A data processing device and method for tumor tracing

Abstract

The present invention provides a data processing device and method for tumor tracing, which relate to the technical field of tumor tracing. The device comprises: a tumor primary location existence region analysis module, which is used to analyze a CT image data set of a detection object to obtain a tumor primary location existence region; a pathological slice evaluation value acquisition module, which is used to analyze and obtain pathological slice evaluation values ​​of each sampling position of each organ in the tumor primary location existence region based on the acquired pathological slice data set of each sampling position of each organ in the tumor primary location existence region; a tumor primary organ location analysis module, which is used to analyze and obtain the tumor primary organ location based on the pathological slice evaluation values ​​of each sampling position of each organ in the tumor primary location existence region; and a tumor cell differentiation source determination module, which is used to determine the tumor cell differentiation source of the tumor primary organ location; thus, the accuracy and safety of tumor tracing are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of tumor tracing, and in particular to a data processing device and method for tumor tracing. Background Art

[0002] Tumor origin tracing refers to the analysis of tumor cells, molecular characteristics and genetic variations of tumor tissue to determine the primary organ of the tumor. Tumor cells may be heterogeneous in different parts, that is, tumor cells in different parts may have different genetic variations and expression characteristics. The analysis of tumor tissue can reveal the heterogeneity of tumor cells, which is helpful to determine the primary organ of the tumor. With the continuous advancement of molecular biology technology, such as gene sequencing, genomics, transcriptomics and other technologies, the genetic variations and expression characteristics of tumor cells can be more comprehensively analyzed, providing more information for tumor tracing. Personalized medicine emphasizes the formulation of treatment plans based on the individual genetic characteristics and disease characteristics of the patient. Determining the primary organ of the tumor is crucial for selecting appropriate treatment plans and prognosis assessment. Therefore, research on tumor origin tracing has attracted much attention. The development of precision medicine has made the treatment of tumors more personalized and precise. By determining the primary organ of the tumor, appropriate treatment methods can be selected more specifically to improve treatment outcomes.

[0003] For example, the invention patent with announcement number CN117316286B discloses a data processing method, device and storage medium for tumor tracing, which relates to a data processing method, device and storage medium for tumor tracing. The method includes: obtaining sequencing data to be processed and its corresponding feature vector; the sequencing data to be processed is obtained by genetic sequencing of the target sample using a preset sequencing method, and the target sample is obtained by sampling the target object; the sequencing data to be processed and its corresponding feature vector are input into the first classification model in the pre-trained tracing prediction model to obtain candidate prediction results, and the sequencing data to be processed and its corresponding feature vector are input into the second classification model in the pre-trained tracing prediction model to obtain reference prediction results; the candidate prediction results are corrected according to the reference prediction results to obtain the tracing prediction results; the tracing prediction results are used to characterize the primary site corresponding to the target sample; the primary site is the site in the target object that has an origin relationship with the target sample. This method can improve the accuracy of tumor tracing prediction and reduce prediction costs.

[0004] At present, there are still some shortcomings in the research on a data processing device and method for tumor tracing, which are specifically reflected in the limitation to determining the primary organ of the tumor, ignoring the determination of the primary location of the primary organ of the tumor, and failing to confirm the differentiation source of the tumor cells. If only the primary organ of the tumor is determined but the primary location is not determined, misdiagnosis or mistreatment may occur, because the same type of tumor may have different treatment strategies in different parts. If the primary location is not accurately determined, poor treatment effect may result. The primary location of the tumor is crucial for the selection of treatment plans. Different primary locations may require different surgical, radiotherapy, chemotherapy and other treatment methods. If the primary location is not determined, an inappropriate treatment plan may be selected. The primary location of the tumor and the degree of cell differentiation have an important impact on prognostic evaluation. Failure to determine the primary location and the source of cell differentiation may lead to inaccurate prognosis assessment of tumor patients, affecting the treatment effect and the survival rate of patients. The differentiation source of tumor cells can provide important information about the development and progression of the tumor. Ignoring the confirmation of the differentiation source of tumor cells may miss important biological characteristics and mechanisms, affecting the in-depth understanding of the tumor and the formulation of treatment plans. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a data processing device and method for tumor tracing, which can effectively solve the problems involved in the above-mentioned background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0007] The first aspect of the present invention provides a data processing device for tumor origin tracing, comprising a tumor primary location region analysis module, a pathological slice evaluation value acquisition module, a tumor primary organ location analysis module and a tumor cell differentiation source determination module; wherein:

[0008] The tumor primary location region analysis module is used to obtain a CT image data set of the detection object, analyze the CT image data set of the detection object, and obtain the tumor primary location region through analysis;

[0009] A pathological slice evaluation value acquisition module is used to acquire a pathological slice data set of each sampling position of each organ in the area where the primary position of the tumor exists, and based on the acquired pathological slice data set of each sampling position of each organ in the area where the primary position of the tumor exists, analyze and obtain the pathological slice evaluation value of each sampling position of each organ in the area where the primary position of the tumor exists;

[0010] The tumor primary organ location analysis module is used to analyze and obtain the tumor primary organ location based on the evaluation values ​​of the pathological sections of each organ and each sampling position in the area where the tumor primary location exists;

[0011] The tumor cell differentiation origin determination module is used to determine the tumor cell differentiation origin of the primary organ location of the tumor.

[0012] Optionally, the CT image data set of the detection object specifically includes the total area of ​​tumor shadows in each region of the CT image of the detection object, the number of tumor shadows in each region of the CT image of the detection object, and the maximum area of ​​tumor shadows in each region of the CT image of the detection object.

