WO2011077303A1

WO2011077303A1 - Methods and apparatuses for prostate cancer detection, staging, and therapy response assessment

Info

Publication number: WO2011077303A1
Application number: PCT/IB2010/055634
Authority: WO
Inventors: Manoj Narayanan; Jens-Christoph Georgi
Original assignee: Koninklijke Philips Electronics N.V.; Philips Intellectual Property & Standards Gmbh
Priority date: 2009-12-23
Filing date: 2010-12-07
Publication date: 2011-06-30

Abstract

A prostate cancer workflow module (30) comprises: a prostate contouring and partitioning sub-module (44) configured to (i) delineate the prostate organ in an image and (ii) partition the delineated prostate organ into a plurality of partitions; a quantitative analysis sub-module (60) configured to perform a quantitative analysis on each partition of an image to produce a quantitative result for each partition; and a report generator sub-module (76) configured to format the quantitative result for each partition into a human readable report and to display the report on a display (36) of a user interface (32). The prostate cancer workflow module (30) may suitably be a single module embodied by a computer or other digital processing device (32).

Description

METHODS AND APPARATUSES FOR PROSTATE CANCER DETECTION, STAGING, AND THERAPY RESPONSE ASSESSMENT

DESCRIPTION

The following relates to the medical arts, medical imaging arts, medical data storage and analysis arts, and related arts.

Prostate cancer is a leading cause of cancer death in older men. Conventionally, histopathology has been a primary tool for prostate cancer detection, staging, and therapy response assessment. More particularly, trans-rectal guided ultrasound biopsy is employed to obtain prostate specimens for histopathology. In this technique, ultrasound imaging is used to guide insertion of the biopsy needle to acquire a set of prostate samples. Conventionally, a sextant analysis is employed, in which the prostate is divided into sextants as follows: the base is defined as the upper third which extends from the vesical margin of the prostate; the mid-region defined as the central third and the apex defined as the remaining inferior third; and each third is further divided into a right and a left side. A prostate specimen is obtained from each sextant in order to provide a crude histopathology "mapping" of the prostate. Not surprisingly, most patients find trans-rectal guided ultrasound biopsy to be an unpleasant procedure, and additionally the crude "mapping" of the prostate is a "hit-or-miss" process which sometimes fails to detect existing prostate cancer.

It is also known to augment histopathology employing trans-rectal guided ultrasound biopsy with medical imaging techniques that have sensitivity to prostate cancer. Magnetic resonance (MR) imaging has been used in conjunction with an endorectal coil balloon to dilate the prostate region, and the resulting imaging data processed on a picture archiving and communication system (PACS) database to perform per-sextant analysis. Positron emission tomography (PET) has also been used for prostate cancer imaging, for example using ^uC-choline or ¹⁸F-choline as a suitable radiotracer targeting prostate cancer. PET studies are also typically analyzes using the conventional sextant partitioning. See, e.g., Graser et al., "Per Sextant Localization and Staging of Prostate Cancer: Correlation of Imaging Findings with Whole-Mount Step Section Histopathology", AJR Am. J. Roentgenol, vol. 188 no. 1 pp. 84-90 (2007); Testa et al., "Prostate cancer: sextant localization with MR imaging, MR spectroscopy, and "C-choline PET/CT", Radiology vol. 244 no. 3 pp. 797-806 (2007); Kwee et al, "Use of step-section histopathology to evaluate ¹⁸F-fluorocholine PET sextant localization of prostate cancer", Mol. Imaging vol. 7 no. 1 pp. 12-20 (2008); and Farsad et al., "Detection and localization of prostate cancer: correlation of ¹ ^-choline PET/CT with histopathologic step-section analysis", J. Nucl. Med. vol. 46 no. 10 pp. 1642-49 (2005).

Imaging-based prostate cancer characterization leverages an existing PACS or other imaging analysis system to perform sextant analysis which is then directly comparable with sextant histopathology samples acquired by trans-rectal guided ultrasound biopsy. In a typical PET analysis, for example, the prostate PET image is divided into sextants, and a standardized uptake value (SUV) is computed for each sextant. The SUV value is an average over the imaged sextant, so that it is not "hit-or-miss" in the same sense as sextant biopsy sampling for histopathology. However, the averaging over the sextant can result in a small cancerous region being missed due to its small impact on the SUV value averaged over the whole sextant.

The histopathology and the imaging are typically performed separately by respective histopathology and medical imaging laboratories. If multiple imaging modalities (e.g., MR and PET) are applied, the MR and PET analyses are also typically performed separately, often at different imaging laboratories and/or by different radiologists. Additional results may come from other sources, such as a prostate-specific antigen (PSA) test result from a hematology test on a drawn blood sample. These diverse results are then delivered to the oncologist who must synthesize the various results in order to make a prostate cancer diagnosis, staging decision, or therapy response assessment. Typically, the oncologist does not question the methodology by which the provided imaging or non-imaging results were obtained, and moreover cannot readily request reanalysis using a different methodology. For example, the oncologist may find it convenient that the imaging results are sextant-based so as to be directly comparable with the sextant-based histopathology results, and may fail to appreciate the potential for error introduced by the sextant partitioning methodology (e.g., sextant sampling error in the case of histopathology, or value averaging over the sextant in the case of imaging).

