EP1728211A2

EP1728211A2 - Systems and methods for treating, diagnosing and predicting the occurrence of a medical condition

Info

Publication number: EP1728211A2
Application number: EP05728291A
Authority: EP
Inventors: Olivier Saidi; David A. Verbel; Mikhail Teverovskiy
Original assignee: Aureon Laboratories Inc
Current assignee: Fundacao D Anna Sommer Champalimaud E Dr Carlos
Priority date: 2004-03-12
Filing date: 2005-03-14
Publication date: 2006-12-06
Also published as: CA2559241C; CA2559241A1; WO2005091203A3; WO2005091203A2

Abstract

Methods and systems are provided that use clinical information, molecular information and computer-generated morphometric information in a predictive model for predicting the occurrence (e.g., recurrence) of a medical condition, for example, cancer.

Description

SYSTEMS AND METHODS FOR TREATING, DIAGNOSING AND PREDICTING THE OCCURRENCE OF A MEDICAL CONDITION

FIELD OF THE INVENTION Embodiments of the invention relate to methods and systems that use clinical information, molecular information and computer-generated morphometric information in a predictive model for predicting the occurrence of a medical condition (e.g., disease or responsiveness or unresponsiveness to treatment). For example, in one embodiment, the invention comprises methods and systems that use clinical, molecular and morphometric information to treat, diagnose and predict the recurrence of prostate cancer. BACKGROUND Physicians are required to make many medical decisions ranging from, for example, whether and when a patient is likely to experience a medical condition to how a patient should be treated once the patient has been diagnosed with the condition. Determining an appropriate course of treatment for a patient may increase the patient's chances for, for example, survival and/or recovery. Similarly, predicting the occurrence of an event advantageously allows individuals to plan for the event. For example, predicting whether a patient is likely to experience occurrence (e.g., recurrence) of a disease may allow a physician to recommend an appropriate course of treatment for that patient. Traditionally, physicians rely heavily on their expertise and training to treat, diagnose and predict the occurrence of medical conditions. For example, pathologists use the Gleason scoring system to evaluate the level of advancement and aggression of prostate cancer, in which cancer is graded based on the appearance of prostate tissue under a microscope as perceived by a physician. Higher Gleason scores are given to samples of prostate tissue that are more undifferentiated [1]. Although Gleason grading is widely considered by pathologists to be reliable, it is a subjective scoring system. Particularly, different pathologists viewing the same tissue samples may make conflicting interpretations. Conventional tools for assisting physicians in medical diagnostics are limited in scope and application. For example, tools for assisting physicians with decisions regarding prostate cancer treatment after a patient has undergone radical prostatectomy are limited to serum- based PSA screening tests and generalized nomograms. One postoperative nomogram, developed by Kattan et al. U.S. Patent No. 6,409,664, is widely used by urologists and allows prediction of the 7-year probability of disease recurrence for patients treated by radical prostatectomy. This nomogram provides information about the likelihood of biochemical failure only (i.e., an increase in PSA level), and does not predict clinical failure (death). Moreover, this nomogram only predicts whether a patient's condition is likely to recur within 7 years, and does not predict when in that interval the patient's condition might recur. Prognostic variables used in this nomogram include pre-treatment serum PSA levels, Gleason score, and microscopic assessment by a pathologist of prostate capsular invasion, surgical margins, seminal vesicle invasion, and lymph node status. Treatment failure is recorded when there is clinical evidence of disease recurrence, a rising serum PSA, or initiation of adjuvant therapy. However, these nomograms have several limitations. Of the most notable limitations is that even the best of these nomograms performs only slightly better than midway between a model with perfect discrimination (concordance index = 1.0) and a model with no discriminating ability (concordance index = 0.5). Furthermore, outcome for the approximately 30% of patients who have nomogram predictions in the mid range (7 -year progression-free survival, 30-70%) is uncertain as the prediction is no more accurate than a coin toss. Techniques in computer-implemented image processing and analysis have emerged that provide significantly increased computational power. In many applications, the ability to extract large amounts of quantitative continuous-valued features automatically from a single image has become a reality. A feature X is said to be continuous-valued if, for some A < B, the set of values for the feature includes all numbers x between A and B. Cancer image analysis systems have been developed for images taken from cytological specimens [2] [3]. However, such systems only capture cells and thus do not utilize all of the architectural information observable at the tissue level, let alone combine that information with clinical and molecular information. Cancer image analysis systems have not been provided for analyzing the structure of different pathological elements at the tissue level, which often plays a more important role in diagnosis (e.g., in Gleason analysis) than the appearance of individual cells. Thus, pathologists have resorted to manual techniques for analyzing the shape and size of the prostate gland to determine the pathologic grade of the cancer [4]. The deficiency of conventional cancer image analysis systems is exacerbated by the fact that tissue images are typically more complex than cellular images and require comprehensive domain expert knowledge to be understood. In view of the foregoing, it would be desirable to provide systems and methods for treating, diagnosing and predicting the occurrence of medical conditions, responses and other medical phenomena with improved predictive power. It would also be desirable to provide computer-implemented systems and methods that utilize information at the tissue level to treat, diagnose and predict the occurrence of medical conditions. SUMMARY OF THE INVENTION Embodiments of the present invention provide automated systems and methods for predicting the occurrence of medical conditions. As used herein, predicting an occurrence of a medical condition may include, for example, predicting whether and/or when a patient will experience occurrence (e.g., recurrence) of disease such as cancer, predicting whether a patient is likely to respond to one or more therapies (e.g., a new pharmaceutical drug), and predicting the occurrence of any other suitable medical condition. Predictions by embodiments of the present invention may be used by physicians or other individuals to, for example, select an appropriate course of treatment for a patient and/or to diagnose a medical condition in the patient. In an aspect of the present invention, systems and methods are provided for generating a model that predicts the occurrence of a medical condition. Generating a predictive model may include using an analytical tool to train a support vector machine (SVM) or a neural network with data for a cohort of patients whose outcomes are at least partially known. In one embodiment, the training data includes clinical data, molecular data, and computer-generated morphometric data. As used herein, "data" of a particular type (e.g., clinical, molecular, or morphometric) may include one or more features of that type. Additionally, morphometric data is defined to include any computer-generated data associated with or derived from an electronic (digital) image of tissue, including but not limited to data regarding structural properties of the tissue or portion thereof (e.g., area, length, width, compactness, and density), spectral properties of the tissue or portion thereof (e.g., red, green, blue (RGB) color channel values, brightness and channel histograms), and f actal properties of the tissue image and/or identified tissue components (e.g., fractal dimension of intraepithelial interface, lumen outline), statistical properties of wavelet decomposition coefficients and/or other image data transforms. In other embodiments, the training data includes computer-generated morphometric data only or the combination of clinical data and computer-generated morphometric data. In one embodiment, systems and methods are provided for generating a predictive model based on one or more computer-generated morphometric features related to stroma, cytoplasm, epithelial nuclei, stroma nuclei, lumen, red blood cells, tissue artifacts, or tissue background, or a combination thereof. The predictive model may be generated based on the computer-generated morphometric features alone or in combination with one or more of the clinical features listed in Table 4 and/or one or more of the molecular features listed in Table 6. For example, the one or more features may be input to an analytical tool that determines an affect of the features on the ability of an associated model to predict a medical condition. Features that increase the predictive power of the model maybe included in the final model, whereas features that do not increase (e.g., or decrease) the predictive power may be removed from consideration. Using the above-described morphornetric features alone or in combination with the clinical and/or morphometric features listed in Tables 4 and/or 6, respectively, as a basis for developing a predictive model may focus the resources of physicians, other individuals, and/or automated processing equipment (e.g., a tissue image analysis system) on obtaining data for patient features that are more likely to be correlated with outcome and therefore useful in the final predictive model. In another aspect of the present invention, a predictive model is provided that evaluates a dataset for a patient in order to evaluate the risk of occurrence of a medical condition in the patient, where the predictive model is based on computer- generated morphometric data alone or in combination with clinical data and/or molecular data. For example, the predictive model may receive the dataset for the patient as input, and may output a "score" indicating the likelihood that the patient will experience one or more outcomes related to the medical condition. In one embodiment, a predictive model is provided for predicting occurrence or recurrence of disease, where the model is based on one or more computer-generated morphometric features related to stroma, cytoplasm, epithelial nuclei, stroma nuclei, lumen, red blood cells, tissue artifacts, or tissue background, or a combination thereof. The predictive model may be based on these computer-generated morphometric features alone or in combination with one or more of the clinical features listed in Table 4 and/or one or more of the molecular features listed in Table 6. In another embodiment, a predictive model is provided for predicting prostate cancer recurrence, where the model is based on one or more of the clinical and/or molecular features set forth in Figure 6 and one or more morphometric features for one or more of the following pathological objects: red blood cell, epithelial nuclei, stroma, lumen, cytoplasm, and tissue background. In yet another embodiment, a predictive model is provided for predicting prostate cancer recurrence, where the model is based on one or more of the clinical and/or molecular features set forth in Figure 9 and one or more morphometric features for one or more of the following pathological objects: red blood cell, epithelial nuclei, stroma, lumen, and cytoplasm. In another embodiment, a predictive model is provided for predicting prostate cancer survivability, where the model is based on one or more of the clinical and/or molecular features set forth in Figure 11 and one or more morphometric features for one or more of the following pathological objects: red blood cell, epithelial nuclei, and stroma. In other embodiments, the predictive model may determine whether a tissue sample is normal or abnormal or may predict whether a patient is likely to experience clinical failure post prostatectomy. In another aspect, systems and methods are provided in which data for a patient is measured at each of a plurality of points in time and evaluated by a predictive model of the present invention. A diagnosis or treatment of the patient may be based on a comparison of the results from each evaluation. Such a comparison may be summarized in, for example, a report output by a computer for use by a physician or other individual. For example, systems and methods may be provided for screening for an inhibitor compound of a medical condition. A first dataset for a patient may be evaluated by a predictive model, where the model is based on clinical data, molecular data, and computer-generated morphometric data. A test compound may be administered to the patient. Following administering of the test compound, a second dataset may be obtained from the patient and evaluated by the predictive model. The results of the evaluation of the first dataset may be compared to the results of the evaluation from the second dataset. A change in the results for the second dataset with respect to the first dataset may indicate that the test compound is an inhibitor compound. In still another aspect of the present invention, a test kit is provided for treating, diagnosing and/or predicting the occurrence of a medical condition. Such a test kit may be situated in a hospital, other medical facility, or any other suitable location. The test kit may receive data for a patient (e.g., including clinical data, molecular data, and/or computer- generated morphometric data), compare the patient's data to a predictive model (e.g., programmed in memory of the test kit) and output the results of the comparison. In some embodiments, the molecular data and/or the computer-generated morphometric data may be at least partially generated by the test kit. For example, the molecular data may be generated by an analytical approach subsequent to receipt of a tissue sample for a patient. The morphometric data may be generated by segmenting an electronic image of the tissue sample into one or more objects, classifying the one or more objects into one or more object classes (e.g., stroma, lumen, red blood cells, etc.), and determining the morphometric data by taking one or more measurements for the one or more object classes. In some embodiments, the test kit may include an input for receiving, for example, updates to the predictive model, h some embodiments, the test kit may include an output for, for example, transmitting data, such as data useful for patient billing and or tracking of usage, to another device or location. BRIEF DESCRIPTION OF THE DRAWINGS For a better understanding of embodiments of the present invention, reference is made to the following description, taken in conjunction with the accompanying drawings, in which like reference characters refer to like parts throughout, and in which: Figures 1 A and IB are block diagrams of systems that use a predictive model to treat, diagnose or predict the occurrence of a medical condition; Figure 1 C is a block diagram of a system for generating a predictive model; Figure 2 shows illustrative results for a patient that may be output by a predictive model; Figure 3 is flowchart of illustrative stages involved in processing tissue images; Figure 4 is a flowchart of illustrative stages involved in screening for an inhibitor compound of a medical condition; Figures 5 a and 5b show grayscale digital images of healthy and abnormal prostate tissue specimens, respectively, after image segmentation and classification; Figure 6 shows various clinical, molecular, and computer-generated morphometric features used by a model to predict prostate cancer recurrence; Figures 7a and 7b show stained tissue specimens demonstrating the presence of two molecular features, particularly Androgen Receptor (AR) and CD34; Figure 8 is a graph of a Kaplan-Meier curve demonstrating a classification of patients as being at low-risk, intermediate-risk, or high-risk for experiencing prostate cancer recurrence as predicted by a model based on the features of Figure 6; Figure 9 shows various clinical, molecular, and computer-generated morphometric features used by a model to predict prostate cancer recurrence; Figure 10 is a graph of a Kaplan-Meier curve demonstrating a classification of patients as being at low-risk, intermediate-risk, or high-risk for experiencing prostate cancer recurrence as predicted by a model based on the features of Figure 9; Figure 11 shows various clinical, molecular, and computer-generated morphometric features used by a model to predict overall survivability of prostate cancer; Figure 12 is a graph of a Kaplan-Meier curve demonstrating a classification of patients as being at low-risk, intermediate-risk, or high-risk of death due to any cause as predicted by a model based on the features of Figure 11 ; and Figure 13 shows various clinical and computer-generated morphometric features used by a model to predict aggressive disease subsequent to a patient having a prostatectomy. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Embodiments of this invention relate to methods and systems that use computer- generated morphometric information alone or in c mbination with clinical information and/or molecular information in a predictive model for predicting the occurrence of a medical condition. For example, in one embodiment of the present invention, clinical, molecular and computer-generated morphometric information is "used to predict the recurrence of prostate cancer. In other embodiments, the teachings provided herein are used to predict the occurrence of other medical conditions such as, for example, other types of disease (e.g., epithelial and mixed-neoplasms including breast, colon, lung, bladder, liver, pancreas, renal cell, and soft tissue) and the responsiveness or unresponsiveness of a patient to one or more therapies (e.g., pharmaceutical drugs). These predictions may be used by physicians or other individuals to, for example, select an appropriate course of treatment for a patient and/or to diagnose a medical condition in the patient. In an aspect of the present invention, an ar alytical tool including a support vector machine (SVM) and/or a neural network may be provided that determines correlations between clinical, molecular, and computer-generated morphometric features and a medical condition. The correlated features may form a model that can be used to predict the occurrence or recurrence of the condition. For example, an analytical tool may be used to generate a predictive model based on data for a cohort of patients whose outcomes with respect to a medical condition (e.g., time to recurrence of cancer) are at least partially known. The model may then be used to evaluate data for a. new patient in order to predict the occurrence of the medical condition for the new patient. In some embodiments, only a subset of the three data types (e.g., clinical and morphometric data only) may be used by the analytical tool to generate the predictive model. The clinical, molecular, and/or morphometric data used by embodiments of the present invention may include any clinical, molecular, and/or morphometric data that is relevant to the diagnosis, treatment and or prediction of a medical condition. Features analyzed for correlations with prostate cancer recurrence and survival in order to generate predictive models are described below in connection with, for example, Tables 1, 2, 4 and/or 6. It will be understood that at least some of these features (e.g., epithelial and mixed- neoplasms) may provide a basis for developing predictive models for other medical conditions (e.g., breast, colon, lung, bladder, liver, pancreas, renal cell, and soft tissue). For example, one or more of the features in Tables 1, 2, 4 and/or 6 maybe assessed for patients having some other medical condition and then input to an analytical tool that determines whether the features correlate with the medical condition. Features that increase the ability of the model to predict the occurrence of the medical condition may be included in the final model, whereas features that do not increase (e.g., or decrease) the predictive power of the model may be removed from consideration. Using the features in Tables 1, 2, 4 and/or 6 as a basis for developing a predictive model may focus the resources of physicians, other individuals, and/or automated processing equipment (e.g., a tissue image analysis system) on obtaining patient data that is more likely to be correlated with outcome and therefore useful in the final predictive model. Moreover, the features determined to be correlated with prostate cancer recurrence and survival are shown in Figures 6, 9, and 11. It will be understood that these features may be included directly in final models predictive of prostate cancer recurrence and or survival, and/or used for developing predictive models for other medical conditions. The morphometric data may include computer-generated data indicating various structural and/or spectral properties of, for example, tissue specimens. In one embodiment, the morphometric data may include data for morphometric features of stroma, cytoplasm, epithelial nuclei, stroma nuclei, lumen, red blood cells, tissue artifacts, tissue background, or a combination thereof. In an aspect of the present invention, a tissue image analysis system is provided for obtaining measurements of the morphometric features from a tissue image. Such a system may be the MAGIC™ system which uses the Definiens Cellenger software. Such a system may receive an H&E stained image as input, and may output various measurements of morphometric features for pathological objects in the image. Additional details regarding systems and methods for obtaining moφhometric features from an image are described below in connection with Figure 3. Clinical features may include or be based on data for one or more patients such as age, race, weight, height, medical history, genotype and disease state, where disease state refers to clinical and pathologic staging characteristics and ∑ar other clinical features gathered specifically for the disease process at hand. Generally, clinical data is gathered by a physician during the course of examining a patient and/or the "tissue or cells of the patient. The clinical data may also include clinical data that may be more specific to a particular medical context. For example, in the context of prostate cancer, the clinical data may include data indicating blood concentration of prostate specific antigen (PSA), the result of a digital rectal exam, Gleason score, and/or other clinical data that may be more specific to prostate cancer. Generally, when any features (i.e., clinical, moφhometric and/or molecular) in Tables 1, 2, 4 and/or 6 and/or Figures 6, 9 and/or 11 are applied to medical contexts other than the prostate, features from these Tables and/or Figures that are more specific to the prostate may not be considered. Optionally, features more specific to the medical context in question may be substituted for the prostate-specific features. For example, other histologic disease-specific features/manifestations may include regions of necrosis (e.g., ductal carcinoma in situ for the breast), size, shape and regional pattern/distribution of epithelial cells (e.g., breast, lung), degree of differentiation (e.g., squamous differentiation with non- small cell lung cancer (NSCLC, mucin production as seen wit i various adenocarcinomas seen in both breast and colon)), moφhological/microscopic distribution of the cells (e.g., lining ducts in breast cancer, lining bronchioles in NSCLC), aαid degree and type of inflammation (e.g., having different characteristics for breast and NSCLC in comparison to prostate). The molecular features may include or be based on data indicating the presence, absence, relative increase or decrease or relative location of biological molecules including nucleic acids, polypeptides, saccharides, steroids and other small molecules or combinations of the above, for example, glycoroteins and protein-RNA complexes. The locations at which these molecules are measured may include glands, tumors, stroma, and/or other locations, and may depend on the particular medical context. Generally, molecular data is gathered using common molecular biological and biochemical techniques including Southern, Western, and Northern blots, polymerase chain reaction (PCR), immunohistochemistry, and immunofluorescence. Further, in situ hybridization may be used to show both the relative abundance and location of molecular biological features. Illustrative methods and systems for in situ hybridization of tissue are described in above-incoφora.ted U.S. Patent Application No. 10/624,233, filed July 21, 2003, and entitled "Methods and compositions for the preparation and use of fixed-treated cell-lines and tissue in fluorescence in situ hybridization." Figures 1 A and IB show illustrative systems that use a predictive model to predict the occurrence of a medical condition in a patient. The arrangement in Figure 1A may be used when, for example, a medical diagnostics lab provides support for a medical decision to a physician or other individual associated with a remote access device. The arrangement in Figure IB may be used when, for example, a test kit including the predictive model is provided for use in a facility such as a hospital, other medical facility, or other suitable location. Referring to Figure 1A, predictive model 102 is located in diagnostics facility 104. Predictive model 102 may include any suitable hardware, software, or combination thereof for receiving data for a patient, evaluating the data in order to predict the occurrence (e.g., recurrence) of a medical condition for the patient, and outputting the results of the evaluation. In another embodiment, model 102 may be used to predict the responsiveness of a patient to particular one or more therapies. Diagnostics facility 104 may receive data for a patient from remote access device 106 via Internet service provider (ISP) 108 and communications networks 110 and 112, and may input the data to predictive model 102 for evaluation. Other arrangements for receiving and evaluating data for a patient from a remote location are of course possible (e.g., via another connection such as a telephone line or through the physical mail). The remotely located physician or individual may acquire the data for the patient in any suitable manner and may use remote access device 106 to transmit the data to diagnostics facility 104. In some embodiments, the data for the patient may be at least partially generated by diagnostics facility 104 or another facility. For example, diagnostics facility 104 may receive a digitized version of an H&E stained image from remote access device 106 or other device and may generate moφhometric data for the patient based on the image. In another example, actual tissue samples may be received and processed by diagnostics facility 104 in order to generate the moφhometric data. In other examples, a third party may receive an image or tissue for a new patient, generate moφhometric data based on the image or tissue, and provide the moφhometric data to diagnostics facility 104. A suitable image processing tool for generating moφhometric data from tissue images and/or samples is described below in connection with Figure 3. Diagnostics facility 104 may provide the results of the evaluation to a physician or individual associated with remote access device 106 through, for example, a transmission to remote access device 106 via ISP 108 and communications networks 110 and 112 or in another manner such as the physical mail or a telephone call. The results may include a diagnostic "score" (e.g., an indication of the likelihood that the patient will experience one or more outcomes related to the medical condition such as the predicted time to recurrence of the event), information indicating one or more features analyzed by predictive model 102 as being correlated with the medical condition, information indicating the sensitivity and/or specificity of the predictive model, or other suitable diagnostic information or a combination thereof. For example, Figure 2 shows an example of a report for a fictional patient that may be output by the predictive model. As shown, the report maps the patient's probability of outcome (e.g., recmxence of prostate cancer; i.e., y-axis) to time in months (x-axis). In this example, the patient has a score of "520" which places the patient in a high-risk category. Such a report may be used by a physician or other individual to assist in determining a more refined clinical-diagnostic tumor grade, develop an effective means to sub-classify patients and finally generate more accurate (and appropriate) treatment option algorithms for the individual patient. The report may also be useful in that it may help the physician or individual to explain the patient's risk to the patient. Remote access device 106 may be any remote device capable of transmitting and/or receiving data from diagnostics facility 104 such as, for example, a personal computer, a wireless device such as a laptop computer, a cell phone or a personal digital assistant (PDA), or any other suitable remote access device. Multiple remote access devices 106 may be included in the system of Figure 1A (e.g., to allow a plurality of physicians or other individuals at a corresponding plurality of remote locations to communicate data with diagnostics facility 104), although only one remote access device 106 has been included in Figure 1A to avoid over-complicating the drawing. Diagnostics facility 104 may include a server capable of receiving and processing communications to and/or from remote access device 106. Such a server may include a distinct component of computing hardware and/or storage, but may also be a software application or a combination of hardware and software. The server may be implemented using one or more computers. 2< Each of communications links 110 and 112 may be any suitable wired or wireless communications path or combination of paths such as, for example, a local area network, wide area network, telephone network, cable television network, intranet, or Internet. Some suitable wireless communications networks may be a global system for mobile communications (GSM) network, a time-division multiple access (TDMA) network, a code- division multiple access (CDMA) network, a Bluetooth network, or any other suitable wireless network. Figure IB shows a system in which test kit 122 including the predictive model of the present invention is provided for use in facility 124, which may be a hospital, a physician's office, or other suitable location. Test kit 122 may include any suitable hardware, software, or combination thereof (e.g., a personal computer) that is adapted to receive data for a patient (e.g., at least one of clinical, moφhometric and molecular data), evaluate the patient's data with a predictive model (e.g., programmed in memory of the test kit), and output the results of the evaluation. For example, test kit 122 may include a computer readable medium encoded with computer executable instructions for performing the functions of the predictive model. The predictive model may be a predetermined model previously generated (e.g., by another system or application such as the system in Figure 1C). hi some embodiments, test kit 122 may optionally include an image processing tool capable of generating data corresponding to moφhometric features from, for example, a tissue sample or image. A suitable image processing tool is described below in connection with Figure 3. In other embodiments, test kit 122 may receive pre-packaged data for the moφhometric features as input from, for example, an input device (e.g., keyboard) or another device or location. Test kit 122 may optionally include an input for receiving, for example, updates to the predictive model. The test kit may also optionally include an output for transmitting data, such as data useful for patient billing and/or tracking of usage, to a main facility or other suitable device or location. The billing data may include, for example, medical insurance information for a patient evaluated by the test kit (e.g., name, insurance provider, and account number). Such information may be useful when, for example, a provider of the test kit charges for the kit on a per-use basis and or when the provider needs patients' insurance information to submit claims to insurance providers. Figure 1C shows an illustrative system for generating a predictive model. The system includes analytical tool 132 (e.g., including a support vector machine (SVM) and/or a neural network) and database 134 of patients whose outcomes are at least partially known. Analytical tool 132 may include any suitable hardware, software, or combination thereof for determining conelations between the data from database 134 and a medical condition. The system in Figure 1C may also include image processmg tool 136 capable of generating moφhometric data based on, for example, a digitized version of an H&E stained tissue image, an actual tissue sample, or both. Tool 136 may generate moφhometric data for, for example, the known patients whose data is included in database 134. A suitable image processing tool 136 is described below in connection with Figure 3. Database 134 may include any suitable patient data such as data for clinical features, moφhometric features, molecular features, or a combination thereof. Database 134 may also include data indicating the outcomes of patients such as whether and when the patients have experienced disease recmrence. For example, database 134 may include uncensored data for patients (i.e., data for patients whose outcomes are completely known) such as data for patients who have experienced a recurrence of a medical condition. Database 134 may alternatively or additionally include censored data for patients (i.e., data for patients whose outcomes are not completely known) such as data for patients who have not shown signs of disease recurrence in one or more follow-up visits to a physician. The use of censored data by analytical tool 132 may increase the amount of data available to generate the predictive model and, therefore, may advantageously improve the reliability and predictive power of the model. Examples of support vector machines (SVM) and neural networks (NNci) that can make use of both censored and uncensored data are described below. In one embodiment, analytical tool 132 may include a support vector machine (SVM). In such an embodiment, tool 132 preferably includes an SVM capable of performing support vector regression on censored data (SVRc). As described in co-pending U.S. Patent Application No. 10/991,240, in SVRc a novel modified loss/penalty function is provided for use within an SVM that may allow the SVM to utilize censored data. Data including clinical, molecular and/or moφhometric features of known patients from database 134 may be input to the SVM to determine parameters for a predictive model. The parameters may indicate the relative importance of input features, and may be adjusted in order to maximize the ability of the SVM to predict the outcomes of the known patients. Additional details regarding the use of SVM to determine correlations of features with a medical condition are described in [5] and [6]. The use of SVRc by analytical tool 132 may include obtaining from database 134 multi-dimensional, non-linear vectors of information indicative of status of patients, where at least one of the vectors lacks an indication of a time of occurrence of an event with respect to a corresponding patient. Analytical tool 132 may then perform regression using the vectors to produce a kernel-based model that provides an output value related to a prediction of time to the event based upon at least some of the information contained in the vectors of information. Analytical tool 132 may use a loss function for each vector containing censored data that is different from a loss function used by tool 132 for vectors comprising uncensored data. A censored data sample may be handled differently because it may provide only "onesided information." For example, in the case of survival time prediction, a censored data sample typically only indicates that the event has not happened within a given time, and there is no indication of when it will happen after the given time, if at all. The loss function used by analytical tool 132 for censored data may be as follows:

where e = f(x) - y ; and /(x) = ^rΦ(x)+b is a linear regression function on a feature space F. Here, W is a vector in F, and Φ(x) maps the input x to a vector in F. In contrast, the loss function used by tool 132 for uncensored data may be: Loss(f(x),y,s = 0) = - 0 -ε_n ≤ e ≤ C_n(ε„ -e) e < -ε_n where e = f(x) - y and ε ^* <ε „ and C ≥ C„

In the above description, the W and b are obtained by solving an optimization problem, the general form of which is: min 1 _, - W^τ W W,b 2 s.t. _yι - (W^τφ(_Xl) + b) ≤ ε (W^τφ(x,) + b)-y_t ≤ ε

This equation, however, assumes the convex optimization problem is always feasible, which may not be the case. Furthermore, it is desired to allow for small errors in the regression estimation. It is for these reasons that a loss function is used for SVRc. The loss allows some leeway for the regression estimation. Ideally, the model built will exactly compute all results accurately, which is infeasible. The loss function allows for a range of error from the ideal, with this range being controlled by slack variables ξ and ξ , and a penalty C. Enors that deviate from the ideal, but are within the range defined by ξ and ξ , are counted, but their contribution is mitigated by C. The more erroneous the instance, the greater the penalty. The less erroneous (closer to the ideal) the instance is, the less the penalty. This concept of increasing penalty with error results in a slope, and C controls this slope. While various loss functions may be used, for an epsilon-insensitive loss function, the general equation transforms into: .t. ^ -(W^τΦ(x,) + b) < ^ +

For an epsilon-insensitive loss function in accordance with the invention (with different loss functions applied to censored and uncensored data), this equation becomes: si. 3A( ^rΦ(_Xi) + b) < , + (W^rΦ(x,) + b)- v, < + / where C ⁵ = s, C? + (l-s, )Cf The optimization criterion penalizes data points whose y- values differ from (x) by more than ε. The slack variables, ξ and ξ , correspond to the size of this excess deviation for positive and negative deviations respectively. This penalty mechanism has two components, one for uncensored data (i.e., not right-censored) and one for censored data. Both components are, here, represented in the form of loss functions that are referred to as ε- insensitive loss functions. Additional details regarding systems and methods for performing support vector regression on censored data (SVRc) are described in above-incoφorated U.S. Patent Application No. 10/991,240, filed November 17, 2004, and U.S. Provisional Patent Application No. 60/520,939, filed November 18, 2003. In another embodiment, analytical tool 132 may include a neural network. In such an embodiment, tool 132 preferably includes a neural network that is capable of utilizing censored data. Additionally, the neural network preferably uses an objective function substantially in accordance with an approximation (e.g., derivative) of the concordance index (CI) to train an associated model (NNci). Though the CI has long been used as a performance indicator for survival analysis [7], the use of the CI to train a neural network has not been proposed previously. The difficulty of using the CI as a training objective function in the past is that the CI is non-differentiable and cannot be optimized by gradient-based methods. As described in co-pending U.S. Patent Application No. / , filed February 25, 2005, and entitled "Methods and Systems for Predicting Occurrence of an

Event," this obstacle may be overcome by using an approximation of the CI as the objective function. For example, when analytical tool 132 includes a neural network that is used to predict prostate cancer recurrence, the neural network may process input data for a cohort of patients whose outcomes with respect to prostate cancer recurrence are at least partially known in order to produce an output. The particular features selected for input to the neural network may be selected through the use of the above-described SVRc (e.g., implemented with a support vector machine of analytical tool 132) or using another suitable feature selection process. An enor module of tool 132 may determine an error between the output and a desired output corresponding to the input data (e.g., the difference between a predicted outcome and the known outcome for a patient). Analytical tool 132 may then use an objective function substantially in accordance with an approximation of the CI to rate the performance of the neural network. Analytical tool 132 may adapt the weighted connections (e.g., relative importance of features) of the neural network based upon the results of the objective function. Additional details regarding adapting the weighed connections of a neural network in order to adjust the correlations of features with a predicted outcome are described in [8] and [9]. The concordance index may be expressed in the form:

where 0 : otherwise and may be based on pair- wise comparisons between the prognostic estimates t₍. and t . for patients i and j, respectively. In this example, Ω consists of all the pairs of patients {i,j} who meet the following conditions: • both patients andy experienced recurrence, and the recurrence time ti of patient i is shorter than patient y's recurrence time tf, or • only patient i experienced recurrence and is shorter than patient y^{' J}s follow-up visit time t_j. The numerator of the CI represents the number of times that the patient predicted to recur earlier by the neural network actually does recur earlier. The denominator is the total number of pairs of patients who meet the predetermined conditions. Generally, when the CI is increased, preferably maximized, the model is more accurate. Thus, by preferably substantially maximizing the CI, or an approximation of the CI, the performance of a model is improved. An embodiment of the present invention provides an approximation of the CI as follows:

where [0 : otherwise J and where 0 < γ < 1 and n > 1. i.(t, , t₇ ) can be regarded as an approximation to I(-t_t ,-t ) . Another approximation of the CI provided by the present invention which has been shown empirically to achieve improved results is the following: α = J(ι,j)e^Q (t, -t ). .(t„t_y) D where

is a normalization factor. Here each R(i_t ,t ) is weighted by the difference between i_t and i . The process of minimizing the C_ω (or Q seeks to move each pair of samples in Ω to satisfy t, - t, > T and thus to make I(i_[ , i_} ) = 1. When the difference between the outputs of a pair in Ω is larger than the margin γ, this pair of samples will stop contributing to the objective function. This mechanism effectively overcomes over-fitting of the data during training of the model and makes the optimization preferably focus on only moving more pairs of samples in Ω to satisfy t_t —i > γ. The influence of the training samples is adaptively adjusted according to the pair- wise comparisons during training. Note that the positive margin γ in R is preferable for improved generalization performance, h other words, the parameters of the neural network are adjusted during training by calculating the CI after all the patient data has been entered. The neural network then adjusts the parameters with the goal of minimizing the objective function and thus maximizing the CI. As used above, over-fitting generally refers to the complexity of the neural network. Specifically, if the network is too complex, the network will react to "noisy" data. Overfitting is risky in that it can easily lead to predictions that are far beyond the range of the training data. Additional details regarding systems and methods for using an objective function substantially in accordance with an approximation of the CI to train a neural network are described in above-incoφorated U.S. Patent Application No. _/___, , filed February 25, 2005, and entitled "Methods and Systems for Predicting Occurrence of an Event" and U.S. Provisional Patent Application Nos. 60/548,322, filed February 27, 2004, and 60/577,051, filed June 4, 2004. Figure 3 is a flowchart of illustrative functions of a suitable image processing tool. The functions in Figure 3 relate primarily to the segmentation of tissue images in order to classify pathological objects in the images (e.g., classifying objects as cytoplasm, lumen, nuclei, stroma, background, artifacts, and red blood cells). In one example, the image processing tool may include a light microscope that captures tissue images at 20X magnification using a SPOT Insight QE Color Digital Camera (KAI2000) and produces images with 1600 x 1200 pixels. The images may be stored as images with 24 bits per pixel in Tiff format. Such equipment is only illustrative and any other suitable image capturing equipment may be used without departing from the scope of the present invention. The image processing tool may also include any suitable hardware, software, or combination thereof for segmenting and classifying objects in the captured images, and then measuring moφhometric features of the objects. In one embodiment, the image processing tool may include the commercially-available Defmiens Cellenger Developer Studio (v. 4.0) adapted to perform the segmenting and classifying of, for example, the various pathological objects described above and to measure various moφhometric features of these objects. Additional details regarding the Defmiens Cellenger product are described in [10]. The image processing tool may measure various moφhometric features of the objects including spectral- based characteristics (red, green, blue (RGB) channel characteristics, such as mean values, standard deviations, etc.), position, size, perimeter, shape (asymmetry, compactness, elliptic fit, etc.) and relationships to neighboring objects (contrast). The image processing tool may measure these features for every instance of every identified pathological object in the image and may output these features for, for example, evaluation by predictive model 102 (Figure 1A), test kit 122 (Figure IB), or analytical tool 132 (Figure 1C). Optionally, the image processing tool may also output an overall statistical summary for the image for each of the measured features. Additional details regarding measuring moφhometric features of the classified pathological objects are described below in connection with Tables 1 and 2. The following is a description of the functions shown in Figure 3 of the image processing tool. Initial Segmentation. In a first stage, the image processing tool may segment an image (e.g., an H&E stained tissue microarray (TMA) image or an H&E of a whole tissue section) into small groups of contiguous pixels known as objects. These objects may be obtained by a region-growing algorithm which finds contiguous regions based on color similarity and shape regularity. The size of the objects can be varied by adjusting a few parameters [11]. In this system, an object rather than a pixel is typically the smallest unit of processing. Thus, all moφhometric feature calculations and operations may be performed with respect to objects. For example, when a threshold is applied to the image, the feature values of the object are subject to the threshold. As a result, all the pixels within an object are assigned to the same class. In one embodiment, the size of objects may be controlled to be 10-20 pixels at the finest level. Based on this level, subsequent higher and coarser levels are built by forming larger objects from the smaller ones in the lower level. Background Extraction. Subsequent to initial segmentation, the image processing tool may segment the image tissue core from the background (transparent region of the slide) using intensity threshold and convex hull. The intensity threshold is an intensity value that separates image pixels in two classes: "tissue core" and "background". Any pixel with an intensity value greater than or equal the threshold is classified as a "tissue core" pixel, otherwise the pixel is classified as a "background" pixel. The convex hull of a geometric object is the smallest convex set (polygon) containing that object. A set S is convex if, whenever two points P and Q are inside S, then the whole line segment PQ is also in S. Coarse Segmentation. In a next stage, the image processing tool may re-segment the foreground (e.g., TMA core) into rough regions corresponding to nuclei and white spaces. For example, the main characterizing feature of nuclei in H&E stained images is that they are stained blue compared to the rest of the pathological objects. Therefore, the difference in the red and blue channels (R-B) intensity values may be used as a distinguishing feature. Particularly, for every image object obtained in the initial segmentation step, the difference between average red and blue pixel intensity values may be determined. The length/width ratio may also be used to determine whether an object should be classified as nuclei area. For example, objects which fall below a (R-B) feature threshold and below a length width threshold may be classified as nuclei area. Similarly, a green channel threshold can be used to classify objects in the tissue core as white spaces. Tissue stroma is dominated by the color red. The intensity difference d, "red ratio" r = R/(R + G + B) and the red channel standard deviation σ_R of image objects may be used to classify stroma objects. Wliite Space Classification. In the stage of coarse segmentation, the white space regions may correspond to both lumen (pathological object) and artifacts (broken tissue areas) in the image. The smaller white space objects (area less than 100 pixels) are usually artifacts. Thus, the image processing tool may apply an area filter to classify them as artifacts. Nuclei De-fusion and Classification. In the stage of coarse segmentation, the nuclei area is often obtained as contiguous fused regions that encompass several real nuclei. Moreover, the nuclei region might also include surrounding misclassified cytoplasm. Thus, these fused nuclei areas may need to be de-fused in order to obtain individual nuclei. The image processing tool may use two different approaches to de-fuse the nuclei. The first approach may be based on a region growing algorithm that fuses the image objects constituting nuclei area under shape constraints (roundness). This approach has been determined to work well when the fusion is not severe. In the case of severe fusion, the image processing tool may use a different approach based on supervised learning. This approach involves manual labeling of the nuclei areas by an expert (pathologist). The features of image objects belonging to the labeled nuclei may be used to design statistical classifiers. In one embodiment, in order to reduce the number of feature space dimensions, feature selection may be performed on the training set using two different classifiers: the Bayesian classifier and the k nearest neighbor classifier [12]. The leave-one-out method [13] may be used for cross-validation, and the sequential forward search algorithm may be used to choose the best features. Finally, two Bayesian classifiers may be designed with number of features equal to 1 and 5, respectively. The class-conditional distributions may be assumed to be Gaussian with diagonal covariance matrices. In some embodiments, the input image may include different kinds of nuclei: epithelial nuclei, fibroblasts, basal nuclei, endothelial nuclei, apoptotic nuclei and red blood cells. Since the number of epithelial nuclei is typically regarded as an important feature in grading the extent of the tumor, it may be important to distinguish the epithelial nuclei from the others. The image processing tool may accomplish this by classifying the detected nuclei into two classes: epithelial nuclei and "the rest" based on shape (eccentricity) and size (area) features. Additional details regarding image segmentation and classification in accordance with the present invention are described in above-incoφorated U.S. Patent Application No. 10/991,897, filed November 17, 2004, and U.S. Provisional Patent Application Nos. 60/520,815, filed November 17, 2003 and 60/552,497, filed March 12, 2004. As described above, the image processing tool may measure various moφhometric features subsequent to the segmenting and classifying of objects in the image by the tool. These moφhometric features may be indicative of one or more properties and/or statistics. The object properties may include both spectral properties (e.g., color channel mean values, standard deviations and brightness) and structural/shape properties (e.g., area, length, width, compactness, density). The statistics may include minimum, maximum, mean and standard deviation and may be computed for each property of an image object. Tables 1 and 2 (appended hereto) show various examples of moφhometric features that may be measured in accordance with the present invention. The moφhometric features in these tables are named using a convention that indicates the various properties and/or statistics measured by these features. The particular naming convention shown in Tables 1 and 2 is adapted from the commercially-available Defmiens software product described above and, therefore, will be understood by one of ordinary skill in the art. It will be understood that the computer-generated moφhometric features shown in Tables 1 and 2 are only illustrative and that any computer- generated moφhometric features may be utilized without departing from the scope of the present invention. For example, Tables 1 and 2 include different sets of moφhometric features. The reduced and modified set of features in Table 2 (i.e., reduced and modified in comparison to the features of Table 1) resulted from additional experimentation in the field of prostate cancer recmrence and survival from the time that the study involving Table 1 was performed. Particularly, the additional experimentation provided additional insight regarding the types of features which may be more likely to correlate with outcome. The inventors expect that continued experimentation and/or the use of other suitable hardware, software, or combination thereof will yield various other sets of computer-generated features (e.g., a subset of the features in Table 2) that may correlate with these and other medical conditions. Referring to Tables 1 and 2, the feature "Lumen. StdDevAreaPxl", "Lumen" indicates a type of image object, "StdDev" indicates a statistic (standard deviation) to be computed using all instances of the identified Lumen, and "AreaPxl" indicates a feature of an object instance (area as a number of pixels) to be evaluated by the statistic. An image processmg tool may measure moφhometric features for all the objects previously segmented and classified in the image. For example, the image processing tool may measure moφhometric features for objects including "Background," "Cytoplasm," "Epithelial nuclei," "Lumen," "Stroma," "Stroma nuclei" and "Red blood cells." "Background" includes portions of the digital image that are not occupied by tissue. "Cytoplasm" refers to the cytoplasm of a cell, which may be an amoφhous area (e.g., pink area that surrounds an epithelial nucleus in an image of, for example, H&E stained tissue). "Epithelial nuclei" refers to the nucleus present within epithelial cells/luminal and basal cells of the glandular unit, ^■which appear as "round" objects surrounded by cytoplasm. "Lumen" refers to central glandular space where secretions are deposited by epithelial cells, which appear as enclosed white areas sunounded by epithelial cells. Occasionally, the lumen can be filled by prostatic fluid (which typically appears pink in H&E stained tissue) or other "debris" (e.g., macrophages, dead cells, etc.). Together the lumen and the epithelial cytoplasm and nuclei form a gland unit. "Stroma" refers to a form of connective tissue with different density that maintains the architecture of the prostatic tissue. Stroma tissue is present between the gland units, and appears as red to pink in H&E stained tissue. "Stroma nuclei" are elongated cells with no or minimal amounts of cytoplasm (fibroblasts). This category may also include endothelial cells and inflammatory cells, and epithelial nuclei may also be found scattered within the stroma if cancer is present. "Red blood cells" are small red round objects usually located within the vessels (arteries or veins), but can also be found dispersed throughout tissue. "C2EN" in the below tables is a relative ratio of nucleus area to the cytoplasm. The more anaplastic/malignant the epithelial cell is, the more area is occupied by the nucleus. "EN2SN" is the percent or relative amount of epithelial to stroma cells present in the digital tissue image. "L2Core" is the number or area of lumen present within the tissue. The higher the Gleason grade, the more aggressive cancer is and therefore the less amount of lumen is present. Generally, this is because epithelial cells replicate in an uncontrolled way when cancer occurs, which causes lumen to become filled with the epithelial cells. "C2L" is relative cytoplasm to lumen. "CEN2L" is relative cytoplasm endothelial cells to lumen. In an aspect of the present invention, systems and methods are provided for screening for an inhibitor compound of a medical condition (e.g., disease). Figure 4 is a flowchart of illustrative stages involved in screening for an inhibitor compound in accordance with an embodiment of the present invention. At stage 402, a first dataset for a patient may be obtained that includes one or more of clinical data, moφhometric data and molecular data. A test compound may be administered to the patient at stage 404. Following stage 404, a second dataset may be obtained from the patient at stage 406. The second dataset may or may not include the same data types (i.e., features) included in the first dataset. At stage 408, the second dataset may be compared to the first dataset, where a change in the second dataset following administration of the test compound indicates that the test compound is an inhibitor compound. Stage 408 of comparing the datasets may include, for example, comparing an output generated by a predictive model of the present invention responsive to an input of the first dataset with an output generated by the predictive model responsive to an input of the second dataset. For example, the inhibitor compound may be a given drug and the present invention may determine whether the drug is effective as a medical treatment for a medical condition. Various illustrative applications of embodiments of the present invention to the prediction of medical conditions will now be described. In a first example, an embodiment of the present invention used clinical and moφhometric data to predict the recurrence of prostate cancer, i a second example, an embodiment of the present invention used clinical, moφhometric, and molecular data to predict the recurrence of prostate cancer and overall survivability. hi a third example, an embodiment of the present invention was used to predict the occurrence of aggressive disease subsequent to a patient prostatectomy. In a fourth example, an embodiment of the present invention was used to predict liver toxicology.

