CN113378879A - Postoperative tumor assessment method and device and computer storage medium - Google Patents
Postoperative tumor assessment method and device and computer storage medium Download PDFInfo
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Abstract
The application provides a method, a device and a computer storage medium for postoperative tumor assessment, which mainly comprise the steps of obtaining preoperative images and a plurality of thermal ablation images of target tumors; obtaining each thermal dose map corresponding to each thermal ablation image according to preset treatment parameters and each thermal ablation image; obtaining a plurality of target characteristic vectors according to each thermal dose map, the preoperative image, each thermal ablation image and the preset treatment parameters; and obtaining a postoperative evaluation result of the target tumor according to each target feature vector. Therefore, the method and the device can quickly and accurately predict the postoperative tumor recurrence risk so as to assist a clinician to control the patient's condition more accurately.
Description
Technical Field
The embodiment of the application relates to the technical field of artificial intelligence, in particular to a method and a device for postoperative tumor assessment and a computer storage medium.
Background
Tumor ablation is a minimally invasive treatment technology, has high safety and small complications, and is an important choice for treating cancer patients who fail chemoradiotherapy or are not suitable for surgical resection.
Existing ablation devices can deliver energy directly to the target tissue and cause physical damage in situ: wherein the thermal ablation therapy is that the temperature of a target tissue is raised to be higher than 60 ℃ by a high-frequency electric field through radio frequency ablation, microwave ablation and the like, so that proteins and cell nucleuses are rapidly denatured; cryoablation therapy is based on the joule-thomson principle, and the target tissue is cooled to a temperature lower than-40 ℃ to destroy the cell structure and make the cell structure undergo the process of apoptosis and necrosis; in addition, ablation therapy with combined heating and cooling can enhance direct thermal damage to the tumor and indirect immune response through rapid changes in temperature and stress within the target tissue.
In recent years, techniques for evaluating tumor ablation procedures, and methods for predicting changes in tumor volume in long-term follow-up after ablation have also been proposed in conjunction with the development of medical imaging.
It should be noted that the sensitivity of the tumor and the precise application of thermal dose are the key factors for the success of the treatment during the tumor ablation treatment. However, the existing post-tumor ablation evaluation has the following partial defects:
firstly, the thermal dose actually accepted by different patients in the treatment process is different and is limited by the compatibility of the ablation device and the imaging device, the temperature field during treatment is difficult to be visualized in a navigation image, and the degree of damage to the tumor to be ablated, especially the safety margin, is difficult to measure only by the experience of a doctor and ideal preoperative planning.
Second, the necrotic lesion remains in the body after ablation and cannot be removed, and it is clinically necessary to evaluate the long-term local progression of the tumor after treatment through more than one year of image follow-up. However, the artificial diagnosis based on image follow-up can only measure the occurrence of imaging and reflect macroscopic tissue changes, neglect the potential manifestations of tumor microcirculation destruction, antigen release and the like in the image and influence the long-term estimation of prognosis.
Thirdly, the current tumor ablation prognosis evaluation method is difficult to evaluate whether the surgery is successful or not immediately after the treatment is finished, and cannot predict the tumor recurrence risk which progresses along with the time, and the main reason is that the lack of related quantitative indexes provides reference with clinical guidance significance for doctors, which delays the optimal complementary treatment time of high-risk patients and brings potential recurrence risk.
In view of the above, how to provide a technique capable of timely and accurately performing postoperative tumor recurrence evaluation is a technical subject to be solved by the present application.
Disclosure of Invention
In view of the above problems, the present application provides a method for evaluating a tumor after an ablation operation, which can quickly predict the risk of local tumor progression after the ablation operation, so as to help a doctor to enhance disease monitoring and timely supplement treatment. A
A first aspect of the present application provides a method for postoperative tumor assessment, comprising: acquiring preoperative images and a plurality of thermal ablation images of a target tumor; obtaining each thermal dose map corresponding to each thermal ablation image according to preset treatment parameters of the target tumor and each thermal ablation image; obtaining a plurality of target characteristic vectors according to each thermal dose map, the preoperative image, each thermal ablation image and the preset treatment parameters; and obtaining a postoperative evaluation result of the target tumor according to each target feature vector.
A second aspect of the present application provides a computer storage medium having stored therein instructions for performing the steps of the post-operative tumor assessment method of the first aspect.
A third aspect of the present application provides a post-operative tumor evaluation apparatus comprising: the acquisition module is used for acquiring preoperative images, a plurality of thermal ablation images and preset treatment parameters of the target tumor; an analysis module, configured to obtain each thermal dose map corresponding to each thermal ablation image according to the preset treatment parameter and each thermal ablation image, and obtain a plurality of target feature vectors according to each thermal dose map, the preoperative image, each thermal ablation image, and the preset treatment parameter; and the evaluation module is used for obtaining the postoperative evaluation result of the target tumor according to each target feature vector.
In summary, the method, the device and the computer storage medium for evaluating the postoperative tumor provided by the embodiment of the present application can quickly and accurately evaluate the risk of local progress of the postoperative tumor by combining the preoperative image, the thermal ablation image and the preset treatment parameters of the analyzed target tumor.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the embodiments of the present application, and other drawings can be obtained by those skilled in the art according to the drawings.
Fig. 1 is a schematic flow chart of a method for postoperative tumor assessment according to a first embodiment of the present application.
Fig. 2 is a schematic flow chart of a method for postoperative tumor assessment according to a second embodiment of the present application.
Fig. 3 is a schematic flow chart of a method for postoperative tumor assessment according to a third embodiment of the present application.
FIG. 4 is a schematic view of an embodiment of a temperature field profile and a thermal dose profile.
Fig. 5 is a schematic flow chart of a method for postoperative tumor assessment according to a fourth embodiment of the present application.
