CN117373545A - Intestinal cancer chemoradiotherapy prediction scoring model, application, system and construction method thereof - Google Patents
Intestinal cancer chemoradiotherapy prediction scoring model, application, system and construction method thereof Download PDFInfo
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Abstract
The invention discloses a prediction scoring model for intestinal cancer chemoradiotherapy, application, a system and a construction method thereof, and the technical scheme is characterized in that fluorescent expression intensities of a plurality of biomarkers in tumor tissues of the intestinal cancer and outside the tumor tissues are obtained, wherein the biomarkers comprise: panCK, CD20 and MHC-II; obtaining the positive rate of the biomarkers in the tumor tissue, outside the tumor tissue and inside and outside the tumor tissue according to the fluorescence expression intensity; screening out characteristic factors from all positive rates by using a stepwise regression method, and constructing a scoring model according to the characteristic factors; the characteristic factors comprise the overall double positive rate of MHC-II and CD20 inside and outside tumor tissues, the double positive rate of MHC-II and CD20 inside tumor tissues, the single positive rate of MHC-II outside tumor tissues, the single positive rate of CD20 outside tumor tissues and the double positive rate of MHC-II and CD20 outside tumor tissues. The invention combines the positive rate of the biomarkers in and out of the tumor tissue, and can construct a scoring model with better scoring prediction effect on the chemoradiotherapy of the intestinal cancer.
Description
Technical Field
The invention belongs to the field of biomedicine, and particularly relates to a prediction scoring model for intestinal cancer chemoradiotherapy, application, a system and a construction method thereof.
Background
Colorectal cancer (CRC) is the third most fatal malignancy worldwide, with up to 50% of CRC patients eventually metastasizing, resulting in poor prognosis. The radiotherapy and chemotherapy in the current treatment of the locally advanced colorectal cancer is a standard treatment mode, and has the advantages of controlling and treating the tumor on one hand, reducing the tumor body on the other hand, facilitating the subsequent operation and the anus protection treatment, and improving the survival quality of patients after the treatment. The method has the defects that 20-40% of patients are insensitive to radiotherapy and chemotherapy, so that treatment resources are wasted, and the patients are additionally subjected to economic pressure and physical and psychological burden. Therefore, developing a suitable biomarker for distinguishing people who can respond to radiotherapy and chemotherapy in patients with locally advanced colorectal cancer has great significance.
The existing biomarkers are not enough to distinguish the chemoradiotherapy response population. New markers require analysis of large sample volumes of clinical data and in-depth knowledge of the disease, as well as expansion of the field of intrinsic knowledge by new technologies. In recent years many newly discovered biomarkers, in particular immunotherapeutic biomarkers, have been associated with the tumor immune microenvironment (tumor microenvironment, TME). The tumor immune microenvironment is a complex comprehensive system, mainly composed of tumor cells, surrounding immune cells, tumor-related fibroblasts, nearby interstitial tissues, microvessels, various cytokines, chemokines and the like, and the occurrence and development of tumors are closely related to the tumor cells and tumor immune microenvironment as well.
The many biomarker interactions that exist within TMEs are interrelated, new spatial genomic and spatial proteomic techniques push the biomarker discovery of TMEs to new heights, and mRNA and protein of tissue specimens can be mapped and quantified by means of multiplex in situ hybridization and multiplex immunohistochemistry, and these indices are statistically analyzed. For a tissue sample of intestinal cancer, not only the distribution condition of the biomarker in the tissue of the intestinal cancer is analyzed, but also the distribution condition of the biomarker in the interstitium is further considered, and the effective biomarker is searched for carrying out the effect prediction by combining the effect difference of the medicines.
Traditional immunohistochemical staining/immunofluorescence staining (i m m u n o h i s t o c h e m i s t r y/immunofluorescence staining, IHC/IF) is the most commonly used detection method for TME studies at present, plays a vital role in the evaluation of pathological types and biomarkers of intestinal cancer, can assist clinicians in making therapeutic decisions timely and accurately, and still has a number of limitations.
Disclosure of Invention
The invention aims to provide a grading model, application, a system and a construction method for predicting grading of intestinal cancer chemoradiotherapy, and can construct a grading model with better effect of predicting therapeutic response of intestinal cancer chemoradiotherapy by combining positive rates of biomarkers in tumor tissues and out of tumor tissues.
The first aspect of the invention provides a method for constructing a prediction scoring model for chemoradiotherapy of intestinal cancer, which comprises the following steps:
obtaining fluorescence expression intensities of a plurality of biomarkers within and outside tumor tissue of a bowel cancer, wherein the biomarkers comprise: panCK, CD20 and MHC-II;
obtaining the positive rate of the biomarkers in the tumor tissue, outside the tumor tissue and inside and outside the tumor tissue according to the fluorescence expression intensity;
screening out characteristic factors from all positive rates by using a stepwise regression method, and constructing a scoring model according to the characteristic factors;
wherein the characteristic factors comprise MHC2_cd20, MHC2_cd20_turner, mhc2_back, cd20_back and MHC2_cd20_back, wherein MHC2_cd20 represents the overall double positive rate of MHC-II and CD20 inside and outside tumor tissues, MHC2_cd20_turner represents the double positive rate of MHC-II and CD20 inside tumor tissues, MHC2_back represents the single positive rate of MHC-II outside tumor tissues, cd20_back represents the single positive rate of CD20 outside tumor tissues, and MHC2_cd20_back represents the double positive rate of MHC-II and CD20 outside tumor tissues.