Prostate Cancer Overview Prostate cancer is the leading cause of death among men in the United States with an anticipated 230,000 newly diagnosed cases and nearly 30,000 deaths in 2004. The expanded use of serum based screening with PSA has offered physicians the ability to detect prostate cancer at an earlier stage (i.e. Tla-c, T2), either localized to the prostate or regionally spread while only a small percentage are detected at the metastatic stage. The reported benefits of early detection and diagnosis have placed enormous pressure on both the patient and the urologist in selecting the course of treatment. The need for accurate prognosis is critical when selecting initial therapeutic intervention, as the majority of tumors are indolent and require minimal intervention (i.e. 'watchful waiting') while others are more aggressive and early intervention (i.e. radiotheraphy / hormonal / adjuvant systemic therapy / clinical trial placement) is recommended. Furthermore, in a randomized trial comparing watchful waiting with radical prostatectomy, only a modest benefit was derived from surgery (6.6% reduction in mortality after prostatectomy) suggesting that better patient stratification measures are needed in order to guide individualized patient care [14]. The natural history of PCa re-emphasizes the challenges facing the patient at the time of their diagnosis [15]. Even though early stage prostate cancer is curable with local therapy, approximately 25 - 40% of men will develop a PSA / biochemical recurrence (BCR). To complicate matters even further, a man with prostate cancer who has had a recurrence can still develop a metastasis some 8 years post PSA / BCR (mean 8 years; median 5 years post BCR), suggesting that identifying this group of patients early in their treatment regimen (both in predicting their time to BCR as well as their propensity to develop metastases) is paramount to their overall survival. Unfortunately, the existing predictive models are limited in their accuracy and are not individualized for the specific patient with respect to their tumor pathology. Although a variety of genetic, environmental and life-style changes have been implicated in the pathogenesis of PCa, at present there is no single biochemical pathway, gene mutation or clinical biomarker which can predict a given patients outcome. Twenty-orxe years after radical prostatectomy became popular again and 15 years after the widespread use of PSA, urologists still cannot tell patients which treatment for localized disease results in the best clinical disease-free or overall survival. Prognostic nomograms based only on clinical feature data do in fact provide useful predictions of clinical states and outcomes, but need improvement in both accuracy and universality [16]. Embodiments of the present invention provide a 'Systems Pathology' approach to successfully improve upon the accuracy of a predictive model for PSA / BCR post prostatectomy. This represents an 'individualized' view of the patients own tumor sample, including quantitative assessment of cellular and microanatomic moφhometric characteristics, clinical profiles and molecular markers to create a highly accurate and integrative model of prediction. By utilizing domain expertise, highly accurate models for predicting PSA recurrence have been developed. These efforts have validated the utility of systems pathology in generating predictive and prognostic models. Furthermore, the analysis demonstrates that a limited set of clinical variables, molecular biomarkers, and tissue moφhometric features can be derived and included in a predictive test used by urologists/pathologists to construct optimal patient treatment plans based on a designated clinical outcome. The selected molecular features which were associated with PSA recurrence suggest convergent roles for mechanisms of growth factor signaling (through the androgen receptor (hereinafter "AR"), described below) and cellular coupled vascularization (through CD34). CD34 is a transmembrane glycoprotein which is present on endothelial cells which line vessels in the human body. Further studies are underway to better understand these observations and the potential impact on predicting prostate cancer progression. Also of note were the selected image segmentation and moφhometric characteristics which represent in part a highly accurate, non-subjective and quantitative Gleason Score in addition to several novel tissue descriptors which were important in model development and accuracy. The defined moφhometric features relating to the Gleason Scoring System include in part the overall appearance of the glandular structures, shape and size (cytoplasmic composition) of the epithelial cells, epithelial cell nuclei and the demonstration of single epithelial cells admixed in the stroma. The androgen receptor protein (AR) receives naturally occurring androgenic hormones (testosterone and its 5 .alpha.-reduced metabolite, dihydrotestosterone) after these hormones are synthesized by the Leydig cells of the male testes. Particularly, after synthesizing, these hormones circulate throughout the body and bind to the AR. Androgens, acting through the receptor AR, stimulate development of the male genitalia and accessory sex glands in the fetus, virilization and growth in the pubertal male, and maintenance of male virility and reproductive function in the adult. The androgen receptor, together with other steroid hormone receptors, constitute a family of trans-acting transcriptional regulatory proteins that control gene transcription through interactions with specific gene sequences. Studies on AR with respect to prostate cancer ha e suggested that a positive conelation may exist between the presence of androgen receptors in cancer cells and their dependence on androgenic hormone stimulation for growth. For example, Sovak et al. U.S. Patent No. 6,472,415 proposes that growth of prostate cancer in early stages is androgen driven and can, at least temporarily, be stopped by androgen deprivation. French et al. U.S. Patent No. 6,821,767 proposes various ways for measuring AR that may allow for the use .of androgen receptor assays in the diagnostic evaluation of prostate cancer by physicians. However, these studies have not proposed using measurements of AR in conjunction with automated models that predict the occurrence of prostate cancer, as disclosed herein.

Example 1 : Prediction of Prostate Cancer Recurreα ce Clinical and Morphometric Data A number of raw moφhometric features initially as large as five hundred was extracted from each prostate tissue image using the MAGrIC tissue image analysis system which is based on Defmiens Cellenger software. The full set of raw features was chosen agnostically to avoid disregarding potentially useful features. However, all of these moφhometric features were not likely to be equally informative, and a prediction model built based on the full feature set would be likely to have poor predictive perfonnance due to the "curse of dimensionality" [13]. So a dimensionality reduction procedure was applied, and a set of eight moφhometric features was finally selected. A study was conducted based on a subset of 153 patients from a cohort of prostate cancer patients who underwent radical prostatectomy. Measurable prostate specific antigen (PSA) after the operation was used to define prostate cancer recurrence (also referred to as a biochemical recurrence (BCR)). Patients were followed post-operatively. Their recurrence status at their last visit, as well as their follow-up time, was recorded, which generated a set of right-censored data. Gleason scores were measured both pre-operatively from the biopsy specimen and post-operatively using the excised prostate gland. The four specific clinical measures, or features, considered in this study were (1) the biopsy Gleason grade, (2) the biopsy Gleason score, (3) the post-operative Gleason grade, and (4) the post-operative Gleason score. The moφhometric features were analyzed separately from the clinically derived Gleason score feature to predict both the probability and the time to PSA/BCR recurrence. The image and Gleason score (features) were then combined to establish a recunence and time to recurrence time prediction. Improved prediction accuracy achieved by this joint set of features indicated that the image features indeed provided additional information and thus enhanced the recurrence prediction rate and the overall prediction model. Because this cohort of patients had right-censored outcome data, survival analysis models had to be built for the prediction of recurrence. In order to avoid the potential algorithmic bias on different types of data, two survival analysis algorithms were used: 1) a Cox regression model [17]; and 2) SVRc which is described above and as applied to a support vector machine. The concordance index estimated using 5-fold cross validation was used to measure the models' predictive accuracy [13] [18]. Both algorithms were applied to three data sets: (1) the Gleason score clinical features alone; (2) the selected moφhometric features alone; and (3) the combination of the moφhometric features and the Gleason score clinical features. The experimental results are listed in Table 3. The clinical features selected in this example were BXGGTOT, BXGG1, GGTOT, and GG1 and the moφhometric features selected related to epithelial nuclei

(EpitheliaLNuclei.MaxCompactness), background (Background.StdDevAreaPxl), and lumen (Lumen.MaxBorderLengthPxl, Lumen.MinRadiusofsmallestenclosinge, Lumen.StdDevBorderLengthPxl, Lumen. SumBorderlengthPxl, Lumen. StdDevAreaPxl, and LumemMinCompactness). More particularly, in this example, moφhometric features related to the area, border length, and shape (compactness) of the lumen were deteπmined to conelate with disease progression. The smaller and more compact the lumen, the more advanced the cancer was likely to be. Indeed, with more aggressive cancer (Gleason grade 4 and 5), it can be expected that lumen will almost or completely disappear from the tissue. It was also determined that the moφhometric feature of compactness of epithelial nuclei conelated with cancer progression, where compactness was calculated by the Defmiens Cellenger software as the ratio of the length and width product of the epithelial nuclei to the epithelial nuclei area. This may be because epithelial nuclei invasion into stroma increases as cancer progresses (i.e., tissue with advanced cancer typically includes an abundanc e of epithelial nuclei). The background-based moφhometric feature that was determined to conelate with outcome in this example measured the actual size of the tissue core used in the analysis.

Table 3 - Comparison of Prediction Accuracy

According to Table 3, the predictive performance of the moφhometric features is comparable with that of the Gleason scores, and the combination of the morphometric features and the Gleason scores achieves a higher predictive rate, which confirms that the moφhometric features extracted by the tissue image analysis system indeed provide extra information beyond the Gleason scores. Therefore, the use of the moφhorαetric measurements can enhance overall recurrence prediction.

Example 2: Prediction of Prostate Cancer Recurrence and Overall Simrvival Clinical, Morphometric and Molecular Data Two studies were conducted which successfully predicted prostate specific antigen (PSA) recurrence with 88% and 87% predictive accuracies, respectively. B y combining clinical, molecular, and moφhometric features with machine learning, a robnst platform was created which has broad applications in patient diagnosis, treatment management and prognostication. A third study was conducted to predict overall survival of prostate cancer patients, where the outcome of interest was death due to any cause. A cohort of 539 patients who underwent radical prostatectomy was studied incoφorating high-density tissue microanays (TMAs) constructed from prostatectomy specimens. Moφhometric studies were performed using hematoxylin and eosin (H&E) stained tissue sections and molecular biological determinants were assessed with immunohistochemistry (IHC). A predictive model for both PSA recunence and overall survival was derived from a selected set of features through supervised multivariate learning. Patients with complete non-missing data in each domain were evaluated with a support vector machine for regression developed to handle censored data (SVRc). Predictive performance of the model was estimated using the concordance index (CI) with generated scores used to define risk groups . Using a cohort of 132 patients, 41 features (including 17 clinical, 14 molecular, and 10 moφhometric) were selected which predicted PSA recunence with 88% accuracy. In a cohort of 268 patients, 10 features (3 clinical, 1 molecular, and 6 moφhometric) were found to be predictive of PSA recunence with 87% accuracy; additionally, 14 features (2 clinical, 1 molecular, and 11 moφhometric) were found to be predictive of overall survival with 80% accuracy. Using the log-rank test, significant differences in tumor recunence and death were observed between risk groups (pθ.0001). The present study reveals an incremental trend of improved prostate cancer recunence prediction through the use of a new systems approach combining clinical variables, molecular markers, and tissue histology, analyzed by machine learning. Patient Clinical Features. A cohort of 539 patients who underwent radical prostatectomy was studied. Seventeen clinical features (shown below in Table 4) were retrospectively collected using de-identified patient information, which included patient age, preoperative PSA, and Gleason Grade.

Table 4. Clinical Features Collected

Feature Description age Age (in years) race Race prepsa Prostate specific antigen (ng/dl) turn TNM clinical stage uicc UICC clinical stage dre Palpable on digital rectal exam

In Lymph node status svi Invasion of the seminal vesicles margins +/- surgical margins ece Tumor located outside capsule bxggl Dominant biopsy Gleason Grade bxggtot Biopsy Gleason Score ggl Dominant post-operative Gleason Grade ggtot Post-operative Gleason Score prsltcd Diploid, Tetraploid, Aneuploid pp_sphas Percent of cells in ploidy in S phase pp_frac Ploidy proliferation fraction

Tissue microarrays (TMAs) were constructed from selected blocks of the prostatectomy specimens. Tissue cores with a diameter of 0.6 mm from each specimen were randomly arrayed in triplicate for each of the recipient paraffin bloclks (Beecher Instruments, Silver Spring, MD). Sections (5 μm) of these TMA blocks were placed on charged poly- lysine-coated slides, and used for moφhometric and immunohistochiemical (EHC) analyses (see below). Missing values for clinical features were imputed with flexible additive regression models containing all of the features to estimate the value of the missing feature without reference to outcome, and only those patients with complete clinical- (after imputation), moφhometric, and molecular data, as well as non-missing outcome information, were further studied. The effective sample size for Study 1 (proof of concept) consisted of 132 patients. The primary classification of interest was whether a patient recurred, or not after surgery for prostate cancer. Patients who had two observed consecutive elevations in PSA > 0.2 ng mL were considered to have recurrent prostate cancer. If a patient did not recur as of his last visit, or the patient outcome was unknown as of his most recent visit (i.e. due to loss-to- follow-up), then the patient's outcome was considered censored. Time to recurrence was defined as the time (in months) from radical prostatectomy until PS . (biochemical) recunence. Study 2 was performed using 268 patients from the original 539 patient cohort including 129 of the 132 patients from Study 1. Instead of utilizing H&E images derived from TMA cores, whole sections from radical prostatectomies were analyzed. Study 3 examined the same 268-patient cohort but was used to predict overall survival, where the outcome of interest was death due to any cause. Image Analysis and Morphometry Studies. Representative areas of the original tumor tissue retrieved from each patient, either from a tissue core or whole section, were digitized and analyzed using the H&E stained slides. Images were captured with a light microscope at 20X magnification using a SPOT Insight QE Color Digital Camera (KAI2000). Only areas containing greater than 80% tumor were selected for optimal image segmentation and quantitative analysis. 1 Molecular Analysis. A panel of 12 biomarkers including Cytokeratin 18 (luminal cells), Cytokeratin 14 (basal cells), CD45 (lymphocytes), CD34 (endothelial cells), CD68 (macrophages), Ki67 (proliferation), PSA (hK-3, kallikrein), PSMA (growth receptor), Cyclin Dl (cell cycle), p27 (cell cycle), Androgen Receptor (endocrine) and Her-2/neu (signaling) were applied across all 7 TMA blocks with standard chromogenic immunohistochemistry. Antigen retrieval was performed with a 0.01M citrate buffer (pH 6) for 30 min in a pressure cooker for all antibodies. Illustrative methods and systems relating to such a process are described in above-incoφorated U.S. Patent Application No. 10/624,233, filed July 21, 2003, and entitled "Methods and compositions for the preparation and use of fixed-treated cell-lines and tissue in fluorescence in situ hybridization." Primary antibodies (shown in Table 5) were diluted in Tris-buffered saline with 0.1% Tween and applied for 16 h at 4 °C followed by biotinylated secondary antibodies (Vector) at 1:1000 dilution for 1 h.

Table 5. List of Antibodies

Biomarker Clone

Ki-67 Clone ki-67 (DAKO)

Cytokeratinlδ Clone DC-10 (Novocastra)

CD45 Clone XI 6/99

CD68 Clone 514H2 (Novocastra UK)

CD34 Clone QBEnd 101 (DAKO)

AR Clone AR27 (Novocastra)

Cytokeratin 14 Clone LL002 (Novocastra)

Cyclin Dl Clone P2D11F11

PSA Clone PA05 (Neomarkers)

PSMA Clone ZMD.80 (Zymed)^p p27 Clone DCS72 (Oncogene)

Her-2/neu KIT DAKO^p polyclonal, the rest are monoclonal

Negative control slides received normal mouse serum (DAKO) as the primary antibody. Slides were counterstained with Harris hematoxylin and reviewed by two independent pathologists with all discrepancies resolved by a third pathologist. The recorded IHC data from all 539 patients and their respective triplicate cores included the percentage and intensity (0-3+) of cells which stained for a particular antigen under investigation. Where applicable, these two measures were combined to create a Staining Index for that particular biomarker (Table 6, below, shows an exemplary list of molecular features). A Staining Index was calculated for AR (Androgen Receptor), CK14 (Cytokeratin 14), Cyclin Dl, PSA

(Prostate Specific Antigen), PSMA (Prostate Specific Membrane Antigen), p27 and Her2/neu while the remaining markers (i.e., Ki67, CK18 (Cytokeratin 18), CD45, CD68) were evaluated based on percentage of positive cells with a given intensity. These biomarkers are further described below. The Staining Index ranged from 0-300, and was calculated as follows: l*(the percentage of cells staining positive with 1+ intensity for a biomarker) + 2*(the percentage of cells staining positive with 2+ intensity for the biomarker) + 3*(the percentage of cells staining positive with 3+ intensity for the biomarker), where the percentage of cells staining positive refers to the number of positive cells identified per every 100 cells counted. Additional details regarding this staining index are described in [19]. Such a staining index is only illustrative and any other suitable way for measuring molecular features may be used without departing from the scope of the present invention. In the discussion of biomarkers above, p27 belongs to the family of cell cycle regulators called cyclin-dependent kinase inhibitors, which bind to cyclin-CDK complexes and cause cell cycle anest in the Gl phase. The biomarker p27 is postulated to promote apoptosis and play a role in terminal differentiation of some tissues. By immunohistochemistry, the loss of nuclear p27 expression is associated with a more aggressive phenotype. Her2/neu is a member of the EGFR family of receptor tyrosine kinases and plays an important role in the patho genesis of certain human cancers. The over- expression of Her2/neu by immunohistochemistry on cellular membranes has been associated with a more aggressive type of breast cancer. Ki67 is one of many proliferative markers that stains the nucleus with varying degrees of intensity and is utilized to assess a proliferative index or measure of cellular activity of the tumor sample in question. CD45 is a cell surface antigen that is used to identify cells that are destined to become immune cells such as lymphocytes (T cells, B-cells, NK cells etc.). The intensity is believed not to be as important as its distribution / presence and association with other histological elements. CD68 is a cytoplasmic antigen closely associated with lysosomes. It is expressed throughout the monocyte differentiation cascade but is usually more intense in macrophages than monocytes.