Fig. 6 is a schematic flow chart of a fifth embodiment of the method for postoperative tumor assessment according to the present application.
Fig. 7 is a schematic structural diagram of a post-operative tumor evaluation apparatus according to a seventh embodiment of the present application.
Element number
700: a post-operative tumor assessment device; 702: an acquisition module; 704: an analysis module; 706: an evaluation module; 708: tumor evaluation model.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, but not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application shall fall within the scope of the protection of the embodiments in the present application.
As described in the background section, the current post-tumor-ablation evaluation technology mainly has the problems that the temperature field during treatment cannot be visually displayed, so that doctors are difficult to measure the degree of damage to a tumor to be ablated, particularly a safety boundary, and to immediately evaluate whether the surgery is successful after the treatment is finished, and prediction cannot be made on the tumor recurrence risk which progresses along with time.
In view of the foregoing, embodiments of the present application provide a post-operative tumor assessment technique that at least partially solves the above-mentioned problems.
First embodiment
Fig. 1 is a schematic flow chart of a method for postoperative tumor assessment according to a first embodiment of the present application. As shown in the figure, the method for postoperative tumor of the present embodiment mainly includes:
step S102, a preoperative image and a plurality of thermal ablation images of the target tumor are acquired.
Alternatively, one preoperative image of the target tumor prior to performing ablation may be acquired, and each thermal ablation image of the target tumor corresponding to each thermal ablation procedure may be acquired.
In this embodiment, the ablation procedure performed on the target tumor may include an ablation treatment protocol such as cold ablation alone, hot ablation alone, a combination of cold and hot ablation, and so on.
Wherein each thermal ablation operation (e.g., radiofrequency ablation) corresponds to a different ablation location of the target tumor.
Optionally, a cold ablation image of the target tumor corresponding to the cold ablation procedure may also be acquired.
In particular, the ablation procedure performed for the target tumor may also include a cold ablation procedure, which may be performed prior to a hot ablation procedure.
Alternatively, the preoperative image, the thermal ablation image, and the cold ablation image (if present) may include MRI image, CT image, or ultrasound image, but not limited thereto, and image types may be used.
In the present embodiment, the preoperative image may be an MRI (magnetic resonance imaging) image, and the thermal ablation image and the cold ablation image may be CT (computed tomography) images.
Alternatively, the ablation needle used to perform the ablation therapy may comprise a monopolar ablation needle, a bipolar ablation needle, a multipolar ablation needle, or the like.
And step S104, obtaining each thermal dose map corresponding to each thermal ablation image according to preset treatment parameters and each thermal ablation image.
Alternatively, preset treatment parameters may be input into a preset simulation model for inverse extrapolation to obtain temperature data and thermal dose data accumulated since the start of each thermal ablation operation.
In the present embodiment, the preset treatment parameters refer to various treatment parameters related to tumor ablation.
Alternatively, the preset treatment parameters may include an initial impedance and an initial temperature of the human tissue, an output power of the ablation needle, the number of thermal ablation operations, a respective length of thermal ablation time corresponding to the respective thermal ablation operation, and the like.
Optionally, the preset treatment parameters may further include the age of the patient, the tumor type, the number of tumors, the treatment position, the number of ablation needles for cold ablation operation, and the like.
And step S106, obtaining a plurality of target characteristic vectors according to each thermal dose map, preoperative images, each thermal ablation image and preset treatment parameters.
In this embodiment, the imaging omics features and the clinical features can be obtained from each thermal dose map, preoperative image, each thermal ablation image, and the preset treatment parameters, and the imaging omics features and the clinical features can be combined arbitrarily to obtain a plurality of target feature vectors.
And step S108, obtaining postoperative evaluation results of the target tumor according to the target characteristic vectors.
Optionally, each target feature vector may be input into a trained tumor assessment model for prediction to output a postoperative assessment result of the target tumor.
Alternatively, the post-operative evaluation result of the target tumor may include a risk score of the target tumor, which is used to indicate the high or low probability of the post-operative recurrence of the target tumor.
Optionally, the postoperative evaluation result of the target tumor may further include survival probability values of the target tumor corresponding to the respective time periods to obtain a survival probability curve of the individual patient.
In summary, the method for evaluating postoperative tumor according to the embodiment of the present application can quickly and accurately evaluate and predict postoperative recurrence risk of a target tumor by combining analysis of preoperative image, thermal ablation image and preset treatment parameters of the target tumor, so as to assist a clinician to better control the condition of a patient.
Second embodiment
Fig. 2 shows a schematic flow chart of a second embodiment of the present application. As shown, the post-operation tumor evaluation method of the present embodiment can be executed after step S102 and before step S104, and mainly includes the following steps:
step S202, performing image registration on the preoperative image and each thermal ablation image of the target tumor.
In the present embodiment, a deformable registration may be performed for the preoperative image and a rigid registration may be performed for each of the other thermal ablation images based on the thermal ablation image of the first thermal ablation operation.
Specifically, an appropriate image registration mode can be selected according to the imaging difference of the image sequence for the same tissue to be ablated (target tumor), so as to correct the posture change during treatment and the tissue deformation caused by multiple needle advancing and retracting operations (one needle advancing and retracting operation can be generated by one thermal ablation operation).
For example, when performing registration of preoperative images and thermal ablation images, large differences in deformation can result from the target tumor and its surrounding tissue being affected by the actual treatment body position, in which case the cross-modality deformable registration of MRI-CT can be selected to eliminate anisotropic voxel movement resulting from the three treatment body positions of supine, lateral, and prone.
For example, when performing registration of respective thermal ablation images corresponding to different thermal ablation operations, a small difference in deformation may occur due to the target tumor and its surrounding tissue being affected by multiple needle advancing and retracting operations, in which case CT-CT co-modal rigid registration may be selected.