Optionally, the formula of the scoring model is:
，
and the Type represents a predictive value, and if the predictive value is larger than a preset threshold value, the response of the intestinal cancer patient is judged to be good, and if the predictive value is not larger than the preset threshold value, the response of the intestinal cancer patient is judged to be poor.
By establishing a corresponding ROC curve for the scoring model, calculating the AUC value of the ROC curve, the corresponding AUC value of the scoring model is 0.91 and is close to 1, and the scoring model is good in performance and good in prediction effect.
Optionally, the step regression method is used to screen out feature factors from all positive rates, including:
fitting all positive rates through a binomial distribution method of a generalized linear model to obtain a plurality of first models to be selected;
establishing corresponding first ROC curves for the first to-be-selected models, and calculating corresponding first AUC values of the first ROC curves;
selecting a first model to be selected with the highest first AUC value as a primary model;
gradually removing the positive rate from the primary model, checking whether the primary model has significant change after removing, if so, reserving the positive rate, and if not, removing the positive rate until all positive rates with significant change on the primary model are reserved as characteristic factors.
The first AUC value corresponding to each first model to be selected is calculated, then the primary model is determined according to the first AUC value, and the first model to be selected with the highest AUC value is taken as the primary model as the best AUC value is indicated to be better in performance of the model, the best first model to be selected can be objectively screened as the primary model, after the primary model is determined, stepwise regression analysis is carried out on the primary model to obtain a scoring model, and the AUC value corresponding to the primary model can be compared with the AUC value corresponding to the scoring model to judge whether the performance of the scoring model is better than that of the primary model.
Optionally, the constructing a scoring model according to the feature factors includes:
fitting all the characteristic factors through a binomial distribution method of a generalized linear model to obtain a plurality of second candidate models;
establishing a corresponding second ROC curve for each second model to be selected, and calculating a corresponding second AUC value of each second ROC curve;
and selecting a second candidate model with the highest second AUC value as a scoring model.
And calculating second AUC values corresponding to the second candidate models, and determining a scoring model according to the second AUC values, wherein the second candidate model with the highest AUC value is taken as a primary model as the better performance of the model is explained due to the fact that the AUC values are approximately close to 1, so that the best second candidate model can be objectively screened out to be taken as the scoring model.
Optionally, the obtaining the positive rate of the biomarker in the tumor tissue, outside the tumor tissue and inside and outside the tumor tissue according to the fluorescence expression intensity includes:
comparing the fluorescence expression intensity of the cells of each biomarker in the tumor tissue of the intestinal cancer with a corresponding preset intensity threshold value, and judging the cells as positive cells corresponding to the biomarkers in the tumor tissue under the condition that the fluorescence expression intensity of the cells of the biomarkers in the tumor tissue of the intestinal cancer is greater than the corresponding intensity threshold value, so as to obtain the single positive rate of each biomarker in the tumor tissue of the intestinal cancer and the double positive rate of the biomarkers in the tumor tissue of the intestinal cancer;
Comparing the fluorescence expression intensity of the cells of each biomarker outside the tumor tissue of the intestinal cancer with a corresponding preset intensity threshold value, and judging the cells as positive cells corresponding to the biomarkers outside the tumor tissue under the condition that the fluorescence expression intensity of the cells of each biomarker outside the tumor tissue of the intestinal cancer is greater than the corresponding intensity threshold value, so as to obtain the single positive rate of each biomarker outside the tumor tissue of the intestinal cancer and the double positive rate of each biomarker inside the tumor tissue of the intestinal cancer;
and obtaining the double positive rate of the biomarker according to the single positive rate of each biomarker in the tumor tissue of the intestinal cancer and the single positive rate of each biomarker outside the tumor tissue of the intestinal cancer.
By comparing the fluorescence expression intensity of each biomarker in the cells with a corresponding preset intensity threshold, whether the cells are positive cells or not is conveniently judged, so that the single positive rate of each biomarker and the double positive rate of the biomarker are calculated.
The second aspect of the invention provides a prediction scoring model for intestinal cancer chemoradiotherapy, which is formed by the construction method.
The third aspect of the invention provides an application of the scoring model in construction of a prediction scoring system or device for chemoradiotherapy of intestinal cancer, and response of an intestinal cancer patient is predicted according to a prediction score calculated by the scoring model.
The fourth aspect of the present invention provides a prediction scoring system for chemoradiotherapy of intestinal cancer, comprising: the grading model is used for predicting the treatment response of the intestinal cancer chemoradiotherapy by taking the positive rate of the biomarker as an input variable.
A fifth aspect of the invention provides a computer device comprising a memory storing a computer program and a processor implementing the steps of the method described above when the processor executes the computer program.
A sixth aspect of the invention provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the method described above.
The technical scheme provided by the invention has the following advantages and effects: combining the positive rates of the three biomarkers of PanCK, CD20 and MHC-II in and out of tumor tissues, and then adopting a stepwise regression method to screen the positive rates of all the biomarkers to obtain characteristic factors, and constructing a scoring model according to the characteristic factors so as to ensure that the prediction effect of the obtained scoring model is better.
