A method, device and medium for constructing a glioma chemotherapy response prediction model

Abstract

This application discloses a method, device, and medium for constructing a glioma chemotherapy response prediction model, relating to the field of medical device technology. Based on radiomics technology, this application extracts and filters features from magnetic resonance images to construct a sample data set. The sample data set is then used to train different machine learning models. The optimal trained machine learning model is then selected as the glioma chemotherapy response prediction model, thereby achieving the prediction of glioma chemotherapy response.

Description

Technical Field

[0001] The present application relates to the field of medical device technology, and in particular to a method, device, and medium for constructing a glioma chemotherapy response prediction model. Background Art

[0002] Because surgery is difficult to completely remove tumor cells from the brain, glioma patients require chemotherapy after neurosurgery. Chemotherapy can cause significant and ongoing physical and mental distress to patients. Common complications include hair loss, vomiting, and a severely weakened immune system, significantly reducing their quality of life. Furthermore, chemotherapy is expensive, placing a significant financial burden on patients' families. Furthermore, gliomas are highly heterogeneous, resulting in significant variations in the effectiveness of chemotherapy. Some patients are insensitive to chemotherapy, meaning they endure significant pain without benefiting from it. However, due to the lack of clarity in clinical practice regarding which patients are most sensitive to chemotherapy, most patients are currently limited to standard chemotherapy, which is essentially a broad-based, trial-and-error approach. Therefore, predicting a patient's response to chemotherapy before it begins, thereby avoiding unnecessary physical and mental distress and financial burden, remains a crucial and pressing issue in the treatment of gliomas. Summary of the Invention

[0003] The purpose of this application is to provide a method, device and medium for constructing a glioma chemotherapy response prediction model, so as to establish a glioma chemotherapy response prediction model and realize the prediction of glioma chemotherapy response.

[0004] To achieve the above objectives, this application provides the following solutions.

[0005] In a first aspect, the present application provides a method for constructing a glioma chemotherapy response prediction model, comprising the following steps.

[0006] Acquire a longitudinal multi-time-point magnetic resonance image set; the longitudinal multi-time-point magnetic resonance image set includes multiple magnetic resonance image sequences, and the magnetic resonance image sequence includes a magnetic resonance image group of high-grade glioma at least three time points of the same patient; the three time points are respectively before surgery, before postoperative chemotherapy, and after the end of chemotherapy; the magnetic resonance image group includes: one or more of: T1-weighted phase, T2-weighted phase, and enhanced T1-weighted phase.

[0007] The volume changes of the residual tumor in the magnetic resonance imaging group after surgery and before chemotherapy and the magnetic resonance imaging group after the completion of chemotherapy in each magnetic resonance imaging sequence are calculated as the volume changes corresponding to each magnetic resonance imaging sequence.

[0008] The chemotherapy sensitivity corresponding to each magnetic resonance imaging sequence is determined according to the volume change corresponding to each magnetic resonance imaging sequence.

[0009] Feature extraction and screening are performed on the pre-operative magnetic resonance image group in each magnetic resonance image sequence to obtain a feature set corresponding to each magnetic resonance image sequence.

[0010] The feature set corresponding to each magnetic resonance imaging sequence is used as input, and the chemotherapy sensitivity corresponding to each magnetic resonance imaging sequence is used as a label to construct a sample data set.

[0011] Based on the sample data set, multiple machine learning models are trained respectively, and the optimal trained machine learning model is selected as the glioma chemotherapy response prediction model.

[0012] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned method for constructing a glioma chemotherapy response prediction model.

[0013] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for constructing a glioma chemotherapy response prediction model.

[0014] According to the specific embodiments provided in this application, this application has the following technical effects.

[0015] The present application provides a method, device and medium for constructing a glioma chemotherapy response prediction model. The present application extracts and screens features of magnetic resonance images based on imaging genomics technology, constructs a sample data set, and then uses the sample data set to train different machine learning models separately. The optimal trained machine learning model is then selected as the glioma chemotherapy response prediction model, thereby realizing the prediction of glioma chemotherapy response. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 A flowchart of a method for constructing a glioma chemotherapy response prediction model provided in one embodiment of the present application.