Optionally, registration may be performed on a cold ablation image based on a thermal ablation image of the first thermal ablation operation, where a registration manner of the cold ablation image may refer to the thermal ablation image, which is not described herein again.
Step S204, determining the region of interest according to the preoperative image and the thermal ablation images after the registration.
In this embodiment, the tumor region of the target tumor may be determined according to the registered preoperative images, the monitoring range region of the target tumor may be determined according to the registered thermal ablation images, and the region of interest may be determined according to the tumor region and the monitoring range region.
In this embodiment, the monitoring range region may include the maximum ablation radius range around the ablation needle and the surrounding tissue of the target tumor.
Optionally, a frozen ice ball of the target tumor can be determined according to the registered cold ablation image.
Step S206, determining the positions of the ablation needles corresponding to the thermal ablation images according to the registered thermal ablation images.
In this embodiment, the position of each ablation needle corresponding to each thermal ablation image (i.e., each thermal ablation operation) can be determined based on the cross-sectional image of each thermal ablation image.
Alternatively, the ablation needle position may include ablation needle tip position coordinates (x1, y1, z1) and needle handle position coordinates (x2, y2, z 2).
In summary, the post-operation tumor assessment method according to the embodiment of the present application improves the accuracy of the post-operation tumor assessment result by performing the registration processing on the acquired preoperative image and the thermal ablation image.
Third embodiment
Fig. 3 shows a schematic flow chart of a method for postoperative tumor assessment according to a third embodiment of the present application. This embodiment is a specific implementation of the step S104, which mainly includes the following steps:
step S302, obtaining each temperature field data and each thermal dose data corresponding to each thermal ablation image according to preset treatment parameters.
Optionally, the preset simulation model may be configured according to the preset treatment parameters, and based on the configured preset simulation model, the temperature field data and the thermal dose data corresponding to each voxel block in the region of interest of the thermal ablation image are obtained.
In this embodiment, the preset simulation model may be used to invert the accumulated steady-state temperature field and thermal dose distribution from each thermal ablation operation according to the preset treatment parameters, and derive the temperature field and thermal dose lattice data in the form of x-coordinate, y-coordinate, z-coordinate, and temperature value T.
Alternatively, the preset treatment parameters may include an initial impedance and an initial temperature of the human tissue, an output power of the ablation needle, a number of times of the thermal ablation operation, a length of time of each of the thermal ablation corresponding to each of the thermal ablation operations, and the like. Wherein, the output power of the ablation needle can be respectively set according to different working sections.
Alternatively, the tip temperature of the ablation needle may be measured directly using a temperature sensor.
Alternatively, thermal dose data may be acquired by coupling a quasi-electrostatic field equation with a biological heat transfer equation.
Specifically, the thermal dose data may be obtained using the following preset conversion rules:
where D represents thermal dose data corresponding to a voxel block, T represents a thermal ablation time period for a thermal ablation operation, and T represents a transient temperature value for the voxel block.
And S304, converting the temperature field data and the thermal dose data corresponding to each thermal ablation image according to each space coordinate of each thermal ablation image to obtain each temperature field map and each thermal dose map corresponding to each thermal ablation image.
In this embodiment, the spatial coordinates of the thermal ablation image can be obtained from the header of the thermal ablation image.
Optionally, each temperature field data and each thermal dose data corresponding to each thermal ablation image may be interpolated according to each spatial coordinate of each thermal ablation image to obtain each temperature field image gray value and each thermal dose image gray value corresponding to each thermal ablation image, and each temperature field map and each thermal dose map corresponding to each thermal ablation image may be obtained according to each temperature field image gray value and each thermal dose image gray value.
Specifically, the size of the interpolation grid can be determined according to the extend, origin, and spacing in the header of the thermal ablation image, and the gray value of each temperature field image and the gray value of each thermal dose image can be interpolated by using a linear interpolation method or a cubic spline interpolation method.
Then, according to the preset temperature field dot array range threshold value and the gray value of each temperature field image corresponding to each thermal ablation image, the gray value of each temperature field image exceeding the preset temperature field dot array range threshold value is marked as 37, so as to obtain each temperature field map corresponding to each thermal ablation image.
Similarly, the gray value of each thermal dose image exceeding the preset thermal dose field dot array range threshold is marked as 0 according to the preset thermal dose field dot array range threshold and the gray value of each thermal dose image corresponding to each thermal ablation image, so as to obtain each thermal dose map corresponding to each thermal ablation image.
Please refer to fig. 4, which shows a temperature field map and a thermal dose map obtained by the method for evaluating postoperative tumor of the present embodiment, wherein the numbers a, b, and c correspond to the three conditions of lying on the back, lying on the side, and lying on the stomach, respectively. The serial numbers 1 and 2 are respectively a temperature field map and a thermal dose map of the thermal ablation operation.
As can be seen from fig. 4, the temperature field map and the thermal dose map constructed in the present embodiment both reflect the degree of heat accumulation in the target tumor tissue, and the thermal dose map is not easily affected by the shape of the ablation needle, so that the boundary damage condition of the tissue to be ablated (i.e., the target tumor) can be better measured, and whether the ablation operation is successful or not can be quickly determined.
Step S306, according to the positions of the ablation needles corresponding to the thermal ablation images, the registration processing is executed aiming at the temperature field maps and the thermal dose maps corresponding to the thermal ablation images.
In the present embodiment, based on the needle tip position coordinates (x1, y1, z1) and the needle shaft position coordinates (x2, y2, z2) of the ablation needle, displacement processing and/or rotation processing may be performed on each temperature field map and each thermal dose map corresponding to each thermal ablation image, so that each temperature field map and each thermal dose map corresponding to each thermal ablation image are respectively aligned with the ablation needle position.