Drawings
FIG. 1 is a flow chart of a method for constructing a prediction scoring model for intestinal cancer chemoradiotherapy provided by the invention;
FIG. 2 is a fluorescent staining pattern of PanCK provided by the present invention;
FIG. 3 is a bright field and negative control plot of PanCK provided by the present invention;
FIG. 4 is a fluorescent staining pattern of MHC-II provided by the present invention;
FIG. 5 is a bright field and negative control of MHC-II provided by the present invention;
FIG. 6 is a fluorescent staining pattern of CD20 provided by the present invention;
FIG. 7 is a bright field and negative control plot of CD20 provided by the present invention;
FIG. 8 is a graph of PanCK and MHC-II double positive rates provided by the present invention;
FIG. 9 is a graph of MHC-II and CD20 double positive rates provided by the present invention;
FIG. 10 is a tissue duty cycle scatter plot of MHC-II provided by the present invention;
FIG. 11 is a plot of tissue occupancy scatter for CD20 provided by the present invention;
FIG. 12 is a positive display in tumor tissue of MHC-II provided by the present invention;
FIG. 13 is a positive display of MHC-II tumor tissue provided by the present invention;
FIG. 14 is a diagram showing the difference between Response classes and Non-Response classes provided by the present invention;
FIG. 15 is a ROC graph of a primary model provided by the present invention;
FIG. 16 is a ROC graph of a scoring model provided by the present invention;
fig. 17 is an internal structural diagram of a computer device provided by the present invention.
Detailed Description
In order that the invention may be readily understood, a more particular description of specific embodiments thereof will be rendered by reference to specific embodiments that are illustrated in the appended drawings.
As used herein, the terms "first and second …" are used merely to distinguish between names and not to represent a particular number or order unless otherwise specified or defined.
The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items, unless specifically stated or otherwise defined.
The term "fixed" or "connected" as used herein may be directly fixed or connected to an element, or indirectly fixed or connected to an element.
The invention analyzes three marks and four colors of intestinal cancer, wherein the three marks and four colors refer to three biomarkers including PanCK, CD20 and MHC-II, and a DAPI is added as a background for cell identification to form four colors. The analysis method covers main immune cells such as B cells, T cells, macrophages and the like, and is added with PanCK cancer cells, MHC-II is a biomarker related to intestinal cancer, and is a critical ring of tumor immunity. Interactions between MHC-II and T cells are necessary to maintain intestinal immune homeostasis, and normal interactions between MHC-II and T cells can maintain a type 1 immune response within TME (tumor microenvironment ) to limit colon cancer progression and invasion. Tumor-infiltrating cd20+ B lymphocytes exhibit a positive effect in colorectal cancer (CRC, colorectal cancer) patient response. In addition, cd20+ B lymphocytes and cytotoxic T lymphocytes have a synergistic effect in the response of CRC patients, which gives important clues to the response of colon cancer and also helps to mine new therapeutic targets. Through the analysis of polychromatic immunohistochemical three-mark four-color, the single positive rate can be obtained, and after tissue identification, the single positive rate of the biomarker inside and outside the tissue is obtained. Meanwhile, the double positive rate between the interesting biomarkers can be obtained, data conversion and statistical analysis are carried out according to the single positive rate and the double positive rate, the conditions of the biomarkers between two groups with different curative effects in different areas are obtained, and the specific conditions of intestinal cancer immune microenvironment can be displayed more intuitively.
Branch fluorescence TSA is a brand new fluorescent signal amplification technology, and TSA is short for tyramine signal amplification technology (Tyramide Signal Amplification). Using the "< primary-secondary-HRP > substrate" principle and procedure based on Immunohistochemistry (IHC), the TSA fluorescent compound binds to tyrosine residues in the vicinity of the target under the catalysis of horseradish peroxidase (HRP) to generate a stable fluorescent compound. Compared with common immunofluorescence, the TSA technology can amplify signals by 10-1000 times, and fluorescent compounds have ideal light, heat and pH stability, so that the multi-color fluorescent markers are not limited by primary antibody species. Compared with large-scale cell detection and single-cell transcription analysis, the method not only can enable the tumor sample to display the relationship between tumor cells and tumor immune microenvironment under better resolution, but also has the advantages of relatively mature technical principle and more convenient operation, and has particular advantages in the current clinical medical research. The TSA technique used may reduce the drawbacks of the experiment compared to other methods.
An embodiment provides a method for constructing a prediction scoring model for radiotherapy and chemotherapy of intestinal cancer, as shown in fig. 1, including:
step 100, obtaining fluorescence expression intensities of a plurality of biomarkers in tumor tissues of intestinal cancer and outside the tumor tissues, wherein the biomarkers comprise: panCK, CD20 and MHC-II;
Specifically, a sample, namely tumor tissue inner cells and tumor tissue outer cells of intestinal cancer, is stained and sealed by using PanCK, CD20 and MHC-II antibodies and using a TSA staining technology to obtain a stained slide, the specific antibody information is shown in Table 1,
TABLE 1
The HLA-DRDPDQ is a specific name of an MHC-II antibody, then the obtained stained slide is scanned by using tissue quest software, a scanned picture is obtained from the tissue quest software, a picture file is imported into Strataquest analysis software to generate an analysis file, and after the analysis file enters a main interface, an ROI is used as a preset mode according to a region selected by a pathologist to enter the analysis interface.
