Glioblastoma multiforme inhibitory effect analysis system and method

Abstract

The present application discloses a system and method for analyzing the inhibitory effect of glioblastoma multiforme, which belongs to the field of medical care systems. The present application analyzes the growth inhibitory effect based on the extracted growth characteristics of glioblastoma multiforme, analyzes the abnormal constitution of the patient during the inhibition process based on the patient's constitution change data, evaluates the inhibitory effect based on the growth inhibitory effect analysis results and the patient's constitution abnormality analysis results, accurately analyzes the inhibitory effect of glioblastoma multiforme based on a comprehensive analysis of the growth change characteristics of glioblastoma multiforme and the patient's constitution change characteristics, improves the safety of the glioblastoma multiforme inhibition process, and solves the technical problem that the inhibitory effect of glioblastoma multiforme cannot be evaluated based on the comprehensive analysis of the growth change characteristics of glioblastoma multiforme and the patient's constitution change characteristics, resulting in a low analysis accuracy.

Description

Technical Field

[0001] The present application belongs to the field of medical care systems, and more specifically, to a system and method for analyzing the inhibitory effect of glioblastoma multiforme. Background Art

[0002] Glioblastoma multiforme (GBM) is a highly invasive central nervous system tumor that is common in adults. It is derived from the malignant transformation of astrocytoma and is the most malignant type of astrocytoma. GBM is generally considered to be a manifestation of progressive anaplasia of astrocytoma, mixed astrocytoma, and oligodendroglioma. The cytological characteristics of GBM are obvious necrotic foci in the tumor tissue, the surrounding tumor cells are arranged in a fence-like manner, and the small blood vessels in the interstitium are obviously proliferated in the form of buds, glomeruli, or curved long ribbons. The tumor cells are often pleomorphic, and atypical multinucleated giant cells are easily seen. Its growth characteristics include extending deep along the nerve bundles in the white matter, and multicentric growth. The treatment of GBM Treatment usually includes surgical removal of the tumor, chemotherapy and radiotherapy, and there is no significant improvement in the patient's survival period, and the prognosis is extremely poor. In order to study the molecular role of receptor inhibitors in the growth, migration and invasion of glioblastoma multiforme cells, a glioblastoma multiforme inhibitory effect analysis system is needed; in the process of analyzing the glioblastoma multiforme inhibitory effect, the prior art is unable to evaluate the glioblastoma multiforme inhibitory effect based on a comprehensive analysis of the growth change characteristics of glioblastoma multiforme and the physical change characteristics of the patient, resulting in a low analysis accuracy. Most of the prior art have the above problems; in view of this, the present application proposes a glioblastoma multiforme inhibitory effect analysis system and method. Summary of the invention

[0003] In order to solve the deficiencies in the prior art mentioned in the background technology, the present application proposes a system and method for analyzing the inhibitory effect of glioblastoma multiforme.

[0004] To achieve the above objectives, the present application provides the following technical solutions: In the first aspect, the present application provides a method for analyzing the inhibitory effect of glioblastoma multiforme, which comprises the following specific steps:

[0005] Step 1, obtaining image data of changes in the growth position of glioblastoma multiforme, and simultaneously obtaining data on changes in the patient's physical condition;

[0006] Step 2: constructing a growth feature extraction model based on the image data of the growth position change of the glioblastoma multiforme to extract the growth features of the glioblastoma multiforme;

[0007] Step 3, analyzing the growth inhibition effect based on the extracted growth characteristics of glioblastoma multiforme;

[0008] Step 4: Analyze the abnormal physical condition of the patient during the inhibition process based on the physical condition change data of the patient;

[0009] Step 5: Evaluate the inhibitory effect based on the growth inhibitory effect analysis results and the patient's physical abnormality analysis results.