[0013] Optionally, the tumor primary location area analysis module is specifically used to obtain a CT image data set of the detection object, analyze the CT image data set of the detection object, obtain evaluation values ​​of each area of ​​the CT image of the detection object, and use the evaluation values ​​of each area of ​​the CT image of the detection object as an analysis basis for determining the existence of the tumor primary location; compare the evaluation values ​​of each area of ​​the CT image of the detection object with reference evaluation values ​​of the CT image area of ​​the detection object stored in a database; if the evaluation value of a certain area of ​​the CT image of the detection object is higher than or equal to the reference evaluation value of the CT image area of ​​the detection object, then mark the area of ​​the CT image of the detection object as the tumor primary location area.

[0014] Optionally, the evaluation value of each area of ​​the CT image of the detection object is expressed as:

[0015]

[0016] In the formula, ψ j is the evaluation value of the jth region of the CT image of the detected object, S j YN is the total area of ​​tumor shadow in the jth region of the CT image of the detected object, j is the number of tumor shadows in the jth region of the CT image of the detected object, SM j is the maximum area of ​​the tumor shadow in the jth area of ​​the CT image of the detection object, S0 is the reference area of ​​the tumor shadow in the CT image area of ​​the detection object stored in the database, YN0 is the number of tumor shadow delimitations in the CT image area of ​​the detection object stored in the database, SM0 is the maximum area of ​​tumor shadow delimitations in the CT image area of ​​the detection object stored in the database, μ1 is the compensation factor of the total area of ​​tumor shadow in the CT image area of ​​the detection object, μ2 is the compensation factor of the number of tumor shadows in the CT image area of ​​the detection object, μ3 is the compensation factor of the maximum area of ​​tumor shadow in the CT image area of ​​the detection object, j is the number of each area of ​​the CT image, j=1,2,3,...,j′, j′ is the total number of CT image areas.

[0017] Optionally, the acquired pathological slice data set of each sampling position of each organ in the area where the primary location of the tumor exists specifically includes pathological slice cell data of each sampling position of each organ in the area where the primary location of the tumor exists and pathological slice cell differentiation data of each sampling position of each organ in the area where the primary location of the tumor exists, wherein the pathological slice cell data of each sampling position of each organ in the area where the primary location of the tumor exists includes the number of tumor cells per unit area of ​​the pathological slice of each sampling position of each organ, the tumor cell density of the pathological slice of each sampling position of each organ, the number of tumor cell types in the pathological slice of each sampling position of each organ, and the number of tumor cell mutations per unit area of ​​the pathological slice of each sampling position of each organ, and the pathological slice cell differentiation data of each sampling position of each organ in the area where the primary location of the tumor exists includes the tumor cell differentiation ratio of the pathological slice of each sampling position of each organ and the maximum number of tumor cell differentiation in the pathological slice of each sampling position of each organ.

[0018] Optionally, the pathological section evaluation value acquisition module is specifically used to obtain the pathological section evaluation values ​​of each organ and each sampling position in the area where the primary location of the tumor exists based on the acquired pathological section cell data of each organ and each sampling position in the area where the primary location of the tumor exists and the pathological section cell differentiation data of each organ and each sampling position in the area where the primary location of the tumor exists, and the pathological section evaluation values ​​of each organ and each sampling position in the area where the primary location of the tumor exists are used as the analysis basis for obtaining the primary organ position of the tumor.

[0019] Optionally, the evaluation value of the pathological section of each sampling position of each organ in the area where the primary tumor exists is expressed as:

[0020]

[0021] In the formula, ω hn is the pathological section evaluation value of the nth sampling position of the hth organ in the area where the primary tumor exists, ω hn1 is the cell evaluation value of the pathological section of the nth sampling position of the hth organ in the area where the primary tumor exists, ω hn2 is the cell differentiation assessment value of the pathological section of the nth sampling position of the hth organ in the area where the primary tumor exists, ε1 is the weight factor of the pathological section cell assessment value of the organ sampling position in the area where the primary tumor exists, ε2 is the weight factor of the pathological section cell differentiation assessment value of the organ sampling position in the area where the primary tumor exists, h is the number of each organ, h=1,2,3,...,h′, h′ is the total number of organs, n is the number of each sampling position, n=1,2,3,...,n′, n′ is the total number of sampling positions.

[0022] Optionally, the tumor primary organ location analysis module is specifically used to obtain reference evaluation values ​​of tumor cell expression in each organ stored in a database; obtain pathological section evaluation values ​​of each sampling position of each organ in the area where the tumor primary location exists, and obtain tumor cell characteristic values ​​of each sampling position of each organ in the area where the tumor primary location exists in combination with the reference evaluation values ​​of tumor cell expression in each organ; compare the tumor cell characteristic values ​​of each sampling position of each organ in the area where the tumor primary location exists to obtain the maximum value of the tumor cell characteristic values ​​of each sampling position of each organ in the area where the tumor primary location exists, and the sampling position of a certain organ corresponding to the maximum value of the tumor cell characteristic values ​​of each sampling position of each organ in the area where the tumor primary location exists is the tumor primary organ location.

[0023] Optionally, the tumor cell differentiation origin determination module is specifically used to determine the tumor cell differentiation origin of the primary organ location of the tumor by comparing the tumor cell staining results of the primary organ location of the tumor with the tumor cell differentiation sources of the primary organ location of the tumor corresponding to the tumor cell staining results of each primary organ location of the tumor stored in the database.