According to experimental records, the setting parameters are shown in table 2, corresponding fluorescence channels are found, and the fluorescence parameters of the 4 channel targets are sequentially converted from 16 bits to 8 bits.
TABLE 2
Wherein, type1 represents a first scan parameter, in other embodiments, other scan parameters can be set, 440N represents 440 channels, 470SN represents 470 frequency multiplication channels, 520N represents 520 channels, 650N represents 650 channels, and in order to identify nuclei first, parameter setting is performed: the fluorescence range is set to-1 to 256; masks are then identified (CD 20 for cell membrane recognition; MHC-II, panCK for cell membrane and cytoplasmic recognition; e.g. channel 440, panCK, parameter set-fluorescence range set-1 to 256, maximum growth steps (Max Growing Steps) set 7.5.
Three regions were then set by StrataQuest software: backspace (representing background area), stroma (representing extra-tumor tissue area), objects (representing intra-tumor tissue area); and the organization is optimized, and the StrataQuest software obtains the organization which is most in line with the reality through multiple times of training.
As shown in fig. 2-7, each biomarker can be screened for a threshold based on the results of the bright field and negative controls, such as: the fluorescence expression intensity threshold parameter corresponding to panck+ cells is set to 37, that is, the preset intensity threshold corresponding to PanCK biomarker is set to 37, the fluorescence expression intensity threshold parameter corresponding to MHC-ii+ cells is set to 25, that is, the preset intensity threshold corresponding to MHC-II biomarker is set to 25, the fluorescence expression intensity threshold parameter corresponding to cd20+ cells is set to 26, that is, the preset intensity threshold corresponding to CD20 biomarker is set to 25, and each cell can be traced back according to a flow-like manner, so that the fluorescence expression intensity of the biomarker on each cell is obtained.
Step 200, obtaining the positive rate of the biomarkers in the tumor tissue, outside the tumor tissue and inside and outside the tumor tissue according to the fluorescence expression intensity, wherein the positive rate comprises the following steps: panck_ MHC2, which indicates the overall double positive rate of PanCK and MHC-II inside and outside tumor tissue; MHC2_cd20, which indicates the overall double positive rate of MHC-II and CD20 in and out of tumor tissue; MHC2_tumor, which indicates MHC-II single positive rate in tumor tissue; cd20_tumor, which represents the single positive rate of CD20 in tumor tissue; MHC2 cd20_tumor, which indicates the double positive rate of MHC-II and CD20 in tumor tissue; MHC2_back, which indicates the single positive rate of MHC-II outside tumor tissue; cd20_back, which indicates single positive rate of CD20 outside tumor tissue; MHC2_cd20_back, indicates the double positive rate of MHC-II and CD20 outside tumor tissue.
Specifically, the step of obtaining the positive rate of the biomarker in the tumor tissue, outside the tumor tissue and inside and outside the tumor tissue according to the fluorescence expression intensity comprises the following steps:
comparing the fluorescence expression intensity of the cells of each biomarker in the tumor tissue of the intestinal cancer with a corresponding preset intensity threshold value, and judging the cells as positive cells corresponding to the biomarkers in the tumor tissue under the condition that the fluorescence expression intensity of the cells of the biomarkers in the tumor tissue of the intestinal cancer is greater than the corresponding intensity threshold value, so as to obtain the single positive rate of each biomarker in the tumor tissue of the intestinal cancer and the double positive rate of the biomarkers in the tumor tissue of the intestinal cancer;
comparing the fluorescence expression intensity of the cells of each biomarker outside the tumor tissue of the intestinal cancer with a corresponding preset intensity threshold value, and judging the cells as positive cells corresponding to the biomarkers outside the tumor tissue under the condition that the fluorescence expression intensity of the cells of each biomarker outside the tumor tissue of the intestinal cancer is greater than the corresponding intensity threshold value, so as to obtain the single positive rate of each biomarker outside the tumor tissue of the intestinal cancer and the double positive rate of each biomarker inside the tumor tissue of the intestinal cancer;
And obtaining the double positive rate of the biomarker according to the single positive rate of each biomarker in the tumor tissue of the intestinal cancer and the single positive rate of each biomarker outside the tumor tissue of the intestinal cancer.
As shown in FIG. 8, showing the double positive rate of PanCK and MHC-II, as shown in FIG. 9, showing the double positive rate of MHC-II and CD20, both of FIG. 10 and FIG. 11 are scatter plots, FIG. 10 shows the tissue ratio of MHC-II, FIG. 10 shows the tissue ratio of CD20, FIG. 10 shows the tissue ratio of MHC-II, and FIG. 11 shows the tissue ratio of Gate 4, and FIG. 5 shows the tissue ratio of MHC-II, and trace back to the tissue, and a distinct tissue inside and outside distinction can be seen, as shown in FIG. 12, showing the positive display of MHC-II in the tumor tissue, and as shown in FIG. 13, showing the positive display of MHC-II outside the tumor tissue.