The following provides new and improved apparatuses and methods which overcome the above -referenced problems and others. In accordance with one disclosed aspect, a prostate cancer workflow module comprises a computer or other digital processing device configured to diagnose, stage, or assess treatment of prostate cancer using a method including delineating a prostate organ in a reference image, partitioning the delineated prostate organ to define a plurality of prostate partitions, performing at least one quantitative analysis on the prostate partitions of at least one prostate image having sensitivity to prostate cancer to generate a quantitative image result probative for diagnosing, staging, or assessing treatment of prostate cancer, and outputting the quantitative image result.

In accordance with another disclosed aspect, a prostate cancer workflow module is disclosed as set forth in the immediately preceding paragraph, wherein the prostate cancer workflow module is a single module embodied by a computer, a multiple processor core (multi-core) computer, a supercomputer that employs multiple processors, a network server, or a plurality of cooperatively operating network servers. In accordance with another disclosed aspect, a storage medium is disclosed storing instructions that are executable on a computer or other digital processing device to define a prostate cancer workflow module as set forth in the immediately preceding paragraph.

In accordance with another disclosed aspect, a prostate cancer workflow module comprises: a prostate contouring and partitioning sub-module configured to (i) delineate the prostate organ in an image and (ii) partition the delineated prostate organ into a plurality of partitions; a quantitative analysis sub-module configured to perform a quantitative analysis on each partition of an image to produce a quantitative result for each partition; and a report generator sub-module configured to format the quantitative result for each partition into a human readable report and to display the report on a display of a user interface. The prostate cancer workflow module may suitably be a single module embodied by a computer or other digital processing device.

One advantage resides in providing more accurate prostate cancer diagnosis, staging and treatment assessment.

Another advantage resides in providing more versatile prostate cancer diagnosis, staging and treatment assessment including facilitating reanalyzing diagnostic data using different image partitioning schemes. Another advantage resides in facilitating correlation of imaging data (which may or may not be multi-modality imaging data) and/or additional nonimaging data in order to obtain a result having improved statistical reliability.

Further advantages will be apparent to those of ordinary skill in the art upon reading and understanding the following detailed description.

FIGURE 1 diagrammatically illustrates a prostate cancer characterization system for cancer diagnosis, staging and treatment assessment.

FIGURE 2 diagrammatically illustrates the prostate cancer workflow module of the system of FIGURE 1.

FIGURES 3 and 4 diagrammatically illustrate prostate delineation and partitioning into sextants (FIGURE 3) or nine partitions (FIGURE 4) as performed by the prostate contouring and partitioning sub-module.

With reference to FIGURE 1, a plurality of representative medical prostate cancer-diagnostic apparatuses or laboratories are shown as illustrative examples. Figure 1 includes: a gamma camera 10 which in the illustrated embodiment is a Skylight^™ gamma camera (available from Koninklijke Philips Electronics N.V., Eindhoven, The Netherlands); a hybrid PET/CT imaging system 12 which in the illustrated embodiment is a GEMINI^™ PET/CT imaging system (available from Koninklijke Philips Electronics N.V., Eindhoven, The Netherlands); and a magnetic resonance (MR) imaging system 14 which in the illustrated embodiment is an Achieva^™ MR system (available from Koninklijke Philips Electronics N.V., Eindhoven, The Netherlands).

The gamma camera 10 is configured to acquire single photon emission computed tomography (SPECT) images. For prostate imaging, a radiotracer is employed that preferentially accumulates or is trapped in prostate cancer tissue. In similar fashion, the hybrid PET/CT imaging system 12 is configured to acquire PET images and CT images of the prostate region. For prostate cancer diagnosis, staging, or therapy assessment, the PET images are acquired using a radiotracer such as ^uC-choline or ¹⁸F-choline that preferentially collects in or is trapped by prostate cancer tissue. The CT images of the prostate region are typically not sensitive to prostate cancer tissue, but instead provide anatomical information enabling delineation of the prostate organ in the CT images. The MR system 14 is configured to acquire MR images including MR spectroscopic images, maps, or data. For prostate cancer diagnosis, staging, or treatment assessment, the MR system 14 is used to acquire MR spectroscopic data from which the concentrations or ratios of various prostate cancer- sensitive metabolites can be determined. For example, it is known in the art that the ratio of choline-plus-creatine-to-citrate peak area ratio in an MR spectrum can be probative of prostate cancer. Another example is the choline-to-creatine peak area ratio which can also be probative of prostate cancer. The MR spectroscopy data is typically acquired with spatial resolution, so that the metabolite concentration or ratio can be mapped out spatially, thus in effect generating an image of the metabolite concentration or ratio. Additionally or alternatively, the MR system 14 can be used to acquire anatomical images that are not sensitive to prostate cancer tissue but which instead provide anatomical information enabling delineation of the prostate organ.

The imaging systems 10, 12, 14 are illustrative examples, and other imaging modalities having sensitivity to prostate cancer are also contemplated. In the illustrated embodiment, an optional picture archiving and communication system (PACS) 16 provides a central database for archiving medical images acquired by the various imaging modalities 10, 12, 14. Additionally or alternatively, the various imaging modalities 10, 12, 14 may include separate image storage components, such as (for example) separate PET/CT and MR image databases.

