CN114141376A - VEGFA-based first-line chemotherapy curative effect prediction model for HER2 negative advanced gastric cancer patient and application thereof - Google Patents
VEGFA-based first-line chemotherapy curative effect prediction model for HER2 negative advanced gastric cancer patient and application thereof Download PDFInfo
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Abstract
The invention discloses a HER2 negative advanced gastric cancer patient first-line chemotherapy curative effect prediction model based on VEGFA and application thereof, comprising a nomogram for evaluating chemotherapy curative effect, wherein scores corresponding to a first line of 4 indexes of BMI, Diagnose Pattern, VEGFA and CA19-9 from a second line to a fifth line are added to obtain a sixth line of patient total score, and the sixth line of patient total score is correspondingly projected to a seventh line to obtain the probability of ineffective patient treatment. The invention integrates the simplicity, conciseness and understandability of the curative effect prediction nomogram of the serum VEGFA and the common baseline characteristic, is popular and easy to understand, and is convenient for the clinician and the patient to carry out operation prediction on the curative effect. Meanwhile, the curative effect nomogram shows a satisfactory prediction result, can better guide clinical judgment and layering, and provides certain help for treating HER2 negative late gastric cancer patients in the future.
Description
Technical Field
The invention belongs to the field of clinical medicine, and particularly relates to a VEGFA-based HER2 negative advanced gastric cancer patient first-line chemotherapy curative effect prediction model and application thereof.
Background
Gastric Cancer (GC) is the fifth leading cancer and fourth leading cause of cancer death in the world. Morbidity and mortality are high in asian countries. Currently, chemotherapy remains the cornerstone of systemic treatment for advanced patients despite immune checkpoint inhibitors in the treatment modality for HER2 negative gastric cancer patients. The response of patients to chemotherapy varies from person to person due to the high heterogeneity and malignancy of gastric cancer. Therefore, a new method is searched for predicting the curative effect of chemotherapy of the patient to make a more appropriate treatment decision, and the improvement of the survival rate of the patient with advanced gastric cancer is very important.
Angiogenesis is one of the hallmarks of malignancy and contributes to the development and progression of cancer. As a major marker of angiogenesis, vascular endothelial growth factor a (vegfa) is thought to be associated with a poor prognosis in various malignancies. We found in previous studies that high levels of serum VEGFA in small cell lung cancer patients are predictive of a poorer prognosis, however, there is still no good prospective study to assess the clinical utility value of VEGFA in GC. In addition, immune inflammatory markers such as systemic immune inflammatory index (SII), C-reactive protein/albumin ratio (CAR), nutritional markers such as Body Mass Index (BMI), hemoglobin (Hb), albumin/globulin ratio (AGR) and classical tumor markers are all associated with gastric cancer or other malignancies. Because the prediction effect of a single marker is limited, the influence of each marker on the treatment effect needs to be reasonably evaluated, and an appropriate marker is selected to establish a prediction model so as to comprehensively evaluate the treatment effect and provide individualized treatment for malignant tumor patients.
Currently, the prediction models of gastric cancer are mainly diagnosis and prognosis models, most of which are based on retrospective research, and some of the models based on gene expression are difficult to popularize clinically. The model is established according to a prospective, single-center and queue research, the first-line chemotherapy curative effect of HER2 negative late gastric cancer is predicted by using clinical pathological characteristics and laboratory hematology indexes, a convenient and universal curative effect prediction model is provided for the treatment of the late gastric cancer, the doctor-patient communication is facilitated, and a clinician is helped to make a better treatment decision.
Disclosure of Invention
The invention aims to provide a VEGFA-based HER2 negative advanced gastric cancer patient first-line chemotherapy curative effect prediction model and application thereof, integrates a curative effect nomogram of serum VEGFA and common baseline characteristics, shows a satisfactory prediction result, and can help clinical judgment and stratification of future research.