Step 300, screening out characteristic factors from all positive rates by using a stepwise regression method, and constructing a scoring model according to the characteristic factors;
wherein the characteristic factors comprise MHC2_cd20, MHC2_cd20_turner, mhc2_back, cd20_back and MHC2_cd20_back, wherein MHC2_cd20 represents the overall double positive rate of MHC-II and CD20 inside and outside tumor tissues, MHC2_cd20_turner represents the double positive rate of MHC-II and CD20 inside tumor tissues, MHC2_back represents the single positive rate of MHC-II outside tumor tissues, cd20_back represents the single positive rate of CD20 outside tumor tissues, and MHC2_cd20_back represents the double positive rate of MHC-II and CD20 outside tumor tissues.
Specifically, the step-by-step regression method is used for screening the characteristic factors from all positive rates, and comprises the following steps:
fitting all positive rates through a binomial distribution method of a generalized linear model to obtain a plurality of first models to be selected;
establishing corresponding first ROC curves for the first to-be-selected models, and calculating corresponding first AUC values of the first ROC curves;
selecting a first model to be selected with the highest first AUC value as a primary model;
gradually removing the positive rate from the primary model, checking whether the primary model has significant change after removing, if so, reserving the positive rate, and if not, removing the positive rate until all positive rates with significant change on the primary model are reserved as characteristic factors.
In this example, 30 samples were randomly divided into 70% and 30%, 70% samples were used as training set, 30% samples were used as test set, and then the samples were divided into two groups according to the mhic pathology image and the clinical efficacy of ROI of each sample, as shown in table 3,
TABLE 3 Table 3
Wherein, response represents a sample with good Response, non-Response represents a sample with poor Response, corresponding labels are marked on each sample according to the classification of table 3, a corresponding staining slide is obtained by using TSA technology to stain each sample, positive rates of biomarkers of each sample are obtained after sequentially using TissueQuest software and StrataQuest analysis software, namely positive rates of biomarkers of each sample of a training set and positive rates of biomarkers of each sample of a test set, as shown in table 4, positive rates of samples 0-10,
TABLE 4 Table 4
The result of the difference between the Response class and the Non-Response class is obtained through statistical analysis, as shown in fig. 14, then a labeled training set is adopted to train each first model to be tested, the positive rate of the biomarker of each sample of the training set is input into the first model to be tested, the first model to be tested is obtained after training, then a labeled testing set is adopted to test each first model to be tested, the positive rate of the biomarker of each sample of the testing set is input into the first model to be tested, the first model to be tested is tested, a corresponding testing result is obtained, a first ROC curve corresponding to each first model to be tested is drawn according to each testing result, the corresponding first AUC value of each first ROC curve is calculated by using a ggplot2, the AUC value is the area under the ROC curve and is used for measuring the performance of the model, the AUC value is close to 1, the performance of the model is good, otherwise, the AUC value is close to 0, the performance is poor, and therefore the AUC value is the first ROC curve which is the primary model which is the primary ROC value of the primary model of 0.86. In practical application, some positive rates are not significant, namely, some positive rates can be removed from the primary model step by step, whether the primary model has significant change after removal is checked, if yes, the positive rate is reserved, and if not, the positive rate is removed until all positive rates with significant change to the primary model are reserved as characteristic factors.
Specifically, constructing a scoring model according to the feature factors, including:
fitting all the characteristic factors through a binomial distribution method of a generalized linear model to obtain a plurality of second candidate models;
establishing a corresponding second ROC curve for each second model to be selected, and calculating a corresponding second AUC value of each second ROC curve;
and selecting a second candidate model with the highest second AUC value as a scoring model.
In this embodiment, a labeled training set is also adopted to train a second candidate model, the positive rate of the biomarker of each sample of the training set is input into the second candidate model, the second candidate model is trained to obtain each trained second candidate model, then the labeled test set is adopted to test each trained second candidate model, the positive rate of the biomarker of each sample of the test set is input into the trained second candidate model, the trained second candidate model is tested to obtain corresponding test results, a second ROC curve corresponding to each second candidate model is drawn according to each test result, the corresponding second AUC value of each second ROC curve is calculated by using the gplot2, then the second candidate model with the highest second AUC value is selected as a scoring model, as shown in fig. 16, the AUC value of the scoring model is 0.91, the sensitivity, specificity, accuracy, backaccuracy and accuracy of the scoring table are shown in the accuracy table 5,
TABLE 5
。
Specifically, the formula of the scoring model is:
，
and the Type represents a predictive value, and if the predictive value is larger than a preset threshold value, the response of the intestinal cancer patient is judged to be good, and if the predictive value is not larger than the preset threshold value, the response of the intestinal cancer patient is judged to be poor.
In this embodiment, the value of the predictive score is between 0 and 1, a median value of 0.5 is selected as a preset threshold, and the overall double positive rate of the MHC-II and CD20 inside and outside the tumor tissue, the single positive rate of the MHC-II outside the tumor tissue, the single positive rate of the CD20 outside the tumor tissue, and the double positive rate of the MHC-II and CD20 outside the tumor tissue are substituted into the formula of the scoring model to obtain the corresponding predictive score, where the predictive score is greater than the preset threshold, and where the predictive score is not greater than the preset threshold, the poor response of the radiotherapy and chemotherapy treatment of the patient with the bowel cancer is indicated.