With continuing reference to FIGURE 1, non-image prostate cancer-diagnostic apparatuses or laboratories are also represented. For example, a diagrammatically indicated hematology laboratory 20 performs hematology tests on drawn blood samples, including a PSA test for measuring the level of prostate-specific antigen (PSA) in the drawn blood sample. As is known in the art, an elevated PSA level for a patient of a given age can be a positive indicator for prostate cancer.

As another illustrative example, an ultrasound-guided biopsy laboratory 22 acquires prostate specimens using an ultrasound-guided biopsy needle or other interventional instrument. Typically, trans-rectal guided ultrasound biopsy is employed to obtain sextant prostate specimens from the six regions of the conventional sextant partitioning of the prostate, with ultrasound imaging enabling the clinician to guide the biopsy needle into each sextant partion in turn to obtain the prostate specimens. A histopathology laboratory 24 performs histopathology analysis on the sextant prostate specimens using known prostate cancer histopathology procedures.

The prostate cancer-sensitive images and anatomical images acquired by the various imaging modalities 10, 12, 14, and the non- imaging test results (e.g., a PSA level 26 determined by the hematology laboratory 20 and sextant histopathology results generated by the histopathology laboratory 24) serve as inputs to a prostate cancer workflow module 30 that collects, analyzes, and reports on various imaging and non-imaging data related to prostate cancer diagnosis, staging, or treatment assessment. In the illustrated embodiment, the prostate cancer workflow module 30 is a component of or otherwise integrated with the PACS 16; as a consequence, the prostate cancer workflow module 30 can retrieve the relevant prostate images from the PACS 16 while the non-imaging results 26, 28 are supplied via a digital data network, or are input by a human user (e.g., radiologist or oncologist) via a computer 32, or so forth. In other embodiments, the prostate cancer workflow module 30 is independent of the PACS 16 and so the relevant prostate images are also supplied via a digital data network, optical disk, or other digital data storage or transfer mechanism.

The prostate cancer workflow module 30 collects, analyzes, and reports on various imaging and non-imaging data related to prostate cancer diagnosis, staging, or treatment assessment. Toward this end, the prostate cancer workflow module 30 is embodied in or interfaces with the computer 30 or other user interfacing device in order to receive input from the user via a keyboard 32, mouse, or other user input device, and in order to display results on a computer display 36, printer, or other output device. The user input to the prostate cancer workflow module 30 may include, for example, the name, hospital identification number, or other identifying information respective to the patient whose prostate cancer-related data are to be analyzed, selections of analysis methodology, manual delineation of the prostate organ and/or partitioning thereof in prostate images, or so forth. The output may include, for example, prostate images, a report setting forth results of the analyses performed by the prostate cancer workflow module 30, or so forth.

The prostate cancer workflow module 30 is suitably emboded by a computer (which may be the illustrated computer 32 which also provides user interfacing, or may be a different computer operatively connected with the computer 32), or a network server, or another digital processing device that includes a digital processor or controller. It will also be appreciated that the prostate cancer workflow module 30 can be embodied by a storage medium storing instruction that are executable on such as computer or network server or other device including a digital processor. The storage medium may, for example, be a hard disk or other magnetic storage medium, an optical disk or other optical storage medium, a random access memory (RAM), read-only memory (ROM), electronically eraseable read-only memory (EEROM), flash memory, or other electronic storage medium, or so forth.

With reference to FIGURE 2, an illustrative embodiment of the prostate cancer workflow module 30 is set forth. Images useful in prostate cancer detection, staging, or therapy response assessment are obtained from a PACS image database 40 of the PACS system 16 (in the illustrated embodiment) or from another source. The images may include one or more reference images that provide anatomical information, for example to enable delineation of the prostate organ. Some suitable reference images include: CT images; MR images acquired using imaging parameters that provide anatomical contrast; or so forth.

The images include at least one prostate image having sensitivity to prostate cancer.

Some suitable images having sensitivity to prostate cancer include: a PET image acquired of a subject who has been administered ^uC-choline or ¹⁸F-choline or another suitable PET radiotracer targeting prostate cancer; a SPECT image using a suitable SPECT radiotracer such as ProstaScint® ("'In-capromab pendetide) targeting prostate cancer; an MR image having spectroscopic content encompassing one or more of choline, creatine, and citrate, or including ratios thereof such as a choline -plus-creatine-to-citrate peak area ratio or a choline-to-creatine peak area ratio; or so forth. The at least one prostate image having sensitivity to prostate cancer may in some embodiments include a time sequence of images, for example encompassing inflow and/or outflow of a radiotracer into or out of the prostate organ, so as to enable performing kinetic analysis of the time sequence of images.

It is to be understood that the at least one prostate image having sensitivity to prostate cancer may or may not actually contain a feature (or multiple features) indicative of prostate cancer. For example, in the case of initial prostate cancer detection the at least one prostate image having sensitivity to prostate cancer is expected to include a feature (or multiple features) indicative of prostate cancer if the patient actually has prostate cancer; on the other hand, if the patient does not have prostate cancer it is expected that the at least one prostate image having sensitivity to prostate cancer will not include any feature indicative of prostate cancer. It is also to be understood that the reference image and the at least one prostate image having sensitivity to prostate cancer may in some embodiments be the same image.