Table 6. Molecular Features Feature Description atki67tl Ki-67 m intensity area 1 (tumor) atla67t2 Ki-67 in intensity area 2 ( umor) atki67t3 Ki-67 in intensity area 3 ( umor) atki67pl Ki-67 in intensity area 1 (PIN) atki67p2 Ki-67 in intensity area 2 (PIN) atki67p3 Ki-67 in intensity area 3 (PIN) atki67al Ki-67 in intensity area 1 (gland) atki67a2 Ki-67 in intensity area 2 (gland) atki67a3 Ki-67 in intensity area 3 (gland) atcl8t3 Cytokeratinl8 (tumor) atcd45t3 CD45 (tumor) atcd68t3 CD68 (tumor) atcd34ρ CD34 (PIN) atcd34s CD34 (stroma) atcd34t CD34 (tumor) atcd34tp CD34 (tumor PIN) atcd34ts CD34 (tumor/stroma) atcd34ps CD34 (PIN/stroma) atcl8p3 Cytokeratin 18 (PIN) atcd45p3 CD45 (PIN) atclδa3 Cytokeratin 18 (gland) atcd45a3 CD45 (gland) arsi AR (tumor) staining index cl4si Cytokeratin 14 (tumor) staining index cdlsi Cyclin Dl (tumor) staining index psasi PSA (tumor) staining index psmasi PSMA (tumor) staining index p27si p27 (tumor) staining index her2si Her-2/neu (tumor) staining index arpsi AR (PIN) staining index cl4psi Cytokeratin 14 (PIN) staining index cdlpsi Cyclin Dl (PIN) staining index psapsi PSA (PIN)staining index psmapsi PSMA (PIN)staining index p27psi p27 (PIN)staining index her2psi Her-2/neu (PIN) staining index arasi AR (gland) staining index cl4asi Cytokeratin 14 (gland) staining index cdlasi Cyclin Dl (gland) staining index psaasi PSA (gland) staining index psmaasi PSMA (gland) staining index p27asi p27 (gland) staining index her2asi Her-2/neu (gland) staining index

Analytical and Statistical Studies. Three studies were conducted: an initial proof of concept analysis (Study 1) with 132 patients and an extended investigation (Study 2 and Study 3) using 268 patients. In both Study 1 and Study 2, the analysis consisted of two steps: identifying features predictive of PSA recunence and developing a model based on those features, with the ultimate objective of using the model to predict biochemical (PSA) recunence in future radical prostatectomy patients. The goals of Study 3 were to identify features and develop a model for predicting overall survival post-prostatectomy. Support Vector Regression for Censored data (SVRc) of the type described above was used to develop the resulting models in each of these studies. Predictive accuracy of a model was evaluated using the concordance index (CI). In dealing with censored outcomes this is often the metric of choice. The concordance index is based on pairwise comparisons between the prognostic scores of two randomly selected patients who meet any one of the following criteria: both patients experienced the event and the event time of the first patient is shorter than that of the second patient or only the first patient experienced the event and his event time is shorter than the second patient's follow-up time. The CI estimates the probability that a patient with the higher prognostic score from the model will experience the event within a shorter time than a patient with a lower score and is tightly associated with the area under the ROC curve (AUC). Other metrics may also be used to measure the ability of a predictive model. For example, sensitivity and specificity maybe used in assessing diagnostics. As another example, a "p-value" may be used that represents the probability that chance alone is responsible for, for example, the observed differences between strata (e.g., see Figures 8, 10, and 12). Therefore, the lower the p-value, the more likely there is a true statistical association with outcome. Typically, the standard is that any p-value less than or equal to 0.05 is statistically significant. Study 1. In this analysis, the above-described SVRc model was applied sequentially to the clinical, molecular, and moφhometric data, with the clinical features first serving as an anchor for a "greedy-forward" feature selection ("FS") algorithm via SVRc run on the molecular data. Following this step, a sepond SVRc greedy-forward feature selection algorithm on the moφhometric data was run, using the combination of the clinical and selected molecular features as the anchor. The last step involved running a greedy-backward selection algorithm on the combination of the clinical, selected molecular and selected moφhometric features to derive a final model. During feature selection, the criterion to determine whether a feature was entered (or kept) in the model was based on whether the presence (or absence) of that feature increased the concordance index, i.e. added predictive information. The model was evaluated for predictive accuracy using both internal and external validation. Internal validation was performed using five-fold cross-validation. In order to perform external validation, a series of test sets of patients was created from the cohort of patients and predicted outcome was compared to actual outcome for these patients via the concordance index. In applying this two-level validation design, a subset of patients were randomly selected from the full set of patient records and only the remaining patients were used to build the predictive model using the procedure just described. The withheld records were then used to apply to the trained model in order to get a predictive accuracy. These two steps were repeated B times to get B predictive rates where the final predictive rate was the average. Features selected for the final model were those that appeared a sufficient amount of times in the B distinct models created. Using the selected feature set, a neural network model was developed via directly maximizing the concordance index. Particularly, a neural network (NNci) of the type described above was used, in which network was trained using an objective function substantially in accordance with an approximation of the concordance index. The output of this final model was used to estimate individual future patient risk for PSA recunence. Study 2. The goals of this study were identical to Study 1; however, different feature selection and validation procedures were used. Instead of using the anchoring approach, all of the features were ranked by their association with time to PSA recunence (measured by the concordance index) and those features which passed a certain pre-determined threshold (CI > 0.60) were selected. This was done after the number of imaging features was reduced by our domain experts, and these features were then evaluated in a series of n-feature models (e.g. 1-feature, 2-feature, 3-feature, etc.). Using a forward feature selection process, the features that maximized the concordance index of each n-feature model were used in the next n+ 1-feature model. This process ended once the CI could not be improved by a predetermined threshold. Then using a backward feature selection process, features were removed in an effort to increase the CI. This process was terminated when the removal of any feature did not improve the CI. A simple bootstrapping technique was used for feature selection. In this approach, patients were sampled with replacement and used as a training set while the model was evaluated on those not selected. As a comparison, this feature selection algorithm was run using only those features found in the Kattan post-operative nomogram, which is described in Kattan et al U.S. Patent No. 6,409,664, which is hereby incoφorated by reference herein in its entirety. The output of the final model was used to estimate individual future patient risk for PSA recunence. Study 3. The goal of this study was to identify features predictive of overall survival using the same cohort and feature set analyzed in Study 2 as well as the same feature selection algorithm. The output of the final model was used to estimate individual future patient risk for death due to any cause. RESULTS The general approach was to apply systems pathology (the combination of moφhometric analyses, molecular signatures and patient clinical profiles) to develop predictive models for PSA recunence and overall survival in a cohort of prostate cancer patients status post prostatectomy. It is important to note that when clinicopathological features alone from Study 1 were utilized in a standard Cox Model analysis, the accuracy for predicting PSA recunence was only 59%. It was only after the integration of moφhometric and molecular features with SVRc that the level of predictive accuracy was increased to 88%. The following sections describe how this improvement was achieved. Study 1. For the 132 patients in this cohort, the median age at diagnosis was 63 years (min: 40, max: 81), and the median pre-operative PSA was 8.2 ng/dl (min: 1.1, max: 81.9). Based on the prostatectomy samples, 32% had a Gleason score less than 7, 60% were Gleason 7 and the remaining 8% were greater than 7. Sixty-nine patients (52%) were pT2N0MO, 40 patients (30%) pT3aN0M0, and the remaining 23 patients (18%) pT3bN0M0 or pTl-3N+. (Table 7 contains a summary list of clinical characteristics for the three studies).

Table 7. Clinical Information Study 1 Study 2 and 3

N 132 268

Age (years) Mean 62 62 Median 63 63 Range 40 - 81 40 - 81 Race Caucasian 120 (90.9%) 241 (89.9%) Hispanic 8 (6.1%) 12 (4.5%) African-American 2 (1.5%) 9 (3.4%) Unknown 2 (1.5%) 6 (2.2%) Pre-operative PSA (ng/dl) Mean 12.2 10.8 Median 8.2 7.8 Range 1.1 - 81.9 0.9 - 81.9 TNM Stage pT2N0 69 (52.3%) 157 (58.6%) pT3aN0 40 (30.3%) 72 (26.9%) pT3bN0 13 (9.8%) 22 (8.2%) pTl-3N+ 10 (7.6%) 17 (6.3%) UICC Stage Tla < 5% 0 (0.0%) 1 (0.3%) Tib > 5% 0 (0.0%) 1 (0.3%) Tic not palpable or visible 49 (37.1%) 112 (41.8%) Study 1 Study 2 and 3 T2a < V- lobe 23 (17.4%) 58 (21.7%) T2b < 1 lobe 27 (20.5%) 45 (16.8%) T2c both lobes 23 (17.4%) 34 (12.7%) T3 a unilateral ECE 8 (6.1%) 15 (5.6%) T3c SV+ 2 (1.5%) 2 (0.8%)

DRE Result Non-palpable 56 (42.4%) 118 (44.0%) Palpable 76 (57.6%) 150 (56.0%)

Lymph Node Involvement Negative 121 (91.7%) 250 (93.3%) Positive 11 (8.3%) 18 (6.7%)

Seminal Vesicle Involvement No 113 (85.6%) 236 (88.0%) Yes 19 (14.4%) 32 (12.0%)

Surgical Margins Negative 108 (81.8%) 217 (81.0%) Positive 24 (18.2%) 51 (19.0%)

Extracapsular Involvement No 70 (53.0%) 159 (59.3%) Yes 62 (47.0%) 109 (40.7%)

Dominant Biopsy Gleason Grade 1 0 (0.0%) 1 (0.4%) 2 24 (18.2%) 43 (16.0%) 3 85 (64.4%) 184 (68.7%) 4 22 (16.7%) 38 (14.2%) 5 1 (0.7%) 2 (0.8%)

Biopsy Gleason Score 2 0 (0.0%) 1 (0.4%) 3 0 (0.0%) 0 (0.0%) 4 6 (4.6%) 7 (2.6%) 5 27 (20.5%) 56 (20.9%) 6 41 (31.1%) 97 (36.2%) 7 48 (36.4%) 90 (33.6%) 8 7 (5.3%) 13 (4.9%) 9 3 (2.3%) 4 (1.5%)

Dominant Post-operative Gleason Grade 2 3 (2.3%) 20 (7.5%) 3 98 (74.2%) 201 (75.0%) 4 31 (23.5%) 47 (17.5%)

Post-operative Gleason Score 5 6 (4.6%) 21 (7.8%) 6 36 (27.3%) 86 (32.1%) 7 79 (59.9%) 148 (55.2%) 8 10 (7.6%) 12 (4.5%) 9 1 (0.8%) 4 (0.4%)

Ploidy Diploid 74 (56.1%) 145 (54.1%) Tetraploid 54 (40.9%) 115 (42.9%) Aneuploid 4 (3.0%) 8 (3.0%)

Percent Ploidy in S Phase (%) Mean 2.3 2.4 Median 1.1 1.1 Range 0.0 - 63.8 0.0 - 66.4

Percent Ploidy Fraction Mean 3.4 3.5 Median 2.6 2.4 Range 0.0 - 20.0 0.0 - 20.0 Twenty (15%) patients experienced PSA recunence, while the remaining patients (85%) were censored. For censored patients, the median follow-up time was 60.8 months, or just over 5 years. The overall median time to PSA recunence was not reached. All seventeen clinical features were selected as being predictive of PSA recunence, with the most informative being annotated as follows (clinicopathological feature and # of times selected by the model): biopsy Gleason grade (112), race (112), UICC clinical stage (110), ploidy (110), and DRE results (109). Image Analysis and Morphometry Studies. Figures 5a and 5b illustrate digitized images of healthy and abnormal prostate tissue, respectively, obtained after segmentation and classification in accordance with the present invention. Various pathological objects have been labeled in the tissue for illustration. A total of 496 moφhometric features (shown in Table 1, above) were generated by the image analysis software. Of the 496 moφhometric features, the 10 moφhometric features shown in Figure 6 were selected as being predictive of PSA recunence. The moφhometric features selected related to the following pathological objects, where the numbers in parentheses next to the features indicate how many times the features were selected as conelated with outcome during generation of the final model: red blood cell, epithelial nuclei, lumen, stroma, cytoplasm, and tissue background (Red Blood Cell Minimum Length in Pixels (20), Epithelial Nuclei Maximum Compactness (17), Lumen Minimum Radius of Smallest Enclosure (14), Epithelial Nuclei Minimum Width in Pixels (11), Stroma Maximum Density (10), Lumen Maximum Border Length in Pixels (10), Epithelial Nuclei Minimum Standard Deviation Channel 2 (10), Epithelial Nuclei Maximum Radius of Smallest Enclosure (10), Cytoplasm Standard Deviation of Border Length in Pixels (10), and Background Standard Deviation of Area in Pixels(lO)). More particularly, in this example, the moφhometric features of length for red blood cell, radius of smallest enclosure and border length for lumen, border length for cytoplasm, density for stroma (e.g., square root of the area covered by a stroma divided by its radius), and area for background were determined to conelate with outcome. The moφhometric features of compactness, width, green channel value, and radius of smallest enclosure for epithelial nuclei (e.g., ellipse with the same area as the object is created and then enlarged until it completely encloses the epithelial nuclei, and the ratio of the radius of the smallest enclosing ellipse to the radius of the original ellipse is computed) were also determined to conelate with outcome. Various possible reasons for at least some of these conelations are described above in connection with Example 1. For example, the morphometric feature of compactness of the epithelial nuclei may be a reflection of the 'back to back' nature of epithelial cells in a circumferential pattern which would suggest a loss of glandular and lumen formation / differentiation and therefore be consistent with a higher Gleason grade (i.e., higher disease progression). Also, the moφhometric feature of the radius of smallest enclosure of the lumen relates to the overall size of the lumen which is dramatically reduced and diminished as the Gleason grade increases. In addition, the conelations determined in this study may be at least partially explained by the hypothesis that epithelial nuclei typically become less diverse in shape (e.g., more round with less variations) and size (e.g., area and border length) and have less color variation as the epithelial nuclei invade the stroma. This invasion of the stroma may also explain why moφhometric features of the stroma have been determined to be conelated with disease progression. Particularly, cancerous images are typically characterized by a small amount of stroma because the stroma area is replaced by epithelial cell cytoplasm as cancer progresses. This causes density values for stroma to be higher because the stroma compactness is reduced and becomes more fractal in shape (the object radius increases more than the area as objects deform and become thinner). Additional reasoning for the conelations determined in this study may be that an abundance of red blood cells traveling through the tissue may reflect some measure of angiogenesis or new blood vessel formation which may be related to disease progression as a means for cells to leave the prostate and seed externally - thus impacting on the clinical outcome of PSA / BCR recunence. As stated above, it will be understood that at least some of the particular moφhometric features determined by the teachings provided herein to conelate with outcome may depend on, for example, the particular hardware, software, or combination thereof that is used by the present invention to calculate the moφhometric features. The Defmiens Cellenger software and the particular morphometric features measured by the software described herein are only illustrative and any other hardware, software, or combination thereof may be used without departing from the scope of the invention. Molecular Analysis. Of the 12 biomarkers that were evaluated by IHC, a total of 43 unique features were recorded. (Tables 8a, 8b, and 8c, below, show a summary of the observed biomarker - molecular features). o

From these 12 antibodies, 8 biomarkers encompassing 14 specific molecular features were selected as being associated with PSA recunence. Some examples of the more highly selected molecular features are annotated as follows (biomarker - # times selected by the model) and include : AR Staining Index - tumor (93), AR Staining Index - atrophic gland (54), CD34 - associated Tumor / PIN (22), Ki-67 - tumor (18) and CD45 - associated with PIN (17), where PIN is an abbreviation for prostatic intraepithelial neoplasm. Figures 7a and 7b illustrate representative fields demonstrating expression profiles for AR and CD34, respectively. The profile of biomarker expression was noteworthy for the highly selected and somewhat heterogeneous expression patterns of AR and CD34. These markers and their relationship to tumor, atrophic glands (for AR) and Tumor / PIN (for CD34) suggest biological and functional significance impacting on the clinical outcome of PSA recurrence. The second group of selected markers included Ki-67 and CD45 both of which had prominent but overall low selection frequency when compared with AR and CD34. Analytical and Statistical Studies. Using both domain expertise and domain-specific feature selection procedure above where 120 random splits were created for training (N=100) and testing (N=32) the models, the final feature set was reduced to 41 total features of which 17 were clinical, 10 moφhometric, and 14 molecular. Figure 5 shows a complete list of the selected features. The 10 moφhometric features are described above. The clinical and molecular features are further described below. Clinical Features

1. Biopsy Gleason Score: the summarized Gleason grades (dominant and secondary) which are assigned to the multiple Needle Biopsy Tissue Samples received by a pathologist. The Gleason scoring system was developed to create a standardized, somewhat subjective, means of representing the architecture of prostatic adenocarcinoma by histology with the production of individual grades. The grades range from 1 - 5 based on the degree of differentiation of the glandular units and epithelial cells. The dominant (primary) and sub-dominant (secondary) patterns are added together to create a Gleason Summary. In addition, the features of overall stromal compactness, epithelial cell size and nnclear features are occasionally considered in the overall grading system.

2. Race (e.g., African American, Caucasian, etc.)

3. UICC Stage: International Union against Cancer TNM staging system use to define clinical staging for cancer, where "T" stands for Tumor size, "N" stands for lymph node involvement and "M" stands for metastasis to a. distant site. . •

4. Ploidy Result: DNA content which is a. reflection of the overall DNA content within the prostate cancer epithelial cells. Benign cells and well-behaved tumor cells grow and divide in an orderly fashion. In the resting state, they contain one complete set of chromosomes (this is the diploid condition). This complete set of chronxosomes consists of 23 chromosomes (or N) from Ma and 23 (N again) chromosomes from Pa (equaling a total of 2N). A cell must double the number of its chromosomes before it can divide, creating two complete sets of chromosomes (this is 4N, or the tetraploid state). After division is completed, each new cell receives half of the genetic material and therefore becomes diploid (2N) once again. If DNA ploidy analysis were to be performed on a group of these cells, one would see that most of the cells would be diploid and a small fraction of them (those getting ready to divide) would be tetraploid. Additionally, in measuring and creating a graph of the amount of genetic material in each cell, one would see a dominant diploid peak and a minor tetraploid peak. The amount of DNA in a cell can be measured by staining it with a dye that binds to the genetic material. The concentration and distribution of this dye (Fuelgen stain) can be measured by image analysis microscopy. When tumors worsen they tend to not divide as orderly as they once did. Instead of the resting state having a complete set of chromosomes, the resting state may only have a set and a half. Such cells would have a DNA content that was neither diploid nor tetraploid but mid-way between. Plotting these cells on the above-described graph above would yield an aneuploid peak midway between the other two peaks. Studies have shown that tumors that have a significant aneuploid peak do not behave as well as those that do not. This is not svuprising because a strong conelation exists between ploidy status and nuclear grade. A nuclear grade can be assessed by any pathologist with enough experience with prostate cancer. The value that DNA ploidy analysis adds is that it is an objective measurement that can be compared between labs using standardized techniques and that can be used to perform a quick check on the approximate accuracy of Gleason scoring. For example, any Gleason score 2+2=4 or 2+3=5 tumor that has an aneuploid peak should potentially be re-evaluated for possible adjustment to the score.

5. DRE Result: Result from a digital rectal exam (e.g., negative or positive) which is utilized to determine extent of disease both within the prostate as well as extra prostatic extension by palpation.

6. Lymph Node Involvement: a measure of the extent to which lymph nodes contain tumor cells (e.g., prostate cancer epithelial cells), which can be assessed either by clinical / surgical inspection or at the time of a prostatectomy.

7. Dominant Biopsy Gleason Grade: See above description of Biopsy Gleason Score. This reflects the dominant Gleason grading pattern seen on either a biopsy or a prostatectomy specimen. 8. Percent Ploidy in S Phase: represents a fraction of the cellular content which is in a proliferative or S phase of the cell cycle and reflects the growth potential of the tumor.

9. Post-operative Gleason Score: Scoring of tissue taken after surgery from various regions of the prostate resection sample.

10. TNM Stage: Tumor, Node and Metastasis based on the UICC criteria post prostatectomy and based on pathologic examination of tissue samples. 11. Dominant Post-operative Gleason Grade: the dominant Gleason grade which represents the most predominant histologic feature present in the prostatectomy specimen.

12. Age

13. Seminal Vesicle Involvement: Invasion of the seminal vesicle by tumor. 14. Pre-operative PSA: PSA level observed prior to surgery

15. Percent Ploidy Fraction: See above description of ploidy result.

16. Surgical Margin Involvement: Involvement of the surgical margins by tumor which reflects the extent to which the bed from which the tumor/prostate vas removed at the time of surgery contained tumor cells. 17. Extracapsular Involvement: Extension of me tumor beyond the capsule of the prostate. Molecular Features

1. AR - tumor: Androgen Receptor (AR) Staining Index for a tumor, which is a measure of the percentage and intensity of cells staining positive for AR. Witn respect to prostate cancer, the staining index may represent the degree of brown reaction prod.nct which is detected in the nuclei of epithelial cells in the prostate samples evaluated.

2. AR - gland: AR Staining Index for a tumor, which is present within a glandular structure.

3. CD34 - tumor/PIN: The localization of CD34 to the endothelial cells of vessels which are associated with tumor and PIN.

4. Ki67 - tumor 2: The identification of ki67 positive nuclei in- tumor epithelial cell nuclei. 5. CD45 - PIN 3: The identification f CD45 positive lymphocytes in association with PIN.

6. CD34 - tumor/stroma: The localization of CD34 vessels which are associated with tumor.

7. Ki-67 - tumor 3: see above.

8. p27 - tumor: The identification of p27 in the nuclei of tumor epithelial cells.