The second embodiment provides a prediction scoring model for radiotherapy and chemotherapy of intestinal cancer, which is formed by using the construction method in the first embodiment, adopting a stepwise regression method to screen out characteristic factors from the pack_ MHC2, MHC _cd20, MHC2_tumor, cd20_tumor, MHC2_cd20_tumor, mhc2_back, cd20_back and MHC2_cd20_back, wherein the characteristic factors comprise MHC2_cd20, MHC2_cd20_tumor, mhc2_back, cd20_back and MHC2_cd20_back, and then fitting according to the characteristic factors by a binomial distribution method of an R language generalized linear model to obtain a plurality of second candidate models.
Training a second to-be-selected model by adopting a labeled training set, inputting the positive rate of the biomarker of each sample of the training set into the second to-be-selected model, training the second to-be-selected model to obtain each trained second to-be-selected model, testing each trained second to-be-selected model by adopting a labeled testing set, inputting the positive rate of the biomarker of each sample of the testing set into the second to-be-selected model after training, testing the second to-be-selected model after training to obtain corresponding testing results, drawing second ROC curves corresponding to each second to-be-selected model by using ggplot2, calculating corresponding second AUC values of each second ROC curve by using R-packet pROC, and selecting the second to-be-selected model with the highest second AUC value as a scoring model.
The third embodiment uses a scoring model in constructing a prediction scoring system or device for the radiotherapy and chemotherapy of intestinal cancer, the model used in constructing the prediction scoring system or device for the radiotherapy and chemotherapy of intestinal cancer is the scoring model in the second embodiment, the scoring model in the second embodiment is adopted to test each sample in the test set to obtain corresponding test results, a ROC curve corresponding to the scoring model is drawn by using ggplot2 according to each test result, an AUC value corresponding to the ROC curve is calculated by using R packet pROC, as shown in fig. 16, the AUC value of the ROC curve is 0.91, and the sensitivity, specificity, accuracy, backtracking accuracy, accuracy and ACU value of the scoring model are shown in table 5.
The fourth embodiment provides a prediction scoring system for radiotherapy and chemotherapy of intestinal cancer, comprising: the prediction scoring model of the intestinal cancer chemoradiotherapy constructed by the construction method in the first embodiment takes the positive rate of the biomarker as an input variable for predicting the therapeutic response of the intestinal cancer chemoradiotherapy.
A fifth embodiment provides a computer device, which may be a server, and an internal structural diagram thereof may be as shown in fig. 17. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to realize a method for constructing a prediction scoring model of intestinal cancer chemoradiotherapy.
It will be appreciated by those skilled in the art that the structure shown in fig. 17 is merely a block diagram of a portion of the structure associated with the present application and is not limiting of the computer device to which the present application applies, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
The computer device provided in this embodiment includes a memory and a processor, where the memory stores a computer program, and the processor implements the following steps when executing the computer program:
obtaining fluorescence expression intensities of a plurality of biomarkers within and outside tumor tissue of a bowel cancer, wherein the biomarkers comprise: panCK, CD20 and MHC-II;
obtaining the positive rate of the biomarkers in the tumor tissue, outside the tumor tissue and inside and outside the tumor tissue according to the fluorescence expression intensity;
screening out characteristic factors from all positive rates by using a stepwise regression method, and constructing a scoring model according to the characteristic factors;
wherein the characteristic factors comprise MHC2_cd20, MHC2_cd20_turner, mhc2_back, cd20_back and MHC2_cd20_back, wherein MHC2_cd20 represents the overall double positive rate of MHC-II and CD20 inside and outside tumor tissues, MHC2_cd20_turner represents the double positive rate of MHC-II and CD20 inside tumor tissues, MHC2_back represents the single positive rate of MHC-II outside tumor tissues, cd20_back represents the single positive rate of CD20 outside tumor tissues, and MHC2_cd20_back represents the double positive rate of MHC-II and CD20 outside tumor tissues.
In one embodiment, the scoring model is formulated as:
/>
，
And the Type represents a predictive value, and if the predictive value is larger than a preset threshold value, the response of the intestinal cancer patient is judged to be good, and if the predictive value is not larger than the preset threshold value, the response of the intestinal cancer patient is judged to be poor.
In one embodiment, the step-by-step regression method is used to screen feature factors from all positive rates, including:
fitting all positive rates through a binomial distribution method of a generalized linear model to obtain a plurality of first models to be selected;
establishing corresponding first ROC curves for the first to-be-selected models, and calculating corresponding first AUC values of the first ROC curves;
selecting a first model to be selected with the highest first AUC value as a primary model;
gradually removing the positive rate from the primary model, checking whether the primary model has significant change after removing, if so, reserving the positive rate, and if not, removing the positive rate until all positive rates with significant change on the primary model are reserved as characteristic factors.
In one embodiment, the constructing a scoring model according to the feature factors includes:
fitting all the characteristic factors through a binomial distribution method of a generalized linear model to obtain a plurality of second candidate models;
Establishing a corresponding second ROC curve for each second model to be selected, and calculating a corresponding second AUC value of each second ROC curve;
and selecting a second candidate model with the highest second AUC value as a scoring model.