Specifically, a rotation angle perpendicular to a cross section of the thermal ablation image can be determined according to needle point position coordinates (x1, y1, z1) and needle handle position coordinates (x2, y2, z2) of the ablation needle, so as to calculate a displacement transformation matrix and a rotation transformation matrix; and performing registration on the temperature field map and the thermal metering map according to the translation transformation matrix and the rotation transformation matrix so as to enable the directions of the maps to be consistent with the direction of the inserting needle of the ablation needle respectively.
And S308, fusing each temperature field map and each thermal dose map after registration, preoperative images after registration and each thermal ablation image after registration based on a preset fusion standard, and outputting a fusion result.
In this embodiment, in a case where multiple thermal ablation operations are performed, the superposition of energy needs to be considered, and the method of this embodiment further includes performing a union processing on the temperature field maps corresponding to the thermal ablation images to obtain a comprehensive temperature field map, and performing an addition processing on the thermal dose maps corresponding to the thermal ablation images to obtain a comprehensive thermal dose map.
Optionally, normalization processing may be performed on the integrated temperature field map and the integrated thermal dose map to facilitate subsequent fusion display and feature extraction processing.
Optionally, based on the preset fusion criteria, fusing the registered temperature field maps and thermal dose maps, the registered preoperative images, and the registered thermal ablation images may include at least one of the following fusion modes:
fusing the comprehensive temperature field map and preoperative images; fusing the comprehensive thermal dose map and preoperative images; fusing any one thermal ablation image and a temperature field map corresponding to the thermal ablation image; any one of the thermal ablation images is fused with a thermal dose map corresponding to the thermal ablation image.
For example, assuming that three thermal ablation operations are performed on a target tumor, wherein three temperature field maps corresponding to the three thermal ablation operations are respectively identified as a1, a2 and A3, a combined temperature field map is identified as a0, three thermal dose maps corresponding to the three thermal ablation operations are respectively identified as B1, B2 and B3, a combined thermal dose map is identified as B0, a preoperative image after registration is identified as C, and three thermal ablation images corresponding to the three thermal ablation operations are respectively identified as D1, D2 and D3, the fusion modes of the above data can include the following four types:
the first method is as follows: fusion of A0+ C; the second method comprises the following steps: fusion of B0+ C; the third method comprises the following steps: fusion of the thermal ablation image and the temperature field map corresponding to the same thermal ablation operation (e.g., a1+ D1, a2+ D2, A3+ D3, and so on); and a fusion mode is as follows: fusion of thermal dose maps and thermal ablation images corresponding to the same thermal ablation procedure (e.g., B1+ D1, B2+ D2, B3+ D3, and so on).
In summary, the postoperative tumor assessment method of the present embodiment inverts the temperature field maps and the thermal dose maps corresponding to each thermal ablation operation according to the preset treatment parameters, and generates a plurality of fusion results by selectively fusing the registered temperature field maps and thermal dose maps, the registered preoperative image, and the registered thermal ablation images, so as to facilitate a doctor to view the edge treatment condition of the tissue to be ablated (target tumor) in a linkage manner, thereby ensuring the technical success of the ablation operation.
Moreover, the post-operation tumor evaluation method of the embodiment considers the specificity of the thermal dose actually received by the patient in the treatment process, precisely inverts the temperature field through clinical treatment parameters, incorporates the influence of the thermal dose into the post-operation evaluation of tumor ablation, and realizes the fusion of the temperature field map and the thermal dose map with the preoperative image and the intraoperative image of the target tumor so as to visually display the temperature and energy information of each voxel on the tumor.
Fourth embodiment
Fig. 5 shows a schematic flow chart of a method for postoperative tumor assessment according to a fourth embodiment of the present application, which mainly shows a specific implementation of step S106 described above, and mainly includes the following steps:
step S502, based on the preoperative image and the comprehensive thermal dose map, a plurality of image omics characteristics corresponding to the tumor region and the monitoring range region are respectively obtained.
Alternatively, the imagery omics features may include any of grayscale features, geometric features, texture features, and higher-order features obtained using neural networks.
In this embodiment, for the preoperative image, the tumor region may be used as a mask, various texture features including Tamara feature, Law feature, gray level co-occurrence matrix feature, and the like are extracted, and the geometric features of the target tumor are extracted.
In this embodiment, for the integrated thermal dose map, the monitoring range region may be used as a mask to extract the first-order grayscale features and the above-mentioned texture features.
In this embodiment, the geometric features of the frozen ice ball can be extracted by using the monitoring area as a mask for the cold ablation image.
Optionally, the 3D region of interest in the preoperative image and the cold ablation image may be divided according to a cross section to obtain a plurality of two-dimensional slices, and the operation of extracting the characteristics of the image group is performed based on each two-dimensional switching.
Step S504, a plurality of clinical characteristics are obtained based on preset treatment parameters.
Optionally, the preset treatment parameters may include medical record information counted by the medical record system and treatment parameters of the ablation device.
For example, the medical history information may include parameters such as age, tumor type, number of tumors, etc., and the treatment parameters may include treatment position, average power of cold ablation operation, average power of hot ablation operation, total duration of cold ablation operation, number of cryoneedles of cold ablation operation, number of hot ablation operations, etc.
Alternatively, normalization processing may be performed for each clinical feature that is quantified and encoding processing (e.g., One-Hot encoding) may be performed for each clinical feature that is not quantified.
And step S506, obtaining each target feature vector according to the imaging group features and the clinical features.
In this embodiment, classification can be performed for each imaging omics feature and each clinical feature based on the treatment order and the feature source to obtain each category feature; screening various category characteristics based on a preset screening rule to obtain various candidate characteristics; and randomly combining the candidate features to obtain the target feature vectors.