The images useful in prostate cancer detection, staging, or therapy response assessment are obtained from the PACS image database 40 by an images selection and spatial registration sub-module 42, which retrieves the images from the database 40 based on a user selection made via the user interface 32, or based on matching a patient identification number or other patient identification information, or so forth. If appropriate, the images are spatially registered by the images selection and spatial registration sub-module 42. Image registration is appropriate when the analysis to be performed involves comparison of corresponding regions of different images. For example, in some embodiments the reference image and the at least one prostate image having sensitivity to prostate cancer are acquired by different modalities. The reference image is used to identify the prostate organ and thereby provide spatial orientation. For this spatial orientation to be useful in analyzing the at least one prostate image having sensitivity to prostate cancer, the reference image and the at least one prostate image having sensitivity to prostate cancer are spatially registered. Another situation in which spatial registration may be useful is where the at least one prostate image having sensitivity to prostate cancer include two prostate images acquired by different imaging modalities or at different times (for example, before and after performing a prostate cancer therapy) and it is desired to be able to directly compare corresponding spatial regions in the two images.

On the other hand, if there is a single image which serves as both the reference image for identifying the prostate organ and as the at least one prostate image having sensitivity to prostate cancer, then no spatial registration is needed. As another example of a situation in which spatial registration may not be needed, if the hybrid PET/CT scanner 12 acquires both a CT reference image and a PET prostate image having sensitivity to prostate cancer, then since the CT and PET scanners of the hybrid PET/CT scanner 12 share a common coordinate system spatial registration may not be needed. Similarly, the same MR scanner 14 may acquire both an anatomical (reference) MR image and a spectroscopic MR image that serves as the prostate image having sensitivity to prostate cancer - although these are different images, they share a common spatial coordinate system and hence no spatial registration is needed. Where appropriate, spatial registration is performed by the sub-module 42 manually and/or using an automated image registration algorithm. In some automated registration embodiments, the image registration may entail nonrigid image adjustment such as stretching or otherwise deforming one or both images to achieve mutual registration. In some semi-automatic embodiments, the images to be registered are displayed on the display 36 and the user identifies a set of corresponding landmarks in the images to be registered using the user interface 32, and the images are shifted, rotated, or optionally stretched or deformed in order to align the corresponding landmarks. In some embodiments, the landmarks are generated automatically by feature identification algorithms. In some embodiments, the landmarks are exogenous fiducial markers placed on the torso or other body part of the patient which contain metal, magnetic material, or some other feature that appears in the images to be spatially registered.

The reference image (which may or may not be the same as the prostate image having sensitivity to prostate cancer) is processed by a prostate contouring and partitioning sub-module 44 which (i) delineates the prostate organ and (ii) partitions the prostate organ into sextants, or optionally into another partitioning. Toward this end, the illustrative prostate contouring and partitioning sub-module 44 includes a manual prostate contouring user interface sub-module 50 and an automatic prostate contouring sub-module 52 that operate together to delineate the prostate organ.

With continuing reference to FIGURE 2 and with further reference to

FIGURES 3 and 4, operation of the prostate contouring sub-modules 50, 52 is described. FIGURES 3 and 4 illustrate a prostate region including a prostate P which is delineated by a contour C. In one suitable approach, an automatic or semi-automatic segmentation algorithm is employed to define the contour C. In one approach, a loop or mesh (for a three dimensional image) is initially positioned around the expected location of the prostate P, and the loop is adjusted by a deformation algorithm respective to an energy minimization criterion or other deformation objective to conform with the boundary of the prostate P. Optionally, the initial loop is drawn by the user via the user interface 32. Optionally, the user can modify the initial and/or fitted contour via the user interface 32. For example, FIGURE 3 illustrates a contour position cursor CC that can be moved by the user (for example, using a mouse, trackball, touch screen or other pointing device) in order to adjust the contour C in the vicinity of the contour position cursor CC. In another approach the user delineates the contour C manually by selecting a number of points along the contour, and these points are connected by straight lines, spline curves, or the like in order to define the contour C. Other approaches that can be used alone or in combination to define the prostate contour C around the prostate P include edge detection and thresholding. It is also to be appreciated that in some embodiments only one of the contouring sub-modules 50, 52 is provided (by way of example, if only manual contouring is provided then the automatic prostate contouring sub-module 52 is suitably omitted; or vice versa).

With continuing reference to FIGURES 2-4, the contour C defines a delineated prostate organ, which is then partitioned by a manual prostate partitioning user interface sub-module 54 and/or by an automatic prostate partitioning sub-module 56. FIGURE 3 illustrates conventional sextant partitioning. In this approach, the prostate is divided into sextants: the base is defined as the upper third which extends from the vesical margin of the prostate; the mid-region defined as the central third and the apex defined as the remaining inferior third; and each third is further divided into a right and a left side. In an automatic approach, the leftmost extremity of the delineated prostate organ (that is, the contour C) is denoted by a left vertical cursor at 0.00, the rightmost extremity of the delineated prostate organ C is denoted by a right vertical cursor at 1.00, and the half-way point is denoted by a vertical cursor at 0.50. In similar fashion the vertical direction is subdivided into equal portions by: a horizontal cursor at the uppermost extremity of the delineated prostate organ C (vertical position 0.00); a horizontal cursor at the lowermost extremity of the delineated prostate organ C (vertical position 1.00); and two equi-spaced horizontal cursors at vertical positions 0.33 and 0.67. This partitioning is readily performed in automated fashion respective to a bounding box BB that bounds the delineated prostate organ C. Optionally, the user can utilize the manual prostate partitioning user interface sub-module 54 operating the user interface 32 to adjust the partition cursors - for example, FIGURE 3 shows a horizontal partition cursor control HCC being used to adjust the cursor initially placed at vertical position 0.33.

