ABSTRACT
Objective:To investigate the establishment and application value of a radio-mics prediction model for lymph node metastasis of gallbladder carcinoma based on dual-phase enhanced computed tomography (CT).Methods:The retrospective cohort study was conducted. The clinicopathological data of 194 patients with gallbladder carcinoma who were admitted to the First Affiliated Hospital of Xi'an Jiaotong University from January 2012 to December 2020 were collected. There were 70 males and 124 females, aged (64±10)years. All patients underwent curative-intent resection of gallbladder carcinoma. A total of 194 patients were randomly divided into 156 cases in training set and 38 cases in test set according to the ratio of 8:2 based on random number method in R software. The training set was used to establish a diagnostic model, and the test set was used to validate the diagnostic model. After the patients undergoing CT examination, image analysis was performed, radiomics features were extracted, and a radiomics model was established. Based on clinicopathological data, a nomogram prediction model was established. Observation indicators: (1) lymph node dissection and histopathological examination results; (2) establishment and characteristic analysis of a radiomics prediction model; (3) analysis of influencing factors for lymph node metastasis of gallbladder carcinoma; (4) establishment of a nomogram prediction model for lymph node metastasis; (5) comparison of the predictive ability between the radiomics prediction model and nomogram prediction model for lymph node metastasis. Measurement data with normal distribution were represented as Mean± SD, and measurement data with skewed distribution were represented as M(range). Count data were expressed as absolute numbers, and comparison between groups was performed by the chi-square test. Univariate analysis was conducted by the chi-square test, and multivariate analysis was performed by the Logistic regression model forward method. The receiver operating characteristic curve was drawn, and the area under curve, decision curve, confusion matrix were used to evaluate the predictive ability of prediction models. Results:(1) Lymph node dissection and histopathological examination results. Of the 194 patients, 182 cases underwent lymph node dissection, with the number of lymph node dissected as 8(range, 1?34) per person and the number of positive lymph node as 0(range, 0?11) per person. Postoperative histopathological examination results of 194 patients: 122 patients were in stage N0, with the number of lymph node dissected as 7(range, 0?27) per person, 48 patients were in stage N1, with the number of lymph node dissected as 8(range, 2?34) per person and the number of positive lymph node as 1(range, 1?3) per person, 24 patients were in stage N2, with the number of lymph node dissected as 11(range, 2?20) per person and the number of positive lymph node as 5(range, 4?11) per person. (2) Establishment and characteristic analysis of a radiomics prediction model. There were 107 radiomics features extracted from 194 patients, including 18 first-order features, 14 shape features and 75 texture features. According to the intra-group correlation coefficient and absolute median difference of each radiomics feature, mutual information, Select K-Best, least absolute shrinkage and selection operator regression were conducted to further reduce dimensionality. By further combining 5 different machine learning algorithms including random forest, gradient boosting secession tree, support vector machine (SVM), K-Nearest Neighbors and Logistic regression, the result showed that the Select K-Best_SVM model had the best predictive performance after analysis, with the area under receiver operating characteristic curve as 0.76 in the test set. (3) Analysis of influencing factors for lymph node metastasis of gallbladder carcinoma. Results of univariate analysis showed that systemic inflammation response index, carcinoembryonic antigen (CEA), CA19-9, CA125, radiological T staging and radiological lymph node status were related factors for lymph node metastasis of patients with gallbladder cancer ( χ2=4.20, 11.39, 5.68, 11.79, 10.83, 18.58, P<0.05). Results of multivariate analysis showed that carcinoembryonic antigen, CA125, radiological T staging (stage T3 versus stage T1?2, stage T4 versus stage T1?2), radiological lymph node status were independent influencing factors for lymph node metastasis of patients with gallbladder carcinoma [ hazard ratio=2.79, 4.41, 5.62, 5.84, 3.99, 95% confidence interval ( CI) as 1.20?6.47, 1.81?10.74, 1.50?21.01, 1.02?33.31, 1.87?8.55, P<0.05]. (4) Establishment of a nomogram prediction model for lymph node metastasis. A nomogram prediction model was established based on the 4 independent influencing factors for lymph node metastasis of gallbladder carcinoma, including CEA, CA125, radiological T staging and radiological lymph node status. The concordance index of the nomogram model was 0.77 (95% CI as 0.75?0.79) in the training set and 0.73 (95% CI as 0.68?0.72) in the test set, respectively. (5) Comparison of the predictive ability between the radiomics predic-tion model and nomogram prediction model for lymph node metastasis. The receiver operating characteristic curve showed that the areas under the curve of Select K-Best_SVM radiomics model were 0.75 (95% CI as 0.74?0.76) in the training set and 0.76 (95% CI as 0.75?0.78) in the test set, respectively. The areas under the curve of nomogram prediction model were 0.77 (95% CI as 0.76?0.78) in the training set and 0.70 (95% CI as 0.68?0.72) in the test set, respectively. The decision curve analysis showed that Select K-Best_SVM radiomics model and nomogram prediction model had a similar ability to predict lymph node metastasis. The confusion matrix showed that Select K-Best_SVM radiomics model had the sensitivity as 64.29% and 75.00%, the specificity as 73.00% and 59.09% in the training set and test set, respectively. The nomogram had the sensitivity as 51.79% and 50.00%, the specificity as 80.00% and 72.27% in the training set and test set, respectively. Conclusion:A dual-phase enhanced CT imaging radiomics prediction model for lymph node metastasis of gallbladder carcinoma is successfully established, and its predictive ability is good and consistent with that of nomogram.