[0024] A second aspect of the present invention provides a data processing method for tumor origin tracing, comprising the following steps:

[0025] Acquire a CT image data set of the detection object, analyze the CT image data set of the detection object, and obtain the area where the primary position of the tumor exists through analysis;

[0026] Obtaining a pathological slice dataset of each sampling position of each organ in the area where the primary position of the tumor exists, and analyzing and obtaining an evaluation value of the pathological slice of each sampling position of each organ in the area where the primary position of the tumor exists based on the acquired pathological slice dataset of each sampling position of each organ in the area where the primary position of the tumor exists;

[0027] Based on the evaluation values ​​of the pathological sections of each organ and each sampling position in the area where the primary tumor exists, the primary organ position of the tumor is analyzed;

[0028] The differentiation origin of tumor cells determines the primary organ location of the tumor.

[0029] Compared with the prior art, the above technical solution has at least the following beneficial effects:

[0030] (1) The present invention provides a data processing device and method for tumor tracing. By analyzing the CT image data set of the detection object, the area where the tumor is located is obtained. The CT image data set can provide high-resolution images to help doctors accurately determine the primary location of the tumor, which helps to accurately locate the location of the tumor. By analyzing the CT image data set, the growth and spread of the tumor at the primary location can be evaluated. The CT image data set can provide three-dimensional structural information. By analyzing the area where the tumor is located, the resection range and the irradiation area of ​​radiotherapy can be determined, thereby improving the accuracy and safety of tumor tracing. By continuously analyzing the CT image data set, the changes of the tumor at the primary location can be monitored. By analyzing the CT image data set, the morphological characteristics and biological characteristics of the area where the tumor is located at the primary location can be understood, which helps to conduct in-depth research on the development mechanism and growth law of the tumor and provide a scientific basis for the formulation of new strategies.

[0031] (2) The present invention determines the primary location of the primary organ of the tumor by combining the reference evaluation values ​​of tumor cell expression of each organ. The reference evaluation values ​​of tumor cell expression of each organ are obtained by analyzing the characteristics of tumor cell expression number and differentiation rate of different organs stored in the database. Different organs have different tumor cell expression reference evaluation values. Combining the reference evaluation values ​​of tumor cell expression of each organ helps to determine the primary location of the primary organ of the tumor in a targeted manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0033] Figure 1 A schematic diagram of the structure of a data processing device for tumor tracing provided by the present invention.

[0034] Figure 2 A schematic flow chart of a data processing method for tumor origin tracing provided by the present invention. DETAILED DESCRIPTION

[0035] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0036] like Figure 1 As shown, the first aspect of the present invention provides a data processing device for tumor tracing, including a tumor primary location region analysis module, a pathological slice evaluation value acquisition module, a tumor primary organ location analysis module and a tumor cell differentiation source determination module; wherein,

[0037] The tumor primary location region analysis module is used to obtain a CT image data set of the detection object, analyze the CT image data set of the detection object, and obtain the tumor primary location region through analysis.

[0038] The pathological slice evaluation value acquisition module is used to obtain the pathological slice data set of each sampling position of each organ in the area where the primary location of the tumor exists, and based on the acquired pathological slice data set of each sampling position of each organ in the area where the primary location of the tumor exists, analyze and obtain the pathological slice evaluation value of each sampling position of each organ in the area where the primary location of the tumor exists.

[0039] The tumor primary organ location analysis module is used to analyze and obtain the tumor primary organ location based on the pathological slice evaluation values ​​of each organ and each sampling position in the area where the tumor primary location exists.

[0040] The tumor cell differentiation origin determination module is used to determine the tumor cell differentiation origin of the primary organ location of the tumor.

[0041] Specifically, the tumor primary location area analysis module is specifically used to obtain a CT image data set of the detection object, analyze the CT image data set of the detection object, obtain evaluation values ​​of each area of ​​the CT image of the detection object, and use the evaluation values ​​of each area of ​​the CT image of the detection object as an analysis basis for determining the tumor primary location area; compare the evaluation values ​​of each area of ​​the CT image of the detection object with the reference evaluation values ​​of the CT image area of ​​the detection object stored in the database; if the evaluation value of a certain area of ​​the CT image of the detection object is higher than or equal to the reference evaluation value of the CT image area of ​​the detection object, then mark the area of ​​the CT image of the detection object as the tumor primary location area.

[0042] In a specific embodiment, by analyzing the CT image data set of the detection object, the area where the primary location of the tumor exists is analyzed. The CT image data set can provide high-resolution images to help doctors accurately determine the primary location of the tumor, which helps to accurately locate the location of the tumor. By analyzing the CT image data set, the growth and spread of the tumor at the primary location can be evaluated. The CT image data set can provide three-dimensional structural information. By analyzing the area where the primary location of the tumor exists, the resection range and the irradiation area of ​​radiotherapy can be determined, thereby improving the accuracy and safety of tumor tracing. By continuously analyzing the CT image data set, the changes of the tumor at the primary location can be monitored. By analyzing the CT image data set, the morphological characteristics and biological characteristics of the area where the tumor exists at the primary location can be understood, which helps to conduct in-depth research on the development mechanism and growth law of the tumor and provide a scientific basis for the formulation of new strategies.