Optionally, the category characteristics include at least one of clinical category characteristics, preoperative tumor category characteristics, and intraoperative thermal dose category characteristics.
Optionally, the preset screening rule may include a non-linear screening manner and/or a linear screening manner.
For example, the candidate features can be obtained by screening the non-zero-valued features by a Lasso screening method (i.e., non-linear screening) and screening the features with coefficient values less than 0.6 by a linear screening method.
It should be noted that other screening methods may also be used to obtain candidate features from the classification features, which is not limited in this application.
In summary, by using the method for evaluating postoperative tumor according to the embodiment of the present application, feature extraction is performed on the thermal dose map, the preoperative image, each thermal ablation image, and the preset treatment parameters to combine into the target feature vector, and the generated target feature vector includes clinical features, preoperative tumor features, and intraoperative thermal dose features, which can not only characterize the tissue characteristics of the tumor, but also reflect the applied specific energy, and the heat is the root cause of the therapeutic effect generated by the thermal physical ablation regardless of direct cellular necrosis or indirect immune injury, so the present application has good prediction potential for short-term prediction and long-term estimation of tumor ablation risk.
Fifth embodiment
Fig. 6 shows a method for postoperative tumor assessment according to a fifth embodiment of the present application, which mainly shows a training procedure of a tumor assessment model, and the method mainly includes:
step S602, training a tumor evaluation model using each target feature vector, and obtaining each performance parameter corresponding to each target feature vector.
In this embodiment, the tumor assessment model may be constructed based on any one of a support vector machine, a random forest, and a neural network.
In this embodiment, different combinations of target feature vectors may be, for example, combinations of clinical features, pre-operative tumor features, conventional post-operative ablation zone features; the combination of clinical features, preoperative tumor features, and intraoperative thermal dose features, but not limited thereto, other combination modes may also be used to generate the target feature vector.
In this embodiment, the tumor assessment model may be trained based on the predetermined optimal hyper-parameters:
for example, an optimal number of tree plants n _ tree for survival of a random forest is determined according to the calculation cost and the prediction error, and in consideration of the calculation cost and the prediction accuracy, the optimal number of tree plants determined in this embodiment may be n _ tree — 880; searching through GridSearchCV grid to determine the optimal hyper-parameters max _ features, max _ depth, min _ node _ size. And then, training a survival random forest model on the training set by using the optimal hyper-parameters, calculating a consistency coefficient C-index by the training set through 10-fold cross validation, and finishing the training of the tumor evaluation model until the training set is iterated to the optimal training set C-index.
The fully trained tumor assessment model can then be validated on the test set.
For example: dividing the test set into 10 parts, and alternately calculating the consistency coefficient C-index of each 9 parts to obtain simple statistical distribution of the performance of the test set; and calculating the time dependent auc (iauc), Brier error (iBS) from the predicted test set risk score to verify the global predictive performance of the tumor assessment model over the entire follow-up time.
In this embodiment, the tumor assessment model may be trained based on preset performance indicators, wherein the preset performance indicators include: c-index (consistency factor) is 0.92 ± 0.012, iAUC (calculation time dependence) is 0.889, and iBS (Brier error) is 0.041.
Step S604, obtaining an optimal combination of the target feature vectors and a tumor assessment model trained and completed based on the target feature vectors of the optimal combination according to the performance parameters corresponding to the target feature vectors.
In this embodiment, the optimal combination mode of the target feature vectors can be obtained by comparing the performance parameters, and the tumor assessment model trained by the target feature vectors based on the optimal combination is used as the optimal tumor assessment model.
In summary, the tumor assessment model constructed by the present embodiment can predict the risk score of the target tumor and the survival probability values of the target tumor corresponding to the time periods, and can provide quantitative information of time dependence.
Sixth embodiment
A sixth embodiment of the present application provides a computer storage medium, which stores instructions for executing each step of the post-operative tumor assessment method according to any one of the first to fifth embodiments.
Seventh embodiment
Fig. 7 shows a post-operative tumor evaluation apparatus according to a seventh embodiment of the present application. As shown in the figure, the post-operation tumor evaluation apparatus 700 of the present embodiment mainly includes: an acquisition module 702, an analysis module 704, and an evaluation module 706.
The obtaining module 702 is configured to obtain preoperative images, multiple thermal ablation images, and preset treatment parameters of the target tumor.
Optionally, the acquiring module 702 is further configured to acquire the preoperative image before the target tumor is ablated; and acquiring each thermal ablation image of the target tumor corresponding to each thermal ablation operation; wherein each of the thermal ablation operations respectively corresponds to a different ablation location of the target tumor.
Optionally, the preoperative image, the thermal ablation image comprise any one of MRI images, CT images, ultrasound images.
The analysis module 704 is configured to obtain each thermal dose map corresponding to each thermal ablation image according to the preset treatment parameter and each thermal ablation image, and obtain a plurality of target feature vectors according to each thermal dose map, the preoperative image, each thermal ablation image, and the preset treatment parameter.
Optionally, the analysis module 704 is further configured to perform image registration for the preoperative image and each of the thermal ablation images of the target tumor; determining a region of interest according to the registered preoperative images and the thermal ablation images; and determining the position of each ablation needle corresponding to each thermal ablation image according to each thermal ablation image after registration.
Optionally, the analysis module 704 is further configured to perform a deformable registration for the preoperative image based on the thermal ablation image of the first thermal ablation operation; and performing a rigid registration for each of the other thermal ablation images based on the thermal ablation image of the first thermal ablation operation.