[0010] As a preferred technical solution of the method for analyzing the inhibitory effect of glioblastoma multiforme, the specific content of obtaining the image data of the change of the growth position of glioblastoma multiforme and simultaneously obtaining the data of the change of the patient's physical condition is:

[0011] Step 11, acquiring image data of changes in the growth position of the glioblastoma multiforme before inhibition through an image acquisition terminal, and acquiring image data of changes in the growth position of the glioblastoma multiforme during the inhibition process, wherein the image data of changes in the growth position of the glioblastoma multiforme is acquired through multiple medical imaging, and the medical imaging includes computer tomography, positron emission tomography, magnetic resonance imaging, and functional magnetic resonance imaging, etc.;

[0012] Step 12: Collect the patient's physical condition change data before and during the inhibition process through the physical condition information collection terminal. The patient's physical condition change data here includes body temperature, blood pressure, heart rate and other data types that reflect the patient's physical condition;

[0013] Step 13: Import the collected data into the storage module for data storage.

[0014] As a preferred technical solution of the method for analyzing the inhibitory effect of glioblastoma multiforme, the method of constructing a growth feature extraction model based on the image data of the growth position change of glioblastoma multiforme to extract the growth features of glioblastoma multiforme includes the following specific steps:

[0015] Step 21, obtaining the specific position of the glioblastoma in the constructed three-dimensional brain image, and simultaneously obtaining the contour data of the glioblastoma;

[0016] It should be noted that how to construct a three-dimensional brain image here is a conventional technical means of three-dimensional construction in the prior art, and obtaining medical imaging pictures can be achieved through existing medical three-dimensional construction software. At the same time, this is not the gist of the invention of this application, so it will not be described in detail;

[0017] Step 22, obtaining the pixel point with the largest difference between the average pixel value of the glioblastoma image contour and the image of the normal brain region as the central pixel point, taking the central pixel point as the center, setting the pixel difference as the pixel gradient, dividing the glioblastoma image contour into a number of closed images, and obtaining the volume and average pixel value data of each closed image;

[0018] Step 23, obtaining the data of the number of neurons in the neural area corresponding to each closed image of the glioblastoma, where the number of neurons is an estimated value, and the value is obtained by taking the average value of the experimental population.

[0019] As a preferred technical solution of the method for analyzing the inhibitory effect of glioblastoma multiforme, the analysis of the growth inhibitory effect based on the extracted growth characteristics of glioblastoma multiforme includes the following specific steps:

[0020] Step 31, obtaining the volume and average pixel value data of each closed image of the glioblastoma image at the corresponding moment, and simultaneously obtaining the number of neurons in the neural area corresponding to each closed image;

[0021] Step 32: The volume of each closed image of the glioblastoma image, the average pixel value data, and the number of neurons in the neural area corresponding to each closed image are imported into the growth abnormality value calculation formula to calculate the growth abnormality value of the glioblastoma, wherein the growth abnormality value calculation formula is: , where L is the average number of neurons in the human body, N is the number of closed images, xi is the average pixel value of the ith closed image, xm is the average pixel value of normal brain tissue in the corresponding area, exp() is the power of the natural constant e, Vi is the volume of the ith closed image, Vm is the set standard value of the volume of the closed image, and Li is the number of neurons in the neural area corresponding to the ith closed image;

[0022] Step 33, obtaining the growth abnormality value of the set period before the inhibition starts and the growth abnormality value of the set period after the inhibition starts, and importing them into the growth inhibition coefficient calculation formula to calculate the growth inhibition coefficient, wherein the growth inhibition coefficient calculation formula is: , where a is the growth rate inhibition ratio, Szq is the growth abnormality value at the beginning of the inhibition, Szc is the growth abnormality value of the set period after the inhibition begins, Szm is the growth abnormality value of the set period before the inhibition begins, and T is the duration of the set period. In this formula To inhibit the overall growth of glioblastoma, To set the growth rate of glioblastoma before the inhibition begins, To inhibit the growth rate of glioblastoma after setting the cycle, It has an inhibitory effect on the growth rate of glioblastoma.

