A method and system for predicting the prognosis of low-grade glioma care risk

Abstract

This application relates to the field of clinical nursing technology, specifically an automated clinical nursing method for cancer patients, and more specifically, a method and system for predicting the prognostic risk of low-grade gliomas. The technical solution provided in this application combines multiple sets of clinical data and multiple imaging feature data to effectively assess and predict the prognostic risk of cancer patients, especially those with low-grade gliomas. This scoring data more intuitively determines the degree of prognostic risk for low-grade glioma patients, thereby achieving risk identification in the prognostic care of cancer patients. Compared to existing technologies, this application combines imaging feature data from MRI images with clinical indicators, resulting in more accurate judgment results.

Description

Technical Field

[0001] This application relates to the field of clinical nursing technology, specifically an automated clinical nursing method for cancer patients, and more specifically a method and system for predicting the prognostic nursing risks of low-grade gliomas. Background Technology

[0002] Low-grade gliomas (DLGGs) are primary brain tumors originating from astrocytes or oligodendrocytes, and are common in young adults. Although classified as low-grade and slow-growing, their diffuse growth pattern often leads to invasion of key brain functional areas, causing severe neurological dysfunction, and the likelihood of recurrence and malignancy is high even after multiple surgeries.

[0003] Traditional clinical prognostic assessment relies on factors such as pathological grade and tumor location. However, due to tumor heterogeneity, invasive growth characteristics, and varying responses to treatment, accurately predicting patient prognosis remains a significant clinical challenge. Summary of the Invention

[0004] To address the technical problems existing in the prior art, this application provides a risk prediction method for clinical nursing, especially for the prognostic care of patients with low-grade gliomas. This method combines computer medical images with multiple clinical indicators. It acquires MRI images of the patient and extracts radiographic features from ultrasound images. The extracted features are then combined with medical data to achieve prognostic care risk identification based on a prediction model.

[0005] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows:

[0006] In a first aspect, a method for predicting the prognostic care risk of low-grade gliomas is provided. The method includes: acquiring medical data of a patient to be diagnosed and imaging feature data of a target region of the patient; the imaging feature data includes magnetic resonance imaging feature data, and the medical data includes clinical data and biophysical and chemical data, wherein the clinical data includes the patient's age, number of gliomas, and resection extent; inputting the medical data and the imaging feature data into a risk prediction model to calculate a risk value; the risk prediction model is a nomogram model.

[0007] Furthermore, the biophysical and chemical data include systemic immune inflammation index, neutrophil-to-lymphocyte ratio, and monocyte-to-lymphocyte ratio.

[0008] Furthermore, the image group feature data includes multiple first-order features, multiple gray-level dependency matrix features, multiple gray-level size region matrix features, and gray-level running length matrix features.

[0009] Furthermore, the first-order features include first-order skewness, first-order deviation, and first-order tenth percentile.

[0010] Furthermore, the multiple gray-level dependency matrix features include small dependency high gray-level emphasis features and small dependency low gray-level emphasis features of the gray-level dependency matrix.

[0011] Furthermore, the multiple gray-scale region matrix features include normalized gray-scale non-uniformity of the gray-scale region matrix, small region emphasis of the gray-scale region matrix, and normalized size region non-uniformity of the gray-scale region matrix.

[0012] Furthermore, the grayscale running length matrix feature includes the running variance of the grayscale running length matrix.

[0013] Furthermore, the step of inputting the medical data and the imaging feature data into the risk prediction model to calculate the risk value includes: obtaining, based on the nomogram, the patient's age, number of gliomas, resection range, systemic immune inflammation index, neutrophil-to-lymphocyte ratio, monocyte-to-lymphocyte ratio, and the first, second, third, fourth, fifth, sixth, and seventh scores corresponding to the imaging feature data within the unit time period, as well as the corresponding total score.

[0014] Furthermore, obtaining the seventh score corresponding to the image group feature data includes: obtaining the sub-scores corresponding to the first-order skewness, first-order bias, first-order tenth percentile, small-dependency high-gray-level emphasis feature of the gray-level dependency matrix, small-dependency low-gray-level emphasis feature of the gray-level dependency matrix, normalized gray-level non-uniformity of the gray-level size region matrix, small-region emphasis of the gray-level size region matrix, normalized size region non-uniformity of the gray-level size region matrix, and running variance of the gray-level running length matrix, and then updating the sub-scores according to the calculation weights corresponding to the above data and weighting them to obtain the seventh score.