Optionally, the analysis module 704 is further configured to determine a tumor region of the target tumor according to the pre-operative image after the registration; determining a monitoring range area of the target tumor according to each thermal ablation image after registration; determining the region of interest in the preoperative image and the thermal ablation image according to the tumor region and the monitoring range region and the tumor region and the monitoring range region.
Optionally, the analysis module 704 is further configured to obtain, according to the preset treatment parameters, each temperature field data and each thermal dose data corresponding to each thermal ablation image; converting the temperature field data and the thermal dose data corresponding to each thermal ablation image according to each spatial coordinate of each thermal ablation image to obtain each temperature field map and each thermal dose map corresponding to each thermal ablation image; according to the positions of the ablation needles corresponding to the thermal ablation images, performing registration processing on the temperature field maps and the thermal dose maps corresponding to the thermal ablation images; fusing each registered temperature field atlas, each registered thermal dose atlas, the pre-operation image after registration and each registered thermal ablation image based on a preset fusion standard, and outputting a fusion result.
Optionally, the analysis module 704 is further configured to configure a preset simulation model according to the preset treatment parameters; and obtaining each temperature field data and each thermal dose data corresponding to each voxel block in the region of interest based on the configured preset simulation model.
Optionally, the analysis module 704 is further configured to obtain the thermal dose data based on a preset conversion rule; the preset conversion rule is expressed as:
wherein D represents the thermal dose data corresponding to the voxel block, T represents a thermal ablation time period for the thermal ablation operation, and T represents a transient temperature value for the voxel block.
Optionally, the preset treatment parameters include an initial impedance and an initial temperature of the human tissue, an output power of an ablation needle, a number of operations of the thermal ablation operation, a respective length of the thermal ablation time corresponding to the respective thermal ablation operation.
Optionally, the analysis module 704 is further configured to interpolate, according to each of the spatial coordinates of each of the thermal ablation images, each of the temperature field data and each of the thermal dose data corresponding to each of the thermal ablation images, so as to obtain each of the temperature field image gray values and each of the thermal dose image gray values corresponding to each of the thermal ablation images; obtaining each temperature field map corresponding to each thermal ablation image according to a preset temperature field dot matrix range threshold value and each temperature field image gray value corresponding to each thermal ablation image; and obtaining each thermal dose map corresponding to each thermal ablation image according to a preset thermal dose field dot matrix range threshold value and each thermal dose image gray value corresponding to each thermal ablation image.
Optionally, the ablation needle position includes needle tip position coordinates and needle handle position coordinates of the ablation needle, and the analysis module 704 is further configured to perform displacement processing and/or rotation processing on each temperature field map and each thermal dose map corresponding to each thermal ablation image according to the needle tip position coordinates and the needle handle position coordinates of the ablation needle.
Optionally, the analyzing module 704 is further configured to perform a union processing on each temperature field map spectrum corresponding to each thermal ablation image to obtain a comprehensive temperature field map; and performing summation processing on each thermal dose map corresponding to each thermal ablation image to obtain a comprehensive thermal dose map.
Optionally, the analyzing module 704 further includes fusing the integrated temperature field map and the preoperative image, and outputting the fused result.
Optionally, the analyzing module 704 further comprises fusing the comprehensive thermal dose map and the preoperative image and outputting the fused result.
Optionally, the analyzing module 704 further comprises fusing any one of the thermal ablation images with the temperature field map corresponding to the thermal ablation image, and outputting the fused result.
Optionally, the analyzing module 704 further comprises fusing any one of the thermal ablation images with the thermal dose map corresponding to the thermal ablation image, and outputting the fused result.
Optionally, the analysis module 704 is further configured to obtain a plurality of imagemics features corresponding to the tumor region and the monitoring range region, respectively, based on the preoperative image and the comprehensive thermal dose map; obtaining a plurality of clinical characteristics based on the preset treatment parameters; and obtaining each target feature vector according to the imaging group features and the clinical features.
Optionally, the imagery omics features include any of grayscale features, geometric features, texture features, and higher-order features obtained using a neural network.
Optionally, the region of interest is a three-dimensional region, and the analysis module 704 is further configured to divide the region of interest in the preoperative image according to a cross section to obtain a plurality of two-dimensional slices; based on each of the two-dimensional slices, obtaining the iconomics features corresponding to the tumor region from the pre-operative image.
Optionally, the analysis module 704 is further configured to perform a normalization process for each of the clinical features that are quantified, and perform an encoding process for each of the clinical features that are not quantified.
Optionally, the analysis module 704 is further configured to perform classification for each of the imaging omics features and each of the clinical features based on the treatment order and the feature source to obtain each class feature; screening each category characteristic based on a preset screening rule to obtain each candidate characteristic; and randomly combining the candidate features to obtain the target feature vectors.
Optionally, the category characteristics include at least one of clinical category characteristics, preoperative tumor category characteristics, and intraoperative thermal dose category characteristics.
Optionally, the analysis module 704 is further configured to use Lasso to filter out non-zero values of each of the classification features, and obtain each of the candidate features.
The evaluation module 706 is configured to obtain a postoperative evaluation result of the target tumor according to each of the target feature vectors.
Optionally, the evaluation module 706 is configured to utilize the trained tumor evaluation model 708 to perform prediction according to the target feature vector to obtain a post-operation evaluation result of the target tumor.
Optionally, the postoperative evaluation result of the target tumor comprises a risk score of the target tumor and survival probability values of the target tumor corresponding to time periods, wherein the risk score of the target tumor is used for indicating the high and low postoperative recurrence probability of the target tumor.
Optionally, the tumor assessment model 708 may be trained by using each of the target feature vectors, obtaining performance parameters corresponding to each of the target feature vectors, and obtaining an optimal combination of the target feature vectors and the tumor assessment model 708 trained based on the target feature vectors of the optimal combination according to the performance parameters corresponding to each of the target feature vectors.