The sextant partitioning shown in FIGURE 3 is advantageous in that is correlates with the conventional sextant analysis typically used in histopathology for diagnosing, staging, or assessing treatment of prostate cancer. This enables quantitative image analyses performed on the prostate partitions defined by the sextant partitioning of FIGURE 3 to be directly compared with non-imaging histopathology results. However, sextant partitionins is relatively coarse, as it divides the prostate into only six partitions. Accordingly, in some embodiments the prostate partitioning sub-module 54, 56 enable the user to select a user-selected number of prostate partitions, and the partitioning partitions the delineated prostate organ C into the user-selected number of prostate partitions. To maintain direct comparisons with conventional sextant-based histopathology results, it is preferable for the user-selected number to be user selectable from a group consisting of the number six (corresponding to conventional sextant partitioning) and at least one number other than the number six.

FIGURE 4 shows an example of partitioning into nine partitions. The partitioning of FIGURE 4 employs the same vertical partitioning as in conventional sextant partitioning, with equipartitioning of the bounding box BB yielding partitioning horizontal cursors at vertical positions 0.00, 0.33, 0.67, and 1.00, each of which optionally is adjustable manually. To obtain nine partitions, the horizontal partitioning is increased in FIGURE 4 as compared with conventional sextant partitioning, so that there are four vertical partition cursors at 0.00, 0.33, 0.67, and 1.00, each of which optionally is adjustable manually. For example, an illustrative vertical partition cursor control VCC is shown for adjusting the vertical cursor at horizontal position 0.67.

In some embodiments, the user selected number of partitions is selected in an mxn format, where m indicates the number of vertical partitions and n indicates the number of horizontal partitions. In such an embodiment, conventional sextant partitioning is selected by selecting 3x2 partitioning, while the partitioning into 9 partitions shown in FIGURE 4 is selected by selecting 3x3 partitioning, and so forth. Analogous with the prostate contouring, it is also to be appreciated that in some embodiments only one of the partitioning sub-modules 54, 56 is provided (by way of example, if only manual partitioning is provided then the automatic partitioning sub-module 56 is suitably omitted; or vice versa). For three-dimensional images, it is also contemplated to select the user selected number of partitions in an mxnxp format, where the third parameter p denotes the number of partitions along the third dimension. For example, a 3x3x3 partitioning would partition a three-dimensional image into 27 partitions.

With continuing reference to FIGURE 2, once the partitioning is complete a quantitative analysis sub-module 60 performs a quantitative analysis on each partition. By way of example, in the illustrative embodiment a standardized uptake value (SUV) calculation sub-module 62 computes an aggregate SUV value for each partition of a PET image. The aggregation may be an average SUV over the partition, a maximum SUV within the partition, or so forth. This analysis operates on the expectation that the radiotracer tends to be trapped or collected in prostate cancer tissue, so that a region of prostate cancer tissue (or, more generally, a region containing a mixture of healthy tissue and prostate cancer tissue) is expected to have an elevated SUV.

The illustrative quantitative analysis sub-module 60 is also configured with a kinetic analysis sub-module 64 that computes a radiotracer transient parameter by kinetic analysis of the time sequence of PET images. For this application, the at least one prostate image having sensitivity to prostate cancer includes a time sequence of PET images spanning a time interval that encompasses radiotracer inflow and/or outflow to and from prostate cancer tissue. The kinetic analysis is performed on a voxel-by-voxel basis or on a per-partition basis in order to compute a radiotracer transient parameter for each partition. Kinetic analysis provides information about metabolic activity of the prostate cancer tissue, and can be useful in identifying regions of prostate cancer tissue necrosis, regions of prostate cancer metastasis, and other clinically useful information. While kinetic analysis of a time sequence of PET images is mentioned by way of example, more generally kinetic analysis can be performed on a time sequence of images acquired by any image modality having sufficient time resolution and sensitivity to a radiotracer or other transient physiological response in order to generate a detectable transient. As another example, a time sequence of blood oxygen level-dependent (BOLD) MR images can be used to measure transients in oxygen metabolism by prostate cancer tissue.