[0015] Secondly, a prognostic care risk prediction system for low-grade gliomas is provided. The system includes: a data acquisition unit for acquiring medical data of a patient to be diagnosed and imaging feature data of a target region of the patient; the imaging feature data includes magnetic resonance imaging feature data, and the medical data includes clinical data and biophysical and chemical data, the clinical data including patient age, number of gliomas, and resection extent; and a risk prediction unit for inputting the medical data and the imaging feature data into a risk prediction model to calculate a risk value; the risk prediction model is a nomogram model.

[0016] The technical solution provided in this application combines multiple sets of clinical data and multiple imaging feature data to effectively assess and predict the prognostic risk of cancer patients, especially those with low-grade gliomas. This scoring data provides a more intuitive determination of the prognostic risk level for patients with low-grade gliomas, thereby enabling risk identification in the prognostic care of cancer patients. Compared to existing technologies, this application combines imaging feature data from MRI images with clinical indicators, resulting in more accurate judgment results. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] The methods, systems, and / or procedures shown in the accompanying drawings will be further described with reference to exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These exemplary embodiments are non-limiting exemplary embodiments, wherein example figures represent similar mechanisms in the various views of the drawings.

[0019] Figure 1 This is a schematic diagram of the prognostic care risk prediction method for low-grade gliomas provided in the embodiments of this application.

[0020] Figure 2 This is a schematic diagram of the line graph model in the embodiments of this application.

[0021] Figure 3 This is a schematic diagram of the system structure provided in the embodiments of this application.

[0022] Figure 4 This is a schematic diagram of the terminal device structure provided in the embodiments of this application. Detailed Implementation

[0023] To better understand the above technical solutions, the technical solutions of this application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of this application and the specific features in the embodiments are detailed descriptions of the technical solutions of this application, rather than limitations on the technical solutions of this application. In the absence of conflict, the embodiments of this application and the technical features in the embodiments can be combined with each other.

[0024] In the detailed description below, numerous specific details are illustrated with examples to provide a comprehensive understanding of the relevant guidance. However, it will be apparent to those skilled in the art that this application can be practiced without these details. In other instances, well-known methods, procedures, systems, components, and / or circuits have been described at a relatively high level without detail to avoid unnecessarily obscuring aspects of this application.

[0025] This application uses flowcharts to illustrate the execution process performed by a system according to embodiments of this application. It should be clearly understood that the execution processes in the flowcharts may not be executed sequentially. Instead, these execution processes may be executed in reverse order or simultaneously. Additionally, at least one other execution process may be added to the flowchart. One or more execution processes may be deleted from the flowchart.

[0026] Before providing a further detailed description of the embodiments of the present invention, the nouns and terms involved in the embodiments of the present invention will be explained, and the nouns and terms involved in the embodiments of the present invention shall be interpreted as follows.

[0027] (1) In response to, used to indicate the conditions or states on which the operation is performed depends. When the conditions or states on which the operation is performed are met, one or more operations may be performed in real time or with a set delay. Unless otherwise specified, there is no restriction on the order in which the multiple operations are performed.

[0028] (2) Based on, used to indicate the conditions or states on which the operation is performed depends. When the conditions or states on which it depends are met, one or more operations can be performed in real time or with a set delay. Unless otherwise specified, there is no restriction on the order of execution of the multiple operations.

[0029] This application provides a method for predicting the nursing risk of patients with tumor prognosis, particularly for low-grade gliomas. In predicting the prognosis of low-grade gliomas, traditional clinical factors remain crucial for prognostic assessment, as these factors can predict the survival rate of patients with low-grade gliomas. However, due to the high heterogeneity of low-grade gliomas, relying solely on clinical factors cannot fully identify the biological characteristics of the tumor, resulting in limited predictive power for individualized treatment. This limitation makes prognostic models based on traditional factors difficult to provide accurate predictions when faced with individual patient differences. In contrast, radiomics can extract a large number of quantitative features from conventional imaging data, discovering valuable information not directly visible in images. This information is crucial for predicting the biological behavior of tumors and patient prognosis, and supports personalized tumor treatment. Unlike traditional methods, radiomics models are non-invasive, high-throughput, and can provide more comprehensive prognostic information without additional clinical procedures.