[0023] As a preferred technical solution of the method for analyzing the inhibitory effect of glioblastoma multiforme, the analysis of abnormal physical condition of patients during the inhibition process based on the data of changes in the physical condition of the patients includes the following specific contents:

[0024] Step 41, obtaining the patient's physical condition change data before inhibition and the patient's physical condition change data during inhibition;

[0025] Step 42: Import the patient's physical condition change data into the physical condition abnormality coefficient calculation formula to calculate the physical condition abnormality coefficient, wherein the physical condition abnormality coefficient calculation formula is: , where M is the type of physical data, cj is the proportion coefficient of the j-th physical data, fj is the average value of the j-th physical data within the set period, fjm is the median value of the safety range of the j-th physical data, fjmax is the maximum value of the safety range of the j-th physical data, and fjmin is the minimum value of the safety range of the j-th physical data;

[0026] Step 43, obtain the physical abnormality coefficient of the set period before inhibition and the physical abnormality coefficient of the set period after inhibition, and substitute them into the physical inhibition effect value calculation formula to calculate the physical inhibition effect value, wherein the physical inhibition effect value calculation formula is: , where Ktq is the physical abnormality coefficient of the set period before inhibition, and Kth is the physical abnormality coefficient of the set period after inhibition.

[0027] As a preferred technical solution for the method for analyzing the inhibitory effect of glioblastoma multiforme, the evaluation of the inhibitory effect based on the growth inhibitory effect analysis results and the patient's physical abnormality analysis results includes the following specific contents:

[0028] Step 51, obtain the calculated growth inhibition coefficient and the inhibitory constitution effect value in the inhibitory process and substitute them into the inhibitory effect value calculation formula to calculate the inhibitory effect value, wherein the inhibitory effect value calculation formula is: , where Rm is the growth inhibition standard value, Km is the physical inhibition standard value, is the growth inhibition weight, is the body mass weight;

[0029] Step 52: Compare the calculated inhibition effect value with the set inhibition effect threshold. If the inhibition effect value is greater than or equal to the set inhibition effect threshold, it means that the inhibition effect has achieved the purpose and there is no need to replace the inhibition means. If the inhibition effect value is less than the set inhibition effect threshold, it means that the inhibition effect has not achieved the purpose and it is recommended to replace the inhibition means.

[0030] In the second aspect, the present application provides a glioblastoma multiforme inhibitory effect analysis system, which is implemented based on the above-mentioned glioblastoma multiforme inhibitory effect analysis method, and specifically includes: a data acquisition module, used to obtain the change image data of the growth position of glioblastoma multiforme, and at the same time obtain the patient's physical change data; a growth feature extraction module, used to construct a growth feature extraction model based on the change image data of the growth position of glioblastoma multiforme to extract the growth characteristics of glioblastoma multiforme; an inhibitory effect analysis module, used to analyze the growth inhibitory effect based on the extracted growth characteristics of glioblastoma multiforme; a physical abnormality analysis module, used to analyze the patient's physical abnormality during the inhibition process based on the patient's physical change data; an inhibitory effect evaluation module, used to evaluate the inhibitory effect based on the growth inhibitory effect analysis results and the patient's physical abnormality analysis results.

[0031] In a third aspect, the present application provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0032] The processor executes the above-mentioned glioblastoma multiforme inhibitory effect analysis method by calling the computer program stored in the memory.

[0033] In a fourth aspect, the present application provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the above-mentioned method for analyzing the inhibitory effect of glioblastoma multiforme.