[0018] Figure 2 This is a receiver operating characteristic curve diagram of the glioma chemotherapy sensitivity prediction model provided in one embodiment of the present application.

[0019] Figure 3A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0021] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0022] Radiomics, a product of advances in image pattern recognition, can extract a large number of objective quantitative features from medical images that are difficult to discern with the naked eye. These include signal intensity distribution, three-dimensional shape, texture characteristics, and wavelet-transformed features. This technology enables the quantitative description of solid tumor image characteristics, providing a new direction for predictive analysis of clinical diagnosis and treatment-related events in these diseases. In glioma research, radiomics has demonstrated broad application potential in multiple fields, particularly in predicting treatment response. For example, a radiomics model developed by Grossmann P, Kickingereder P, and others can effectively screen patients with recurrent glioblastoma who may benefit from bevacizumab (anti-angiogenic therapy). Similarly, George E, et al. constructed another radiomics model to identify patients with recurrent glioblastoma who may benefit from durvalumab (immunotherapy). These models provide important support for personalized treatment and hold the potential to improve patient outcomes.

[0023] Magnetic resonance imaging (MRI) plays a crucial role in the diagnosis, treatment planning, and follow-up monitoring of gliomas. Different MRI sequences, including T1-weighted (T1), T2-weighted (T2), enhanced T1-weighted (CE), and fluid-attenuated inversion recovery (FLAIR), provide complementary information for assessing imaging features such as tumor structure, edema, and necrosis. In clinical practice, the treatment response of gliomas is often evaluated based on the Response Assessment in Neuro-Oncology (RANO) criteria, which is based on longitudinal MRI scans at multiple time points and assesses treatment efficacy by monitoring dynamic changes in tumor volume. Given the central role of MRI in glioma management, radiomics models based on MRI data have become ideal tools for predicting glioma chemotherapy sensitivity, helping to implement personalized treatment plans.

[0024] Using MRI data to construct a glioma chemotherapy response prediction model to predict the treatment response of high-grade glioma patients to chemotherapy is expected to assist doctors in selecting the most appropriate individualized treatment plan, improve treatment efficacy, and avoid unnecessary physical and mental pain and economic burden.

[0025] In an exemplary embodiment, Figure 1 As shown, a method for constructing a glioma chemotherapy response prediction model is provided, including the following steps 101 to 106.

[0026] Step 101: Acquire a longitudinal multi-time point magnetic resonance image set; the longitudinal multi-time point magnetic resonance image set includes multiple magnetic resonance image sequences, and the magnetic resonance image sequence includes a magnetic resonance image group of high-grade glioma of the same patient at least three time points; the three time points are respectively located before surgery, before postoperative chemotherapy, and after the end of chemotherapy; the magnetic resonance image group includes: one or more of: T1-weighted phase, T2-weighted phase, and enhanced T1-weighted phase.

[0027] Step 102 : Calculate the volume change of the residual tumor in the magnetic resonance image group before chemotherapy and the magnetic resonance image group after chemotherapy in each magnetic resonance image sequence as the volume change corresponding to each magnetic resonance image sequence.

[0028] Step 103 : determining the chemotherapy sensitivity corresponding to each magnetic resonance imaging sequence according to the volume change corresponding to each magnetic resonance imaging sequence.

[0029] Step 104 : extracting and screening features of the pre-operative magnetic resonance image groups in each magnetic resonance image sequence to obtain a feature set corresponding to each magnetic resonance image sequence.

[0030] Step 105 : Taking the feature set corresponding to each magnetic resonance imaging sequence as input and the chemotherapy sensitivity corresponding to each magnetic resonance imaging sequence as a label, a sample data set is constructed.

[0031] Step 106: train multiple machine learning models based on the sample data set, and select the best trained machine learning model as the glioma chemotherapy response prediction model.