[0030] Therefore, this application provides a method for predicting the prognostic care risk of low-grade gliomas. This method combines clinical indicators with radiographic feature data from magnetic resonance imaging (MRI) images to identify the prognostic risk of patients with low-grade gliomas. For more information on this method, please refer to [link to relevant documentation]. Figure 1 This includes the following steps:

[0031] Step S11. Obtain the medical data of the patient to be diagnosed and the image group feature data of the target area of ​​the patient to be diagnosed.

[0032] In this embodiment, the method employs a combined prediction approach to assess the prognostic care risk of low-grade gliomas. The logic of this combined prediction approach is to obtain the score corresponding to each indicator based on a nomogram using a prediction model, and then obtain the total score. The total score is used to determine the risk level of stroke, especially the risk level of prognostic care for low-grade gliomas.

[0033] In this embodiment, the medical data includes clinical data and biophysical and chemical data. Furthermore, the clinical data includes patient age, number of gliomas, and extent of resection, while the biophysical and chemical data includes systemic immune inflammation indices, neutrophil-to-lymphocyte ratios, and monocyte-to-lymphocyte ratios.

[0034] In this embodiment, cellular inflammatory components are ubiquitous in the microenvironment of many cancer tissues. These components include eosinophils, neutrophils, monocytes, B lymphocytes and T lymphocytes, tumor-associated macrophages, and myeloid-derived suppressor cells. Inflammatory cells induce and develop tumors by secreting large amounts of pro-inflammatory chemokines, cytokines, and growth factors, promoting tumor proliferation, invasion, metastasis, angiogenesis, anti-tumor immunosuppression, and facilitating the escape of their products from the immune system. Increased neutrophils or decreased lymphocytes indicate an elevated inflammatory state and weakened immune response in patients. Neutrophils suppress the lymphocyte-mediated immune system and promote the formation of an inflammatory microenvironment by secreting large amounts of inflammatory mediators, such as interleukin-6, IL-8, and vascular endothelial growth factor. Increased neutrophil recruitment is associated with tumor grading, resistance to anti-vascular endothelial growth factor therapy, and progression of gliomas with stromal features. Lymphocytes are an important cellular population in the body's immune system and a crucial factor in regulating immunity, playing a vital role in cell-mediated anti-tumor immune responses. Lymphocytes, by secreting cytokines and other substances, inhibit tumor proliferation, growth and invasion, becoming a key component of anti-tumor immunity and improving patient prognosis.

[0035] In this embodiment, the image group feature data refers to MRI image feature data. Furthermore, these images were acquired using a GE Signa HDx 3.0 T MRI scanner (8-channel standard head coil) and a Siemens Magnetom Skyra 1.5 T MRI scanner (32-channel standard head coil) for cranial MRI scans. The scanning parameters were as follows: TR / TE were 1913 or 330 ms / 21.2 or 8.7 ms respectively; slice thickness was 1 mm; and the matrix size was 192×192.

[0036] Based on the acquired images, the tumor volume of interest (MOI) is segmented, and features are extracted from the segmented images. In this embodiment, the feature extraction is implemented using a self-supervised learning ResNet3D-18 model. Specifically, based on the acquired MOI, a minimum bounding rectangle is determined in the original 3D image space, which completely contains the target tumor tissue. Next, based on the aforementioned localization information, the region containing the lesion is cropped from the original 3D image data, and spatial resampling technology is used to standardize it to a uniform spatial resolution, effectively eliminating scale differences between original data and ensuring the consistency of the model input data dimensions. Finally, the preprocessed 3D volume data is input into the pre-trained ResNet 3D-18 model for feature extraction, resulting in a 512-dimensional deep feature vector. Ultimately, 1223 radiomics features and 512 deep learning features are obtained, and z-score is used to standardize and normalize all features.

[0037] Lasso regression dimensionality reduction is performed using the "glmnet" package in R. Lasso employs a minimum criterion of 10-fold cross-validation to identify optimal radiomics features, selecting features with larger coefficients to effectively capture key information in the image data, simplifying the model and improving the performance of the prediction model. Finally, this embodiment uses a support vector machine method to construct the radiomics model. Specifically, the selected image group feature data includes multiple first-order features, multiple gray-level dependency matrix features, multiple gray-level size region matrix features, and gray-level running length matrix features.

