A method and system for predicting the risk of uterine fibroid development

Abstract

This application relates to the field of automated gynecological disease technology, specifically a method and system for predicting the risk of uterine fibroids. By combining multiple sets of clinical indicators and multiple sets of imaging feature data, this application can effectively assess and predict the preoperative risk of uterine fibroids, especially for high-intensity focused ultrasound ablation. This scoring data provides a more intuitive determination of preoperative risk, thereby achieving an accurate assessment of the preoperative risk of uterine fibroids. Compared to existing technologies that rely solely on physician clinical experience, this application combines imaging feature data with clinical indicators, resulting in a more accurate assessment.

Description

Technical Field

[0001] This application relates to the field of automated technology for gynecological diseases, specifically a method and system for predicting the risk of uterine fibroids. Background Technology

[0002] Uterine fibroids are the most common benign tumors of the uterus in women. Although many patients are asymptomatic, some patients experience clinical symptoms that seriously affect their quality of life, such as anemia, increased menstrual flow, prolonged menstrual periods, frequent and urgent urination, and lower back pain. Larger fibroids may negatively impact fertility.

[0003] Currently, high-intensity focused ultrasound (HIFU) ablation is widely used for the ablation of uterine fibroids. Because treatment outcomes vary among different fibroid patients, accurate preoperative prediction of the ablation rate after HIFU ablation is crucial for guiding clinical decision-making. Summary of the Invention

[0004] To achieve the above-mentioned technical effects, this application provides a method for predicting the risk of uterine fibroids, especially for identifying the preoperative risk of high-intensity focused ultrasound ablation. It is an auxiliary diagnostic method and system that combines computer medical images and multiple clinical indicators. By acquiring MRI images of the target area and extracting radiographic features from the images, the extracted features and clinical data are combined with a prediction model to identify the preoperative risk of uterine fibroids.

[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 risk of uterine fibroids is provided. The method includes: acquiring clinical data of a patient to be diagnosed and imaging feature data of a target region of the patient to be diagnosed; the imaging feature data includes MRI image feature data, and the clinical data includes basic medical data and biophysical and chemical data; inputting the clinical data and the imaging feature data into a risk prediction model to calculate a risk value; the risk value is a nomogram model.

[0007] Furthermore, the image group feature data includes multiple first-order features, gray-level dependency matrix features, multiple gray-level co-occurrence matrix features, and multiple gray-level region size matrix features.

[0008] Furthermore, the first-order features include first-order three-dimensional skewness, first-order LLH subband median, and first-order dispersion.

[0009] Furthermore, the gray-level dependency matrix features include the three-dimensional dependency variance of the gray-level dependency matrix.

[0010] Furthermore, the multiple gray-level co-occurrence matrix features include the Imc1 coefficients of the LHH subbands of the gray-level co-occurrence matrix and the three-dimensional clustering shadows of the gray-level co-occurrence matrix.

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

[0012] Furthermore, the basic medical data includes age and menstrual status, and the biophysical and chemical data includes the ratio of neutrophils to lymphocytes, the ratio of platelets to lymphocytes, and the ratio of monocytes to lymphocytes.

[0013] Furthermore, the step of inputting the clinical data and the imaging feature data into the risk prediction model to calculate the risk value includes: obtaining, based on the nomogram, the first score, second score, third score, fourth score, fifth score, and sixth score corresponding to the age, the menstrual status, the neutrophil-to-lymphocyte ratio, the platelet-to-lymphocyte ratio, the monocyte-to-lymphocyte ratio, and the imaging feature data, as well as the corresponding total score.

[0014] Furthermore, obtaining the sixth score corresponding to the image group feature data includes: obtaining the sub-scores corresponding to the first-order three-dimensional skewness, the first-order LLH sub-band median, the first-order dispersion, the three-dimensional dependency variance of the gray-level dependency matrix, the Imc1 coefficient of the gray-level co-occurrence matrix LHH sub-band, the three-dimensional clustering shadow of the gray-level co-occurrence matrix, the small region emphasis feature of the gray-level region size matrix HLH sub-band, the size region non-uniformity normalization feature of the gray-level region size matrix HLH sub-band, and the small region emphasis feature of the gray-level region size matrix LHL sub-band, and then updating the sub-scores according to the calculation weights corresponding to the above data and weighting them to obtain the sixth score.

[0015] Secondly, a risk prediction system for uterine fibroids is provided. The system includes: a data acquisition unit for acquiring clinical data of a patient to be diagnosed and imaging feature data of a target region of the patient to be diagnosed; the imaging feature data includes MRI image feature data, and the clinical data includes basic medical data and biophysical and chemical data; and a risk prediction unit for inputting the clinical data and the imaging feature data into a risk prediction model to calculate a risk value.