[0043] It should be explained that the evaluation values ​​of each area of ​​the CT image of the above-mentioned detection object can not only be obtained by further analysis through the machine learning integration model, using the K-means clustering model to combine the prediction results of multiple basic models to obtain more accurate evaluation values ​​of each area of ​​the CT image of the detection object, but also can be calculated in the following way. The calculation formula for the evaluation values ​​of each area of ​​the CT image of the detection object is:

[0044]

[0045] In the formula, ψ j is the evaluation value of the jth region of the CT image of the detected object, S j YN is the total area of ​​tumor shadow in the jth region of the CT image of the detected object, j is the number of tumor shadows in the jth region of the CT image of the detected object, SM j is the maximum area of ​​the tumor shadow in the jth area of ​​the CT image of the detection object, S0 is the reference area of ​​the tumor shadow in the CT image area of ​​the detection object stored in the database, YN0 is the number of tumor shadow delimitations in the CT image area of ​​the detection object stored in the database, SM0 is the maximum area of ​​tumor shadow delimitations in the CT image area of ​​the detection object stored in the database, μ1 is the compensation factor of the total area of ​​tumor shadow in the CT image area of ​​the detection object, μ2 is the compensation factor of the number of tumor shadows in the CT image area of ​​the detection object, μ3 is the compensation factor of the maximum area of ​​tumor shadow in the CT image area of ​​the detection object, j is the number of each area of ​​the CT image, j=1,2,3,...,j′, j′ is the total number of CT image areas.

[0046] In a specific embodiment, it needs to be explained that the evaluation values ​​of each area of ​​the CT image of the above-mentioned detection object are calculated by the total area of ​​the tumor shadows in each area of ​​the CT image of the detection object, the number of tumor shadows in each area of ​​the CT image of the detection object, and the maximum area of ​​the tumor shadows in each area of ​​the CT image of the detection object. By comprehensively considering factors such as the total area of ​​the tumor shadows in each area of ​​the CT image of the detection object, the number of tumor shadows in each area of ​​the CT image of the detection object, and the maximum area of ​​the tumor shadows in each area of ​​the CT image of the detection object, by comprehensively considering the evaluation values ​​of different aspects, more comprehensive tumor characteristics and property information can be obtained, which helps doctors to more accurately understand the size, number and maximum range of the tumor, thereby providing a more comprehensive basis for tumor tracing, and comprehensively considering multiple factors can better formulate personalized plans, without Comprehensive analysis of the total area, number and maximum area of ​​tumor shadows in the same area can help doctors trace the source of the tumor. Comprehensive consideration of multiple factors can more accurately evaluate the biological behavior and prognosis of the tumor. Comprehensive consideration of multiple factors can provide doctors with a scientific basis to support their judgment and choice in clinical decision-making. By comprehensively considering factors such as the total area, number and maximum area of ​​tumor shadows, we can have a more comprehensive understanding of the development of tumors in different areas, which helps to reveal the growth pattern and evolution process of the tumor, and provide important clues for tracing the development of lesions. Comprehensive consideration of tumor shadow characteristics in different areas can also help evaluate the metastasis of the tumor. By analyzing the distribution and characteristics of tumor shadows, we can understand whether the tumor has metastasized, as well as the possible pathways and extent of metastasis, providing important information for the selection of treatment strategies.

[0047] Furthermore, the acquired pathological section data set of each sampling position of each organ in the area where the primary location of the tumor exists specifically includes pathological section cell data of each sampling position of each organ in the area where the primary location of the tumor exists and pathological section cell differentiation data of each sampling position of each organ in the area where the primary location of the tumor exists, wherein the pathological section cell data of each sampling position of each organ in the area where the primary location of the tumor exists includes the number of tumor cells per unit area of ​​the pathological section of each sampling position of each organ, the tumor cell density of the pathological section of each sampling position of each organ, the number of tumor cell types in the pathological section of each sampling position of each organ, and the number of tumor cell mutations per unit area of ​​the pathological section of each sampling position of each organ, and the pathological section cell differentiation data of each sampling position of each organ in the area where the primary location of the tumor exists includes the tumor cell differentiation ratio of the pathological section of each sampling position of each organ and the highest number of tumor cell differentiation in the pathological section of each sampling position of each organ.

[0048] Specifically, the pathological section evaluation value acquisition module is specifically used to obtain the pathological section evaluation values ​​of each organ and each sampling position in the area where the primary position of the tumor exists based on the acquired pathological section cell data of each organ and each sampling position in the area where the primary position of the tumor exists and the pathological section cell differentiation data of each organ and each sampling position in the area where the primary position of the tumor exists, and the pathological section evaluation values ​​of each organ and each sampling position in the area where the primary position of the tumor exists are used as the analysis basis for obtaining the primary organ position of the tumor.

[0049] It needs to be explained that the above-mentioned comprehensive analysis of the evaluation values ​​of pathological sections of each organ and each sampling position in the area where the primary location of the tumor exists can more accurately determine the primary organ of the tumor. Taking into account the pathological section data of different organs and different sampling positions, multiple aspects of information can be comprehensively considered to obtain a more comprehensive tumor evaluation value, which helps to fully understand the nature and characteristics of the tumor. By obtaining the evaluation values ​​of pathological sections of each organ and each sampling position in the area where the primary location of the tumor exists, a more targeted basis can be provided for personalized tumor tracing. The comprehensive analysis of the evaluation values ​​of pathological sections of each organ and each sampling position in the area where the primary location of the tumor exists provides a scientific basis for doctors to support their judgment and choice in clinical decision-making.