[0038] Specifically, the first-order features include first-order skewness, first-order bias, and first-order tenth percentile. Among these, the multiple gray-level dependency matrix features include small-dependency high-gray-level emphasis features and small-dependency low-gray-level emphasis features; the multiple gray-level size region matrix features include normalized gray-level non-uniformity of the gray-level size region matrix, small-region emphasis of the gray-level size region matrix, and normalized size region non-uniformity of the gray-level size region matrix; and the gray-level running length matrix feature includes the running variance of the gray-level running length matrix.

[0039] Step S12. Input the clinical data and the imaging group feature data into the risk prediction model to calculate the risk value.

[0040] In this embodiment, the risk prediction model is a nomogram model. The nomogram model obtains the total score by mapping the scores corresponding to the multiple medical data and imaging group feature data obtained in step S11, and based on the score corresponding to each indicator and imaging group feature data. This total score is the risk value in this embodiment, which is used to characterize the risk level corresponding to the prognostic care of low-grade glioma.

[0041] Specifically, based on the nomogram, the patient's age, number of gliomas, resection range, systemic immune inflammation index, neutrophil-to-lymphocyte ratio, monocyte-to-lymphocyte ratio, and the first, second, third, fourth, fifth, sixth, and seventh scores corresponding to the image group feature data within a unit time period are obtained, as well as the corresponding total score.

[0042] For the calculation results of this nomogram model, please refer to [link / reference]. Figure 2 Radscore is the overall calculated score of the image group feature data, i.e., the sixth score value. The Radscore score is calculated based on the following formula: Radscore = -0.139448 × first-order skewness - 0.02263 × first-order bias - 0.060787 × first-order tenth percentile - 0.460558 × small-dependency high-gray-level emphasis feature of gray-level dependency matrix - 0.097354 × small-dependency low-gray-level emphasis feature of gray-level dependency matrix - 0.505509 × running variance of gray-level running length matrix + 0.0719 × normalized gray-level non-uniformity of gray-level size region matrix - 0.0788 × small-region emphasis of gray-level size region matrix - 0.0534 × normalized size region non-uniformity of gray-level size region matrix.

[0043] For the specific calculation process of the nodal graph in this embodiment, please refer directly to [the relevant documentation / reference]. Figure 2 As shown, the calculation logic of the nodal chart can be used to achieve this, and will not be elaborated further in this embodiment.

[0044] In summary, this embodiment combines multiple sets of clinical indicators and multiple imaging feature data to effectively assess and predict the prognostic care risk of low-grade gliomas. This scoring data provides a more intuitive way to determine the survival status of patients with low-grade gliomas, thereby enabling the identification of prognostic risks for these patients.

[0045] See Figure 3In addition to the method provided in steps S11-S12, this embodiment also provides a prediction system 30, which includes:

[0046] Data acquisition unit 31 is used to acquire medical data of the patient to be diagnosed and image group feature data of the target area of ​​the patient to be diagnosed;

[0047] The risk prediction unit 32 is used to input the medical data and the image group feature data into the risk prediction model to calculate the risk value.

[0048] In this embodiment, the risk prediction model is a nomogram model.

[0049] See Figure 4 The above methods can also be integrated into the provided terminal device 40. Since the device may vary significantly due to differences in configuration or performance, it may include one or more processors 401 and memories 402. The memories 402 may store one or more application programs or data. The memories 402 can be temporary or persistent storage. The application programs stored in the memories 402 may include one or more modules (not shown in the figure), each module may include a series of computer-executable instructions from the terminal device. Furthermore, the processor 401 may be configured to communicate with the memories 402, and the terminal device may execute the series of computer-executable instructions stored in the memories 402. The terminal device may also include one or more power supplies 403, one or more wired or wireless network interfaces 404, one or more input / output interfaces 405, one or more keyboards 406, etc.

[0050] In one specific embodiment, the terminal device includes a memory and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer-executable instructions for use in the terminal device, and is configured to be executed by one or more processors. The one or more programs include computer-executable instructions for performing the following:

[0051] Acquire clinical data of the patient to be diagnosed and radiographic feature data of the target region of the patient to be diagnosed;

[0052] The clinical data and the imaging feature data are input into the risk prediction model to calculate the risk value.

[0053] The following is a detailed introduction to each component of the processor:

[0054] In this embodiment, the processor is an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0055] Optionally, the processor can perform various functions, such as the above-mentioned functions, by running or executing software programs stored in memory and by calling data stored in memory. Figure 1 The method shown.

[0056] In a specific implementation, as one example, the processor may include one or more microprocessors.