[0034] Compared with the prior art, the beneficial effects of the present application are as follows: 1. The present application analyzes the growth inhibition effect based on the extracted growth characteristics of glioblastoma multiforme, analyzes the abnormal physical condition of the patient during the inhibition process based on the data of the patient's physical condition change, evaluates the inhibition effect based on the growth inhibition effect analysis results and the patient's physical condition abnormality analysis results, accurately analyzes the glioblastoma multiforme inhibition effect based on the comprehensive analysis of the growth change characteristics of glioblastoma multiforme and the patient's physical condition change characteristics, and improves the safety of the glioblastoma multiforme inhibition process;

[0035] 2. When analyzing the growth change characteristics of glioblastoma multiforme, the present application uses a multi-source data fusion approach to extract and comprehensively analyze the data types related to the growth change characteristics, thereby further improving the accuracy of the effect analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings;

[0037] Figure 1This is a schematic diagram of the overall process of the method for analyzing the inhibitory effect of glioblastoma multiforme in this application;

[0038] Figure 2 This is a schematic diagram of the specific steps of step 3 of the method for analyzing the inhibitory effect of glioblastoma multiforme of this application;

[0039] Figure 3 This is a schematic diagram of the specific steps of step 4 of the method for analyzing the inhibitory effect of glioblastoma multiforme of this application;

[0040] Figure 4 A schematic diagram of the process of obtaining volume data of corresponding images for the method for analyzing the inhibitory effect of glioblastoma multiforme in this application;

[0041] Figure 5 This is a schematic diagram of the overall framework of the glioblastoma multiforme inhibitory effect analysis system of the present application. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present application and its application or use.

[0043] In order to solve the technical problems raised in the background technology, the present application provides a preferred embodiment: Figure 1 As shown, the method for analyzing the inhibitory effect of glioblastoma multiforme comprises the following specific steps:

[0044] Step 1, obtaining image data of changes in the growth position of glioblastoma multiforme, and simultaneously obtaining data on changes in the patient's physical condition;

[0045] In this embodiment, the specific content of step 1 is: step 11, acquiring the image data of the change of the growth position of the glioblastoma multiforme before the inhibition through the image acquisition terminal, and acquiring the image data of the change of the growth position of the glioblastoma multiforme during the inhibition process, wherein the image data of the change of the growth position of the glioblastoma multiforme is acquired through multiple medical imaging, and the medical imaging includes computer tomography, positron emission tomography, magnetic resonance imaging, and functional magnetic resonance imaging, etc.;

[0046] Step 12: Collect the patient's physical condition change data before and during the inhibition process through the physical condition information collection terminal. The patient's physical condition change data here includes body temperature, blood pressure, heart rate and other data types that reflect the patient's physical condition;

[0047] Step 13: Import the collected data into a storage module for data storage;

[0048] Step 2: constructing a growth feature extraction model based on the image data of the growth position change of the glioblastoma multiforme to extract the growth features of the glioblastoma multiforme;

[0049] In this embodiment, the specific content of step 2 is: step 21, obtaining the specific position of the glioblastoma in the image on the constructed three-dimensional brain image, and obtaining the contour data of the glioblastoma;

[0050] It should be noted that in this embodiment, how to construct a three-dimensional brain image is a conventional technical means of three-dimensional construction in the prior art, and obtaining medical imaging pictures can be achieved through existing medical three-dimensional construction software. At the same time, this is not the main point of the invention of this application, so it will not be described in detail;

[0051] Step 22, obtaining the pixel point with the largest difference between the average pixel value of the glioblastoma image contour and the image of the normal brain region as the central pixel point, taking the central pixel point as the center, setting the pixel difference as the pixel gradient, dividing the glioblastoma image contour into a number of closed images, and obtaining the volume and average pixel value data of each closed image;

[0052] Among them, the specific steps of obtaining the volume data of the corresponding image are: Step 221, data preparation: first, obtain the contour data of the glioblastoma, which usually exists in the form of medical images, such as MRI or CT scan images; Step 222, contour extraction: extract the glioblastoma contour data, wherein the contour data usually includes a set of coordinate points of the tumor boundary; Step 223, data import fitting software: import the extracted contour data into a fitting software or 3D modeling software suitable for volume calculation, such as MATLAB, Python's NumPy and SciPy libraries, or special volume calculation software.