9. C14 - PIN: The identification of cytokeratin 14 in the (epithelial) basal cells of the glandular unit.

10. CD34 - tumor: The localization of CD34 to vessels which are associated with the tumor.

11. PSA - gland: The identification of PSA to the luminal epitlxelial cells of the gland unit.

12. PSMA - PIN: The identification of PSMA to the glandular J luminal cells of regions identified as PIN. 13. CD34 - PIN/stroma: The localization of CD34 to vessels associated with PIN. 14. CD45 - tumor 3: The identification of CD45 positive lymphocytes which are associated with tumor. As each domain of data was analyzed during this process using SNRc, the predictive accuracy of the models increased. Using internal validation, when looking at the clinical data alone, the concordance index was 0.79. By adding features from the molecular domain, the concordance index increased to 0.81. The final model, formed by the addition of the moφhometric features, reached a concordance index of 0.84. Each of these internally- validated models was also validated externally (as described above in Materials and Methods) with the same trend being noted. Using ΝΝci on the final selected set of features, the concordance index reached 0.88. The resulting output of the ΝΝci and the SVRc models can ^"be inteφreted as a relative risk estimate of PSA recunence for an individual patient. Using the quartiles of this score (<25%, >25%-75%, >75%), risk groups of patients were created; the Kaplan-Meier estimates of recurrence for each risk group according to the ΝΝci model are presented in Figure 8. The groups showed a statistically significant difference in time to PSA recurrence (log-rank test, p- value < 0.0001). The p-value represents the probability that chance alone is responsible for the observed differences between strata (risk groups in these examples) . Therefore, the lower the p- value, the more likely you are seeing a true statistical association. Generally, any p-value less than or equal to 0.05 is statistically significant. Study 2. For the 268 patients in this cohort, hich contains 129 of the 132 patients analyzed in Study 1, the median age at diagnosis was 63 years (min: 38, max: 81), and the median PSA prior to radical prostatectomy was 7.8 ng/dl (min: 0.9., max: 81.9). Based on the prostatectomy samples, 40% of tumors had a Gleason Score less than 7, while 55% of the prostatectomies had a Gleason 7. The remaining 5% of prostatectomies had a Gleason Score greater than 7. One hundred fifty-seven patients (59%) were diagno sed as having pT2Ν0M0 disease, 72 patients (27%) as pT3aN0M0, and the remaining 39 patients (14%) as pT3bN0M0 or pTl-3N+. (See Table 5, supra for details of all analyzed clinicopatbological features for this cohort). Thirty-eight (14%) patients experienced PSA recurrence, while the remaining patients (86%) were censored. For censored patients, the median follow-up time was 58.7 months, or just under 5 years. The overall median time to PSA recunence was not reached. Three clinical features were selected as being predictive of PSA recunence: TNM clinical stage, surgical margins, and lymph nodes. Image Analysis and Morphometry Studies. Using an updated version of the image analysis software but analyzing the same H&E stained slides, a total of 350 moφhometric features were generated (shown in Table 2, above). Figure 9 shows that, of the 350 features, 6 moφhometric features were selected as being predictive of PSA recurrence, where these moφhometric features related to the pathological objects of epithelial nuclei, stroma, cytoplasm, red blood cell, and lumen (i.e., EpithelialNucleiMinCompactne0215, StromaMaxStddevCharιnel30569, CytoplasmStddevMaxDiff0148, RedBloodCellMeanAreaPxl0386,

RedBloodCellStddevAreaPxl0388, and LumenMinAsymmetry0295). More particularly, in this study, the moφhometric features of compactness of epithelial nuclei, blue channel value for stroma, max difference for cytoplasm (e.g., minimum mean value belonging to cytoplasm subtracted from its maximum value over all color channels for the cytoplasm, where the result is divided by the object brightness), area for red blood cells, and asymrrietry of lumen were selected as being conelated with outcome. Various possible reasons for at least some of these conelations are described above in connection with Example 1 and/or Study 1. For example, moφhometric features including the compactness of the epithelial cells, the variation and disruption of the stroma by infiltrating epithelial cells, and the evidence of reduced lumen size would all pro-vide histologic evidence of a higher Gleason grade (i.e., higher disease progression). A higher Gleason grade suggests a more aggressive prostate tumor which would support metastasis and or extension of tumor supporting PSA recunence post surgery, hi addition, the identification of red blood cells in various formats would suggest an abundance of vessels. The evidence of additional vessels would create a possible route for which epithelial cells could exit the prostate and be distributed in external locations producing PSA. Clinical and molecular features selected in study 2 are shown in Figure 9 and listed below. Descriptions of these clinical and molecular features are provided above. Clinical Features 1. TNM stage

2. Surgical Margin Involvement 3. Lymph Node Involvement Molecular Feature

1. AR Staining Index (tumor) Each number in Figure 9 represents the concordance index of a predictive model based on the conesponding feature and all other feature(s) in Figure 9 having smaller number(s). For example, 0.8483 is the CI of a model based on features TNM Clinical Stage, Surgical Margins, EpithelialNucleiMinCompactne0215, Lymph Nodes, and StromaMaxStddevChannel30569. The CI of a model based on the same 5 features plus AR Staining Index (tumor) is 0.8528. In other words, the addition of the AR Staining Index molecular feature to the model increases the predictive power of the model. Molecular Analysis. No additional inrmunohistochemistry studies were necessary. The data originally collected was used as described in Materials and Methods (see Appendix, Tables 9a, 8b, and 9c for a complete summary of the molecular features).

24817-524-061

A single molecular feature was selected as being predictive of PSA recunence: AR Staining Index - tumor. Analytical and Statistical Studies. Using domain expertise and simple bootstrapping, the algorithm found a subset of 10 features (3 clinicopathological, 6 moφhometric, and 1 molecular) that had a concordance index (CI) of 0.87 (Table 9, above, shows the complete list of selected features). The resulting output of the SVRc model can also be inteφreted as a relative risk estimate of PSA recunence for an individual patient. Using the quartiles of this score (<25%, >25%-75%, >75%), risk groups of patients were created; the Kaplan-Meier estimates of recunence for each risk group as predicted by the SVRc model are presented in Figure 10. The groups showed a statistically significant difference in time to PSA recurrence (log-rank test, p-value < 0.0001). Study 3. This study used the same cohort as that of Study 2 so that the clinicopathological characteristics of the patients are identical. In terms of outcome, nineteen (7%) patients died due to any cause, while the remaining patients (93%) were alive as of their last visit and censored. For censored patients, the median follow-up time was 64.8 months, or just over 5 years. The overall median time to death was not reached. Two clinical features were selected as being predictive of death due to any cause: TNM clinical stage and patient age. Image Analysis and Morpho etry Studies. The same set of 350 moφhometric features from Study 2 was used in this study. Figure 11 shows that, of the 350 features, 11 moφhometric features were selected as being predictive of death due to any cause, where these features related to the pathological objects of stroma, red blood cell, and epithelial nuclei (i.e., StromaMinMeanChannell0535, RedBloodCellMeanStddevChann30474, StromaMinMeanChannel20539, RedBloodCellMinMeanChannel20443, RedBloodCellStddeStddeChann20472, StromaMaxMaxDiff0529, EpitheNucleMeanBordeLengtPxl0206, EpithelialNucleiMeanAreaPxl0194, EpithelNucleiStddevElliptFit0228, RedBloodCellStddeStddeChann30476, and RedBloodCellStddevElliρtiFit0420, where "channel" refers to the red (R), green (G), and blue (B) color channels of an image). More particularly, in this study, the moφhometric features of mean value of red color channel, mean value of blue color channel and max difference for stroma were detennined to be conelated with outcome. The moφhometric features of mean and standard deviation of red channel, mean and standard deviation of green channel and elliptic fit for red blood cell were determined to be conelated with outcome. To determine the moφhometric feature of elliptic fit, an ellipse with the same area as the red blood cell was created, the area of the red blood cell outside the ellipse was compared with the area inside the ellipse that was not filled with the red blood cell, and a value of 0 was assigned where there was no fit whereas a value of 1 was assigned for a complete fitting object. The moφhometric features of border length, area and elliptic fit for epithelial nuclei were determined to be conelated with outcome. Various possible reasons for at least some of these conelations are described above in connection with Example 1 and/or Study 1. For example, the overall shape of the epithelial nuclei reflects a histologic appearance of a higher Gleason grade. Additionally, in this study, the conelation with respect to stroma may be explained by the understanding that stroma will exhibit a reduced contrast (as measured by the max difference moφhometric feature) as cancer progresses due to its interruption with epithelial cells. Molecular Analysis. The same set of molecular features from Study 2 was used in this study. A single feature was selected as being predictive of death due to any cause: PSA Staining Index — atrophic gland. Analytical and Statistical Studies. In this cohort, a total of 14 features (2 clinicopathological, 11 moφhometric, and 1 molecular) were selected. The final model had a concordance index (CI) of 0.80. The complete list of selected features are shown in Figure 11 and listed below. The clinical and molecular features selected are listed below. Descriptions of the clinical features are provided above. Clinical Features 1. TNM stage

2. age

Molecular Feature

1. psapsi: refers to the staining index for prostate specific antigen (PSA) in the prostatic intraepithelial neoplasm (PIN). Each number in Figure 11 represents the concordance index of a predictive model based on the conesponding feature and all other feature(s) in Figure 11 having smaller number(s). For example, 0.6804 is the CI of a model based on StromaMinMeanChannell0535 and 0.7362 is the CI when the model is based on both StromaMinMeanChannell0535 and TNM. The resulting output of the SVRc model can also be inteφreted as a relative risk estimate of death for an individual patient. Using the quartiles of this score (<25%, >25%-75%, >75%), risk groups of patients were created; the Kaplan-Meier estimates of recunence for each risk group as predicted by the SVRc model are presented in Figure 12. Using the log-rank test, a significant difference in survival was observed between risk groups (p < 0.0001).

Discussion of Results (Example 2) The observed reduction of (composite) selected features from Study 1 (41) to Study 2 (10) while retaining the predictive accuracy of the model emphasized the precision and filtering attributes that were achieved through different machine learning algorithms. The concordance index of the model that was developed in the 268-patient cohort was 0.87; by comparison, when the Kattan nomogram [20] is applied to this cohort it achieved a concordance index of 0.78.

Perhaps more striking is the ability of the above model as discussed in Study 2 to conectly classify patients with early PSA recunences (within 5 years) with a sensitivity of 80%. By comparison, the Kattan nomogram is able to make the same prediction with a sensitivity of only 54%. This further emphasizes the role that such a predictive test would serve in decision making for early intervention. Finally, the output of the model presented can be used to estimate the likelihood of a patient recurring over time, as opposed to offering a single estimate of the probability of a patient recurring within a given number of years without any indication as to when within that time frame. In Study 3 the objective was to utilize the existing domain knowledge derived from Study 2 and develop a predictive model for overall survival. The successful end result was the ability to predict with 80% accuracy an individual's overall survival and time to death utilizing a total of 14 combined domain features. Although limited by the small number of events (7% dead from any cause) and absence of a comparable published nomogram, the results further support the use of a systems approach for developing these types of predictive tests. Additional efforts are underway with respect to expanding this Overall survival' analysis to include clinical measures of poor outcome (i.e., metastasis and or death due to prostate cancer) utilizing a retrospective multi-institutional population with an independent external validation study. In addition, a 'Systems Pathology' approach recently has been initiated to intenogate diagnostic needle biopsies in order to have an impact on treatment issues prior to surgery. The foregoing example demonstrates that a 'Systems Pathology' platform has been successfully developed which integrates clinical features, tumor tissue moφhometrics and molecular analyses. By using domain expertise and support vector regression for censored data (SVRc), features were selected from the three domains and used to develop a predictive model for PSA recunence and overall survival. It will be understood that this novel 'Systems Pathology' approach has broad application in the field of personalized medicine as it relates to tumor diagnostics, patient prognostication, and as a tool for predicting response to specific therapeutics.

Example 3: Prediction of Aggressive Disease Subsequent to Prostatectomy Clinical and Morphometric Data This study was undertaken to predict aggressive disease (i.e., clinical failure as demonstrated by a positive bone scan representing metastatic prostate cancer to bone) subsequent to a patient having a prostatectomy. Prior to the present invention, no accurate analytical tools existed for providing such a prediction. As described above, the systems pathology approach of the present invention has been shown to accurately predict PSA recunence. This study demonstrates that the present invention can also be used to accurately predict distant bone metastasis after prostatectomy. A cohort of 1 19 patients who underwent radical prostatectomy was studied incoφorating tissue microanays (TMAs) constructed from prostatectomy specimens. Moφhometric (i.e., image analysis) studies were performed using hematoxylin and eosin (H&E) stained tissue sections, and biological determinants were assessed with irnmunohistochemistry (IHC) utilizing a series of biomarkers selected for their potential biological relevance for prostate cancer progression. A predictive model for clinical failure (i.e., positive bone scan) was derived from a selected set of features through supervised multivariate learning. Patients with complete non- missing data (n=l 16) in each domain were evaluated with a support vector machine for regression developed to handle censored data (SVRc). Predictive perfonnance of the model was estimated using the concordance index (CI) with generated scores used to define risk groups. From the 116 patients, a subset of 61 patients was selected based on their clinical features, including 20 individuals with clinical failure as identified by bone metastasis. This cohort was used to create a model for predicting the likelihood of a positive bone scan within 5 years of prostatectomy. The seven features shown in Figure 13 (including four clinical and three moφhometric features) were selected which predicted clinical failure with 89 percent accuracy and a sensitivity and specificity of 86 and 85 percent, respectively. The selected moφhometric features were related to the pathological objects of cytoplasm and lumen. More particularly, the selected moφhometric features were area of cytoplasm divided by the total tissue area, area of lumen divided by total tissue area, and cytoplasm standard deviation of mean red channel. The clinical features are listed below. Clinical Features 1. Extracapsular Extension (ECE) 2. Seminal Vesicle Invasion (SVI)

3. Dominant Prostatectomy Gleason Grade (PGG1 ) 4. Lymph Node Invasion (LNI) Conclusion The integration of clinical features with moφhometric features resulted in the first, accurate prognostic test for predicting clinical failure within 5 years after prostatectomy. As described, the test can predict with 89% accuracy which patients are most likely to have a clinical failure (and when) within a 5 year period post prostatectomy. The results of adding molecular features to the clinical and moφhometric features of the model are cunently pending.

Example 4: Liver Toxicology Morphometric Data This study was undertaken to demonstrate image analysis and statistical modeling capabilities in the area of toxicology. Specifically, the study called for the acquisition and analysis of sections of rat liver with the overall objective being to classify the sections as normal or abnormal. Being able to automate this process while simultaneously achieving a high-level of classification accuracy could allow for the creation of a high-throughput platform used to objectively screen for toxicities in pre-clinical studies. The study was divided into two phases. The initial phase used a set of 100 rat liver sections as a training set; 80 normal liver sections and 20 abnormal. This set of sections was used to develop an image analysis application using the tissue image analysis system described above as well as perform feature and model selection to classify the sections. The established image analysis process was then applied to an unlabeled set of 100 rat liver sections in the second phase of the study in which the statistical models designed in the training phase were tested. Segmentation Accuracy The global segmentation accuracy for all objects, as measured by a pathologist's assessment, was 80% - 90%. Statistics The statistical component of the study involved two steps. The first step involved selecting features from the imaging data generated by the image analysis of the sections. Reducing the number of features used for classification may improve the robustness and reliability of the classification of the sections. The second step involved both training a model using the selected feature set and labels for each section (abnormal, normal) and then testing the model by predicting the classification of an independent set of rat liver sections where the labels were unknown. Feature Selection The statistical measurements generated for each of the above objects were: — Number of obj ects — Relative area (percent, in relation to total area of image) — Minimum size (in pixels) — Maximum size (in pixels) — Average size (in pixels) — Standard deviation of the size Since multiples images which were analyzed per section, these measures were themselves averaged across all images for an individual rat liver section. The total number of original features was 378. Feature selection also involved two steps. The first step utilized domain expertise. A pathologist selected features from the original feature list generated by the image analysis of the sections. The decision to include or exclude features was based on the understanding of the pathology of the liver and potential abnormalities/toxicities that could be encountered. From the original set of 378 features, 90 features were selected using domain knowledge. These features were then examined using stepwise discriminant analysis to fiirther reduce the number of features for classification. The set of features that made up each class were assumed to be multivariate normal with a common covariance matrix. Features were chosen to enter or leave the model according to the significance level of an E-test from an analysis of covariance, where the features already chosen act as the covariates and the feature under consideration is the dependent variable. A significance level of 0.15 was used. — Stepwise selection began with no features in the model. At each step, the model was examined. — If the feature in the model that contributed least to the discriminatory power of the model as measured by Wilks' lambda (the likelihood criterion) failed to meet the criterion to stay, then that feature was removed. — Otherwise, the feature not in the model that contributed most to the discriminatory power of the model was entered. — When all features in the model met the criterion to stay and none of the other features met the criterion to enter, the stepwise selection process stopped.

Classification/Model Training The selected features were then entered into a linear discriminant analysis (LDA) which classified each of the liver sections as abnormal or normal. The output of the model was conected for potential bias via cross-validation. Neural networks were also explored as a classifier. The selected features were used as the inputs to the neural network model, which is a standard multilayer perceptron (MLP) structure with zero hidden units and direct connection between the input and output layers. The model was trained by trying to directly maximize an approximation to the area under the ROC curve, which is explained below. It was found that the MLP model trained by this criterion achieves better accuracy than an MLP model trained by the typical criteria, e.g., mean square enor and cross entropy. The output from both models were used to create a receiver operating characteristic

(ROC) curve by choosing a different value of the model output as a cut point, calculating the sensitivity and specificity for each cut point, and plotting these in a 2-dimensional plot (sensitivity along the y-axis and specificity along the x-axis). The area under the ROC curve (AUC) uses both measures to assess each model's accuracy and can be inteφreted as the ability of the model to conectly classify the liver sections as abnormal or normal. Typically, sensitivity and specificity are described in terms of the true positive rate and true negative rate, respectively. Thus in the context of this study, the abnormal class was considered as a 'positive' result, while the normal class was considered as a 'negative' result. Sensitivity, therefore, is the true positive rate, i.e. the proportion of liver sections conectly classified as abnormal; the specificity, on the other hand, is the true negative rate, i.e., the proportion of liver sections conectly classified as normal. From the ROC curves, selected sensitivities and specificities from the training set are provided in the Results section below. Model Testing Once developed, the parameters of both the linear discriminant function and the neural network were locked. Upon receipt of the statistical measurements from the test set of rat liver images, both classifiers were applied using an individual cut point estimated using the cross validation results of each of the model outputs respectively. The cut points both conesponded to a sensitivity of 100% and a specificity of 90% (both based on cross validation) for a future industrial-grade application. For the initial evaluation of this external validation set of livers, assessment of the models' accuracies was performed by another party who was unblinded to the true classification of the liver sections. This other party then also provided the test key to verify the results. Results The area under the ROC curve for both models is very close to 1, indicating almost perfect discrimination between abnormal and normal liver sections. The function derived using LDA has an AUC of 0.99; the function derived using neural networks has an AUC of 0.98. Also observed in the ROC curves was the sensitivity and specificity of each model, depending on the cut point applied to the model outputs to classify a liver section as abnormal or normal. Table 10 summarizes a selection of sensitivity-specificity pairs.

51 ^■

Table 10

Testing The test key labels were compared with the predicted classifications of the linear discriminant function and those of the neural networks. Based on the key, the results are summarized in Tables I la and ll^"b as follows:

Test Key Label Abnormal Normal LDA Label Abnormal Nor-mal 49 51 100

Sensitivity = TP/(TP+FN) x 100 = 42/(42+7) x 100 = (42/49) x 100 = 86%

Specificity = TN/(FP+TN) x 100 = 32/(19+32) x 100 = (32/51) x 100 = 63%

Table I la Test Key Label Abnormal Normal NN Label Abnormal Normal 49 51 100

Sensitivity = TP/(TP+FN) x 100 = 36/(36+13) x 100 = (36/49) x 100 = 73%

Specificity = TN/(FP+TN) x 100 = 32/(19+32) x 100 = (32/51) x 100 = 63%

Table lib

The cut point used for the LDA classifier equaled 0.0031 ; the cut point used for the NN classifier equaled 0.0002. Both conespond to the system requirements of 100% sensitivity and 90% specificity. Discussion Based on the sensitivity and specificity of each classifier after applying them to the test set, LDA outperformed NN. The LDA classifier achieved a sensitivity of 86% which means that this classifier conectly labeled the abnormal rat liver sections as abnormal 86% of the time, as opposed to the neural network classifier which achieved a sensitivity of 73%. Specificity for both classifiers was 63%. Both the sensitivity and the specificity of each model are lower than previously observed, but this is not suφrising as generalizing any classifier to an external set often leads to a drop in its accuracy. This study demonstrated the successful application of technologies for imaging and statistical modeling. ADDITIONAL EMBODIMENTS Thus it is seen that methods and systems are provided for predicting the occurrence of a medical condition. Although particular embodiments have been disclosed herein in detail, this has been done byway of example for puφoses of illustration only, and is not intended to be limiting with respect to the scope of the appended claims, which follow. In particular, it is contemplated by the inventors that various substitutions, alterations, and modifications may be made without departing from the spirit and scope of the invention as defined by the claims. Other aspects, advantages, and modifications are considered to be within the scope of the following claims. The claims presented are representative of the inventions disclosed herein. Other, unclaimed inventions are also contemplated. Applicants reserve the right to pursue such inventions in later claims. Insofar as embodiments of the invention described above are irnplementable, at least in part, using a computer system, it will be appreciated that a computer program for implementing at least part of the described methods and/or the described systems is envisaged as an aspect of the present invention. The computer system may be any suitable apparatus, system or device. For example, the computer system may be a programmable data processing apparatus, a general puφose computer, a Digital Signal Processor or a microprocessor. The computer program may be embodied as source code and undergo compilation for implementation on a computer, or may be embodied as object code, for example. It is also conceivable that some or all of the functionality ascribed to the computer program or computer system aforementioned may be implemented in hardware, for example by means of one or more application specific integrated circuits. Suitably, the computer program can be stored on a carrier medium in computer usable form, which is also envisaged as an aspect of the present invention. For example, the carrier medium may be solid-state memory, optical or magneto-optical- memory such as a readable and/or writable disk for example a compact disk (CD) or a digital versatile disk (DVD), or magnetic memory such as disc or tape, and the computer system can utilize the program to configure it for operation. The computer program may also be supplied from a remote source embodied in a carrier medium such as an electronic signal, including a radio frequency carrier wave or an optical carrier wave. Whilst presently prefened embodiments of the present i-nvention, and presently prefened features of those embodiments, are set out in the accompanying; claims, the scope of the present invention also extends to encompass the features identified in thie following numbered paragraphs: 1. A method of evaluating a risk of prostate cancer recunence in a patient, the method comprising: receiving a patient dataset for the patient; and evaluating the patient dataset with a model predictive of prostate cancer recunence, wherein the model is based on a staining index of an Androgen Receptor (AR), thereby evaluating the risk of prostate cancer recunence in the patient.