[0016] The technical solution provided in this application combines multiple sets of clinical indicators and multiple sets of imaging feature data to effectively assess and predict the preoperative risks of uterine fibroids, especially for high-intensity focused ultrasound ablation. This scoring data provides a more intuitive determination of preoperative risk, thereby achieving an accurate assessment of the preoperative risk of uterine fibroids. Compared to the existing assessment strategy that relies solely on the doctor's clinical experience, this application combines imaging feature data with clinical indicators, resulting in a more accurate judgment. 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 process for predicting the risk of uterine fibroids 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 risk of uterine fibroid incidence, particularly a method for identifying preoperative risks associated with high-intensity focused ultrasound (HIFU) ablation. HIFU has been widely used for uterine fibroid ablation; however, due to varying treatment outcomes among different fibroid patients, accurate preoperative prediction of the postoperative ablation rate is crucial for guiding clinical decision-making. In existing technologies, the accuracy of prediction results is primarily related to the experience and skill of clinicians, and is subjective due to its non-quantitative nature.

[0030] Therefore, in order to achieve accurate and automated prediction and identification, this application provides a method for predicting the risk of uterine fibroids. This method combines clinical indicators with image group feature data from MRI images to identify the risk of uterine fibroids, especially the preoperative risk. For details on this method, please refer to [link to relevant documentation]. Figure 1 This includes the following steps:

[0031] Step S11. Obtain the clinical data of the patient to be diagnosed and the image group feature data of the target region of the patient to be diagnosed.

[0032] In this embodiment, the method uses a combined prediction approach to assess the preoperative risk of uterine fibroids. The logic of the combined prediction approach is to obtain the score corresponding to each indicator based on the nomogram through the prediction model, and then obtain the total score. The total score is used to determine the degree of possible risk, especially the degree of preoperative risk for uterine fibroids.

[0033] In this embodiment, the image group feature data is MRI image feature data, and the clinical data includes basic medical data and biophysical and chemical data. The basic medical data includes age and menstrual status, and the biophysical and chemical data includes the ratio of neutrophils to lymphocytes, the ratio of platelets to lymphocytes, and the ratio of monocytes to lymphocytes.

[0034] The acquisition of imaging feature data first involved collecting the DWI signal intensity and T1WI enhancement degree of the fibroid. For DWI signal intensity, fibroids were categorized as low signal (below skeletal muscle), isointense (between skeletal muscle and myometrium), and high signal (above myometrium). For T1WI enhancement degree, fibroids were categorized as mild (below the myometrium), moderate (similar to the myometrium), and significant (above the myometrium). Additionally, the abdominal-skin distance, dorsal-skin distance, and rectus abdominis muscle thickness of the fibroid were measured on T2WI sagittal images.

[0035] On preoperative T2WI-FS and CE-MRI delayed-phase transverse images, the region of interest (ROI) was delineated layer by layer along the edge of the fibroid to obtain the ROI volume. Feature extraction was performed on the ROI, yielding a total of 4460 features, including shape-based features, first-order features, and texture features. Inter-observer consistency was assessed by analyzing inter-group correlation coefficients. Features with ICC > 0.75 were retained for feature selection. Feature data were preprocessed using Z-score normalization, and then selected using K-optimization, recursive feature elimination, univariate / multivariate logistic regression, minimum absolute shrinkage, and selection operators. Finally, 9 omics features and 8 omics features were selected from T2WI-FS and CE-MRI delayed-phase images, respectively. Lasso was then used to retain three first-order features, one gray-level dependency matrix feature, two gray-level co-occurrence matrix features, and three gray-level region size matrix features.

[0036] The first-order features include first-order three-dimensional skewness, first-order LLH subband median, and first-order dispersion; the gray-level dependency matrix features include gray-level dependency matrix three-dimensional dependency variance; the gray-level co-occurrence matrix features include gray-level co-occurrence matrix LHH subband Imc1 coefficient and gray-level co-occurrence matrix three-dimensional clustering shadow; and the gray-level region size matrix features include gray-level region size matrix HLH subband small region emphasis features, gray-level region size matrix HLH subband size region non-uniformity normalization features, and gray-level region size matrix LHL subband small region emphasis features.

[0037] Step S12. Input the clinical data and the imaging group feature data into the risk prediction model to calculate the risk value.

[0038] 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 biophysical and chemical data and image group feature data obtained in step S11, and based on the score corresponding to each indicator and image group feature data. This total score is the risk value in this embodiment, which is used to characterize the risk level corresponding to uterine fibroid surgery.

[0039] Specifically, based on the nomogram, the first score, second score, third score, fourth score, fifth score, and sixth score corresponding to age, menstrual status, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, monocyte-to-lymphocyte ratio, and image group feature data, as well as the corresponding total score, are obtained.