[0057] The memory is used to store the software program that executes the solution of this application, and the execution is controlled by the processor. The specific implementation method can be referred to the above method embodiment, which will not be repeated here.

[0058] Optionally, the memory can be read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions, random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory can be integrated with the processor or exist independently and coupled to the processing unit through the processor's interface circuitry; this application embodiment does not specifically limit this.

[0059] It should be noted that the processor structure shown in this embodiment does not constitute a limitation on the device. The actual device may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0060] Furthermore, the technical effects of the processor can be referred to the technical effects of the methods described in the above-described method embodiments, and will not be repeated here.

[0061] It should be understood that the processor in the embodiments of this application may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0062] It should also be understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0063] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0064] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0065] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0066] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0067] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0068] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0069] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0070] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0071] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0072] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for predicting the prognostic care risk of low-grade glioma, characterized in that, The method includes: Acquire medical data of the patient to be diagnosed and imaging feature data of the target region of the patient to be diagnosed; the imaging feature data includes magnetic resonance imaging feature data, and the medical data includes clinical data and biophysical and chemical data, the clinical data including patient age, number of gliomas and resection extent; The medical data and the image group feature data are input into a risk prediction model to calculate the risk value; the risk prediction model is a nomogram model.

2. The method for predicting the prognostic care risk of low-grade glioma according to claim 1, characterized in that, The biophysical and chemical data include systemic immune inflammation index, neutrophil-to-lymphocyte ratio, and monocyte-to-lymphocyte ratio.

3. The method for predicting the incidence risk of cardiovascular diseases according to claim 1, characterized in that, The image group feature data includes multiple first-order features, multiple gray-level dependency matrix features, multiple gray-level size region matrix features, and gray-level running length matrix features.

4. The method for predicting the prognostic care risk of low-grade glioma according to claim 3, characterized in that, The first-order features include first-order skewness, first-order deviation, and first-order tenth percentile.

5. The method for predicting the prognostic care risk of low-grade glioma according to claim 3, characterized in that, The gray-level dependency matrix features include small dependency high gray-level emphasis features and small dependency low gray-level emphasis features.

6. The method for predicting the prognostic care risk of low-grade glioma according to claim 3, characterized in that, The multiple gray-scale region matrix features include normalized gray-scale non-uniformity of the gray-scale region matrix, small region emphasis of the gray-scale region matrix, and normalized size region non-uniformity of the gray-scale region matrix.

7. The method for predicting the prognostic care risk of low-grade glioma according to claim 3, characterized in that, The grayscale running length matrix feature includes the running variance of the grayscale running length matrix.

8. The method for predicting the prognostic care risk of low-grade glioma according to any one of claims 1-7, characterized in that, The step of inputting the medical data and the imaging feature data into the risk prediction model to calculate the risk value includes: obtaining, based on the nomogram, the patient's age, number of gliomas, resection range, systemic immune inflammation index, neutrophil-to-lymphocyte ratio, monocyte-to-lymphocyte ratio, and the first, second, third, fourth, fifth, sixth, and seventh scores corresponding to the imaging feature data within the unit time, as well as the corresponding total score.

9. The method for predicting the prognostic care risk of low-grade glioma according to claim 8, characterized in that, The process of obtaining the seventh score corresponding to the image group feature data includes: obtaining the sub-scores corresponding to the first-order skewness, first-order bias, first-order tenth percentile, small-dependency high-gray-level emphasis feature of the gray-level dependency matrix, small-dependency low-gray-level emphasis feature of the gray-level dependency matrix, normalized gray-level non-uniformity of the gray-level size region matrix, small-region emphasis of the gray-level size region matrix, normalized size region non-uniformity of the gray-level size region matrix, and running variance of the gray-level running length matrix; and updating the sub-scores according to the calculation weights corresponding to the above data and then weighting them to obtain the seventh score.

10. A prognostic care risk prediction system for low-grade gliomas, characterized in that, The system includes: The data acquisition unit is used to acquire medical data of the patient to be diagnosed and imaging feature data of the target area of ​​the patient to be diagnosed; the imaging feature data includes magnetic resonance image feature data, the medical data includes clinical data and biophysical and chemical data, and the clinical data includes the patient's age, number of gliomas and resection range; The risk prediction unit is used to input the medical data and the image group feature data into the risk prediction model to calculate the risk value; the risk prediction model is a nomogram model.

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