2. A method of generating a model predictive of a medical condition, the method comprising: receiving for each of two or more subjects whose outcome with respect to the medical condition is at least partially known, a training subject dataset comprising one or more clinical feature(s), one or more molecular feature(s), and one or more computer-generated moφhometric feature(s) generated from a tissue image; and performing a multivariate analysis on the training subject datasets, thereby generating the model predictive of the medical condition.

3. The method of paragraph 2, further comprising generati ig the one or more moφhometric feature(s) from the tissue image with a computer, wherein said generating the moφhometric feature(s) comprises: segmenting a tissue image into one or more objects; classifying the one or more objects into one or more obj ect classes; and determining the moφhometric features by taking one or more measurements for the one or more object classes. 4. The method of paragraph 3, wherein said classifying the one or more objects into one or more object classes comprises classifying each of one or more of the objects into a class from the group of classes consisting of stroma, cytoplasm, epithelial nuclei, stroma nuclei, lumen, red blood cells, tissue artifacts, and tissue background. 5. The method of paragraph 3, wherein said taking one or more measurements pertaining to the one or more object classes comprises taking one or more measurements of one or more spectral properties and/or one or more shape properties of the one or more object classes.

6. The method of paragraph 2, wherein one or more of the clinical feature(s) are from the group of clinical features listed in Table 4.

7. The method of paragraph 2, wherein one or more of the molecular feature(s) is from the group of molecular features listed in Table 6. 8. The method of paragraph 2, wherein the model is based on a selected subset of said one or more clinical feature(s), one or more molecular feature(s), and one or more computer-generated moφhometric feature(s) in said training subject datasets, which subset is selected as being predictive of said medical condition. 9. The method of paragraph 8, wherein the model is predictive of prostate cancer recunence and the selected subset of feature(s) comprises one or more of the moφhometric feature(s) from the group of moφhometric features consisting of a moφhometric feature of a red blood cell, a moφhometric feature of epithelial nuclei, a moφhometric feature of stroma, a moφhometric feature of lumen, a moφhometric feature of cytoplasm, and a moφhometric feature of tissue background.

10. The method of paragraph 9, wherein the selected subset of feature(s) comprises one or more of the clinical feature(s) listed in Figure 6. 11. The method of paragraph 10, wherein the selected subset of feature(s) comprises one or more of the molecular feature(s) listed in Figure 6.

12. The method of paragraph 11, wherein the predictive model comprises a concordance index of at least about 0.88. 13. The method of paragraph 11, wherein the predictive model comprises a p value less than about 0.0001 for a log-rank test.

14. The method of paragraph 8, wherein the model is predictive of prostate cancer recunence and the selected subset of feature(s) comprises one or more of the moφhometric feature(s) from the group of moφhometric features consisting of a moφhometric feature of a red blood cell, a moφhometric feature of epithelial nuclei, a moφhometric feature of stroma, a moφhometric feature of lumen, and a moφhometric feature of cytoplasm. 15. The method of paragraph 14, wherein the selected subset of feature(s) comprises one or more of the clinical feature(s) from the group of clinical features listed in Figure 9.

16. The method of paragraph 15, wherein the selected subset of feature(s) comprises one or more of the molecular feature(s) from the group of molecular features listed in Figure 9.

17. The method of paragraph 16, wherein the predictive model comprises a concordance index of at least about 0.87.

18. The method of paragraph 16, wherein the predictive model comprises a p value less than about 0.0001 for a log-rank test.

19. The method of paragraph 8, wherein the model is predictive of prostate cancer survival and the selected subset of feature(s) comprises one or more of the moφhometric feature(s) from the group of moφhometric features consisting of a moφhometric feature of a red blood cell, and a moφhometric feature of epithelial nuclei, a moφhometric feature of stroma.

20. The method of paragraph 19, wherein the selected subset of feature(s) comprises one or more of the clinical feature(s) from the group of clinical features listed in Figure 11. 21. The method of paragraph 20, wherein the selected subset of feature(s) comprises one or more of the molecular feature(s) from the group of molecular features listed in Figure 11. 22. The method of paragraph 21 , wherein the predictive model comprises a concordance index of at least about 0.80. 23. The method of paragraph 21 , wherein the predictive model comprises a p value less than about 0.0001 using a log-rank test.

24. The method of paragraph 2, wherein said performing a multivariate analysis comprises performing support vector regression on said datasets.

25. The method of paragraph 2, wherein said performing a multivariate analysis comprises using an objective function substantially in accordance with an approximation of the concordance index (CI) to train a neural network. 26. A method of screening for an inhibitor compound, the method comprising: receiving a first dataset for a patient; evaluating the first dataset with a model predictive of a medical condition, wherein the model is based on one or more clinical feature(s), one or more molecular feature(s), and one or more computer-generated moφhometric feature(s) generated from one or more tissue image(s); administering to the patient a test compound; receiving a second dataset for the patient following the administering of the test compound; evaluating the second dataset with the model; and comparing results of the evaluation of the first dataset with results of the evaluation of the second dataset, wherein a change in the results for the second dataset with respect to the results for the first dataset indicates the test compound is an inhibitor compound.

27. An apparatus for evaluating a risk of prostate cancer recunence in a patient, the apparatus comprising: a model predictive of prostate cancer recunence, wherein the model is based on a staining index of an Androgen Receptor (AR), and wherein the model is configured to: receive a patient dataset for the patient; and evaluate the patient dataset, thereby evaluating the risk of prostate cancer recunence in the patient.

28. An apparatus for generating a model predictive of a medical condition, the apparatus comprising: an analytical tool configured to: receive for each of two or more subjects whose outcome with respect to the medical condition is at least partially known, a training subject dataset comprising one or more clinical feature(s), one or more molecular feature(s), and one or more computer-generated moφhometric feature(s) generated from a tissue image; and perform a multivariate analysis on the training subject datasets, thereby generating the model predictive of the medical condition.

29. The apparatus of paragraph 28, further comprising an image processing tool configured to generate the one or more moφhometric feature(s) from the tissue image with a computer, wherein said generating the moφhometric feature(s) comprises: segmenting the tissue image into one or more objects with the image processing tool; classifying the one or more objects into one or more object classes by the image processing tool; and determining the moφhometric features by taking one or more measurements for the one or more object classes with the image processing tool.

30. The apparatus of paragraph 29, wherein said classifying the one or more objects into one or more object classes by the image processing tool comprises classifying by the image processing tool each of one or more of the objects into a class from the group of classes consisting of stroma, cytoplasm, epithelial nuclei, stroma nuclei, lumen, red blood cells, tissue artifacts, and tissue background. 31. The apparatus of paragraph 29, wherein said taking one or more measurements pertaining to the one or more object classes with the image processing tool comprises taking with the image processing tool one or more measurements of one or more spectral properties and/or one or more shape properties of the one or more object classes.

32. The apparatus of paragraph 28, wherein one or more of the clinical feature(s) are from the group of clinical features listed in Table 4. 33. The apparatus of paragraph 28, wherein one or more of the molecular feature(s) is from the group of molecular features listed in Table 6.

34. The apparatus of paragraph 28, wherein the model is based on a selected subset of said one or more clinical feature(s), one or more molecular feature(s), and one or more computer-generated moφhometric feature(s) in said training subject datasets, which subset is selected as being predictive of said medical condition.

35. The apparatus of paragraph 34, wherein the model is predictive of prostate cancer recunence and the selected subset of feature(s) comprises one or more of the moφhometric feature(s) from the group of moφhometric features consisting of a moφhometric feature of a red blood cell, a moφhometric feature of epithelial nuclei, a moφhometric feature of stroma, a moφhometric feature of lumen, a moφhometric feature of cytoplasm, and a moφhometric feature of tissue background. 36. The apparatus of paragraph 35, wherein the selected subset of feature(s) comprises one or more of the clinical feature(s) listed in Figure 6.

37. The apparatus of paragraph 36, wherein the selected subset of feature(s) comprises one or more of the molecular feature(s) listed in Figure 6. 38. The apparatus of paragraph 37, wherein the predictive model comprises a concordance index of at least about 0.88.

39. The apparatus of paragraph 37, wherem the predictive model comprises a p value less than about 0.0001 for a log-rank test.

40. The apparatus of paragraph 34, wherein the model is predictive of prostate cancer recunence and the selected subset of feature(s) comprises one or more of the moφhometric feature(s) from the group of moφhometric features consisting of a moφhometric feature of a red blood cell, a moφhometric feature of epithelial nuclei, a moφhometric feature of stroma, a moφhometric feature of lumen, and a moφhometric feature of cytoplasm.

41. The apparatus of paragraph 40, wherein the selected subset of feature(s) comprises one or more of the clinical feature(s) from the group of clinical features listed in Figure 9.

42. The apparatus of paragraph 41, wherein the selected subset of feature(s) comprises one or more of the molecular feature(s) from the group of molecular features listed in Figure 9.

43. The apparatus of paragraph 42, wherein the predictive model comprises a concordance index of at least about 0.87.

44. The apparatus of paragraph 42, wherein the predictive model comprises a p value less than about 0.0001 for a log-rank test.

45. The apparatus of paragraph 34, wherein the model is predictive of prostate cancer survival and the selected subset of feature(s) comprises one or more of the moφhometric feature(s) from the group of moφhometric features consisting of a moφhometric feature of a red blood cell, a moφhometric feature of epithelial nuclei, and a moφhometric feature of stroma. 46. The apparatus of paragraph 45, wherein the selected subset of feature(s) comprises one or more of the clinical feature(s) from the group of clinical features listed in Figure 11. 47. The apparatus of paragraph 46, wherein the selected subset of feature(s) comprises one or more of the molecular feature(s) from the group of molecular features Usted in Figure 11.

48. The apparatus of paragraph 47, wherein the predictive model comprises a concordance index of at least about 0.80.

49. The apparatus of paragraph 47, wherein the predictive model comprises a p value less than about 0.0001 using a log-rank test. 50. The apparatus of paragraph 28, wherein the analytical tool is configured to perform support vector regression on said datasets.

51. The apparatus of paragraph 28, wherein the analytical tool comprises a neural network and the analytical tool is configured to use an objective function substantially in accordance with an approximation of the concordance index (CI) to train the neural network.

52. An apparatus for screening for an inhibitor compound, the apparatus comprising: a predictive model based on one or more clinical feature(s), one or more molecular feature(s), and one or more computer-generated moφhometric feature(s) generated from one or more tissue image(s), wherein the predictive model is configured to: receive a first dataset for a patient; evaluate the first dataset according to the model; receive a second dataset for the patient following the administering of a test compound to the patient; and evaluate the second dataset according to the model, wherein a comparison of results from the evaluation of the first dataset with results from the evaluation of the second dataset indicates that the test compound is an inhibitor compound when there is a change in the results for the second dataset with respect to the results for the first dataset.

53. An apparatus for evaluating the risk of occuπence of a medical condition in a patient, the apparatus comprising: a model predictive of the medical condition, wherein the model is based on one or more computer-generated moφhometric feature(s) generated from one or more tissue image(s) and wherein the model is configured to: receive a patient dataset for the patient; and evaluate the patient dataset according to the model, thereby evaluating the risk of occurrence of the medical condition in the patient. 54. The apparatus of paragraph 53, wherein the patient dataset comprises a patient dataset based on a liver tissue image for the patient and wherein the predictive model is configured to determine whether the liver tissue is normal or abnormal. 55. The apparatus of paragraph 53, wherein said model is based on the one or more computer-generated mormoφhometric feature(s) and one or more clinical feature(s).

56. The apparatus of paragraph 55, wherein the patient dataset comprises a patient dataset based on a prostate tissue image for the patient and wherein the predictive model is configured to make a prediction with respect to prostate cancer recurrence for the patient. 57. The apparatus of paragraph 55, wherein the patient dataset comprises a patient dataset based on a prostate tissue image for the patient and wherein the predictive model is configured to make a prediction with respect to clinical failure for the patient. REFERENCES

The following references refened to above are all hereby incoφorated by reference herein in their entireties:

[1] Schen D., et al, Urology. 61 (2 Suppl 1): 14-24, Feb. 2003, Swindle P.W., et al, Urologic Clinics of North America. 30(2):377-401, May 2003.

[2] Wahlby C, et al.. Analytical Cellular Pathology 24. 101-111, 2002.

[3] Street W.N., "Xcyt: A System for Remote Cytological Diagnosis and Prognosis of Breast Cancer," In Soft Computing Techniques in Breast Cancer Prognosis and Diagnosis. L.C. Jain (ed.), CRC Press, 1999

[4] Gleason D.F., "The Veteran's Administration Cooperative Urologic Research Group: Histologic Grading and Clinical Staging of Prostatic Carcinoma," In Urologic Pathology: The Prostate, Tannenbaum M. (ed.), 171-198, Lea and Febiger, Philadelphia, 1977.

[5] Cristianni et al., An Introduction to Support Vector Machines, Cambridge University Press (2000).

[6] Hastie, The Elements of Statistical Learning, Springer (2001).

[7] F.E. Hanell et al, "Evaluating the yield of medical tests," JAMA, 247(18):2543-2546, 1982.

[8] Bishop, C, Neural Networks for Pattern Recognition, Oxford University Press (1995).

[9] Fausett, L., Fundamentals of Neural Networks, New York, Prentice Hall (1994).

[10] Definiens Cellenger Architecture: A Technical Review, April 2004. [11] Baatz M. and Schape A., "Multiresolution Segmentation - An Optimization Approach for High Quality Multi-scale Image Segmentation," In Angewandte Geographische hrfoπnationsverarbeitung XII. Strobl, J., Blaschke, T., Griesebner, G. (eds.), Wichmann- Verlag, Heidelberg, 12-23, 2000.

[12] Fukunaga K., Introduction to Statistical Pattern Recognition, 2nd Edition, Boston: Academic Press, 1990.

[13] Duda R.O. et al, Pattern Classification, 2nd Edition, John Wiley & Sons Inc., 2001.

[14] Holmberg L. et al., A randomized trial comparing radical prostatectomy with watchful waiting in early prostate cancer, N. Engl. M. Med., 347:781-789 (2002).

[ 15] Pound CR et al. , Natural history of progression after PSA elevation following radical prostatectomy, JAMA 1999, 281:1591-1597.

[16] Kumar-Sinha C. et al. , Molecular markers to identify patients at risk for recunence after primary treatment for prostate cancer, Urology 2003; 62 Suppl. 1:19-35.

[17] Cox D.R., "Regression Models and Life Tables," Journal of the Royal Statistical Society, B 34, 187-220, 1972.

[18] Hanell F.E., Regression Modeling Strategies, Springer- Verlag 2001.

[19] Tuxhorn et al., "Reactive Stroma in Human Prostate Cancer: Induction of Myofibroblast Phenotype and Extracellular Matrix Remodeling" Clinical Cancer Research 2912 Vol. 8, 2912-2923, September 2002.

[20] Kattan et al, "Postoperative Nomogram for Disease Recunence After Radical Prostatectomy for Prostate Cancer," Journal of Clinical Oncology, Vol. 17, No. 5 (May), 1999: pp 1499-1507. Table 1. Morphometric Features Script vl.O (496 Features) Feature B ackground .Max AreaPxl Background.MeanAreaPxl Background.MinAreaPxl B ackground. StdDev AreaPxl B ackground. SumAreaPxl Cytoplasm.Obj ects Cytoplasm. ObjectsPct Cytoplasm.MaxAreaPxl Cytoplasm.MeanAreaPxl Cytoplasm.MinAreaPxl Cytoplasm.StdDev AreaPxl Cytoplasm.SumAreaPxl Cytoplasm.Max Asymmetry Cytoplasm.MeanAsymmetry Cytoplasm.MinAsymmetry Cytoplasm.StdDevAsymmetry Cytoplasm.MaxBorderlengthPxl

Cytoplasm.MeanBorderlengthPxl

Cytoplasm.MinBorderlengthPxl

Cytoplasm.StdDevBorderlengthPxl

Cytoplasm.SumBorderlengthPxl

Cytoplasm.MaxBrightness

Cytoplasm.MeanBriglitness

Cytoplasm.MinBrightness

Cytoplasm.StdDevBrightness

Cytoplasm.MaxCompactness

Cytoplasm.MeanCompactness

Cytoplasm.MinCompactness

Cytoplasm.StdDevCompactness

Cytoplasm.MaxDensity

Cytoplasm.MeanDensity

Cytoplasm.MinDensity

Cytoplasm.StdDevDensity

Cytoplasm.MaxDiff.ofenclosing.enclo

Cytoplasm.MeanDiff.ofenclosing.encl

Cytoplasm.MinDiff.ofenclosing.enclo

Cytoplasm.StdDevDiff.ofenclosing.en Feature

Cytoplasm.MaxEllipticFit

Cytoplasm.MeanEllipticFit

CytoplasmMinEllipticFit

Cytoplasm.StdDevEllipticFit

Cytoplasm.MaxLengt Pxl

Cytoplasm.MeanLengthPxl

Cytoplasm.MinLengthPxl

Cytoplasm.StdDev engthPxl

Cytoplasm.SumLengthPxl

Cytoplasm.MaxMax.Diff.

Cytoplasm.MeanMax.Diff.

Cytoplasni.MinMax.Diff.

Cytoplasm.StdDevMax.Diff.

Cytoplasm.MaxMeanChannell

Cytoplasm.MeanMeanChannell

Cytoplasm.MinMeanChannel 1

Cytoplasm.StdDevMeanChannel 1

Cytoρlasm.MaxMeanChannel2

Cytoplasm.MeanMeanChannel2

Cytoplasm.MinMeanChannel2

Cytoplasm.StdDevMeanChannel2

Cytoplasm.MaxMeanChannel3

Cytoplasm.MeanMeanChanneB

Cytoplasm.MinMeanChannel3

Cytoplasm . StdDe vMeanChanneB

Cytoplasm.MaxRadiusoflargestenclose

Cytoplasm.MeanRadiusoflargestenclos

Cytoplasm.MinRadiusoflargestenclose

Cytoplasm.StdDevRadiusoflargestencl

Cytoplasm.MaxRadiusofsmallestenclos

Cytoplasm.MeanRadiusofsmallestenclo

Cytoplasm.MinRadiusofs allestenclos

Cytoplasm.StdDevRadiusofsmallestenc

Cytoplasm.MaxStdevChannel 1

Cytoplasm.MeanStdevChannel 1

Cytoplasm.MinStdevChannell

Cytoplasm.StdDevStdevChannel 1

Cytoplasm.MaxStdevChannel2

Cytoplasm.MeanStdevChannel2 Feature

Cytoplasm.MinStdevChannel2

Cytoplasm.StdDevStdevChannel2

Cytoplasm.MaxStdevChanneB

Cytoplasm.MeanStdevChanneB

Cytoplasm.MinStdevChannel3

Cytoplasm.StdDevStdevChanneB

Cytoplasm.Max WidthPxl

Cytoplasm.MeanWidthPxl

Cytoplasm.MinWidtlιPxl

Cytoplasm.StdDevWidthPxl

Epithelial.Nuclei . Obj ects

Epithelial .Nuclei . Obj ectsPct

Epithelial.Nuclei.MaxAreaPxl

Epithelial.Nuclei.MeanAreaPxl

Epithelial.Nuclei.MinAreaPxl

Epithelial .Nuclei . StdDe v AreaPxl

Epithelial.Nuclei.SumAreaPxl

Epithelial.Nuclei .MaxAsymmetry

Epithelial.Nuclei.MeanAsymmetry

Epithelial .Nuclei .Min Asymmetry

Epithelial.Nuclei.StdDev Asymmetry

Epithelial .Nuclei .MaxB orderlengthPx

Epithelial.Nuclei.MeanBorderlengthP

Epithelial.Nuclei.MinBorderlengthPx

Epithelial.Nuclei.StdDevBorderlengt

Epithelial.Nuclei.SumBorderlengthPx

Epithelial.Nuclei.MaxBrightness

Epithelial .Nuclei .MeanBrightness

Epithelial.Nuclei. MinBrightness

Epithelial.Nuclei. StdDevBrightness

Epithelial.Nuclei.MaxCompactness

Epithelial.Nuclei.MeanCompactness

Epithelial.Nuclei.MinCompactness

Epithelial.Nuclei. StdDevCompactness

Epithelial .Nuclei .MaxDensity

Epithelial.Nuclei.MeanDensity

Epithelial .Nuclei .MinDensity

Epithelial.Nuclei. StdDevDensity

Epithelial.Nuclei.MaxDiff.ofenclosi Feature

Epithehal.Nuclei.MeanDiff.ofenclos

Epithelial.Nuclei.MinDiff.ofenclosi

Epithelial.Nuclei.StdDevDiff.ofencl

Epithelial.Nuclei.MaxEllipticFit

Epithelial.Nuclei.MeanEllipticFit

Epithelial.Nuclei.MinEllipticFit

Ep.thehal.Nuclei.StdDevEllipticFit

Epithelial .Nuclei .MaxLengthPxl

Epithelial .Nuclei .MeanLengthPxl

Epithelial.Nuclei.MmLengthPxl

Epithelial.Nuclei.StdDevLengthPxl

Epithelial .Nuclei . SumLengthPxl

Epithehal.Nuclei.MaxMax.Diff.