In order to achieve the purpose, the invention adopts the following technical scheme:
a VEGFA-based first-line chemotherapy efficacy prediction model for HER2 negative advanced gastric cancer patients, which is characterized by comprising the following components:
a. a nomogram for predicting first-line chemotherapy efficacy of HER2 negative advanced gastric cancer patients, wherein the nomogram comprises a first line of score scales, and the score ranges from 0 to 100; the body mass index BMI of the patient in the second behavior is more than or equal to 18.5kg/m2And BMI < 18.5kg/m2A respective score corresponding to the first row; the third row is Diagnose Pattern (diagnosis mode), and the early treatment late stage and postoperative recurrence correspond to the corresponding scores of the first row respectively; the fourth row is VEGFA, and VEGFA is less than 179.9 pg/ml and VEGFA is more than or equal to 179.9 pg/ml which respectively correspond to a corresponding score of the first row; the fifth row CA19-9 is less than 79.8U/ml and CA19-9 is more than or equal to 79.8U/ml respectively corresponds to a corresponding score of the first row; a sixth action patient total score scale, wherein the score range is 0-260, the starting end 0 is the same as the starting end of the first action score scale, and the tail end 260 corresponds to the tail end 100 of the first action score scale;
the seventh line is a probability score scale for ineffective treatment of the patients, the score range is 0.1-0.9, and the starting end 0.1 and the tail end 0.9 of the probability score scale correspond to the starting end 45 and the tail end 215 of the total score scale of the patients in the sixth line respectively;
b. and adding the scores of the 4 indexes BMI, Diagnose Pattern, VEGFA and CA19-9 in the second row to the fifth row corresponding to the first row to obtain the total score of the patients in the sixth row, and correspondingly projecting the total score of the patients in the sixth row to the seventh row to obtain the probability of ineffective treatment of the patients.
The invention also provides the following steps for establishing a first-line chemotherapy curative effect histogram model of a HER2 negative late gastric cancer patient:
(1) collecting M clinical pathological characteristics of HER2 negative advanced gastric cancer patients, N laboratory indexes including VEGFA, and evaluating the first-line chemotherapy curative effect of the patients;
the M clinical pathology information collected included gender, age, height, weight, american eastern cooperative cohort of tumors score (ECOG PS), diagnostic mode (advanced stage of treatment or postoperative recurrence), histopathology (adenocarcinoma, signet ring cell carcinoma or others) and location (cardia, corpus or distal), number of metastases, and whether serosal cavity fluid pooling was combined;
laboratory indices include VEGFA, CEA, CA19-9, Total Protein (TP), Albumin (ALB), hemoglobin (Hb), C-reactive protein (CRP), low density lipoprotein cholesterol (LDL-C), and high density lipoprotein cholesterol (HDL-C);
the overall index is calculated according to the following formula, AGR = ALB/(TP-ALB), BMI = height/weight2CAR = CRP/ALB, low density lipoprotein cholesterol/high density lipoprotein cholesterol ratio (LHR) = LDL-C/HDL-C, SII = platelet count by neutrophil count/lymphocyte count;
(2) carrying out regression analysis on M clinical pathological characteristics and N laboratory indexes by adopting single-factor Logistic regression analysis, preliminarily screening single-factor predictive variables related to curative effect, and screening single-factor predictive variables related to curative effectP <The variable of 0.05, the variable with a single factor P < 0.05, which is the left column of the forest map of FIG. 2, is a variable obtained by a statistical method, the forest map has limited space and does not list the specific P value of the left column, and only the variable with single factor significance can be included in the multi-factor regression, which is that the statistical method is thatIs known in the art. Meanwhile, P < 0.05 is a defined value of a statistical hypothesis test, is a regulation, is not calculated, is subjected to multi-factor Logistic regression analysis, and is screened out an independent curative effect prediction variable (the P value on the right side of the figure 2 is the P value obtained by multi-factor regression, and is considered to be significant to the curative effect according to the statistically specified P < 0.05);
(3) performing regression analysis on the M clinical pathological characteristics and the N laboratory indexes by adopting an LASSO regression method, and screening predictive variables related to the curative effect;
(4) finally selecting BMI, Diagnose Pattern, VEGFA and CA19-9 to construct a curative effect histogram model according to the variables and clinical meanings screened by the LASSO-Logistic regression model.