[0040] For details regarding the calculation results of this line graph 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 = +0.0977 × first-order 3D skewness + 0.0711 × gray-level dependency matrix 3D dependency variance + 0.0691 × first-order LLH subband median + 0.0376 × first-order dispersion + 0.0258 × gray-level co-occurrence matrix LHH subband Imc1 coefficient + 0.0142 × gray-level co-occurrence matrix 3D clustering shadow - 0.0041 × gray-level region size matrix HLH subband small region emphasis feature - 0.1003 × gray-level region size matrix HLH subband size region non-uniformity normalization feature - 0.1228 × gray-level region size matrix LHL subband small region emphasis feature.

[0041] 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.

[0042] In summary, by combining the above-mentioned multiple sets of clinical indicators and multiple sets of imaging feature data in this embodiment, the preoperative risk of uterine fibroids can be effectively assessed and predicted. This scoring data can more intuitively determine the feasibility of high-intensity focused ultrasound ablation.

[0043] See Figure 3 In addition to the method provided in steps S11-S12, this embodiment also provides a prediction system 30, which includes:

[0044] Data acquisition unit 31 is used to acquire clinical data of the patient to be diagnosed and image group feature data of the target area of ​​the patient to be diagnosed;

[0045] The risk prediction unit 32 is used to input the clinical data and the imaging group feature data into the risk prediction model to calculate the risk value.

[0046] In this embodiment, the risk prediction model is a nomogram model.

[0047] 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.

[0048] 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:

[0049] Acquire clinical data of the patient to be diagnosed and radiographic feature data of the target region of the patient to be diagnosed;

[0050] The clinical data and the imaging feature data are input into the risk prediction model to calculate the risk value.

[0051] The following is a detailed introduction to each component of the processor:

[0052] 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).

[0053] 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.

[0054] In a specific implementation, as one example, the processor may include one or more microprocessors.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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).

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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 risk of developing uterine fibroids, characterized in that, The method includes: Acquire clinical 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 MRI image feature data, and the clinical data includes basic medical data and biophysical and chemical data; The clinical data and the imaging feature data are input into a risk prediction model to calculate the risk value; the risk value is a nomogram model.

2. The method for predicting the risk of uterine fibroids according to claim 1, characterized in that, The image group feature data includes multiple first-order features, gray-level dependency matrix features, multiple gray-level co-occurrence matrix features, and multiple gray-level region size matrix features.

3. The method for predicting the risk of uterine fibroids according to claim 2, characterized in that, The first-order features include first-order three-dimensional skewness, first-order LLH subband median, and first-order dispersion.

4. The method for predicting the risk of uterine fibroids according to claim 2, characterized in that, The gray-level dependency matrix features include the three-dimensional dependency variance of the gray-level dependency matrix.

5. The method for predicting the risk of uterine fibroids according to claim 2, characterized in that, The gray-level co-occurrence matrix features include the Imc1 coefficients of the LHH subbands of the gray-level co-occurrence matrix and the three-dimensional clustering shadows of the gray-level co-occurrence matrix.

6. The method for predicting the risk of uterine fibroids according to claim 2, characterized in that, The multiple gray-scale region size matrix features include small region emphasis features of the HLH subband of the gray-scale region size matrix, size region non-uniformity normalization features of the HLH subband of the gray-scale region size matrix, and small region emphasis features of the LHL subband of the gray-scale region size matrix.

7. The method for predicting the risk of uterine fibroids according to claim 1, characterized in that, The basic medical data includes age and menstrual status, and the biophysical and chemical data includes the neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and monocyte-to-lymphocyte ratio.

8. The method for predicting the risk of uterine fibroids according to any one of claims 1-7, characterized in that, The step of inputting the clinical data and the imaging feature data into the risk prediction model to calculate the risk value includes: obtaining the first score, second score, third score, fourth score, fifth score, and sixth score, and the corresponding total score, based on the nomogram, corresponding to the age, menstrual status, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, monocyte-to-lymphocyte ratio, and imaging feature data.

9. The method for predicting the risk of uterine fibroids according to claim 8, characterized in that, The process of obtaining the sixth score corresponding to the image group feature data includes: obtaining the sub-scores corresponding to the first-order three-dimensional skewness, the first-order LLH sub-band median, the first-order dispersion, the three-dimensional dependency variance of the gray-level dependency matrix, the Imc1 coefficient of the gray-level co-occurrence matrix LHH sub-band, the three-dimensional clustering shadow of the gray-level co-occurrence matrix, the small region emphasis feature of the gray-level region size matrix HLH sub-band, the size region non-uniformity normalization feature of the gray-level region size matrix HLH sub-band, and the small region emphasis feature of the gray-level region size matrix LHL sub-band; and updating the sub-scores according to the calculation weights corresponding to the above data and then weighting them to obtain the sixth score.

10. A system for predicting the risk of developing uterine fibroids, characterized in that, The system includes: The data acquisition unit is used to acquire clinical 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 MRI image feature data, and the clinical data includes basic medical data and biophysical and chemical data. The risk prediction unit is used to input the clinical data and the imaging group feature data into the risk prediction model to calculate the risk value.

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