[0050] It should be explained that the evaluation values ​​of the pathological sections of each organ and each sampling position in the area where the primary location of the tumor exists can not only be obtained by further analysis through a machine learning integrated model, using an integrated method such as Graded Boosting Machine or Random Forest to combine the prediction results of multiple basic models to obtain a more accurate evaluation value of the pathological sections of each organ and each sampling position in the area where the primary location of the tumor exists, but can also be calculated in the following way. The calculation formula for the evaluation values ​​of the pathological sections of each organ and each sampling position in the area where the primary location of the tumor exists is:

[0051]

[0052] In the formula, ω hn is the pathological section evaluation value of the nth sampling position of the hth organ in the area where the primary tumor exists, ω hn1 is the cell evaluation value of the pathological section of the nth sampling position of the hth organ in the area where the primary tumor exists, ω hn2is the cell differentiation assessment value of the pathological section of the nth sampling position of the hth organ in the area where the primary tumor exists, ε1 is the weight factor of the pathological section cell assessment value of the organ sampling position in the area where the primary tumor exists, ε2 is the weight factor of the pathological section cell differentiation assessment value of the organ sampling position in the area where the primary tumor exists, h is the number of each organ, h=1,2,3,...,h′, h′ is the total number of organs, n is the number of each sampling position, n=1,2,3,...,n′, n′ is the total number of sampling positions.

[0053] It should be explained that the evaluation values ​​of the pathological sections of each organ and each sampling position in the area where the primary location of the tumor exists are obtained by analyzing the pathological section data sets of each organ and each sampling position in the area where the primary location of the tumor exists, and comprehensively analyzing the pathological section cell data of each organ and each sampling position in the area where the primary location of the tumor exists and the pathological section cell differentiation data of each organ and each sampling position in the area where the primary location of the tumor exists. By analyzing the cell morphological characteristics, the morphological characteristics of tumor cells can be revealed, which is helpful to determine the type and origin of the tumor. The cell morphological characteristics of different organs and sampling positions can provide clues about the source of tumor cells and help trace the primary location of the tumor. Analysis of the differentiation state of tumor cells can evaluate the malignancy and development stage of the tumor. Combining the cell differentiation data of different organs and sampling positions can understand the changes in the differentiation state of tumor cells, which provides important information for judging the development trend and pathological characteristics of the tumor. By comprehensively analyzing the pathological section cell data and differentiation data of each organ and each sampling position, the development path and evolution process of the tumor can be revealed, and the changes in the cell characteristics of the tumor in different organs and positions can be understood, which is helpful to infer the origin and development trajectory of the tumor.

[0054] It should be explained that the above-mentioned pathological section cell evaluation values ​​of each organ and each sampling position in the area where the primary location of the tumor exists are used to evaluate the cells of the pathological sections of each organ and each sampling position in the area where the primary location of the tumor exists, as the analysis basis for analyzing the location of the primary organ of the tumor. The pathological section cell evaluation values ​​of each organ and each sampling position in the area where the primary location of the tumor exists can not only be further analyzed by the machine learning integrated model, using the K-means clustering model, the support vector machine model or CatBoost, combining the prediction results of multiple basic models to obtain more accurate pathological section cell evaluation values ​​of each organ and each sampling position in the area where the primary location of the tumor exists, but also can be calculated in the following way. The calculation formula for the pathological section cell evaluation values ​​of each organ and each sampling position in the area where the primary location of the tumor exists is:

[0055]

[0056] In the formula, ω hn1ZN is the cell evaluation value of the pathological section of the nth sampling position of the hth organ in the area where the primary tumor exists. hn is the number of tumor cells per unit area of ​​the pathological section at the nth sampling position of the hth organ, ρ hn is the tumor cell density of the pathological section of the hth organ at the nth sampling position, ZL hn is the number of tumor cell types in the pathological section of the hth organ at the nth sampling position, TN hn is the number of tumor cell mutations per unit area of ​​the pathological section at the nth sampling position of the hth organ, ZN0 is the defined number of tumor cells per unit area of ​​the pathological section at the organ sampling position stored in the database, ρ0 is the reference density of tumor cells in the pathological section at the organ sampling position stored in the database, ZL0 is the defined number of tumor cell types in the pathological section at the organ sampling position stored in the database, TN0 is the defined number of tumor cell mutations per unit area of ​​the pathological section at the organ sampling position stored in the database, σ1 is the compensation factor for the set number of tumor cells per unit area of ​​the pathological section at the organ sampling position, σ2 is the compensation factor for the set density of tumor cells in the pathological section at the organ sampling position, σ3 is the compensation factor for the set number of tumor cell types in the pathological section at the organ sampling position, σ4 is the compensation factor for the set number of tumor cell mutations per unit area of ​​the pathological section at the organ sampling position, h is the number of each organ, h=1,2,3,...,h′, h′ is the total number of organs, n is the number of each sampling position, n=1,2,3,...,n′, n′ is the total number of sampling positions, and e is a natural constant.