[0032] By implementing the above steps 101 to 106, a glioma chemotherapy response prediction model can be established to achieve the prediction of chemotherapy response.

[0033] In another exemplary embodiment of the present application, the above step 101 is implemented in the following manner.

[0034] Longitudinal multi-time-point magnetic resonance imaging (MRI) sets of high-grade (World Health Organization, WHO grade 3-4) gliomas were collected from a glioma database. These MRI sets included multiple MRI sequences, each of which included MRI sets of high-grade gliomas from the same patient at at least three time points. The MRI sets included T1-weighted phases, T2-weighted phases, and enhanced T1-weighted phases. The specific time points for collection were before surgery, within one week before postoperative chemotherapy, and four months after the completion of postoperative chemotherapy.

[0035] Magnetic resonance imaging (MRI) technology can highlight different types of tissue or lesions through different scanning sequences and parameter settings. T1-weighted phase, T2-weighted phase, and enhanced T1-weighted phase are common imaging sequences that are important in various clinical applications.

[0036] T1-weighted images are acquired using short repetition time (TR) and echo time (TE). T1 stands for "spin-lattice relaxation time," which refers to the time required for water molecules to return to equilibrium due to their interaction with surrounding tissue. T1-weighted images are commonly used to visualize anatomical structures.

[0037] T2-weighted images are acquired with longer repetition and echo times. T2 stands for "spin-spin relaxation time," which refers to the time it takes for energy to dissipate as water molecules interact with their surroundings. T2-weighted images are suitable for demonstrating brain edema, tumors, inflammation, and other pathological changes.

[0038] Enhanced T1-weighted imaging uses a contrast agent (typically one containing gadolinium) to enhance the image signal, improving the visibility of certain lesions. The contrast agent is injected intravenously into the bloodstream and accumulates at specific lesions, thereby enhancing the signal there. Enhanced T1-weighted imaging is widely used to detect and locate tumors and assess their blood supply. It can also be used to identify other lesions, such as infections and spinal cord lesions.

[0039] In another exemplary embodiment of the present application, step 102 of the present application calculates the volume change of the residual tumor before and after chemotherapy based on the enhanced T1-weighted phases collected in step 101 before and after chemotherapy, and specifically includes the following steps 201 to 204.

[0040] Step 201: Automatically label the tumor area on the enhanced T1-weighted phases before and after chemotherapy using a deep learning model.

[0041] Step 202: extract the number of voxels and volume information of each voxel in the residual tumor area.

[0042] Step 203, according to the formula and , calculate the residual tumor volume before and after chemotherapy; where, is the volume of the residual tumor in the magnetic resonance imaging group after surgery and before chemotherapy, that is, the first volume, is the number of voxels contained in the residual tumor area in the magnetic resonance imaging group after surgery and before chemotherapy, is the volume of a single voxel, is the volume of the residual tumor in the magnetic resonance imaging group after chemotherapy, that is, the second volume, is the number of voxels contained in the residual tumor area in the magnetic resonance imaging group after the end of chemotherapy.

[0043] Step 204, according to the formula , calculate the volume change; where, is the volume change.

[0044] In another exemplary embodiment of the present application, in step 103, the changes in tumor volume before and after chemotherapy are compared according to neuro-oncology response evaluation criteria to determine chemotherapy sensitivity. The specific evaluation criteria are as follows.

[0045] Complete tumor disappearance after treatment is considered a complete response; a 65% or greater reduction in tumor volume after treatment is considered a partial response; a reduction of <65% but an increase of <40% is considered stable disease; and an increase of 40% or greater is considered progressive disease. Complete and partial responses are considered chemotherapy-sensitive, while stable and progressive disease are considered chemotherapy-resistant.

[0046] In another exemplary embodiment of the present application, the above step 104 extracts the imaging omics features on the pre-operative magnetic resonance imaging group, including T1-weighted phase, T2-weighted phase and enhanced T1-weighted phase, and then screens the obtained imaging omics features to screen out the differential imaging omics features of patients with different treatment responses.