[0053] Step 23, obtaining the data of the number of neurons in the neural regions corresponding to each closed image of glioblastoma. The number of neurons here is an estimated value, and the value is obtained by taking the average value of the experimental population. It is mainly to analyze the importance of the neural regions corresponding to each closed image of glioblastoma. For example, if there are more neurons in a region, then this region is more important for glioblastoma.

[0054] Step 3, analyzing the growth inhibition effect based on the extracted growth characteristics of glioblastoma multiforme;

[0055] In this embodiment, the specific content of step 3 is: step 31, obtaining the volume and average pixel value data of each closed image of the glioblastoma image at the corresponding moment, and simultaneously obtaining the neuron number data of the neural area corresponding to each closed image;

[0056] Step 32: The volume of each closed image of the glioblastoma image, the average pixel value data, and the number of neurons in the neural area corresponding to each closed image are imported into the growth abnormality value calculation formula to calculate the growth abnormality value of the glioblastoma, wherein the growth abnormality value calculation formula is: , where L is the average number of neurons in the human body, N is the number of closed images, xi is the average pixel value of the ith closed image, xm is the average pixel value of normal brain tissue in the corresponding area, exp() is the power of the natural constant e, Vi is the volume of the ith closed image, Vm is the set standard value of the volume of the closed image, and Li is the number of neurons in the neural area corresponding to the ith closed image;

[0057] Step 33, obtaining the growth abnormality value of the set period before the inhibition starts and the growth abnormality value of the set period after the inhibition starts, and importing them into the growth inhibition coefficient calculation formula to calculate the growth inhibition coefficient, wherein the growth inhibition coefficient calculation formula is: , where a is the growth rate inhibition ratio, Szq is the growth abnormality value at the beginning of the inhibition, Szc is the growth abnormality value of the set period after the inhibition begins, Szm is the growth abnormality value of the set period before the inhibition begins, and T is the duration of the set period. In this formula To inhibit the overall growth of glioblastoma, To set the growth rate of glioblastoma before the inhibition begins, To inhibit the growth rate of glioblastoma after setting the cycle, For the inhibitory effect on the growth rate of glioblastoma;

[0058] In this way, when analyzing the growth change characteristics of glioblastoma multiforme, the data types related to the growth change characteristics are extracted and comprehensively analyzed by means of multi-source data fusion, thereby further improving the accuracy of effect analysis;

[0059] Step 4: Analyze the abnormal physical condition of the patient during the inhibition process based on the physical condition change data of the patient;

[0060] In this embodiment, the specific content of step 4 is: step 41, obtaining the patient's physical condition change data before inhibition and the patient's physical condition change data during inhibition;

[0061] Step 42: Import the patient's physical condition change data into the physical condition abnormality coefficient calculation formula to calculate the physical condition abnormality coefficient, wherein the physical condition abnormality coefficient calculation formula is: , where M is the type of physical data, cj is the proportion coefficient of the j-th physical data, fj is the average value of the j-th physical data within the set period, fjm is the median value of the safety range of the j-th physical data, fjmax is the maximum value of the safety range of the j-th physical data, and fjmin is the minimum value of the safety range of the j-th physical data;

[0062] Step 43, obtain the physical abnormality coefficient of the set period before inhibition and the physical abnormality coefficient of the set period after inhibition, and substitute them into the physical inhibition effect value calculation formula to calculate the physical inhibition effect value, wherein the physical inhibition effect value calculation formula is: , where Ktq is the physical abnormality coefficient of the set period before inhibition, and Kth is the physical abnormality coefficient of the set period after inhibition;

[0063] Step 5: evaluating the inhibitory effect based on the growth inhibitory effect analysis results and the patient's physical abnormality analysis results;