Epithelial.Nuclei.MeanMax.Diff.

Epithelial .Nuclei .MmMax.Diff .

Epithelial.Nuclei.StdDevMax Diff.

Epithelial Nuclei.MaxMeanChannell

Epithelial.Nuclei.MeanMeanChannell

Epithelial.Nuclei.MmMeanChannell

Epithelial.Nuclei.StdDevMeanChannel

Epιthelial.Nucleι.MaxMeanChannel2

Epιthelιal.Nucleι.MeanMeanChannel2

Eριthelιal.Nucleι.MmMeanChannel2

Epithelial.Nuclei.StdDevMeanChannel

Epithelial.Nuclei.MaxMeanChanneB

Epithelial.Nuclei.MeanMeanChanneB

Epithelial.Nuclei.MinMeanChanneB

Epithelial.Nuclei.StdDevMeanChannel

Epithelial.Nuclei.MaxRadiusoflarges

Epithelial.Nuclei.MeanRadiusoflarge

Epithelial.Nuclei.MinRadiusoflarges

Epithelial .Nuclei . StdDe vRadi usoflar

Epithelial.Nuclei.MaxRadiusofsmalle

Epithelial.Nuclei.MeanRadiusofs all

Epithelial.Nuclei.MinRadiusofsmalle

Epithelial Nuclei. StdDevRadiusofsma

Epithelial.Nuclei.MaxStdevChannell

Epithelial.Nuclei.MeanStdevChannell

Epithelial.Nuclei.MinStdevChannell Feature

Epithelial.Nuclei.StdDevStdevChanne

Epithelial .Nuclei .MaxStde vChannel2

Epithelial.Nuclei .MeanStdevChannel2

Eρithelial.Nuclei.MinStdevChannel2

Epithelial.Nuclei.StdDevStdevC anne

Epithelial .Nuclei .MaxStdevChanneB

Epithelial .Nuclei .MeanStdevChanneB

Epithelial .Nuclei .MinStdevChanneB

Epithelial.Nuclei. StdDevStdevChanne

Epithelial.Nuclei.MaxWidthPxl

Epithelial .Nuclei .Mean WidthPxl

Epithelial.Nuclei.MinWidthPxl

Epithelial.Nuclei.StdDevWidthPxl

Lumen.Objects

Lumen.ObjectsPct

Lumen.MaxAreaPxl

Lumen.MeanAreaPxl

Lumen.MinAreaPxl

Lumen . StdDe vAreaPxl

Lumen . SumAreaPxl

Lumen.MaxAsymmetry

Lumen.MeanAsymmetry

Lumen .Min Asymmetry

Lumen . StdDe vAsymmetry

Lumen .MaxB orderlengthPxl

Lumen.MeanBorderlengthPxl

Lumen.MinBorderlengthPxl

Lumen. StdDevBorderlengthPxl

Lumen . SumB orderlengthPxl

Lumen.MaxB lightness

Lumen.MeanBrightness

Lumen.MinBrightness

Lumen. StdDevBrightness

Lumen.MaxCompactness

Lu en.MeanCompactness

Lumen.MinCompactness

Lumen. StdDevCompactness

Lumen.MaxDensity

Lumen.MeanDensity Feature

Lumen.MinDensity

Lumen.StdDevDensity

Lumen.MaxDiff.ofenclosing.enclosede

Lumen.MeanDiff.ofenclosing.enclosed

Lumen.MinDiff.ofenclosing.enclosede

Lumen.StdDevDiff.ofenclosing.enclos

Lumen.MaxEllipticFit

Lumen.MeanEllipticFit

Lumen.MinEllipticFit

Lumen.StdDevEllipticFit

Lumen .MaxLengthPxl

Lumen .MeanLengthPxl

Lumen .MinLengthPxl

Lumen.StdDevLengthPxl

Lumen.SumLengthPxl

Lumen.MaxMax.Diff.

Lumen.MeanMax.Diff.

Lumen.MinMax.Diff.

Lumen.StdDevMax.Diff.

Lumen.MaxMeanChannel 1

Lumen.MeanMeanChannel 1

Lumen.MinMeanChannell

Lumen.StdDevMeanChannell

Lumen .MaxMeanChannel2

Lumen.MeanMeanChannel2

Lumen.MinMeanChannel2

Lumen.StdDevMeanChannel2

Lumen.MaxMeanChanneB

Lumen .MeanMeanChanneB

Lumen.MinMeanChanneB

Lumen.StdDevMeanChanneB

Lumen.MaxRadiusoflargestenclosedell

Lumen.MeanRadiusoflargestenclosedel

Lumen.MinRadiusoflargestenclosedell

Lumen.StdDevRadiusoflargestenclosed

Lumen.MaxRadiusofsmallestenclosinge

Lumen.MeanRadiusofsmallestenclosing

Lumen.MinRadiusofsmallestenclosinge

Lumen.StdDevRadiusofsmallestenclosi Feature

Lumen.MaxStdevChannel 1

Lumen.MeanStdevChannel 1

Lumen.MinStdevChannel 1

Lumen.StdDevStdevChannell

Lumen.MaxStdevChannel2

Lumen .MeanStdevChannel2

Lumen.MinStdevChannel2

Lumen.StdDevStdevChannel2

Lumen.MaxStdevChanneB

Lumen.MeanStdevChanneB

Lumen.MinStdevChanneB

Lumen.StdDevStdevChanneB

Lumen.MaxWidthPxl

Lumen.MeanWidthPxl

Lumen.MinWidthPxl

Lumen.StdDevWidthPxl

Red.Blood.Cell.Objects

Red.Blood.Cell.ObjectsPct

Red.Blood.Cell.MaxAreaPxl

Red.Blood.Cell.MeanAreaPxl

Red.Blood.CelLMinAreaPxl

Red.Blood.Cell.StdDevAreaPxl

Red.Blood.Cell.SumAreaPxl

Red.Blood.Cell.MaxAsymmetry

Red.Blood.Cell.MeanAsymmetry

Red.Blood.Cell.MinAsymmetry

Red.Blood.Cell.StdDevAsy metry

Red.Blood.Cell.MaxBorderlengthPxl

Red.Blood.Cell.MeanBorderlengthPxl

Red.Blood.CelLMinBorderlengthPxl

Red.Blood.Cell.StdDevBorderlengthPx

Red.Blood.Cell.SumBorderlengthPxl

Red.Blood.CelLMaxBrightness

Red.Blood.Cell.MeaiiBrightness

Red.Blood.Cell.MinBrightness

Red.Blood.Cell.StdDevBrightness

Red.Blood.Cell.MaxCompactness

Red.Blood.Cell.MeanCompactness

Red.Blood.Cell.MinCompactness

Red.Blood.Cell.StdDevCompactness

Red.Blood.Cell.MaxDensity

Red.Blood.Cell.MeanDensity Feature

Red.Blood.Cell.MinDensity

Red.Blood.Cell.StdDevDensity

Red.Blood.Cell.MaxDiff.ofenclosing.

Red.Blood.Cell.MeanDiff.ofenclosing

Red.Blood.Cell.MinDiff.ofenclosing.

Red.Blood.Cell.StdDevDiff.ofenclosi

Red.Blood.Cell.MaxEllipticFit

Red.Blood.Cell.MeanEllipticFit

Red.Blood.Cell.MinEUipticFit

Red.Blood.Cell.StdDevEllipticFit

Red.Blood.Cell.MaxLengthPxl

Red.Blood.CelLMeanLengthPxl

Red.Blood.Cell.MinLengthPxl

Red.Blood.Cell.StdDevLengthPxl

Red.Blood.Cell.SumLengthPxl

Red.Blood.Cell.MaxMax.Diff.

Red.Blood.Cell.MeanMax.Diff.

Red.Blood.Cell.MinMax.Diff.

Red.Blood.Cell.StdDevMax.Diff.

Red.Blood.Cell.MaxMeanChannel 1

Red.Blood.Cell.MeanMeanChannel 1

Red.Blood.Cell.MinMeanChannell

Red.Blood.Cell.StdDevMeanChannell

Red.Blood.Cell.MaxMeanChannel2

Red.Blood.Cell.MeanMeanChannel2

Red.Blood.Cell.MinMeanChannel2

Red.Blood.Cell.StdDevMeanChannel2

Red.Blood.Cell.MaxMeanChanneB

Red.Blood.Cell.MeanMeanChanneB

Red.Blood.Cell.MinMeanChanneB

Red.Blood.Cell.StdDevMeanChanneB

Red.Blood.Cell.MaxRadiusoflargesten

Red.Blood.Cell.MeanRadiusoflargeste

Red.Blood.Cell.MinRadiusoflargesten

Red.Blood.Cell.StdDevRadiusoflarges

Red.Blood.Cell.MaxRadiusofsmalleste

Red.Blood.Cell.MeanRadiusofsmallest

Red.Blood.Cell.MinRadiusofsmalleste

Red.Blood.Cell.StdDevRadiusofsmalle Feature

Red.Blood.Cell.MaxStdevChannell

Red.Blood.Cell.MeanStdevChannell

Red.Blood.Cell.MinStdevChannell

Red.Blood.Cell.StdDevStdevChannell

Red.Blood.Cell.MaxStdevChannel2

Red.Blood.Cell.MeanStdevChannel2

Red.Blood.Cell.MinStdevChannel2

Red.Blood.Cell.StdDevStdevChannel2

Red.Blood.Cell.MaxStdevChanneB

Red.Blood.Cell.MeanStdevChannel3

Red.Blood.Cell.MinStdevChanneB

Red.Blood.Cell.StdDevStdevChanneB

Red.Blood.Cell.MaxWidthPxl

Red.Blood.Cell.MeanWidthPxl

Red.Blood.Cell.MinWidthPxl

Red.Blood.Cell.StdDevWidthPxl

Stroma.Objects

Stroma.Obj ectsPct

Stroma.MaxAreaPxl

Stroma.MeanAreaPxl

Stroma.MinAreaPxl

Stroma.StdDev AreaPxl

Stroma.SumAreaPxl

Stroma.MaxAsymmetry

Stroma.MeanAsymmetry

Stroma.MinAsymmetry

Stroma.StdDevAsymmetry

Stroma.MaxBorderlengthPxl

Stroma.MeanBorderlengthPxl

Stroma.MinBorderlengthPxl

Stroma.StdDevBorderlengthPxl

Stroma. SumB orderlengthPxl

Stroma.MaxBrightness

Stroma.MeanBrightness

Stroma.MinBrightness

Stroma.StdDevBrightness

Stroma.MaxCompactness

Stroma.MeanCompactness

Stro a.MinCompactness Feature

Stroma.StdDevCompactness

Stroma.MaxDensity

Stroma.MeanDensity

Stroma.MinDensity

Stroma.StdDevDensity

Stroma.MaxDiff.ofenclosing.enclosed

Stroma.MeanDiff.ofenclosing.enclose

Stroma.MinDiff.ofenclosing.enclosed

Stroma.StdDevDiff.ofenclosing.enclo

Stroma.MaxEllipticFit

Stroma.MeanEllipticFit

Stroma.MinEllipticFit

Stroma.StdDevEllipticFit

Stroma.MaxLengthPxl

Stroma.MeanLengthPxl

Stroma.MinLengthPxl

Stroma.StdDevLengthPxl

Stroma.SumLengthPxl

Stroma.MaxMax.Diff.

Stroma.MeanMax.Diff.

Stroma.MinMax.Diff.

Stroma.StdDevMax.Diff.

Stroma.MaxMeanChannel 1

Stroma.MeanMeanChannel 1

Stroma.MinMeanChannel 1

Stroma.StdDevMeanChannel 1

Stroma.MaxMeanChannel2

Stroma.MeanMeanChannel2

Stroma.MinMeanChannel2

Stroma.StdDevMeanChannel2

Stroma.MaxMeanChannel3

Stroma.MeanMeanChanneB

Stroma. MinMeanChanneB

Stroma.StdDevMeanChanneB

Stroma.MaxRadiusoflargestenclosedel

Stroma.MeanRadiusoflargestenclosede

Stroma.MinRadiusoflargestenclosedel

Stroma.StdDevRadiusoflargestenclose

Stroma.MaxRadiusofsmallestenclosing Feature

Stroma.MeanRadiusofsmallestenclosin

Stroma.MinRadiusofsrnallestenclosing

Stroma.StdDevRadiusofsmallestenclos

Stroma.MaxStdevChannel 1

Stroma.MeanStdevChannel 1

Stro a.MinStdevChannell

Stroma.StdDevStdevChannell

Stroma.MaxStdevChannel2

Stroma.MeanStdevChannel2

Stroma.MinStdevChannel2

Stroma.StdDevStdevChannel2

Stroma.MaxStdevChanneB

Stroma.MeanStdevChanneB

Stroma.MinStdevChanneB

Stroma.StdDevStdevChannel3

Stroma.MaxWidthPxl

Stroma.MeanWidthPxl

Stroma.MinWidthPxl

Stroma.StdDevWidthPxl

Stroma.Nuclei . Obj ects

Stroma.Nuclei.ObjectsPct

Stroma.Nuclei. Max AreaPxl

Stroma.Nuclei.MeanAreaPxl

Stroma.Nuclei.MinAreaPxl

Stroma.Nuclei. StdDev AreaPxl

Strørna.Nuclei.SumAreaPxl

Stroma.Nuclei.MaxAsymmetry

Stroma.Nuclei .MeanAsymmetry

Stroma.Nuclei.MinAsynimetry

Stroma.Nuclei.StdDevAsymmetry

Stroma.Nuclei .MaxB orderlengthPxl

Stroma.Nuclei .MeanBorderlengthPxl

Stroma.Nuclei.MinBorderlengthPxl

Stroma.Nuclei. StdDevBorderlengtliPxl

Stroma.Nuclei. SumBorderlengthPxl

Stroma.Nuclei. MaxBrightness

Stroma.Nuclei. MeanBrightness

Stioma.Nuclei.MinB lightness

Stroma.Nuclei. StdDevBrightness Feature

Stroma.Nucleii.MaxCompactness Stroma.Nuclei ^' .MeanCompactness Stroma.Nucleii.MinCompactness Stroma.Nucleii.StdDevCompactness Stroma.Nuclei ^'.MaxDensity Stroma.Nucleii.MeanDensity Stroma.Nucleii.MinDensity Stroma.Nucleii.StdDevDensity Stroma.Nucleii.MaxDiff.ofenclosing.e Stroma.Nucleii .MeanDiff. of enclosing. Stroma.Nucleii.MinDiff.ofendosing.e

Stroma.Nuclei .StdDevDif .ofenclosin Stroma.Nuclei MaxEllipticFit Stroma.Nuclei .MeanEllipticFit Stroma.Nuclei.MinEllipticFit

Stroma.Nuclei StdDevEllipticFit

Stroma.Nuclei.MaxLengthPxl

Stroma.Nuclei .MeanLengthPxl Stroma.Nuclei .MinLengthPxl Stroma.Nuclei .StdDevLengthPxl Stroma.Nuclei .SumLengthPxl Stroma.Nuclei .MaxMax.Diff. Stroma.Nuclei .MeanMax.Diff. Stroma.Nuclei .MinMax.Diff. Stroma.Nuclei .StdDevMax.Diff. Stroma.Nuclei .MaxMeanChannel 1 Stroma.Nuclei MeanMeanChannell Stroma.Nuclei .MinMeanChannel 1 Stroma.Nuclei .StdDevMeanChannell

Stroma.Nuclei MaxMeanChannel2 Stroma.Nuclei MeanMeanChannel2 Stroma.Nuclei .MinMeanChannel2 Stroma.Nuclei .StdDevMeanChannel2 Stroma.Nuclei .MaxMeanChanneB Stroma.Nuclei MeanMeanChanneB Stroma.Nuclei MinMeanChanneB Stroma.Nuclei StdDevMeanChanneB Stroma.Nuclei MaxRadiusofiargestenc Stroma.Nuclei MeanRadiusoflargesten Stroma.Nuclei .MinRadiusoflargestenc Stroma.Nuclei. StdDevRadiusoflargest Feature Stroma.Nuclei.MaxRadiusofsmallesten Stroma.Nuclei.MeanRadiusofsmalleste Stroma.Nuclei.MinRadmsofsmallesten Stroma.Nuclei.StdDevRadiusofsmalles Stroma.Nuclei.MaxStdevChannell Stroma.Nuclei.MeanStdevChannell Stroma.Nuclei .MmStdevChannel 1 Stroma Nuclei StdDevStdevChannell Stroma.Nucleι.MaxStdevChannel2 Stroma.Nuclei. MeanStdevChannel2 Stroma.Nuclei .Mm StdevChannel2 Stroma.Nucleι.StdDevStdevChannel2 Stroma.Nuclei. MaxStdevChanneB Stroma.Nuclei.MeanStdevChanneB Stroma.Nuclei.MmStdevChanneB Stro a.Nuclei.StdDevStdevChanneB Stroma.Nuclei.MaxWidthPxl Stroma.Nuclei.MeanWidthPxl Stroma.Nucleι.MmWidthPxl Stroma.Nucleι.StdDevWιdthPxl C2EN EN2SN L2Core C2L CEN2L

Table 2. Morphometric Features Script v2.0 (350 features)

Feature

Artifact Mean Area Pxl

Artifact StdDev Area Pxl

Artifact Mean Asymmetry

Artifact StdDev Asymmetry

Artifact Mean Border index

Artifact StdDev Border index

Artifact Mean Border length Pxl

Artifact StdDev Border length Pxl

Artifact Mean Brightness

Artifact StdDev Brightness

Artifact Mean Compactness

Artifact StdDev Compactness

Artifact Mean Density

Artifact StdDev Density

Artifact Mean Diff. of enclosing/enclosed ellipse

Artifact StdDev Diff. of enclosing/enclosed ellipse

Artifact Mean Elliptic Fit

Artifact StdDev Elliptic Fit Feature

Artifact Mean Length Pxl

Artifact StdDev Length Pxl

Artifact Mean Lengthwidth

Artifact StdDev Length/width

Artifact Mean Main direction

Artifact StdDev Main direction

Artifact Mean Max.Diff.

Artifact StdDev Max.Diff.

Artifact Mean Mean Channel 1

Artifact StdDev Mean Channel 1

Artifact Mean Mean Channel 2

Artifact StdDev Mean Channel 2

Artifact Mean Mean Channel 3

Artifact StdDev Mean Channel 3

Artifact Mean Radius of largest enclosed ellipse

Artifact StdDev Radius of largest enclosed ellipse

Artifact Mean Radius of smallest enclosing ellipse

Artifact StdDev Radius of smallest enclosing ellipse

Artifact Mean Rectangular Fit

Artifact StdDev Rectangular Fit

Artifact Mean Shape index

Artifact StdDev Shape index

Artifact Mean Stddev Channel 1

Artifact StdDev Stddev Channel 1

Artifact Mean Stddev Channel 2

Artifact StdDev Stddev Channel 2

Artifact Mean Stddev Channel 3

Artifact StdDev Stddev Channel 3

Artifact Mean Width Pxl

Artifact StdDev Width Pxl

Cytoplasm Mean Area Pxl

Cytoplasm StdDev Area Pxl

Cytoplasm Mean Asymmetry

Cytoplasm StdDev Asymmetry

Cytoplasm Mean Border index

Cytoplasm StdDev Border index

Cytoplasm Mean Border length Pxl

Cytoplasm StdDev Border length Pxl

Cytoplasm Mean Brightness

Cytoplasm StdDev Brightness

Cytoplasm Mean Compactness

Cytoplasm StdDev Compactness

Cytoplasm Mean Density

Cytoplasm StdDev Density

Cytoplasm Mean Diff. of enclosing/enclosed ellipse

Cytoplasm StdDev Diff. of enclosing/enclosed ellipse Feature

Cytoplasm Mean Elliptic Fit

Cytoplasm StdDev Elliptic Fit

Cytoplasm Mean Length Pxl

Cytoplasm StdDev Length Pxl

Cytoplasm Mean Length/width

Cytoplasm StdDev Length/width

Cytoplasm Mean Main direction

Cytoplasm StdDev Main direction

Cytoplasm Mean Max.Diff.

Cytoplasm StdDev Max.Diff.