[0047] The obtained radiomics features were screened to identify differential radiomics features between patients with different treatment responses. Specifically, the maximum relevancy minimum redundancy (mRMR) algorithm was used to identify the most discriminative feature subset, retaining the top 50 features. Subsequently, Spearman correlation analysis was performed to assess the correlation between these highly correlated features (r>0.9 and p<0.05, where r is the Spearman correlation coefficient and p is the statistical test value). Only features with larger variance and greater differential information were retained. Finally, the least absolute shrinkage and selection operator (LASSO) algorithm, combined with ten-fold cross-validation, was used to further select the most predictive radiomics features from the remaining features to form a feature set. For example, the radiomics features selected in this step include the following 12 items.

[0048] 1. original_ngtdm_Strength_T2-T1: original neighborhood grayscale difference matrix strength_T2-T1.

[0049] 2. log-sigma-5-0-mm-3D_glszm_ZoneVariance_T1: log-sigma-5-0-mm-3D grayscale size zone matrix interval variance_T1.

[0050] 3. log-sigma-5-0-mm-3D_glszm_ZonePercentage_T1: logarithmic-σ-5-0-mm-3D grayscale size zone matrix interval percentage_T1.

[0051] 4. wavelet-LLH_glcm_JointAverage_CE: wavelet-LLH gray-level co-occurrence matrix joint average_CE.

[0052] 5. log-sigma-5-0-mm-3D_ngtdm_Contrast_T2: logarithmic-σ-5-0-mm-3D neighborhood grayscale difference matrix contrast_T2.

[0053] 6. wavelet-LLH_glszm_GrayLevelVariance_T2: wavelet-LLH grayscale size matrix grayscale variance_T2.

[0054] 7. wavelet-LLH_glrlm_RunVariance_T2: wavelet-LLH grayscale run matrix run variance_T2.

[0055] 8. wavelet-LLH_glrlm_ShortRunEmphasis_CE: wavelet-LLH grayscale run-length matrix short run emphasis_CE.

[0056] 9. wavelet-LLH_glrlm_RunLengthNonUniformityNormalized_T2: wavelet-LLH grayscale run length matrix normalized run length non-uniformity_T2.

[0057] 10. wavelet-LLH_glrlm_RunLengthNonUniformity_T2: wavelet-LLH grayscale run length matrix run length non-uniformity_T2.

[0058] 11. wavelet-LLH_glrlm_RunEntropy_T2: wavelet-LLH grayscale run-length matrix run entropy_T2.

[0059] 12. wavelet-LLH_glrlm_LongRunLowGrayLevelEmphasis_T2: wavelet-LLH grayscale run-length matrix long run low grayscale emphasis_T2.

[0060] The suffixes T2, T1, and CE indicate that the feature is extracted from the T2-weighted phase, T1-weighted phase, and enhanced T1-weighted phase, respectively; T2-T1 indicates that the feature is the difference between the feature values ​​extracted from the T2-weighted phase and the T1-weighted phase.

[0061] In an exemplary embodiment, in step 106 , the screened imaging genomics features and their labels are used to train a machine learning model to form a glioma chemotherapy response prediction model, specifically as follows: steps 301 to 303 .

[0062] In step 301, the sample data set is first divided into a training set and a test set in a ratio of 8:2.

[0063] In step 302 , a random forest, a support vector machine, an extreme gradient boosting model, and a linear regression model are trained on the training set using a machine learning algorithm.

[0064] In step 303 , the performance of each model is evaluated on the test set, and its AUC (Area Under Curve), sensitivity, specificity, and accuracy are calculated. The best performing model is selected as the glioma chemotherapy response prediction model.

[0065] For example, after comparison, the linear regression model was found to be the best, and the final glioma chemotherapy response prediction model was shown in the following formula.

[0066] .