[0064] In this embodiment, the specific content of step 5 is: step 51, obtaining the calculated growth inhibition coefficient and the inhibitory constitution effect value in the inhibitory process and substituting them into the inhibitory effect value calculation formula to calculate the inhibitory effect value, wherein the inhibitory effect value calculation formula is: , where Rm is the growth inhibition standard value, Km is the physical inhibition standard value, is the growth inhibition weight, is the body weight, and ;

[0065] Step 52: Compare the calculated inhibition effect value with the set inhibition effect threshold. If the inhibition effect value is greater than or equal to the set inhibition effect threshold, it means that the inhibition effect has achieved the purpose and there is no need to replace the inhibition means. If the inhibition effect value is less than the set inhibition effect threshold, it means that the inhibition effect has not achieved the purpose and it is recommended to replace the inhibition means.

[0066] It should be noted here that the preset parameters in this embodiment are obtained by those skilled in the art through experiments, and the preferred preset parameters (such as the inhibition effect threshold, growth inhibition weight, physical fitness weight, the proportion coefficient of the j-th physical fitness data, the growth rate inhibition proportion and the volume standard value of the closed image, etc.) are obtained in the following manner: obtaining several groups of image data of changes in the growth position of glioblastoma multiforme and physical fitness change data of the patient, calculating the inhibition effect value through these parameters, obtaining the judgment result of the medical expert on whether the inhibition effect has achieved the purpose, importing the judgment result of whether the inhibition effect has achieved the purpose and the corresponding inhibition effect value into the fitting software, and outputting the values ​​of the preset parameters (such as the inhibition effect threshold, growth inhibition weight, physical fitness weight, the proportion coefficient of the j-th physical fitness data, the growth rate inhibition proportion and the volume standard value of the closed image, etc.) that meet the maximum judgment accuracy.

[0067] It should be noted here that the beneficial effects of this embodiment compared with the prior art are: analyzing the growth inhibition effect based on the extracted growth characteristics of glioblastoma multiforme, analyzing the patient's physical abnormalities during the inhibition process based on the patient's physical condition change data, evaluating the inhibition effect based on the growth inhibition effect analysis results and the patient's physical abnormality analysis results, accurately analyzing the glioblastoma multiforme inhibition effect based on a comprehensive analysis of the growth change characteristics of glioblastoma multiforme and the patient's physical condition change characteristics, and improving the safety of the glioblastoma multiforme inhibition process.

[0068] like Figure 5 As shown, the present embodiment also provides a glioblastoma multiforme inhibitory effect analysis system, which is implemented based on the above-mentioned glioblastoma multiforme inhibitory effect analysis method, and specifically includes: a data acquisition module, used to obtain the image data of the growth position change of the glioblastoma multiforme, and at the same time obtain the patient's physical change data; a growth feature extraction module, used to construct a growth feature extraction model based on the image data of the growth position change of the glioblastoma multiforme to extract the growth characteristics of the glioblastoma multiforme; an inhibitory effect analysis module, used to analyze the growth inhibitory effect based on the extracted growth characteristics of the glioblastoma multiforme; a physical abnormality analysis module, used to analyze the patient's physical abnormality during the inhibition process based on the patient's physical change data; an inhibitory effect evaluation module, used to evaluate the inhibitory effect based on the growth inhibitory effect analysis results and the patient's physical abnormality analysis results, Figure 5 The arrow in the figure indicates the data transmission direction of the system. The specific steps of the system module in this embodiment have been fully mentioned in the above method embodiment, so they will not be repeated here in detail.

[0069] At the same time, this embodiment also provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0070] The processor executes the above-mentioned glioblastoma multiforme inhibitory effect analysis method by calling the computer program stored in the memory.

[0071] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the glioblastoma multiforme inhibitory effect analysis method provided by the above method embodiment. The electronic device may also include other components for implementing the functions of the device, for example, the electronic device may also have components such as a wired or wireless network interface and an input / output interface to input and output data. This embodiment will not be described in detail here.