[0057] It should be explained that the cell evaluation value of the pathological sections of each organ and each sampling position in the area where the above-mentioned primary tumor exists is calculated by the number of tumor cells per unit area of ​​the pathological sections of each sampling position of each organ, the tumor cell density of the pathological sections of each sampling position of each organ, the number of tumor cell types of the pathological sections of each sampling position of each organ, and the number of tumor cell mutations per unit area of ​​the pathological sections of each sampling position of each organ. By comprehensively considering factors such as the number of tumor cells per unit area of ​​the pathological sections of each sampling position of each organ, the tumor cell density of the pathological sections of each sampling position of each organ, the number of tumor cell types of the pathological sections of each sampling position of each organ, and the number of tumor cell mutations per unit area of ​​the pathological sections of each sampling position of each organ, the evaluation value of the cell evaluation value of the pathological sections of each sampling position of each organ and the area where the primary tumor exists is calculated by comprehensively considering factors such as the number of tumor cells per unit area of ​​the pathological sections of each sampling position of each organ, the tumor cell density of the pathological sections of each sampling position of each organ, the number of tumor cell types of the pathological sections of each sampling position of each organ, and the number of tumor cell mutations per unit area of ​​the pathological sections of each sampling position of each organ. Multiple factors such as the number of tumor cells and the number of mutations can be used to achieve multi-dimensional analysis of tumor cell characteristics, which helps to more comprehensively understand the performance of tumor cells in different organs and sampling locations, and provide more information and clues for tracing the origin of the tumor. Comprehensive consideration of cell evaluation values ​​can help accurately locate the primary location of the tumor. By analyzing the cell data of different organs and sampling locations, the distribution and characteristics of tumor cells can be determined, which helps to accurately find the primary focus of the tumor. Comprehensive consideration of factors such as the number, density, number of types and number of mutations of tumor cells can infer the development path and evolution process of the tumor. Understanding the performance of tumor cells in different situations can reveal the origin and development trajectory of the tumor. Through comprehensive analysis of cell evaluation values, a basis can be provided for formulating personalized strategies.

[0058] It should be explained that the cell differentiation evaluation values ​​of the pathological sections of each sampling position of each organ in the region where the primary location of the tumor exists are used to evaluate the degree of cell differentiation of the pathological sections of each sampling position of each organ in the region where the primary location of the tumor exists, as the basis for analyzing the location of the primary organ of the tumor. The cell differentiation evaluation values ​​of the pathological sections of each sampling position of each organ in the region where the primary location of the tumor exists can not only be further analyzed by the machine learning integrated model, using random forest or LightGBM, combining the prediction results of multiple basic models to obtain more accurate cell differentiation evaluation values ​​of the pathological sections of each sampling position of each organ in the region where the primary location of the tumor exists, but can also be calculated in the following way. The calculation formula for the cell differentiation evaluation values ​​of the pathological sections of each sampling position of each organ in the region where the primary location of the tumor exists is:

[0059]

[0060] In the formula, ω hn2 FH is the cell differentiation assessment value of the pathological section of the nth sampling position of the hth organ in the area where the primary tumor exists. hn FN is the tumor cell differentiation ratio of the pathological section of the hth organ at the nth sampling position, hnis the highest number of tumor cell differentiation in the pathological section of the hth organ at the nth sampling position, FH0 is the defined ratio of tumor cell differentiation in the pathological section of the organ sampling position stored in the database, FN0 is the reference number of tumor cell differentiation in the pathological section of the organ sampling position stored in the database, τ1 is the compensation factor of the set tumor cell differentiation ratio in the pathological section of the organ sampling position, τ2 is the compensation factor of the highest number of tumor cell differentiation in the pathological section of the organ sampling position, h is the number of each organ, h=1,2,3,...,h′, h′ is the total number of organs, n is the number of each sampling position, n=1,2,3,...,n′, n′ is the total number of sampling positions, and e is a natural constant.

[0061] It should be explained that the cell differentiation assessment values ​​of the pathological sections of each organ and each sampling position in the area where the above-mentioned primary tumor exists are calculated by the tumor cell differentiation ratio of the pathological sections of each sampling position of each organ, and the highest number of tumor cell differentiation in the pathological sections of each sampling position of each organ. Taking into account factors such as the tumor cell differentiation ratio of the pathological sections of each sampling position of each organ, and the highest number of tumor cell differentiation in the pathological sections of each sampling position of each organ, the degree of differentiation of tumor cells reflects the degree of difference between them and normal cells. The higher the degree of differentiation, the more similar the tumor cells are to normal cells. By analyzing the differentiation ratio and the highest number of differentiation of tumor cells, we can understand the differentiation state of tumor cells, which is helpful to determine the type and source of the tumor. The differentiation degree of tumor cells is closely related to the growth rate and invasiveness of the tumor. Tumors with low differentiation usually grow faster and are more invasive, while tumors with high differentiation grow slowly and are less invasive. By analyzing the differentiation ratio and the highest number of differentiation of tumor cells, we can predict the prognosis of the tumor, which is helpful to confirm personalized plans.

[0062] Specifically, the tumor primary organ location analysis module is specifically used to obtain reference evaluation values ​​of tumor cell expression in each organ stored in a database; obtain pathological section evaluation values ​​of each sampling position of each organ in the area where the tumor primary location exists, and obtain tumor cell characteristic values ​​of each sampling position of each organ in the area where the tumor primary location exists in combination with the reference evaluation values ​​of tumor cell expression in each organ; compare the tumor cell characteristic values ​​of each sampling position of each organ in the area where the tumor primary location exists, and obtain the maximum value of the tumor cell characteristic values ​​of each sampling position of each organ in the area where the tumor primary location exists, and the sampling position of a certain organ corresponding to the maximum value of the tumor cell characteristic values ​​of each sampling position of each organ in the area where the tumor primary location exists is the tumor primary organ location.

[0063] It should be explained that the specific process of combining the evaluation values ​​of the pathological sections of each sampling position of each organ in the area where the primary tumor exists with the reference evaluation values ​​of the tumor cell expression of each organ to obtain the characteristic values ​​of the tumor cells of each sampling position of each organ in the area where the primary tumor exists is as follows: In the formula, Ehn is the characteristic value of tumor cells at the nth sampling position of the hth organ in the area where the primary tumor exists, ω hn is the pathological section evaluation value of the nth sampling position of the hth organ in the area where the primary tumor exists, ω h0 is the reference evaluation value of tumor cell expression in the hth organ, β is the correction factor of the tumor cell characteristic value at each sampling position of each organ in the area where the primary tumor exists, h is the number of each organ, h=1,2,3,...,h′, h′ is the total number of organs, n is the number of each sampling position, n=1,2,3,...,n′, n′ is the total number of sampling positions.