[0067] in, is the chemotherapy sensitivity score, when If the prediction result is positive, it is determined to be chemotherapy sensitive; otherwise, it is determined to be radiotherapy resistant. 、 、 、 、 、 、 、 、 、 、 、 They are the original neighborhood grayscale difference matrix intensity, log-σ-5-0-mm-3D grayscale size area matrix interval variance, log-σ-5-0-mm-3D grayscale size area matrix interval percentage, wavelet-LLH grayscale co-occurrence matrix joint average, log-σ-5-0-mm-3D neighborhood grayscale difference matrix contrast, wavelet-LLH grayscale size area matrix grayscale variance, wavelet-LLH grayscale run matrix run variance, wavelet-LLH grayscale run matrix short run emphasis, wavelet-LLH grayscale run matrix standardized run length unevenness, wavelet-LLH grayscale run matrix run length unevenness, wavelet-LLH grayscale run matrix run entropy, and wavelet-LLH grayscale run matrix long run low grayscale emphasis.

[0068] like Figure 2 As shown, Figure 2The receiver operating characteristic (ROC) curve for the glioma chemotherapy sensitivity prediction model is shown in Figure 2. The X-axis represents "1 - specificity," indicating the proportion of negative samples that the model mistakenly identifies as positive; the Y-axis represents "sensitivity," indicating the proportion of positive samples correctly identified by the model. The area under the curve for this glioma chemotherapy sensitivity prediction model is 0.977, demonstrating its excellent overall performance and suitability for high-precision classification tasks.

[0069] According to the specific embodiments provided in this application, this application has the following technical effects.

[0070] This example first extracts high-throughput features from preoperative magnetic resonance imaging (MRI) images of glioma patients. Based on this, it identifies the most critical features for predicting postoperative chemotherapy response. These key features are then used to train different machine learning models to predict patients' postoperative chemotherapy responses.

[0071] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for constructing a glioma chemotherapy response prediction model is implemented.

[0072] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0073] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0074] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0075] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0076] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0077] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0078] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0079] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for constructing a glioma chemotherapy response prediction model, characterized in that: include: Acquire a longitudinal multi-time point magnetic resonance image set; the longitudinal multi-time point magnetic resonance image set includes a plurality of magnetic resonance image sequences, and the magnetic resonance image sequence includes a magnetic resonance image group of high-grade glioma at least three time points of the same patient; The three time points were before surgery, before chemotherapy after surgery, and after chemotherapy. The magnetic resonance imaging group included one or more of the following: T1-weighted phase, T2-weighted phase, and enhanced T1-weighted phase. Calculating the volume change of the residual tumor in the magnetic resonance imaging group before surgery and chemotherapy and the magnetic resonance imaging group after the completion of chemotherapy in each magnetic resonance imaging sequence as the volume change corresponding to each magnetic resonance imaging sequence; Determine the chemotherapy sensitivity corresponding to each magnetic resonance imaging sequence based on the volume change corresponding to each magnetic resonance imaging sequence; Extracting and screening features of the pre-operative magnetic resonance image group in each magnetic resonance image sequence to obtain a feature set corresponding to each magnetic resonance image sequence; The feature set corresponding to each magnetic resonance imaging sequence is used as input, and the chemotherapy sensitivity corresponding to each magnetic resonance imaging sequence is used as a label to construct a sample data set; Training multiple machine learning models based on the sample data set, and selecting the best trained machine learning model as a glioma chemotherapy response prediction model; Feature extraction and screening are performed on the pre-operative MRI image groups in each MRI sequence to obtain a feature set corresponding to each MRI sequence, specifically including: Extracting features from a pre-operative magnetic resonance image group in a magnetic resonance image sequence to obtain imaging omics features of the pre-operative magnetic resonance image group in the magnetic resonance image sequence; The mRMR algorithm was used to calculate the discriminability of each radiomics feature, and the radiomics features were sorted in descending order of discriminability to obtain the radiomics feature sequence; Performing a correlation analysis on a pre-set number of radiomics features in the radiomics feature sequence, selecting radiomics features that meet preset conditions from the pre-set number of radiomics features in the radiomics feature sequence to form a radiomics feature candidate set; the preset conditions are: r>0.9 and p<0.05, where r is the correlation coefficient of the Spearman correlation test and p is the statistical test value; A LASSO algorithm is used to select a second preset number of radiomics features from the radiomics feature candidate set to form a feature set; The volume changes of the residual tumor in the magnetic resonance imaging group before surgery and chemotherapy and the magnetic resonance imaging group after the end of chemotherapy in each magnetic resonance imaging sequence were calculated, specifically including: The volume of the residual tumor in the magnetic resonance imaging group after surgery and before chemotherapy was calculated as the first volume using the following formula; ； in, is the volume of the residual tumor in the magnetic resonance imaging group after surgery and before chemotherapy, that is, the first volume, is the number of voxels contained in the residual tumor area in the magnetic resonance imaging group after surgery and before chemotherapy, is the volume of a single voxel; The volume of the residual tumor in the magnetic resonance imaging group after the completion of concurrent chemotherapy was calculated as the second volume using the following formula; ； in, is the volume of the residual tumor in the magnetic resonance imaging group after the end of chemotherapy, that is, the second volume, is the number of voxels contained in the residual tumor area in the magnetic resonance imaging group after the end of chemotherapy; Calculate the volume change according to the first volume and the second volume using the following formula: ； in, is the volume change.