The illustrative quantitative analysis sub-module 60 is also configured with an MR spectroscopy ratioing sub-module 66 that computes clinically informative MR spectrum peak ratios. For example, the ratio of choline-plus-creatine-to-citrate peak area ratio in an MR spectrum can be probative of prostate cancer. Another example is the choline-to-creatine peak area ratio which can also be probative of prostate cancer. The MR spectroscopy ratioing sub-module 66 computes one or more such ratios aggregated over each partition, so as to provide an MR spectral peak ratio metric for each partition. For example, the maximum peak ratio, or average peak ratio, or other aggregation may be computed by the sub-module 66 for each partition. The illustrative quantitative analysis sub-module 60 is also configured with a multi-session trends analysis sub-module 68 that computes clinically informative trends for multiple imaging sessions. For example, one imaging session may be conducted prior to initiation of a prostate cancer treatment so as to acquire a set of "before" images, and another imaging sesson may be conducted after the prostate cancer treatment so as to acquire a set of "after" images. One or more quantitative analyses are performed on each set of images, and the values are compared by subtraction (for example, SUV_after-SUV_before) in order to provide a quantitative assessment measure for assessing the effectiveness of the prostate cancer treatment. While SUV is mentioned by way of example, the multi-session trends analysis sub-module 68 can, in general, provide such a quantitative assessment measure for assessing the effectiveness of the prostate cancer treatment based on any image metric (e.g., SUV, MR spectroscopy peak ratios, radiotracer transient parameter obtained by kinetic analysis, or so forth).

The sub-modules 62, 64, 66, 68 are illustrative examples, and it is contemplated for the quantitative analysis sub-module 60 to include other quantitative image analysis capabilities which are compatible with the available imaging modalities and which are probative for prostate cancer detection, staging, and/or treatment assessment.

With continuing reference to FIGURE 2, the prostate cancer workflow module 30 optionally receives further information, such as non-imaging results 70 (e.g., PSA level from a hemotology laboratory, sextant histopathology results, or so forth) and/or similar case histories obtained by querying a comparative patients database 72. Optionally, a correlations sub-module 74 compares or correlates the quantitative imaging result or results obtained from PET images, MR images, SPECT images, or other images with the non-imaging results 70, or compares or correlates the quantitative imaging result or results with imaging results recorded in similar case histories retrieved from the patients database 72. For example, correlation of sextant-partitioned SUV results and histopathology results may entail identifying and associating the corresponding sextant partitions used in the sextant-partitioned SUV analysis and in the histopathology. Correlation of multi-session trends analysis may entail identifying and associating imaging and non-imaging quantitative treatment assessment measures generated by corresponding "before" and "after" data. Correlation with case histories of the comparative patients database 72 may, by way of example, entail querying the patients database 72 using the imaging results generated by the quantitative analysis sub-module 60 as query parameters (for example, to retrieve a set of case histories that record the most similar SUV and MR spectral peak ratios). The query may also include non-imaging results such as PSA level.

A report generator sub-module 76 formats the information provided by the quantitative analysis sub-module 60 and the correlations sub-module 74 into a human-readable report that is displayed on the user interface display 36, or printed by a printer or other marking engine, or communicated to the patient's physician via email or another transmission pathway, or otherwise utilized. As used herein, the term "report" denotes any human readable representation of the quantitative imaging result (and optionally other results such as nonimaging results, correlative results, ROC analysis, or so forth) displayed on the display 36, or printed on paper by a printer, or otherwise rendered in human-readable form. The report may optionally include information such as patient identification, patient characteristics (e.g., age, gender, medical diagnosis, or so forth), report generation date, or so forth.

In some embodiments, the report generator sub-module 76 receives the one or more of the non-imaging results 70 directly (that is, without correlation with imaging results) for inclusion in the report. For example, the PSA level is not a sextant-based analysis - in other words, the PSA level is a single number for the patient, and is not associated with any particular prostate organ sextant partition. Accordingly, the PSA level may be inputted to the report generator sub-module 76 and included in the report without correlation. (On the other hand, if PSA level was measured before and after a prostate cancer treatment, then it may be useful for the correlations sub-module 74 to correlate the "before" and "after" PSA levels with the "before" and "after" SUV levels or with other "before" and "after" image results).

With continuing reference to FIGURES 1 and 2, the illustrated prostate cancer workflow module 30 is advantageously a single module (although it is to be appreciated that the single workflow module 30 may be embodied by a multiple processor core (multi-core) computer, or by a "supercomputer" that employs multiple processors, or by a plurality of cooperatively operating network servers, so forth). As a consequence, the illustrated single prostate cancer workflow module 30 can perform and store quantitative analyses for multiple imaging modalities (e.g., PET, MR, SPECT), and for multiple quantitative assessments (e.g., SUV, kinetic analysis, MR spectroscopy), and can further incorporate nonimaging results 70 and/or comparative patient case histories. This enables the prostate cancer workflow performed by the workflow module 30 to be streamlined and efficient, and to synergistically utilize all available probative information (including both imaging information and nonimaging information). In some embodiments, the correlations sub-module 74 is configured to compute a ROC analysis of the combined imaging data (which may or may not be multi-modality imaging data) and/or additional nonimaging data in order to obtain a result having improved statistical reliability as measured by sensitivity, specificity, or another statistical reliability measure.

Another advantage of the illustrative prostate cancer workflow module 30 is that it enables reanalyzing images using different partitionings of the delineated prostate organ. By way of example, using the conventional sextant partitioning enables convenient comparison of the quantitative imaging results with sextant-based histopathology results, and so running an image analysis using sextant partitioning is useful for this purpose. However, if the sextant-partitioned image analysis does not detect prostate cancer or provides an ambiguous result, then the oncologist may want to re -run the image analysis using a more fine partitioning (that is, a partitioning that breaks the delineated prostate organ into a larger number of smaller partitions) in order to leverage the higher resolution of imaging (as compared with the coarse biopsy sampling used in histopathology) to possibly detect small or incipient regions of prostate cancer tissue.