Optionally, the tumor assessment model 708 is constructed based on any of support vector machines, random forests, neural networks.
Optionally, the tumor assessment model 708 may be trained based on preset performance indicators, including a consistency factor of 0.92 ± 0.012, a calculated time dependence of 0.889, and a Brier error of 0.041.
In addition, the device 700 for evaluating a tumor after surgery of this embodiment can also be used to implement other steps in the method for evaluating a tumor after surgery of any one of the first to fifth embodiments, and has the advantages of the corresponding method step embodiments, which are not repeated herein.
As can be seen from the above, the method, the device and the computer storage medium for evaluating the postoperative tumor provided in the embodiments of the present application can quickly and accurately predict the risk of recurrence of the postoperative tumor, so as to assist the clinician to control the disease condition of the patient more accurately.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the embodiments of the present application, and are not limited thereto; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (30)
1. A method of post-operative tumor assessment, comprising:
acquiring preoperative images and a plurality of thermal ablation images of a target tumor;
obtaining each thermal dose map corresponding to each thermal ablation image according to preset treatment parameters of the target tumor and each thermal ablation image;
obtaining a plurality of target characteristic vectors according to each thermal dose map, the preoperative image, each thermal ablation image and the preset treatment parameters; and
and obtaining a postoperative evaluation result of the target tumor according to each target feature vector.
2. The method of post-operative tumor assessment according to claim 1, wherein said obtaining a pre-operative image and a plurality of thermal ablation images of a target tumor comprises:
acquiring the preoperative image before the target tumor is ablated; and
acquiring each thermal ablation image of the target tumor corresponding to each thermal ablation operation;
wherein each of the thermal ablation operations respectively corresponds to a different ablation location of the target tumor.
3. The method of post-operative tumor assessment according to claim 2, wherein said pre-operative image, said thermal ablation image comprises any of MRI images, CT images, ultrasound images.
4. The method of post-operative tumor assessment according to claim 2, further comprising:
performing image registration for the preoperative image and each of the thermal ablation images of the target tumor;
determining a region of interest according to the registered preoperative images and the thermal ablation images; and
and determining the position of each ablation needle corresponding to each thermal ablation image according to each thermal ablation image after registration.
5. The method of post-operative tumor assessment according to claim 4, wherein said performing image registration for said pre-operative image and each of said thermal ablation images of said target tumor comprises:
performing a deformable registration for the preoperative image based on the thermal ablation image of the first thermal ablation operation; and
performing a rigid registration for each of the other thermal ablation images based on the thermal ablation image of the first thermal ablation operation.
6. The post-operative tumor assessment method according to claim 4, wherein said determining a region of interest from said pre-operative images and each of said thermal ablation images after registration comprises:
determining a tumor region of the target tumor according to the registered preoperative image;
determining a monitoring range area of the target tumor according to each thermal ablation image after registration; and
determining the region of interest in the preoperative image and the thermal ablation image according to the tumor region and the monitoring range region.
7. The method of claim 6, wherein obtaining each thermal dose map corresponding to each thermal ablation image according to the predetermined treatment parameters and each thermal ablation image comprises:
obtaining each temperature field data and each thermal dose data corresponding to each thermal ablation image according to the preset treatment parameters;
converting the temperature field data and the thermal dose data corresponding to each thermal ablation image according to each spatial coordinate of each thermal ablation image to obtain each temperature field map and each thermal dose map corresponding to each thermal ablation image;
according to the positions of the ablation needles corresponding to the thermal ablation images, performing registration processing on the temperature field maps and the thermal dose maps corresponding to the thermal ablation images;
fusing each registered temperature field atlas, each registered thermal dose atlas, the pre-operation image after registration and each registered thermal ablation image based on a preset fusion standard, and outputting a fusion result.
8. The method of claim 7, wherein obtaining the temperature field data and the thermal dose data corresponding to the thermal ablation images according to the predetermined treatment parameters comprises:
configuring a preset simulation model according to the preset treatment parameters;
and obtaining each temperature field data and each thermal dose data corresponding to each voxel block in the region of interest based on the configured preset simulation model.
9. The method of post-operative tumor assessment according to claim 8, further comprising obtaining said thermal dose data based on a preset scaling rule;
the preset conversion rule is expressed as:
D＝ln[∫0 tR(-T-43)dt]
wherein D represents the thermal dose data corresponding to the voxel block, T represents a thermal ablation time period for the thermal ablation operation, and T represents a transient temperature value for the voxel block.
10. The method of post-operative tumor assessment according to claim 8, wherein said preset treatment parameters comprise initial impedance and initial temperature of human tissue, output power of an ablation needle, number of said thermal ablation operations, each said thermal ablation time period corresponding to each said thermal ablation operation.
11. The method of claim 7, wherein transforming the temperature field data and the thermal dose data corresponding to each thermal ablation image according to the spatial coordinates of each thermal ablation image to obtain the temperature field map and the thermal dose map corresponding to each thermal ablation image comprises:
according to the space coordinates of the thermal ablation images, performing interpolation calculation on the temperature field data and the thermal dose data corresponding to the thermal ablation images to obtain the gray values of the temperature field images and the gray values of the thermal dose images corresponding to the thermal ablation images;
obtaining each temperature field map corresponding to each thermal ablation image according to a preset temperature field dot matrix range threshold value and each temperature field image gray value corresponding to each thermal ablation image;
and obtaining each thermal dose map corresponding to each thermal ablation image according to a preset thermal dose field dot matrix range threshold value and each thermal dose image gray value corresponding to each thermal ablation image.