[0064] It should be explained that the characteristic values ​​of tumor cells at each sampling position in each organ in the area where the primary location of the above-mentioned tumor exists serve as the analysis basis for confirming the location of the primary organ of the tumor. The characteristic values ​​of tumor cells at each sampling position in each organ in the area where the primary location of the tumor exists can also be obtained through support vector machine model analysis. By combining the pathological section evaluation values ​​of each organ and each sampling position with the reference evaluation values ​​of tumor cell expression in each organ, the primary location of the tumor can be located more accurately. By comprehensively considering the characteristic values ​​of tumor cells at each sampling position in each organ, it can help reveal the metastatic pathway of the tumor and provide a reference for predicting the metastatic risk of the tumor.

[0065] It should be explained that the above-mentioned determination of the primary location of the primary organ of the tumor by combining the reference evaluation values ​​of tumor cell expression of each organ is the reference evaluation values ​​of tumor cell expression of each organ obtained by analyzing the characteristics such as the number of tumor cells expressed and the differentiation rate of different organs stored in the database. Different organs have different reference evaluation values ​​of tumor cell expression. Combining the reference evaluation values ​​of tumor cell expression of each organ helps to determine the primary location of the primary organ of the tumor in a targeted manner.

[0066] Specifically, the tumor cell differentiation origin determination module is specifically used to determine the tumor cell differentiation origin at the primary organ location of the tumor by comparing the tumor cell staining results at the primary organ location of the tumor with the tumor cell differentiation sources at the primary organ location of the tumor corresponding to the tumor cell staining results at the primary organ locations of each tumor stored in the database.

[0067] In a specific embodiment, immunohistochemical staining technology can be used to detect tumor cells at the primary organ location of the tumor, detect the expression of specific proteins on the surface or inside of the tumor cells at the primary organ location of the tumor, and obtain the staining results of the tumor cells at the primary organ location of the tumor.

[0068] In a specific embodiment, the differentiation origin of tumor cells is confirmed by utilizing immunohistochemical staining technology. The specific antigen expression of tumor cells can be detected by immunohistochemical staining technology, which helps to determine the type of tumor. Confirming the differentiation origin of tumor cells by immunohistochemical staining technology can provide important information for doctors. The degree of differentiation of tumor cells is usually related to the aggressiveness of the tumor. Confirming the differentiation origin of tumor cells by immunohistochemical staining technology can help evaluate the aggressiveness of the tumor. In some cases, the histological type of the tumor may not be clear enough. Immunohistochemical staining technology can provide additional information, providing important information and support for personalized tumor tracing and patient management.

[0069] Reference Figure 2 As shown, a second aspect of an embodiment of the present invention provides a data processing method for tumor origin tracing, comprising:

[0070] Acquire a CT image data set of the detection object, analyze the CT image data set of the detection object, and obtain the area where the primary position of the tumor exists through analysis;

[0071] Obtaining a pathological slice dataset of each sampling position of each organ in the area where the primary position of the tumor exists, and analyzing and obtaining an evaluation value of the pathological slice of each sampling position of each organ in the area where the primary position of the tumor exists based on the acquired pathological slice dataset of each sampling position of each organ in the area where the primary position of the tumor exists;

[0072] Based on the evaluation values ​​of the pathological sections of each organ and each sampling position in the area where the primary tumor exists, the primary organ position of the tumor is analyzed;

[0073] The differentiation origin of tumor cells determines the primary organ location of the tumor.