In a similar vein, if the sextant-partitioned image analysis does detect prostate cancer, then the oncologist may again want to re-run the image analysis using a more fine partitioning (that is, a partitioning that breaks the delineated prostate organ into a larger number of smaller partitions) in order to more precisely localize the detected regions of prostate cancer tissue and/or to more precisely assess the spatial extent of a detected prostate cancer nodule.

As yet another example of this flexibility, if the oncologist has available both PET and MR images, the oncologist may choose to use different partitionings for the PET and MR images based on the native resolution of the different imaging modalities. For analysis of an imaging modality having coarse resolution the sextant partitioning may be chosen in order to facilitate comparison with histopathology results; whereas, for analysis of a different imaging modality having finer native spatial resolution a finer partitioning may be chosen in order to utilize the higher native spatial resolution. The illustrated single prostate cancer workflow module 30 makes available in a single module the quantitative imaging results obtained from multiple different imaging modalities, and or additional nonimaging results. As a result, correlations between diverse data are more likely to be recognized by the workflow module 30 as compared with a more conventional approach in which each imaging modality is performed and quantified independently, and are brought together along with nonimaging results only when the various independent analyses are collected in the patient case file.

The prostate cancer workflow module 30 may in some embodiments be used by an oncologist or other medical professional who lacks extensive expertise in any particular imaging modality or in any particular nonimaging diagnostic. Accordingly, the prostate cancer workflow module 30 optionally includes a user guidance and online help sub-module 80 which provides context-sensitive help responsive to the user pressing a particular key or otherwise selecting the "help" option. The user guidance and online help sub-module 80 may optionally also be configured to activate automatically when certain conditions or operational states are detected. For example, the automatic prostate contouring sub-module 52 is used to generate the contour C defining the delineated prostate organ as shown in FIGURE 3, the help sub-module 80 may optionally display instructions informing the user that he or she may adjust the contour C using the contour cursor CC. By way of additional example, the help sub-module 80 may optionally suggest that the user obtain one or more additional diagnostic imaging or nonimaging tests if the correlations sub-module 74 identifies a low sensitivity or specificity or identifies another indication that the statistical reliability of a correlated result is low.

This application has described one or more preferred embodiments. Modifications and alterations may occur to others upon reading and understanding the preceding detailed description. It is intended that the application be construed as including all such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims

CLAIMS Having thus described the preferred embodiments, the invention is now claimed to be:

1. A prostate cancer workflow module (30) comprising:

a computer or other digital processing device (32) configured to diagnose, stage, or assess treatment of prostate cancer using a method including:

delineating a prostate organ in a reference image,

partitioning the delineated prostate organ to define a

plurality of prostate partitions,

performing at least one quantitative analysis on the

prostate partitions of at least one prostate image having sensitivity to prostate cancer to generate a quantitative image result probative for diagnosing, staging, or assessing treatment of prostate cancer, and

outputting the quantitative image result.

2. The prostate cancer workflow module (30) as set forth in claim 1, wherein the delineating a prostate organ in a reference image comprises:

performing model-based segmentation of the reference image to delineate the prostate organ in the reference image.

3. The prostate cancer workflow module (30) as set forth in any one of claims 1-2, wherein the prostate cancer workflow module is configured to receive a user-selected number of prostate partitions wherein the user-selected number is user selectable from a group consisting of the number six and at least one number other than the number six, and the partitioning comprises:

partitioning the delineated prostate organ into the user-selected number of prostate partitions.

4. The prostate cancer workflow module (30) as set forth in any one of claims 1-3, wherein the partitioning comprises:

displaying the delineated prostate organ and delineation cursors; and

moving the delineation cursors responsive to user input to define the prostate partitions.

5. The prostate cancer workflow module (30) as set forth in any one of claims 1-4, wherein the partitioning further comprises:

automatically partitioning a bounding box that bounds the delineated prostate organ into partitions of equal size.

6. The prostate cancer workflow module (30) as set forth in any one of claims 1-5, wherein the reference image is different from the at least one prostate image having sensitivity to prostate cancer on which is performed the at least one quantitative analysis.

7. The prostate cancer workflow module (30) as set forth in claim 6, wherein the reference image is a computed tomography (CT) reference image and the at least one prostate image having sensitivity to prostate cancer on which is performed the at least one quantitative analysis is selected from a group consisting of a positron emission tomography (PET) image, a magnetic resonance (MR) spectroscopic image, and a single photon emission computed tomography (SPECT) image.

8. The prostate cancer workflow module (30) as set forth in claim 6, wherein the reference image is a magnetic resonance (MR) reference image that does not have sensitivity to prostate cancer and the at least one prostate image having sensitivity to prostate cancer on which is performed the at least one quantitative analysis is selected from a group consisting of a positron emission tomography (PET) image, a magnetic resonance (MR) spectroscopic image, and a single photon emission computed tomography (SPECT) image.

9. The prostate cancer workflow module (30) as set forth in any one of claims 6-8, wherein the method further comprises: spatially registering the reference image and the at least one prostate image having sensitivity to prostate cancer on which is performed the at least one quantitative analysis.