The invention also provides an application of the VEGFA-based HER2 negative advanced gastric cancer patient first-line chemotherapy curative effect prediction model, and the VEGFA-based HER2 negative advanced gastric cancer patient first-line chemotherapy curative effect prediction model is applied to evaluating the treatment ineffectiveness probability of the following patients: histologically confirmed HER2 negative patients, patients who have not received anti-tumor therapy for recurrent or metastatic gastric cancer, patients who are not suitable or willing to receive surgery or radiation therapy, patients with target lesions for which efficacy can be assessed.
In addition, the first-line chemotherapy efficacy prediction model for VEGFA-based HER2 negative advanced gastric cancer patients excludes the following patient criteria: (iii) incorporation of other tumors or subtypes; patients with severe center of gravity, liver, kidney disease; patients with severe bleeding or infectious diseases.
The invention also provides accuracy verification of a HER2 negative late gastric cancer patient first-line chemotherapy curative effect prediction model based on VEGFA, the discrimination of the established model is judged by adopting an ROC curve of an R language drawn model according to the established model, and the calibration of the established model is judged by drawing a calibration curve of the model.
In conclusion, the beneficial effects of the invention are as follows:
the nomogram disclosed by the invention is a first-line chemotherapy curative effect prediction model based on VEGFA and established for HER2 negative late gastric cancer patients for the first time, and integrates the simple and concise curative effect nomograms of serum VEGFA and common baseline characteristics, is popular and easy to understand, and is convenient for clinicians and patients to operate and predict the curative effect. Meanwhile, the curative effect nomogram shows a satisfactory prediction result, can better guide clinical judgment and layering, and provides certain help for treating HER2 negative late gastric cancer in the future.
Drawings
FIG. 1 is a nomogram of a predictive model of the probability of ineffectiveness of first-line chemotherapy treatment for HER2 negative advanced gastric cancer patients based on VEGFA in accordance with the present invention;
FIG. 2 is a forest diagram of the results of Logistic regression analysis, in which the Factors listed in the forest diagram, i.e., single factor Logistic regression analysis, were used to perform regression analysis on M clinical pathological features, N laboratory indexes including VEGFA to screen out the prediction variables with P < 0.05 related to therapeutic effect including BMI, presence OR absence of serous cavity effusion (serous effusions), VEGFA, CEA, CA19-9, LHR, CAR, and to obtain the corresponding OR value and 95% CI (95% confidence interval) for each group, where an OR value greater than 1 indicates that the factor is a risk factor, e.g., BMI < 18.5kg/M2The possibility of therapeutic ineffectiveness of the patient of (a) is that the BMI is 18.5kg/m or more23.24 times of patients. The predictive variables which are screened out by the single-factor Logistic regression analysis and are related to the curative effect and have the P less than 0.05 are brought into the multi-factor Logistic regression analysis to obtain a multi-factor regression result, wherein the predictive variables which are related to the curative effect and have the P less than 0.05 only have VEGFA;
FIG. 3 is a motion trajectory of each variable in the LASSO regression analysis;
FIG. 4 is a graph of LASSO regression analysis screening variables using a LASSO regression model for screening predictive variables with 10-fold cross validation against minimum criteria;
FIG. 5 is a ROC curve for the model created to predict the efficacy of a patient treatment. The ordinate is the True Positive Rate (TPR), which is True therapy ineffective and the model also determines the number of patients whose therapy is ineffective divided by the number of patients whose total True therapy is ineffective. The abscissa is the False Positive Rate (FPR), which is the number of patients that are therapeutically effective but for which the model predicts that treatment is ineffective divided by the number of patients for which all actual treatments are effective. The TPR true yang rate is the finding rate of people who do not have effective treatment, and the number of people who do not have effective treatment is found; the FPR false positive rate is the false detection rate of the effective treatment personnel, and the number of the effective treatment personnel is detected by mistake. AUC, Area under the Curve of ROC (AUC ROC). As shown, i.e., the area under the red curve and the blue dashed line.