[0074] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A data processing device for tumor tracing, characterized in that: It includes a tumor primary location area analysis module, a pathological section evaluation value acquisition module, a tumor primary organ location analysis module and a tumor cell differentiation source determination module; among which: The tumor primary location region analysis module is used to obtain a CT image data set of the detection object, analyze the CT image data set of the detection object, and obtain the tumor primary location region through analysis; The CT image data set of the detection object specifically includes the total area of ​​tumor shadows in each region of the CT image of the detection object, the number of tumor shadows in each region of the CT image of the detection object, and the maximum area of ​​tumor shadows in each region of the CT image of the detection object; The tumor primary location region analysis module is specifically used to obtain a CT image data set of the detection object, analyze the CT image data set of the detection object, obtain evaluation values ​​of each region of the CT image of the detection object, and use the evaluation values ​​of each region of the CT image of the detection object as an analysis basis for determining the tumor primary location region; Comparing the evaluation values ​​of each region of the CT image of the detection object with the reference evaluation values ​​of the CT image region of the detection object stored in the database; If the evaluation value of a certain area of ​​the CT image of the detected object is higher than or equal to the reference evaluation value of the area of ​​the CT image of the detected object, the area of ​​the CT image of the detected object is marked as an area where the primary location of the tumor exists; The evaluation values ​​of each region of the CT image of the detection object are expressed as: In the formula, Ψ j is the evaluation value of the jth region of the CT image of the detected object, S j YN is the total area of ​​tumor shadow in the jth region of the CT image of the detected object, j is the number of tumor shadows in the jth region of the CT image of the detected object, SM j is the maximum area of ​​the tumor shadow of the jth area of ​​the CT image of the detection object, S0 is the reference area of ​​the tumor shadow of the CT image area of ​​the detection object stored in the database, YN0 is the number of tumor shadows defined in the CT image area of ​​the detection object stored in the database, SM0 is the maximum area of ​​tumor shadows defined in the CT image area of ​​the detection object stored in the database, μ1 is the compensation factor of the total area of ​​tumor shadows in the CT image area of ​​the detection object, μ2 is the compensation factor of the number of tumor shadows in the CT image area of ​​the detection object, μ3 is the compensation factor of the maximum area of ​​tumor shadows in the CT image area of ​​the detection object, j is the number of each area of ​​the CT image, j=1,2,3,......,j', j' is the total number of CT image areas; A pathological slice evaluation value acquisition module is used to acquire a pathological slice data set of each sampling position of each organ in the area where the primary position of the tumor exists, and based on the acquired pathological slice data set of each sampling position of each organ in the area where the primary position of the tumor exists, analyze and obtain the pathological slice evaluation value of each sampling position of each organ in the area where the primary position of the tumor exists; The acquired pathological slice data set of each sampling position of each organ in the area where the primary position of the tumor exists specifically includes pathological slice cell data of each sampling position of each organ in the area where the primary position of the tumor exists and pathological slice cell differentiation data of each sampling position of each organ in the area where the primary position of the tumor exists, wherein the pathological slice cell data of each sampling position of each organ in the area where the primary position of the tumor exists includes the number of tumor cells per unit area of ​​the pathological slice of each sampling position of each organ, the tumor cell density of the pathological slice of each sampling position of each organ, the number of tumor cell types of the pathological slice of each sampling position of each organ, and the number of tumor cell mutations per unit area of ​​the pathological slice of each sampling position of each organ, and the pathological slice cell differentiation data of each sampling position of each organ in the area where the primary position of the tumor exists includes the tumor cell differentiation ratio of the pathological slice of each sampling position of each organ and the highest number of tumor cell differentiation of the pathological slice of each sampling position of each organ; The pathological slice evaluation value acquisition module is specifically used to obtain the pathological slice evaluation value of each organ and each sampling position in the area where the primary position of the tumor exists based on the acquired pathological slice cell data of each organ and each sampling position in the area where the primary position of the tumor exists and the pathological slice cell differentiation data of each organ and each sampling position in the area where the primary position of the tumor exists, and the pathological slice evaluation value of each organ and each sampling position in the area where the primary position of the tumor exists is used as the analysis basis for analyzing the primary organ position of the tumor; The evaluation value of the pathological sections of each sampling position of each organ in the area where the primary tumor exists is expressed as: In the formula, ω hn is the pathological section evaluation value of the nth sampling position of the hth organ in the area where the primary tumor exists, ω hn1 is the cell evaluation value of the pathological section of the nth sampling position of the hth organ in the area where the primary tumor exists, ω hn2 is the cell differentiation evaluation value of the pathological section of the nth sampling position of the hth organ in the area where the primary tumor exists, ε1 is the weight factor of the cell differentiation evaluation value of the pathological section of the organ sampling position in the area where the primary tumor exists, ε2 is the weight factor of the cell differentiation evaluation value of the pathological section of the organ sampling position in the area where the primary tumor exists, h is the number of each organ, h=1,2,3,......,h', h' is the total number of organs, n is the number of each sampling position, n=1,2,3,......,n', n' is the total number of sampling positions; The tumor primary organ location analysis module is used to analyze and obtain the tumor primary organ location based on the evaluation values ​​of the pathological sections of each organ and each sampling position in the area where the tumor primary location exists; The tumor primary organ location analysis module is specifically used to obtain the reference evaluation value of tumor cell expression in each organ stored in the database; Obtain the pathological section evaluation value of each sampling position of each organ in the area where the primary tumor exists, and combine it with the reference evaluation value of tumor cell expression in each organ to obtain the characteristic value of tumor cells in each sampling position of each organ in the area where the primary tumor exists; Compare the tumor cell characteristic values ​​of each sampling position of each organ in the area where the primary tumor position exists, and obtain the maximum value of the tumor cell characteristic values ​​of each sampling position of each organ in the area where the primary tumor position exists. The sampling position of a certain organ corresponding to the maximum value of the tumor cell characteristic values ​​of each sampling position of each organ in the area where the primary tumor position exists is the primary organ position of the tumor; A tumor cell differentiation origin determination module is used to determine the tumor cell differentiation origin of the primary organ of the tumor; The tumor cell differentiation origin determination module is specifically used to determine the tumor cell differentiation origin at the primary organ location of the tumor by comparing the tumor cell staining results at the primary organ location of the tumor with the tumor cell differentiation origins at the primary organ location of the tumor corresponding to the tumor cell staining results at the primary organ locations of each tumor stored in the database.

2. A data processing method for tumor tracing, the method being based on the device according to claim 1, characterized in that: The following steps are involved: Acquire a CT image data set of the detection object, analyze the CT image data set of the detection object, and obtain the area where the primary position of the tumor exists through analysis; Obtaining a pathological slice dataset of each sampling position of each organ in the area where the primary position of the tumor exists, and analyzing and obtaining an evaluation value of the pathological slice of each sampling position of each organ in the area where the primary position of the tumor exists based on the acquired pathological slice dataset of each sampling position of each organ in the area where the primary position of the tumor exists; Based on the evaluation values ​​of the pathological sections of each organ and each sampling position in the area where the primary tumor exists, the primary organ position of the tumor is analyzed; The differentiation origin of tumor cells determines the primary organ location of the tumor.

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