2. The method for constructing a glioma chemotherapy response prediction model according to claim 1, wherein: The chemotherapy sensitivity corresponding to each magnetic resonance imaging sequence is determined based on the volume changes corresponding to each magnetic resonance imaging sequence, specifically including: like , the chemotherapy sensitivity is determined to be chemotherapy sensitive, otherwise, the chemotherapy sensitivity is determined to be chemotherapy resistant.

3. The method for constructing a glioma chemotherapy response prediction model according to claim 1, wherein: Based on the sample data set, multiple machine learning models are trained respectively, and the optimal trained machine learning model is selected as the glioma chemotherapy response prediction model, specifically including: Dividing the sample data set into a training set and a test set; Using the training set to train the plurality of machine learning models respectively to obtain a plurality of trained machine learning models; Testing the plurality of trained machine learning models using the test set to obtain a test result for each trained machine learning model; the test result comprising one or more of: AUC, sensitivity, specificity, and accuracy; The trained machine learning model with the best test results is selected as the glioma chemotherapy response prediction model.

4. The method for constructing a glioma chemotherapy response prediction model according to claim 1 or 3, wherein: Multiple machine learning models include random forest models, support vector machine models, extreme gradient boosting models, and linear regression models.

5. The method for constructing a glioma chemotherapy response prediction model according to claim 1, wherein: The glioma chemotherapy response prediction model is: ； in, is the chemotherapy sensitivity score, when If the prediction result is positive, it is determined to be chemotherapy sensitive; otherwise, it is determined to be chemotherapy resistant. 、 、 、 、 、 、 、 、 、 、 、 They are the original neighborhood grayscale difference matrix intensity, log-σ-5-0-mm-3D grayscale size area matrix interval variance, log-σ-5-0-mm-3D grayscale size area matrix interval percentage, wavelet-LLH grayscale co-occurrence matrix joint average, log-σ-5-0-mm-3D neighborhood grayscale difference matrix contrast, wavelet-LLH grayscale size area matrix grayscale variance, wavelet-LLH grayscale run matrix run variance, wavelet-LLH grayscale run matrix short run emphasis, wavelet-LLH grayscale run matrix standardized run length unevenness, wavelet-LLH grayscale run matrix run length unevenness, wavelet-LLH grayscale run matrix run entropy, and wavelet-LLH grayscale run matrix long run low grayscale emphasis.

6. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for constructing a glioma chemotherapy response prediction model according to any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for constructing a glioma chemotherapy response prediction model according to any one of claims 1 to 5 is implemented.

Images