Fig. 6 is a calibration curve of the probability of the patient's disease progression predicted by the model and the actual patient's disease progression, the long dashed line is a straight line with a slope of 45 ° and is equal to the ideal probability of the patient's disease progression predicted by the model, the short dashed line is the probability of the patient's disease progression predicted by the model and the actual patient's disease progression predicted by the model, and the solid line is the calibration curve of the probability of the patient's disease progression predicted by the model after internal verification and calibration.
Detailed Description
The invention relates to the field of clinical medicine, in particular to a model which is established based on clinical pathological characteristics and laboratory test indexes of HER2 negative late gastric cancer patients and can predict the individual treatment effect of the population, and the model is applied in a nomogram form; the first-line chemotherapy curative effect of HER2 negative advanced gastric cancer patients can be judged and predicted more accurately based on VEGFA, clinical judgment and layering can be guided better, and certain help is provided for treatment of HER2 negative advanced gastric cancer patients in the future. The inventor of the application develops a nomogram based on clinical pathological characteristics and laboratory test indexes for predicting the treatment efficacy of the population aiming at HER2 negative advanced gastric cancer patients, and performs modeling and verification in the specific population.
For better illustrating the objects, technical solutions and advantages of the present invention, the present invention will be further described with reference to the accompanying drawings and specific embodiments.
Example 1
The construction method of the model for predicting the first-line chemotherapy curative effect and prognosis of the HER2 negative advanced gastric cancer patient based on VEGFA comprises the following steps:
(1) and modeling crowd screening: in total, 111 patients were enrolled from 11 months 2017 to 9 months 2021, and were initially treated with postoperative recurrence or metastatic HER2 negative advanced gastric cancer at the first subsidiary hospital of the Unionidae Hospital. Inclusion criteria were as follows: histologically confirmed HER2 negative patients; patients who have not received anti-tumor therapy for recurrent or metastatic gastric cancer; patients who are not suitable or willing to undergo surgery or radiotherapy; patients with target lesions for evaluating therapeutic effects. Exclusion criteria were as follows: (iii) incorporation of other tumors or subtypes; patients with severe center of gravity, liver, kidney disease; patients with severe bleeding or infectious diseases. Median follow-up time for the patients in the cohort was 13.6 months (95% CI: 10.0-15.8 months). Among 111 patients with advanced gastric cancer who were HER2 negative, 71 (64.0%) men and 40 (36.0%) women were present. Of 111 patients, 70 had effective treatment, i.e., complete remission or partial remission or stable disease, and 41 had ineffective treatment, i.e., disease Progression (PD).
(2) And a predicted variable:
the M clinical pathology information collected included sex, age, height, weight, American eastern cooperative group of tumors score (ECOG PS), diagnostic mode (diagnoses Pattern: advanced stage of treatment or postoperative recurrence), histopathology (adenocarcinoma, signet cell carcinoma or others) and location (cardia, corpus or distal), number of metastases and whether serosal cavity fluid was pooled.
Laboratory indices include VEGFA, CEA, CA19-9, Total Protein (TP), Albumin (ALB), hemoglobin (Hb), C-reactive protein (CRP), low density lipoprotein cholesterol (LDL-C), and high density lipoprotein cholesterol (HDL-C).
The overall index is calculated according to the following formula, AGR = ALB/(TP-ALB), BMI = height/weight2CAR = CRP/ALB, low density lipoprotein cholesterol/high density lipoprotein cholesterol ratio (LHR) = LDL-C/HDL-C, SII = platelet count x neutrophil count/lymphocyte count.