[0072] This embodiment also provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0073] When the computer program is executed on a computer device, the computer device is caused to execute the above-mentioned method for analyzing the inhibitory effect of glioblastoma multiforme.

[0074] For example, the computer readable storage medium can be a read-only memory, a random access memory, a read-only CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0075] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through a wired network or / and a wireless network. 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 contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0076] The terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or also includes elements inherent to such process, method, article, or apparatus.

[0077] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of application involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned application concept. For example, the above features are replaced with (but not limited to) technical features with similar functions applied in the present application.

Claims

1. A method for analyzing the inhibitory effect on glioblastoma multiforme, characterized in that: It includes the following specific steps: Step 1, obtaining image data of changes in the growth position of glioblastoma multiforme, and simultaneously obtaining data on changes in the patient's physical condition; Step 2: constructing a growth feature extraction model based on the image data of the growth position change of the glioblastoma multiforme to extract the growth features of the glioblastoma multiforme; Step 3, analyzing the growth inhibition effect based on the extracted growth characteristics of glioblastoma multiforme; Step 4: Analyze the abnormal physical condition of the patient during the inhibition process based on the physical condition change data of the patient; Step 5: evaluating the inhibitory effect based on the growth inhibitory effect analysis results and the patient's physical abnormality analysis results; The method of constructing a growth feature extraction model based on the image data of the growth position change of glioblastoma multiforme to extract the growth feature of glioblastoma multiforme comprises the following specific steps: Step 21, obtaining the specific position of the glioblastoma in the constructed three-dimensional brain image, and simultaneously obtaining the contour data of the glioblastoma; Step 22, obtaining the pixel point with the largest difference between the average pixel value of the glioblastoma image contour and the image of the normal brain region as the central pixel point, taking the central pixel point as the center, setting the pixel difference as the pixel gradient, dividing the glioblastoma image contour into a number of closed images, and obtaining the volume and average pixel value data of each closed image; Step 23, obtaining neuron quantity data of the neural region corresponding to each closed image of the glioblastoma; The analysis of the growth inhibition effect based on the extracted growth characteristics of glioblastoma multiforme comprises the following specific steps: Step 31, obtaining the volume and average pixel value data of each closed image of the glioblastoma image at the corresponding moment, and simultaneously obtaining the number of neurons in the neural area corresponding to each closed image; Step 32: The volume of each closed image of the glioblastoma image, the average pixel value data, and the number of neurons in the neural area corresponding to each closed image are imported into the growth abnormality value calculation formula to calculate the growth abnormality value of the glioblastoma, wherein the growth abnormality value calculation formula is: , where L is the average number of neurons in the human body, N is the number of closed images, xi is the average pixel value of the ith closed image, xm is the average pixel value of normal brain tissue in the corresponding area, exp() is the power of the natural constant e, Vi is the volume of the ith closed image, Vm is the set standard value of the volume of the closed image, and Li is the number of neurons in the neural area corresponding to the ith closed image; Step 33, obtaining the growth abnormality value of the set period before the inhibition starts and the growth abnormality value of the set period after the inhibition starts, and importing them into the growth inhibition coefficient calculation formula to calculate the growth inhibition coefficient, wherein the growth inhibition coefficient calculation formula is: , where a is the growth rate inhibition ratio, Szq is the growth abnormality value at the beginning of the inhibition, Szc is the growth abnormality value of the set period after the inhibition begins, Szm is the growth abnormality value of the set period before the inhibition begins, and T is the duration of the set period.