10. The prostate cancer workflow module (30) as set forth in any one of claims 1-5, wherein the reference image is the same image as the at least one prostate image having sensitivity to prostate cancer on which is performed the at least one quantitative analysis.

11. The prostate cancer workflow module (30) as set forth in any one of claims 1-10, wherein the at least one quantitative analysis is selected from a group consisting of:

computing a standardized uptake value (SUV) for a

partition of a positron emission tomography (PET) image, and

computing a ratio of spectral peak characteristics for

a partition of a magnetic resonance (MR) image having spectroscopic contrast.

12. The prostate cancer workflow module (30) as set forth in any one of claims 1-11, wherein the at least one quantitative analysis includes computing a radiotracer transient parameter by kinetic analysis of a time sequence of positron emission tomography (PET) images.

13. The prostate cancer workflow module (30) as set forth in any one of claims 1-12, wherein the performing at least one quantitative analysis on the prostate partitions of at least one prostate image having sensitivity to prostate cancer is repeated for images acquired before and after a prostate cancer treatment, and the method further comprises: comparing the quantitative image result for the image acquired before the prostate cancer treatment and the quantitative image result for the image acquired after the prostate cancer treatment to generate a quantitative treatment assessment measure;

wherein the outputting further comprises outputting the quantitative treatment assessment measure.

14. The prostate cancer workflow module (30) as set forth in any one of claims 1-13, wherein the performing at least one quantitative analysis on the prostate partitions of at least one prostate image having sensitivity to prostate cancer comprises:

performing at least one quantitative analysis on the prostate partitions of a first prostate image acquired using a first imaging modality and having sensitivity to prostate cancer to generate a first quantitative image result probative for diagnosing, staging, or assessing treatment of prostate cancer; and

performing at least one quantitative analysis on the prostate partitions of a second prostate image acquired using a second imaging modality different from the first imaging modality and having sensitivity to prostate cancer to generate a second quantitative image result probative for diagnosing, staging, or assessing treatment of prostate cancer.

15. The prostate cancer workflow module (30) as set forth in any one of claims 1-14, wherein the method further comprises:

outputting at least one non-image result that is probative for diagnosing, staging, or assessing treatment of prostate cancer together with the outputting the quantitative image result.

16. The prostate cancer workflow module (30) as set forth in any one of claims 1-14, wherein the method further comprises:

outputting a correlation of the quantitative image result with at least one non-image result that is probative for diagnosing, staging, or assessing treatment of prostate cancer.

17. The prostate cancer workflow module (30) as set forth in claim 16, wherein the partitioning defines sextant prostate partitions and the outputting a correlation comprises:

outputting the quantitative image result for each sextant partition together with a corresponding sextant histopathology result that is probative for diagnosing, staging, or assessing treatment of prostate cancer.

18. The prostate cancer workflow module (30) as set forth in any one of claims 1-17, wherein the prostate cancer workflow module is a single module embodied by a computer, a multiple processor core (multi-core) computer, a supercomputer that employs multiple processors, a network server, or a plurality of cooperatively operating network servers.

19. A storage medium storing instructions executable on a computer or other digital processing device (32) to define a prostate cancer workflow module (30) as set forth in any one of claims 1-18.

20. A prostate cancer workflow module (30) comprising:

a prostate contouring and partitioning sub-module (44) configured to (i) delineate the prostate organ in an image and (ii) partition the delineated prostate organ into a plurality of partitions;

a quantitative analysis sub-module (60) configured to perform a quantitative analysis on each partition of an image to produce a quantitative result for each partition; and

a report generator sub-module (76) configured to format the quantitative result for each partition into a human-readable report and to display the report on a display (36) of a user interface (32);

wherein the prostate cancer workflow module (30) is a single module embodied by a computer or other digital processing device (32).

21. The prostate cancer workflow module (30) as set forth in claim 20, wherein the prostate cancer workflow module (30) is a single module embodied by a digital processing device selected from the group consisting of a computer, a multiple processor core (multi-core) computer, a supercomputer that employs multiple processors, a network server, or a plurality of cooperatively operating network servers.

22. The prostate cancer workflow module (30) as set forth in any one of claims 20-21, wherein the prostate contouring and partitioning sub-module (44) is configured to receive a user-selected number of prostate partitions via the user interface (32) to partition the delineated prostate organ into the user-selected number of prostate partitions.

23. The prostate cancer workflow module (30) as set forth in any one of claims 20-22, wherein the prostate contouring and partitioning sub-module (44) is configured to operate on a first image and the quantitative analysis sub-module (60) is configured to operate on a second image different from the first image, and the prostate cancer workflow module further comprises:

an images selection and spatial registration sub-module (42) configured to spatially register the first image and the second image.

24. The prostate cancer workflow module (30) as set forth in any one of claims 20-23, wherein the quantitative analysis sub-module (60) is configured to perform at least one quantitative analysis selected from a group consisting of:

computing a standardized uptake value (SUV) for a

partition of a positron emission tomography (PET) image,

computing a ratio of spectral peak characteristics for

a partition of a magnetic resonance (MR) image having spectroscopic contrast, and

computing a radiotracer transient parameter by

kinetic analysis of a time sequence of PET images.