(3) Detection of VEGFA and other laboratory indices: before treatment (at baseline), 5 ml of non-anticoagulated peripheral venous blood and 3 ml of EDTA-K2-anticoagulated peripheral venous blood were collected from each patient. The non-anticoagulated peripheral venous blood was centrifuged at 3000 rpm for 10 minutes to separate the serum, and the supernatant was rapidly frozen and stored in a refrigerator at-20 ℃. VEGFA detection was performed using a weigao JR-1 chemiluminescence immunoassay analyzer and a vascular endothelial growth factor detection kit (chemiluminescence, yawegao college medical polymers ltd, wei sea, china) according to the manufacturer's instructions. Peripheral venous blood anticoagulated with EDTA-K2 was detected with a Sysmex XE-2100 automatic blood analyzer (Sysmex corporation, Japan). CEA (chemiluminescence micro-particle immunoassay) and CA19-9 (chemiluminescence micro-particle immunoassay) were detected by the Yapeh I-2000 chemiluminescence immunoassay according to the standard of laboratory SOP documents and reagent descriptions, and all reagents, calibration and quality control products were obtained from Yapeh trade, Inc. (Shanghai). Albumin (colorimetry), TP (colorimetry), CRP (immunoturbidimetry), LDL-C (homogenized enzyme colorimetry), HDL-C (homogenized enzyme colorimetry), were tested by the Roche cobalt 8000C 701 fully automated biochemical analyzer, and all reagents, calibrations and quality control products were supplied by the Roche diagnostics products company, Shanghai. Hb detection was performed using a Sysmex XE-2100 fully automated hematology analyzer and the prohibitions, calibration and quality control products (Sysmex, Japan Konjac.). All test technicians are professionally trained and do not come into contact with clinical data.
(4) And a treatment scheme: all patients were treated with the recommended regimen in accordance with the Chinese gastric cancer diagnosis and treatment code (2011 edition). Systemic chemotherapy drugs include: commonly used systemic chemotherapeutic drugs include: fluorouracil (5-FU), capecitabine, tegafur, cisplatin, epirubicin, docetaxel, paclitaxel, oxaliplatin, irinotecan and the like. For patients with recurrent metastatic gastric cancer, a two-medicine combination scheme is adopted under the condition of no obvious contraindication; for patients with poor physical condition, advanced age and intolerance of the two-drug combination scheme, the single-drug chemotherapy of oral fluorouracil drugs is considered; the 3-medicine scheme is suitable for patients with large tumor load and good physical condition. The combination and dosage of treatment will be determined by the physician in light of the patient's circumstances.
(5) And evaluating the curative effect of chemotherapy: the efficacy of chemotherapy was evaluated on patients every 2 cycles according to the response evaluation criteria for solid tumors (RECIST), version 1.1, and the best efficacy was recorded for the treatment of patients, with Complete Remission (CR), Partial Remission (PR), and Stable Disease (SD) as effective treatment and disease Progression (PD) as ineffective treatment. The efficacy of patients who died before the first assessment was considered PD.
(6) Follow-up and results: patients were followed up by telephone or hospital review until disease progression, death, or lost follow-up opportunities.
(7) And statistical analysis: for all in this patentData are allR for Windows (version 4.1.0, https:// www.r-project. org /) was used as the primary tool for analysis and mapping. In the research, R software packages including "DynNom", "dplyr", "forest plot", "ggcorrplot", "ggDCA", "glmnet", "maxstat", "pyr", "rms", "rock" and "rsconnect" are used as required. All statistical tests are two-sided,P <0.05 is considered statistically significant (this is the setting for a hypothesis test in statistics, which is statistically specified). The continuous variables of the two independent groups of samples were compared using the Wilcoxon test. The continuous variables of two or more independent samples were compared using the Kruskal-Wallis test. Correlations between continuous variables were performed using Spearman correlation analysis.