In one embodiment, the obtaining the positive rate of the biomarker in the tumor tissue, outside the tumor tissue and inside and outside the tumor tissue according to the fluorescence expression intensity comprises:
comparing the fluorescence expression intensity of the cells of each biomarker in the tumor tissue of the intestinal cancer with a corresponding preset intensity threshold value, and judging the cells as positive cells corresponding to the biomarkers in the tumor tissue under the condition that the fluorescence expression intensity of the cells of the biomarkers in the tumor tissue of the intestinal cancer is greater than the corresponding intensity threshold value, so as to obtain the single positive rate of each biomarker in the tumor tissue of the intestinal cancer and the double positive rate of the biomarkers in the tumor tissue of the intestinal cancer;
comparing the fluorescence expression intensity of the cells of each biomarker outside the tumor tissue of the intestinal cancer with a corresponding preset intensity threshold value, and judging the cells as positive cells corresponding to the biomarkers outside the tumor tissue under the condition that the fluorescence expression intensity of the cells of each biomarker outside the tumor tissue of the intestinal cancer is greater than the corresponding intensity threshold value, so as to obtain the single positive rate of each biomarker outside the tumor tissue of the intestinal cancer and the double positive rate of each biomarker inside the tumor tissue of the intestinal cancer;
And obtaining the double positive rate of the biomarker according to the single positive rate of each biomarker in the tumor tissue of the intestinal cancer and the single positive rate of each biomarker outside the tumor tissue of the intestinal cancer.
A sixth embodiment provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:
obtaining fluorescence expression intensities of a plurality of biomarkers within and outside tumor tissue of a bowel cancer, wherein the biomarkers comprise: panCK, CD20 and MHC-II;
obtaining the positive rate of the biomarkers in the tumor tissue, outside the tumor tissue and inside and outside the tumor tissue according to the fluorescence expression intensity;
screening out characteristic factors from all positive rates by using a stepwise regression method, and constructing a scoring model according to the characteristic factors;
wherein the characteristic factors comprise MHC2_cd20, MHC2_cd20_turner, mhc2_back, cd20_back and MHC2_cd20_back, wherein MHC2_cd20 represents the overall double positive rate of MHC-II and CD20 inside and outside tumor tissues, MHC2_cd20_turner represents the double positive rate of MHC-II and CD20 inside tumor tissues, MHC2_back represents the single positive rate of MHC-II outside tumor tissues, cd20_back represents the single positive rate of CD20 outside tumor tissues, and MHC2_cd20_back represents the double positive rate of MHC-II and CD20 outside tumor tissues.
In one embodiment, the scoring model is formulated as:
，
and the Type represents a predictive value, and if the predictive value is larger than a preset threshold value, the response of the intestinal cancer patient is judged to be good, and if the predictive value is not larger than the preset threshold value, the response of the intestinal cancer patient is judged to be poor.
In one embodiment, the step-by-step regression method is used to screen feature factors from all positive rates, including:
fitting all positive rates through a binomial distribution method of a generalized linear model to obtain a plurality of first models to be selected;
establishing corresponding first ROC curves for the first to-be-selected models, and calculating corresponding first AUC values of the first ROC curves;
selecting a first model to be selected with the highest first AUC value as a primary model;
gradually removing the positive rate from the primary model, checking whether the primary model has significant change after removing, if so, reserving the positive rate, and if not, removing the positive rate until all positive rates with significant change on the primary model are reserved as characteristic factors.
In one embodiment, the constructing a scoring model according to the feature factors includes:
Fitting all the characteristic factors through a binomial distribution method of a generalized linear model to obtain a plurality of second candidate models;
establishing a corresponding second ROC curve for each second model to be selected, and calculating a corresponding second AUC value of each second ROC curve;
and selecting a second candidate model with the highest second AUC value as a scoring model.
In one embodiment, the obtaining the positive rate of the biomarker in the tumor tissue, outside the tumor tissue and inside and outside the tumor tissue according to the fluorescence expression intensity comprises:
comparing the fluorescence expression intensity of the cells of each biomarker in the tumor tissue of the intestinal cancer with a corresponding preset intensity threshold value, and judging the cells as positive cells corresponding to the biomarkers in the tumor tissue under the condition that the fluorescence expression intensity of the cells of the biomarkers in the tumor tissue of the intestinal cancer is greater than the corresponding intensity threshold value, so as to obtain the single positive rate of each biomarker in the tumor tissue of the intestinal cancer and the double positive rate of the biomarkers in the tumor tissue of the intestinal cancer;
comparing the fluorescence expression intensity of the cells of each biomarker outside the tumor tissue of the intestinal cancer with a corresponding preset intensity threshold value, and judging the cells as positive cells corresponding to the biomarkers outside the tumor tissue under the condition that the fluorescence expression intensity of the cells of each biomarker outside the tumor tissue of the intestinal cancer is greater than the corresponding intensity threshold value, so as to obtain the single positive rate of each biomarker outside the tumor tissue of the intestinal cancer and the double positive rate of each biomarker inside the tumor tissue of the intestinal cancer;
And obtaining the double positive rate of the biomarker according to the single positive rate of each biomarker in the tumor tissue of the intestinal cancer and the single positive rate of each biomarker outside the tumor tissue of the intestinal cancer.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
Claims (10)
1. The construction method of the prediction scoring model for the radiotherapy and chemotherapy of the intestinal cancer is characterized by comprising the following steps:
obtaining fluorescence expression intensities of a plurality of biomarkers within and outside tumor tissue of a bowel cancer, wherein the biomarkers comprise: panCK, CD20 and MHC-II;
obtaining the positive rate of the biomarkers in the tumor tissue, outside the tumor tissue and inside and outside the tumor tissue according to the fluorescence expression intensity;
screening out characteristic factors from all positive rates by using a stepwise regression method, and constructing a scoring model according to the characteristic factors;
wherein the characteristic factors comprise MHC2_cd20, MHC2_cd20_turner, mhc2_back, cd20_back and MHC2_cd20_back, wherein MHC2_cd20 represents the overall double positive rate of MHC-II and CD20 inside and outside tumor tissues, MHC2_cd20_turner represents the double positive rate of MHC-II and CD20 inside tumor tissues, MHC2_back represents the single positive rate of MHC-II outside tumor tissues, cd20_back represents the single positive rate of CD20 outside tumor tissues, and MHC2_cd20_back represents the double positive rate of MHC-II and CD20 outside tumor tissues.