12. The method of post-operative tumor assessment according to claim 7,
the ablation needle position comprises a needle point position coordinate and a needle handle position coordinate of the ablation needle;
the performing, according to the ablation needle positions corresponding to the thermal ablation images, registration processing on the temperature field maps and the thermal dose maps corresponding to the thermal ablation images comprises;
and performing displacement processing and/or rotation processing on each temperature field map and each thermal dose map corresponding to each thermal ablation image according to the needle point position coordinate and the needle handle position coordinate of the ablation needle.
13. The method of post-operative tumor assessment according to claim 7, further comprising:
performing union processing on the temperature field map spectrums corresponding to the thermal ablation images to obtain a comprehensive temperature field map;
and performing summation processing on each thermal dose map corresponding to each thermal ablation image to obtain a comprehensive thermal dose map.
14. The method of post-operative tumor assessment according to claim 13, wherein said fusing each of said registered temperature field maps and each of said thermal dose maps, said pre-operative images after registration, each of said thermal ablation images after registration based on a preset fusion criterion and outputting a fusion result comprises at least one of the following fusion modes:
fusing the comprehensive temperature field map and the preoperative image and outputting a fusion result;
fusing the comprehensive thermal dose map and the preoperative image, and outputting a fusion result;
fusing any one of the thermal ablation images with the temperature field map corresponding to the thermal ablation image, and outputting the fusion result;
fusing any one of the thermal ablation images with the thermal dose map corresponding to the thermal ablation image, and outputting the fusion result.
15. The method of claim 13, wherein obtaining a plurality of target features from each of the thermal dose profiles, the preoperative images, the thermal ablation images, and the preset treatment parameters comprises:
obtaining a plurality of imagemics features corresponding to the tumor region and the monitoring range region, respectively, based on the pre-operative image and the comprehensive thermal dose map;
obtaining a plurality of clinical characteristics based on the preset treatment parameters;
and obtaining each target feature vector according to the imaging group features and the clinical features.
16. The method of post-operative tumor assessment according to claim 15,
the imagery omics features include any of grayscale features, geometric features, texture features, and higher-order features obtained using a neural network.
17. The method of post-operative tumor assessment according to claim 15, wherein said region of interest is a three-dimensional region, said method comprising:
dividing the region of interest in the preoperative image according to a cross section to obtain a plurality of two-dimensional slices;
based on each of the two-dimensional slices, obtaining the iconomics features corresponding to the tumor region from the pre-operative image.
18. The method of post-operative tumor assessment according to claim 15, further comprising:
normalization processing is performed for each of the clinical features that are digitized, and encoding processing is performed for each of the clinical features that are not digitized.
19. The method of post-operative tumor assessment according to claim 18, wherein said obtaining each of said target feature vectors from said imagemics features and said clinical features comprises:
performing classification for each of the omics features and each of the clinical features based on treatment order and feature source to obtain each class feature;
screening each category characteristic based on a preset screening rule to obtain each candidate characteristic;
and randomly combining the candidate features to obtain the target feature vectors.
20. The method of claim 19, wherein the category characteristics include at least one of clinical category characteristics, preoperative tumor category characteristics, and intraoperative thermal dose category characteristics.
21. The method of post-operative tumor assessment according to claim 19,
the preset screening rule comprises a non-linear screening mode and/or a linear screening mode;
and wherein the nonlinear screening means comprises at least Lasso screening.
22. The method of claim 19, wherein obtaining a postoperative assessment of the target tumor from each of the target feature vectors comprises:
and performing prediction according to the target characteristic vector by utilizing the trained tumor evaluation model to obtain a postoperative evaluation result of the target tumor.
23. The method of claim 22, wherein the postoperative assessment result of the target tumor comprises a risk score of the target tumor and survival probability values of the target tumor corresponding to time periods, wherein the risk score of the target tumor is used to indicate the postoperative recurrence probability of the target tumor.
24. The method of post-operative tumor assessment according to claim 22, further comprising training said tumor assessment model comprising:
respectively training the tumor evaluation model by using each target feature vector to obtain each performance parameter corresponding to each target feature vector;
and obtaining the optimal combination of the target characteristic vectors and a tumor evaluation model trained and completed based on the target characteristic vectors of the optimal combination according to the performance parameters corresponding to the target characteristic vectors.
25. The method of claim 24, wherein the tumor assessment model is constructed based on any one of a support vector machine, a random forest, and a neural network.
26. The method of post-operative tumor assessment according to claim 24, further comprising training said tumor assessment model based on preset performance indicators, wherein said preset performance indicators comprise a consistency factor of 0.92 ± 0.012, a calculated time dependence of 0.889, and a Brier error of 0.041.
27. The method of post-operative tumor assessment according to claim 2, further comprising:
obtaining a cold ablation image of the target tumor corresponding to a cold ablation operation;
performing registration for the cold ablation image based on the thermal ablation image of the first thermal ablation operation;
determining a frozen ice ball region according to the cold ablation image after registration;
based on the cold ablation image, obtaining the iconomics features corresponding to the frozen puck region.
28. The method of post-operative tumor assessment according to claim 27, wherein said preset treatment parameters further comprise the number of ablation needles of said cold ablation procedure.
29. A computer storage medium having stored therein instructions for performing the steps of the post-operative tumor assessment method according to any one of claims 1 to 28.
30. A post-operative tumor assessment apparatus, comprising:
the acquisition module is used for acquiring preoperative images, a plurality of thermal ablation images and preset treatment parameters of the target tumor;
an analysis module, configured to obtain each thermal dose map corresponding to each thermal ablation image according to the preset treatment parameter and each thermal ablation image, and obtain a plurality of target feature vectors according to each thermal dose map, the preoperative image, each thermal ablation image, and the preset treatment parameter;
and the evaluation module is used for obtaining the postoperative evaluation result of the target tumor according to each target feature vector.
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