2. The method for analyzing the inhibitory effect on glioblastoma multiforme according to claim 1, characterized in that: The analysis of abnormal physical condition of patients during the inhibition process based on the data of physical condition changes of patients includes the following specific contents: Step 41, obtaining the patient's physical condition change data before inhibition and the patient's physical condition change data during inhibition; Step 42: Import the patient's physical condition change data into the physical condition abnormality coefficient calculation formula to calculate the physical condition abnormality coefficient, wherein the physical condition abnormality coefficient calculation formula is: , where M is the type of physical data, cj is the proportion coefficient of the j-th physical data, fj is the average value of the j-th physical data within the set period, fjm is the median value of the safety range of the j-th physical data, fjmax is the maximum value of the safety range of the j-th physical data, and fjmin is the minimum value of the safety range of the j-th physical data; Step 43, obtain the physical abnormality coefficient of the set period before inhibition and the physical abnormality coefficient of the set period after inhibition, and substitute them into the physical inhibition effect value calculation formula to calculate the physical inhibition effect value, wherein the physical inhibition effect value calculation formula is: , where Ktq is the physical abnormality coefficient of the set period before inhibition, and Kth is the physical abnormality coefficient of the set period after inhibition.

3. The method for analyzing the inhibitory effect on glioblastoma multiforme according to claim 2, characterized in that: The evaluation of the inhibitory effect based on the growth inhibitory effect analysis results and the patient's physical abnormality analysis results includes the following specific contents: Step 51, obtain the calculated growth inhibition coefficient and the inhibitory constitution effect value in the inhibitory process and substitute them into the inhibitory effect value calculation formula to calculate the inhibitory effect value, wherein the inhibitory effect value calculation formula is: , where Rm is the growth inhibition standard value, Km is the physical inhibition standard value, is the growth inhibition weight, is the body mass weight; Step 52: Compare the calculated inhibition effect value with the set inhibition effect threshold. If the inhibition effect value is greater than or equal to the set inhibition effect threshold, it means that the inhibition effect has achieved the purpose and there is no need to replace the inhibition means. If the inhibition effect value is less than the set inhibition effect threshold, it means that the inhibition effect has not achieved the purpose and it is recommended to replace the inhibition means.

4. The method for analyzing the inhibitory effect on glioblastoma multiforme according to claim 3, characterized in that: The specific contents of obtaining the image data of the growth position change of glioblastoma multiforme and simultaneously obtaining the data of the patient's physical condition change are: The image acquisition terminal is used to acquire image data of changes in the growth position of the glioblastoma multiforme before inhibition, and the image data of changes in the growth position of the glioblastoma multiforme during the inhibition process are acquired at the same time; the physical condition information acquisition terminal is used to acquire the physical condition change data of the patient before inhibition and the physical condition change data of the patient during inhibition; and the acquired data is imported into a storage module for data storage.

5. A system for analyzing the inhibitory effect of glioblastoma multiforme, which is implemented based on the method for analyzing the inhibitory effect of glioblastoma multiforme according to any one of claims 1 to 4, characterized in that: It specifically includes: a data acquisition module, which is used to obtain image data of changes in the growth position of glioblastoma multiforme and simultaneously obtain data on changes in the patient's physical condition; a growth feature extraction module, which is used to construct a growth feature extraction model based on the image data of changes in the growth position of glioblastoma multiforme to extract the growth characteristics of glioblastoma multiforme; an inhibition effect analysis module, which is used to analyze the growth inhibition effect based on the extracted growth characteristics of glioblastoma multiforme; a physical abnormality analysis module, which is used to analyze the physical abnormality of the patient during the inhibition process based on the data on changes in the patient's physical condition; and an inhibition effect evaluation module, which is used to evaluate the inhibition effect based on the growth inhibition effect analysis results and the patient's physical abnormality analysis results.

6. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; It is characterized in that the processor executes the method for analyzing the inhibitory effect of glioblastoma multiforme according to any one of claims 1 to 4 by calling the computer program stored in the memory.

7. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer is caused to execute the method for analyzing the inhibitory effect of glioblastoma multiforme according to any one of claims 1 to 4.

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