Clinical recommendation is less than 18.5kg/m2The optimal cut-off value for BMI was set at 18.5kg/m for weight loss2VEGFA, CEA, CA19-9, LHR, CAR determine the optimal cut-off values for the efficacy-related continuous variables based on the maximum johnson index.
(8) Performing regression analysis on M clinical pathological characteristics and N laboratory indexes by adopting single-factor Logistic regression analysis, and preliminarily screening single-factor predictive variables related to curative effect to obtain, wherein the single-factor Logistic regression analysis result is shown in Factors on the left side of a graph 2: BMI, serous effusions, VEGFA, CEA, CA19-9, LHR and CAR were associated with therapeutic efficacy in HER2 negative advanced gastric cancer patients. The fact that factors related to curative effect in single-factor Logistic regression analysis are included in multi-factor Logistic analysis shows that only VEGFAPThe value is less than 0.05, and VEGFA is an independent predictor of the curative effect of HER2 negative advanced gastric cancer patients.
(9) Screening for efficacy-related variables using LASSO regression, including BMI, diagnostic mode, presence or absence of serosal cavity fluid, VEGFA, CA199, CAR predictor variables.
(10) And finally selecting 4 variables BMI, a diagnosis mode, VEGFA and CA19-9 to fit the curative effect prediction model through the variables and clinical meanings screened by the LASSO-Logistic regression model.
(11) The discrimination of the efficacy prediction model was evaluated using the receiver operating characteristic curve (ROC curve) shown in fig. 5, and the area under the curve (AUC) was calculated. The probability that the model built predicts the disease progression of the patient versus the actual patient disease progression is evaluated using the calibration curve shown in figure 6. The internal verification of the model is realized by a Bootstrap method, namely, 1000 samples are randomly extracted from the original data set to form a new sample, and the internal verification of the model is carried out.
(12) And constructing a therapeutic effect prediction nomogram
As shown in fig. 1, the alignment chart for predicting the first-line chemotherapy effect of HER2 negative advanced gastric cancer patients is established in this example. The nomogram can be used for predicting the probability of ineffective treatment of HER2 negative early-stage gastric cancer patients. The alignment chart comprises a score scale of a first row, wherein the score range is 0-100; second behavior patient Body Mass Index (BMI), BMI ≥ 18.5kg/m2And BMI < 18.5kg/m2A respective score corresponding to the first row; the third row is Diagnose Pattern (diagnosis mode), and the early treatment late stage and postoperative recurrence correspond to the corresponding scores of the first row respectively; the fourth row is VEGFA, and VEGFA is less than 179.9 pg/ml and VEGFA is more than or equal to 179.9 pg/ml which respectively correspond to a corresponding score of the first row; the fifth row CA19-9 is less than 79.8U/ml and CA19-9 is more than or equal to 79.8U/ml respectively corresponds to a corresponding score of the first row; and the total score of the patient in the sixth row is obtained by adding the scores of the 4 indexes from the second row to the fifth row in the first row to obtain the total score of the patient, and the total score of the patient in the sixth row is correspondingly projected to the seventh row to obtain the probability of invalidity of the patient after treatment.
For example, if 1 HER2 negative primary treatment late stage gastric cancer patient meeting the inclusion/exclusion standard is diagnosed, clinical and pathological information and relevant laboratory examination indexes at baseline are obtained, such as postoperative recurrence of the patient, 20.1 kg/m2 of BMI, 165.4 pg/ml of VEGFA and 80.0U/ml of CA 19-9. On the basis of the curative effect prediction nomogram, the second line BMI obtains 0 score corresponding to the first line, the third line diagnosis mode obtains 47.5 scores corresponding to the first line, the fourth line VEGFA obtains 0 score corresponding to the first line, the fifth line CA199 obtains 50 scores corresponding to the first line, the total score obtained by the patient is 97.5, and the 97.5 scores obtained by the sixth line are projected to the seventh line, so that the probability of ineffective treatment of the patient is about 30% after the patient is treated by adopting a standard treatment scheme (Chinese gastric cancer diagnosis and treatment standard mentioned in the embodiment).