Cytoplasm Mean Mean Channel 1

Cytoplasm StdDev Mean Channel 1

Cytoplasm Mean Mean Channel 2

Cytoplasm StdDev Mean Channel 2

Cytoplasm Mean Mean Channel 3

Cytoplasm StdDev Mean Channel 3

Cytoplasm Mean Radius of largest enclosed ellipse

Cytoplasm StdDev Radius of largest enclosed ellipse

Cytoplasm Mean Radius of smallest enclosing ellipse

Cytoplasm StdDev Radius of smallest enclosing ellipse

Cytoplasm Mean Rectangular Fit

Cytoplasm StdDev Rectangular Fit

Cytoplasm Mean Shape index

Cytoplasm StdDev Shape index

Cytoplasm Mean Stddev Channel 1

Cytoplasm StdDev Stddev Channel 1

Cytoplasm Mean Stddev Channel 2

Cytoplasm StdDev Stddev Channel 2

Cytoplasm Mean Stddev Channel 3

Cytoplasm StdDev Stddev Channel 3

Cytoplasm Mean Width Pxl

Cytoplasm StdDev Width Pxl

Epithelial Nuclei Mean Area Pxl

Epithelial Nuclei StdDev Area Pxl

Epithelial Nuclei Mean Asymmetry

Epithelial Nuclei StdDev Asymmetry

Epithelial Nuclei Mean Border index

Epithelial Nuclei StdDev Border index

Epithelial Nuclei Mean Border length Pxl

Epithelial Nuclei StdDev Border length Pxl

Epithelial Nuclei Mean Brightness

Epithelial Nuclei StdDev Brightness

Epithelial Nuclei Mean Compactness

Epithelial Nuclei StdDev Compactness

Epithelial Nuclei Mean Density

Epithelial Nuclei StdDev Density Feature

Epithelial Nuclei Mean Diff. of enclosing/enclosed ellipse

Epithelial Nuclei StdDev Diff. of enclosing/enclosed ellipse

Epithelial Nuclei Mean Elliptic Fit

Epithelial Nuclei StdDev Elliptic Fit

Epithelial Nuclei Mean Length Pxl

Epithelial Nuclei StdDev Length Pxl

Epithelial Nuclei Mean Length width

Epithelial Nuclei StdDev Length/width

Epithelial Nuclei Mean Main direction

Epithelial Nuclei StdDev Main direction

Epithelial Nuclei Mean Max.Diff.

Epithelial Nuclei StdDev Max.Diff.

Epithelial Nuclei Mean Mean Channel 1

Epithelial Nuclei StdDev Mean Channel 1

Epithelial Nuclei Mean Mean Channel 2

Epithelial Nuclei StdDev Mean Channel 2

Epithelial Nuclei Mean Mean Channel 3

Epithelial Nuclei StdDev Mean Channel 3

Epithelial Nuclei Mean Radius of largest enclosed ellipse

Epithelial Nuclei StdDev Radius of largest enclosed ellipse

Epithelial Nuclei Mean Radius of smallest enclosing ellipse

Epithelial Nuclei StdDev Radius of smallest enclosing ellipse

Epithelial Nuclei Mean Rectangular Fit

Epithelial Nuclei StdDev Rectangular Fit

Epithelial Nuclei Mean Shape index

Epithelial Nuclei StdDev Shape index

Epithelial Nuclei Mean Stddev Channel 1

Epithelial Nuclei StdDev Stddev Channel 1

Epithelial Nuclei Mean Stddev Channel 2

Epithelial Nuclei StdDev Stddev Channel 2

Epithelial Nuclei Mean Stddev Channel 3

Epithelial Nuclei StdDev Stddev Channel 3

Epithelial Nuclei Mean Width Pxl

Epithelial Nuclei StdDev Width Pxl

Lumen Mean Area Pxl

Lumen StdDev Area Pxl

Lumen Mean Asymmetry

Lumen StdDev Asymmetry

Lumen Mean Border index

Lumen StdDev Border index

Lumen Mean Border length Pxl

Lumen StdDev Border length Pxl

Lumen Mean Brightness

Lumen StdDev Brightness

Lumen Mean Compactness

Lumen StdDev Compactness Feature

Lumen Mean Density

Lumen StdDev Density

Lumen Mean Diff. of enclosing/enclosed ellipse

Lumen StdDev Diff. of enclosing/enclosed ellipse

Lumen Mean Elliptic Fit

Lumen StdDev Elliptic Fit

Lumen Mean Length Pxl

Lumen StdDev Length Pxl

Lumen Mean Length/width

Lumen StdDev Length/width

Lumen Mean Main direction

Lumen StdDev Main direction

Lumen Mean Max.Diff.

Lumen StdDev Max.Diff.

Lumen Mean Mean Channel 1

Lumen StdDev Mean Channel 1

Lumen Mean Mean Channel 2

Lumen StdDev Mean Channel 2

Lumen Mean Mean Channel 3

Lumen StdDev Mean Channel 3

Lumen Mean Radius of largest enclosed ellipse

Lumen StdDev Radius of largest enclosed ellipse

Lumen Mean Radius of smallest enclosing ellipse

Lumen StdDev Radius of smallest enclosing ellipse

Lumen Mean Rectangular Fit

Lumen StdDev Rectangular Fit

Lumen Mean Shape index

Lumen StdDev Shape index

Lumen Mean Stddev Channel 1

Lumen StdDev Stddev Channel 1

Lumen Mean Stddev Channel 2

Lumen StdDev Stddev Channel 2

Lumen Mean Stddev Channel 3

Lumen StdDev Stddev Channel 3

Lumen Mean Width Pxl

Lumen StdDev Width Pxl

Stroma Mean Area Pxl

Stroma StdDev Area Pxl

Stroma Mean Asymmetry

Stroma StdDev Asymmetry

Stroma Mean Border index

Stroma StdDev Border index

Stroma Mean Border length Pxl

Stroma StdDev Border length Pxl

Stroma Mean Brightness

Stroma StdDev Brightness Feature

Stroma Mean Compactness

Stroma StdDev Compactness

Stroma Mean Density

Stroma StdDev Density

Stroma Mean Diff. of enclosing/enclosed ellipse

Stroma StdDev Diff. of enclosing/enclosed ellipse

Stroma Mean Elliptic Fit

Stroma StdDev Elliptic Fit

Stroma Mean Length Pxl

Stroma StdDev Length Pxl

Stroma Mean Length/width

Stroma StdDev Length/width

Stroma Mean Main direction

Stroma StdDev Main direction

Stroma Mean Max.Diff.

Stroma StdDev MaxOiff.

Stroma Mean Mean Channel 1

Stroma StdDev Mean Channel 1

Stroma Mean Mean Channel 2

Stroma StdDev Mean Channel 2

Stroma Mean Mean Channel 3

Stroma StdDev Mean Channel 3

Stroma Mean Radius of largest enclosed ellipse

Stroma StdDev Radius of largest enclosed ellipse

Stroma Mean Radius of smallest enclosing ellipse

Stroma StdDev Radius of smallest enclosing ellipse

Stroma Mean Rectangular Fit

Stroma StdDev Rectangular Fit

Stroma Mean Shape index

Stroma StdDev Shape index

Stroma Mean Stddev Channel 1

Stroma StdDev Stddev Channel 1

Stroma Mean Stddev Channel 2

Stroma StdDev Stddev Channel 2

Stroma Mean Stddev Channel 3

Stroma StdDev Stddev Channel 3

Stroma Mean Width Pxl

Stroma StdDev Width Pxl

Stroma Nuclei Mean Area Pxl

Stroma Nuclei StdDev Area Pxl

Stroma Nuclei Mean Asymmetry

Stroma Nuclei StdDev Asymmetry

Stroma Nuclei Mean Border index

Stroma Nuclei StdDev Border index

Stroma Nuclei Mean Border length Pxl

Stroma Nuclei StdDev Border length Pxl Feature

Stroma Nuclei Mean Brightness

Stroma Nuclei StdDev Bnghtness

Stroma Nuclei Mean Compactness

Stroma Nuclei StdDev Compactness

Stroma Nuclei Mean Density

Stroma Nuclei StdDev Density

Stroma Nuclei Mean Diff of enclosing enclosed ellipse

Stroma Nuclei StdDev Diff of enclosing/enclosed ellipse

Stroma Nuclei Mean Elliptic Fit

Stroma Nuclei StdDev Elliptic Fit

Stroma Nuclei Mean Length Pxl

Stroma Nuclei StdDev Length Pxl

Stroma Nuclei Mean Length/width

Stroma Nuclei StdDev Length width

Stroma Nuclei Mean Mam direction

Stroma Nuclei StdDev Mam direction

Stroma Nuclei Mean Max Diff

Stroma Nuclei StdDev Max Diff

Stroma Nuclei Mean Mean Channel 1

Stroma Nuclei StdDev Mean Channel 1

Stroma Nuclei Mean Mean Channel 2

Stroma Nuclei StdDev Mean Channel 2

Stroma Nuclei Mean Mean Channel 3

Stroma Nuclei StdDev Mean Channel 3

Stroma Nuclei Mean Radius of largest enclosed ellipse

Stroma Nuclei StdDev Radius of largest enclosed ellipse

Stroma Nuclei Mean Radius of smallest enclosing ellipse

Stroma Nuclei StdDev Radius of smallest enclosing ellipse

Stroma Nuclei Mean Rectangular Fit

Stroma Nuclei StdDev Rectangular Fit

Stroma Nuclei Mean Shape index

Stroma Nuclei StdDev Shape index

Stroma Nuclei Mean Stddev Channel 1

Stroma Nuclei StdDev Stddev Channel 1

Stroma Nuclei Mean Stddev Channel 2

Stroma Nuclei StdDev Stddev Channel 2

Stroma Nuclei Mean Stddev Channel 3

Stroma Nuclei StdDev Stddev Channel 3

Stroma Nuclei Mean Width Pxl

Stroma Nuclei StdDev Width Pxl

Area of Artifact Pxl

Area of Cytoplasm Pxl

Area of Epithelial Nuclei Pxl

Area of Lumen Pxl

Area of Red Blood Cell Pxl

Area of Stroma Pxl Feature

Area of Stroma Nuclei Pxl

Number of objects of Artifact

Number of objects of Cytoplasm

Number of objects of Epithelial Nuclei

Number of objects of Lumen

Number of objects of Red Blood Cell

Number of objects of Stroma

Number of objects of Stroma Nuclei

Red Blood Cell Mean Area Pxl

Red Blood Cell StdDev Area Pxl

Red Blood Cell Mean Asymmetry

Red Blood Cell StdDev Asymmetry

Red Blood Cell Mean Border index

Red Blood Cell StdDev Border index

Red Blood Cell Mean Border length Pxl

Red Blood Cell StdDev Border length Pxl

Red Blood Cell Mean Brightness

Red Blood Cell StdDev Brightness

Red Blood Cell Mean Compactness

Red Blood Cell StdDev Compactness

Red Blood Cell Mean Density

Red Blood Cell StdDev Density

Red Blood Cell Mean Diff. of enclosing/enclosed ellipse

Red Blood Cell StdDev Diff. of enclosing/enclosed ellipse

Red Blood Cell Mean Elliptic Fit

Red Blood Cell StdDev Elliptic Fit

Red Blood Cell Mean Length Pxl

Red Blood Cell StdDev Length Pxl

Red Blood Cell Mean Length/width

Red Blood Cell StdDev Length/width

Red Blood Cell Mean Main direction

Red Blood Cell StdDev Main direction

Red Blood Cell Mean Max.Diff.

Red Blood Cell StdDev Max.Diff.

Red Blood Cell Mean Mean Channel 1

Red Blood Cell StdDev Mean Channel 1

Red Blood Cell Mean Mean Channel 2

Red Blood Cell StdDev Mean Channel 2

Red Blood Cell Mean Mean Channel 3

Red Blood Cell StdDev Mean Channel 3

Red Blood Cell Mean Radius of largest enclosed ellipse

Red Blood Cell StdDev Radius of largest enclosed ellipse

Red Blood Cell Mean Radius of smallest enclosing ellipse

Red Blood Cell StdDev Radius of smallest enclosing ellipse

Red Blood Cell Mean Rectangular Fit

Red Blood Cell StdDev Rectangular Fit Feature

Red Blood Cell Mean Shape index Red Blood Cell StdDev Shape index Red Blood Cell Mean Stddev Channel 1 Red Blood Cell StdDev Stddev Channel 1 Red Blood Cell Mean Stddev Channel 2 Red Blood Cell StdDev Stddev Channel 2 Red Blood Cell Mean Stddev Channel 3 Red Blood Cell StdDev Stddev Channel 3 Red Blood Cell Mean Width Pxl Red Blood Cell StdDev Width Pxl

Claims

What is claimed is: 1. A method of evaluating a risk of occuπence of a medical condition in a patient, the method comprising: receiving a patient dataset for the patient; and evaluating the patient dataset with a model predictive of the medical condition, wherein the model is based on one or more clinical feature(s), one or more molecular feature(s), and one or more computer-generated moφhometric feature(s) generated from one or more tissue image(s), thereby evaluating the risk of occuπence of the medical condition in the patient.

2. The method of claim 1, further comprising generating with a computer one or more moφhometric feature(s) from a tissue image for the patient for inclusion in the patient dataset, wherein said generating the moφhometric feature(s) comprises: segmenting the tissue image into one or more objects; classifying the one or more objects into one or more object classes; and determining the moφhometric features by taking one or more measurements for the one or more object classes.

3. The method of claim 2, wherein said classifying the one or more objects into one or more object classes comprises classifying each of one or more of the objects into a class from the group of classes consisting of stroma, cytoplasm, epithelial nuclei, stroma nuclei, lumen, red blood cells, tissue artifacts, and tissue background.

4. The method of claim 2, wherein said taking one or more measurements pertaining to the one or more object classes comprises taking one or more measurements of one or more spectral properties and/or one or more shape properties of the one or more object classes.

5. The method of claim 1 , wherein the model is predictive of prostate cancer recunence and one or more of the moφhometric feature(s) are from the group of moφhometric features consisting of a moφhometric feature of a red blood cell, a moφhometric feature of epithelial nuclei, a moφhometric feature of stroma, a moφhometric feature of lumen, a moφhometric feature of cytoplasm, and a moφhometric feature of tissue background.

6. The method of claim 5, wherein one or more of the clinical feature(s) are from the group of clinical features listed in Figure 6.

7. The method of claim 6, wherein one or more of the molecular feature(s) are from the group of molecular features listed in Figure 6.

8. The method of claim 7, wherein the predictive model comprises a concordance index of at least about 0.88.

9. The method of claim 7, wherein the predictive model comprises a p value less than about 0.0001 for a log-rank test.

10. The method of claim 1 , wherein the model is predictive of prostate cancer recunence and one or more of the moφhometric feature(s) are from the group of moφhometric features consisting of a moφhometric feature of a red blood cell, a moφhometric feature of epithelial nuclei, a moφhometric feature of stroma, a moφhometric feature of lumen, and a moφhometric feature of cytoplasm.

11. The method of claim 10, wherein one or more of the clinical feature(s) are from the group of clinical features listed in Figure 9.

12. The method of claim 11, wherein one or more of the molecular feature(s) are from the group of molecular features listed in Figure 9.

13. The method of claim 12, wherein the predictive model comprises a concordance index of at least about 0.87.

14. The method of claim 12, wherem the predictive model comprises a p value less than about 0.0001 for a log-rank test.

15. The method of claim 1 , wherein the model is predictive of prostate cancer survival and one or more of the morphometric feature(s) are from the group of moφhometric features consisting of a moφhometric feature of a red blood cell, a moφhometric feature of epithelial nuclei, and a moφhometric feature of stroma.

16. The method of claim 15, wherein one or more of the clinical feature(s) are from the group of clinical features listed in Figure 11.

17. The method of claim 1 , wherein one or more of the molecular feature(s) are from the group of molecular features listed in Figure 11.

18. The method of claim 17, wherein the predictive model comprises a concordance index of at least about 0.80.

19. The method of claim 17, wherein the predictive model comprises a p value less than about 0.0001 for a log-rank test.

20. The method of claim 1 , wherein said evaluating the patient dataset with the predictive model comprises determining a diagnostic score for the patient.

21. The method of claim 1 , wherein said evaluating the risk of occuπence of a medical condition comprises determining a likely time to occurrence of the medical condition in the patient.

22. The method of claim 1 , wherein said evaluating the risk of occuπence of a medical condition comprises detennining a likely responsiveness or unresponsiveness of the patient to a therapy.

23. The method of claim 1 , further comprising outputting data indicating results of the evaluation.

24. The method of claim 23, wherein the results comprise results from the group of results consisting of a diagnostic score, information indicating one or more features of the patient dataset that were analyzed by the predictive model, information indicating an accuracy of the predictive model, or a combination thereof.

25. The method of claim 23, wherein said receiving a patient dataset for the patient comprises receiving data for the patient from a remote location and wherein said outputting data indicating results of the evaluation comprises transmitting the results to the remote location.

26. The method of claim 25, wherein said receiving and said transmitting comprise receiving and transmitting over one or more communications networks.

27. An apparatus for evaluating a risk of occurrence of a medical condition in a patient, the apparatus comprising: a model predictive of the medical condition, wherem the model is based on one or more clinical feature(s), one or more molecular feature(s), and one or more computer-generated moφhometric feature(s) generated from one or more tissue image(s), wherein the model is configured to: receive a patient dataset for the patient; and evaluate the patient dataset according to the model, thereby evaluating the risk of occuπence of the medical condition in the patient.

28. The apparatus of claim 27, further comprising an image processing tool configured to generate one or more moφhometric feature(s) from a tissue image for the patient for inclusion in the patient dataset, wherein said generating the moφhometric feature(s) comprises: segmenting the tissue image into one or more objects with the image processing tool; classifying the one or more objects into one or more object classes by the image processing tool; and determining the moφhometric features by taking one or more measurements for the one or more object classes with the image processing tool.

29. The apparatus of claim 28, wherein said classifying the one or more objects into one or more object classes by the image processing tool comprises classifying by the image processing tool each of one or more of the objects into a class from the group of classes consisting of stroma, cytoplasm, epithelial nuclei, stroma nuclei, lumen, red blood cells, tissue artifacts, and tissue background.

30. The apparatus of claim 28, wherem said taking one or more measurements pertaining to the one or more object classes with the image processing tool comprises taking with the image processing tool one or more measurements of one or more spectral properties and/or one or more shape properties of the one or more object classes.

31. The apparatus of claim 27, wherein the model is predictive of prostate cancer recunence and one or more of the moφhometric feature(s) are from the group of moφhometric features consisting of a moφhometric feature of a red blood cell, a moφhometric feature of epithelial nuclei, a moφhometric feature of stroma, a moφhometric feature of lumen, a moφhometric feature of cytoplasm, and a moφhometric feature of tissue background.

32. The apparatus of claim 31, wherein one or more of the clinical feature(s) are from the group of clinical features listed in Figure 6.

33. The apparatus of claim 32, wherein one or more of the molecular feature(s) are from the group of molecular features listed in Figure 6.

34. The apparatus of claim 33, wherein the predictive model comprises a concordance index of at least about 0.88.

35. The apparatus of claim 33, wherein the predictive model comprises a p value less than about 0.0001 for a log-rank test.

36. The apparatus of claim 27, wherein the model is predictive of prostate cancer recunence and one or more of the moφhometric feature(s) are from the group of moφhometric features consisting of a moφhometric feature of a red blood cell, a moφhometric feature of epithelial nuclei, a moφhometric feature of stroma, a moφhoir-tetric feature of lumen, and a moφhometric feature of cytoplasm.

37. The apparatus of claim 36, wherein one or more of the clinical feature(s) are from the group of clinical features listed in Figure 9.

38. The apparatus of claim 37, wherein one or more of the molecular feature(s) are from the group of molecular features listed in Figure 9.

39. The apparatus of claim 38, wherein the predictive model comprises a concordance index of at least about 0.87.

40. The apparatus of claim 38, wherein the predictive model comprises a p value less than about 0.0001 for a log-rank test. . .

41. The apparatus of claim 27, wherein the model is predictive of prostate cancer survival and one or more of the moφhometric feature(s) are froxn the group of moφhometric features consisting of a moφhometric feature of a red blood cell, a moφhometric feature of epithelial nuclei, and a moφhometric feature of stroma.

42. The apparatus of claim 41, wherein one or more of the clinical feature(s) are from the group of clinical features listed in Figure 11.

43. The apparatus of claim 42, wherein one or more of the molecular feature(s) are from the group of molecular features listed in Figure 11.

44. The apparatus of claim 43, wherein the predictive model comprises a concordance index of at least about 0.80.

45. The apparatus of claim 43, wherein the predictive model comprises a p value less than about 0.0001 for a log-rank test.

46. The apparatus of claim 27, wherein the predictive model is configured to detennine a diagnostic score for the patient.

47. The apparatus of claim 27, wherein the predictive model is configured to determine a likely time to occunence of the medical condition in the patient.

48. The apparatus of claim 27, wherein the predictive model is configured to determine a likely responsiveness or unresponsiveness of the patient to a therapy.

49. The apparatus of claim 27, wherein the predictive model is further configured to output data indicating results of the evaluation.

50. The apparatus of claim 49, wherein the results comprise results selected from the group of results consisting of a diagnostic score, information indicating one or more features of the patient dataset that were analyzed by the predictive model, information indicating an accuracy of the predictive model, or a combination thereof.

51. The apparatus of claim 49, wherein said predictive model is configured to receive the patient dataset for the patient from a remote location and output the results for transmission to the remote location.

52. The apparatus of claim 51, wherein the patient dataset is received and the results are transmitted over one or more communications networks.

53. The apparatus of claim 27, wherein said predictive model comprises a neural network.

54. The apparatus of claim 27, wherein said predictive model comprises a support vector machine.

55. A computer program comprising one or more computer program elements operable when executed in an execution environment to implement one or more of the functions claimed in any of method claims 1 to 26.

56. A computer usable carrier medium canying a computer program according to Claim 55.