2. The method for constructing a predictive scoring model for radiotherapy and chemotherapy of intestinal cancer according to claim 1, wherein the formula of the scoring model is:
，
and the Type represents a predictive value, and if the predictive value is larger than a preset threshold value, the response of the intestinal cancer patient is judged to be good, and if the predictive value is not larger than the preset threshold value, the response of the intestinal cancer patient is judged to be poor.
3. The method for constructing a predictive scoring model for chemoradiotherapy of intestinal cancer according to claim 1, wherein the step-by-step regression method is used for screening feature factors from all positive rates, and comprises the following steps:
fitting all positive rates through a binomial distribution method of a generalized linear model to obtain a plurality of first models to be selected;
establishing corresponding first ROC curves for the first to-be-selected models, and calculating corresponding first AUC values of the first ROC curves;
selecting a first model to be selected with the highest first AUC value as a primary model;
gradually removing the positive rate from the primary model, checking whether the primary model has significant change after removing, if so, reserving the positive rate, and if not, removing the positive rate until all positive rates with significant change on the primary model are reserved as characteristic factors.
4. The method for constructing a predictive scoring model for radiotherapy and chemotherapy of intestinal cancer according to claim 1, wherein the constructing the scoring model according to the characteristic factors comprises:
fitting all the characteristic factors through a binomial distribution method of a generalized linear model to obtain a plurality of second candidate models;
establishing a corresponding second ROC curve for each second model to be selected, and calculating a corresponding second AUC value of each second ROC curve;
and selecting a second candidate model with the highest second AUC value as a scoring model.
5. The method for constructing a predictive scoring model for chemoradiotherapy of intestinal cancer according to claim 1, wherein the obtaining the positive rate of the biomarker in the tumor tissue, outside the tumor tissue and inside and outside the tumor tissue according to the fluorescence expression intensity comprises:
comparing the fluorescence expression intensity of the cells of each biomarker in the tumor tissue of the intestinal cancer with a corresponding preset intensity threshold value, and judging the cells as positive cells corresponding to the biomarkers in the tumor tissue under the condition that the fluorescence expression intensity of the cells of the biomarkers in the tumor tissue of the intestinal cancer is greater than the corresponding intensity threshold value, so as to obtain the single positive rate of each biomarker in the tumor tissue of the intestinal cancer and the double positive rate of the biomarkers in the tumor tissue of the intestinal cancer;
Comparing the fluorescence expression intensity of the cells of each biomarker outside the tumor tissue of the intestinal cancer with a corresponding preset intensity threshold value, and judging the cells as positive cells corresponding to the biomarkers outside the tumor tissue under the condition that the fluorescence expression intensity of the cells of each biomarker outside the tumor tissue of the intestinal cancer is greater than the corresponding intensity threshold value, so as to obtain the single positive rate of each biomarker outside the tumor tissue of the intestinal cancer and the double positive rate of each biomarker inside the tumor tissue of the intestinal cancer;
and obtaining the double positive rate of the biomarker according to the single positive rate of each biomarker in the tumor tissue of the intestinal cancer and the single positive rate of each biomarker outside the tumor tissue of the intestinal cancer.
6. The prediction scoring model for the radiotherapy and chemotherapy of the intestinal cancer is characterized by being formed by the construction method of any one of claims 1-5.
7. The use of a scoring model according to claim 6 in the construction of a predictive scoring system or device for chemoradiotherapy of intestinal cancer, wherein the response of an intestinal cancer patient is predicted from a predictive score calculated from the scoring model.
8. A system for predicting score of radiotherapy and chemotherapy for intestinal cancer, comprising: a prediction score model for bowel cancer chemoradiotherapy constituted by the construction method according to any one of claims 1 to 5, wherein the score model uses the positive rate of the biomarker as an input variable for predicting the therapeutic response of bowel cancer chemoradiotherapy.
9. Computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1-5 when the computer program is executed.
10. Computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any of claims 1-5.
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