Example 2
The ROC curve of fig. 5 and the calibration curve of fig. 6 are used to verify the discrimination and calibration of the efficacy prediction model in the examples, respectively.
The model of example 1 was evaluated based on the patient population data included in example 1. The discrimination of the model is represented by the ROC curve (fig. 5), the ordinate is the True Positive Rate (TPR), which is the True treatment ineffective and the model also judges the number of patients with ineffective treatment divided by the number of all patients with ineffective True treatment. The abscissa is the False Positive Rate (FPR), which is the number of patients that are therapeutically effective but for which the model predicts that treatment is ineffective divided by the number of patients for which all actual treatments are effective. The TPR true yang rate is the finding rate of people who do not have effective treatment, and the number of people who do not have effective treatment is found; the FPR false positive rate is the false detection rate of the effective treatment personnel, and the number of the effective treatment personnel is detected by mistake. AUC, Area under the Curve of ROC (AUC ROC). As shown in FIG. 5, the area under the red curve and the blue dotted line is 0.5-1, the area under the curve is closer to 1, the discrimination of the model is better, and the AUC (intrinsic temperature coefficient), namely C index, of the model is 0.85 (95% CI: 0.78-0.93), which proves that the model has better discrimination. The inventor verifies the model internally by a Bootstrap method, namely, 1000 samples are randomly drawn from the original data set to form a new sample, and the C index is recalculated, and finally, after the calibration of internal verification by using Bootstrap (1,000 times), the C index is 0.84, which indicates that the model of the embodiment 1 has better discrimination.
The calibration curve of the prediction model is shown in FIG. 6, which is an evaluation of a disease risk model to predict a certain futureThe important index of the accuracy of the probability of the individual occurrence outcome event reflects the degree of consistency between the model predicted risk and the actual occurrence risk, and therefore, the index can also be called consistency. The calibration degree is good, the accuracy of the prediction model is high, and the calibration degree is poor, so that the model can possibly overestimate or underestimate the occurrence risk of diseases. The degree of calibration of the prediction model by the present inventors was evaluated by fitting test statistics of calibration curves, Hosmer and Lemeshow, corresponding toPThe larger the value, the better the degree of calibration of the predictive model is suggested. Statistics of the Hosmer-Lemeshow test obtained by the present inventorsP =0.962, the model has a better degree of calibration, and the inventors plotted a calibration curve for the probability of predicted and observed patient treatment ineffectiveness.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting the protection scope of the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (5)
1. VEGFA-based first-line chemotherapy efficacy prediction model for HER2 negative advanced gastric cancer patients, which is characterized by comprising the following components:
a nomogram for predicting the treatment efficacy of HER2 patients with negative advanced gastric cancer, wherein the nomogram comprises a point scale Points on a first line, and the point scale is 0-100; the body mass index BMI of the patient in the second behavior is more than or equal to 18.5kg/m2And BMI < 18.5kg/m2A respective score corresponding to the first row; the third line is diagnosis mode Diagnose Pattern, the first treatment late stage and postoperative recurrence correspond to the first line with a corresponding score respectively; the fourth row is VEGFA, and VEGFA is less than 179.9 pg/ml and VEGFA is more than or equal to 179.9 pg/ml which respectively correspond to a corresponding score of the first row; the fifth row CA19-9 is less than 79.8U/ml and CA19-9 is more than or equal to 79.8U/ml respectively corresponds to a corresponding score of the first row; the total score scale of the patients in the sixth action has a score range of 0-260, wherein the starting end 0 is the same as the starting end of the score scale in the first action, and the tail end 260 is the same as the tail end 100 of the score scale in the first actionCorresponding;
the seventh line is a probability score scale for ineffective treatment of the patients, the score range is 0.1-0.9, and the starting end 0.1 and the tail end 0.9 of the probability score scale correspond to the starting end 45 and the tail end 215 of the total score scale of the patients in the sixth line respectively;
and adding the scores of the 4 indexes BMI, Diagnose Pattern, VEGFA and CA19-9 in the second row to the fifth row corresponding to the first row to obtain the total score of the patients in the sixth row, and correspondingly projecting the total score of the patients in the sixth row to the seventh row to obtain the probability of ineffective treatment of the patients.
2. The VEGFA-based prediction model of first-line chemotherapy efficacy of HER2 negative advanced gastric cancer patient according to claim 1, wherein the first-line chemotherapy efficacy histogram model of HER2 negative advanced gastric cancer patient is established by the following steps:
collecting M clinical pathological characteristics of HER2 negative advanced gastric cancer patients, N laboratory indexes including VEGFA, and evaluating the first-line chemotherapy curative effect of the patients;
the collected M clinical pathological information comprises sex, age, height, weight, American eastern cooperative group of tumors (ECOG PS), diagnosis mode Diagnose Pattern early treatment late stage or postoperative recurrence, histopathology and position of primary focus, number of metastasis and whether serosal cavity effusion is merged or not;
laboratory indices include VEGFA, CEA, CA19-9, total protein TP, albumin ALB, hemoglobin Hb, C-reactive protein CRP, low density lipoprotein cholesterol LDL-C, and high density lipoprotein cholesterol HDL-C;
the overall index is calculated according to the following formula, AGR = ALB/TP-ALB, BMI = height/weight2CAR = CRP/ALB, low density lipoprotein cholesterol/high density lipoprotein cholesterol ratio LHR = LDL-C/HDL-C, SII = platelet count by neutrophil count/lymphocyte count;
carrying out regression analysis on M clinical pathological characteristics and N laboratory indexes by adopting single-factor Logistic regression analysis, preliminarily screening single-factor predictive variables related to curative effect, and screening single-factor predictive variables related to curative effectP <0.05 variable is included in the multi-factor Logistic regression analysis, and the independent curative effect is screened outPredicting variables;
performing regression analysis on the M clinical pathological characteristics and the N laboratory indexes by adopting an LASSO regression method, and screening predictive variables related to the curative effect;
(4) and finally selecting BMI, Diagnose Pattern, VEGFA and CA19-9 to construct a curative effect histogram model according to the variables and clinical meanings screened by the LASSO-Logistic regression model.
3. Use of the model for predicting the efficacy of first-line chemotherapy in a VEGFA-based HER2 negative advanced gastric cancer patient according to claim 1, wherein the model comprises: the VEGFA-based prediction model for first-line chemotherapy efficacy of HER2 negative advanced gastric cancer patients is applied to evaluate the probability of ineffective treatment of the following patients: histologically confirmed HER2 negative patients, patients who have not received anti-tumor therapy for recurrent or metastatic gastric cancer, patients who are not suitable or willing to receive surgery or radiation therapy, patients with target lesions for which efficacy can be assessed.
4. Use of the model for predicting the efficacy of first-line chemotherapy in VEGFA-based HER2 negative advanced gastric cancer patients according to claim 1; the prediction model of first-line chemotherapy efficacy of HER2 negative advanced gastric cancer patients based on VEGFA is applied to evaluate the probability of ineffective treatment of the following patients, and the exclusion patient criteria are as follows: (iii) incorporation of other tumors or subtypes; patients with severe center of gravity, liver, kidney disease; patients with severe bleeding or infectious diseases.
5. Use of the predictive model of first-line chemotherapy efficacy of VEGFA-based HER2 negative advanced gastric cancer patient according to claim 3, wherein: the accuracy of the VEGFA-based HER2 negative advanced gastric cancer patient first-line chemotherapy curative effect prediction model is verified by the following method: and judging the discrimination of the established model by adopting an ROC curve of the R language drawing model according to the established model, and judging the calibration of the established model by drawing a calibration curve of the model.
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