ABSTRACT
【Objective】 To explore the different expressions of TP53, P16 and K-ras in gallbladder high-grade intraepithelial neoplasia and early carcinoma, and establish their mutation random forest prediction model. 【Methods】 We retrospectively analyzed the clinicopathological data of 71 patients who underwent cholecystectomy at The First Affiliated Hospital of Xi’an Jiaotong University from January 2013 to December 2018, including 20 cases of chronic cholecystitis, 28 cases of gallbladder high-grade intraepithelial neoplasia, and 23 cases of early gallbladder carcinoma. The immunohistochemical SP method was conducted to detect the expressions of TP53, P16 and K-ras in the gallbladder pathological tissues; the correlation between the above genes and clinicopathological data was analyzed. A random forest prediction model of each gene mutation was established based on the clinicopathological data and gene expression. 【Results】 The positive expressions of TP53, P16 and K-ras were related to the gallbladder with cholecystolithiasis or polyps and gallbladder pathological tissue type. The positive rates of the three genes in the gallbladder polyps were significantly higher than those in the cholecystolithiasis group (P<0.05). The positive rates of the three genes in the latter two groups of gallbladder high-grade intraepithelial neoplasia and early gallbladder carcinoma were significantly higher than those in the chronic cholecystitis (P<0.05), while there was no statistical difference between the latter two groups (P>0.05). The mutations of TP53, P16 and K-ras had a certain correlation (χ2=6.285, 19.595, 4.070, r=0.298, 0.525, 0.239, P<0.05). TP53, P16 and K-ras mutation prediction models based on random forest had good accuracy (AUC=77.42%, 80.06%, 71.75%, accuracy=76.06%, 76.06%, 67.61%). 【Conclusion】 TP53, P16 and K-ras gene mutations promote the transformation of chronic cholecystitis to gallbladder carcinoma. The mutation prediction model based on random forest has a good accuracy, which can provide an important reference for carcinogenesis and early diagnosis of gallbladder carcinoma.
ABSTRACT
Objective@#To investigate the clinical value of Bayesian network in predicting survival of patients with advanced gallbladder cancer(GBC)who underwent curative intent surgery.@*Methods@#The clinical data of patients with advanced GBC who underwent curative intent surgery in 9 institutions from January 2010 to December 2015 were analyzed retrospectively.A median survival time model based on a tree augmented naïve Bayes algorithm was established by Bayesia Lab software.The survival time, number of metastatic lymph nodes(NMLN), T stage, pathological grade, margin, jaundice, liver invasion, age, sex and tumor morphology were included in this model.Confusion matrix, the receiver operating characteristic curve and area under the curve were used to evaluate the accuracy of the model.A priori statistical analysis of these 10 variables and a posterior analysis(survival time as the target variable, the remaining factors as the attribute variables)was performed.The importance rankings of each variable was calculated with the polymorphic Birnbaum importance calculation based on the posterior analysis results.The survival probability forecast table was constructed based on the top 4 prognosis factors. The survival curve was drawn by the Kaplan-Meier method, and differences in survival curves were compared using the Log-rank test.@*Results@#A total of 316 patients were enrolled, including 109 males and 207 females.The ratio of male to female was 1.0∶1.9, the age was (62.0±10.8)years.There was 298 cases(94.3%) R0 resection and 18 cases(5.7%) R1 resection.T staging: 287 cases(90.8%) T3 and 29 cases(9.2%) T4.The median survival time(MST) was 23.77 months, and the 1, 3, 5-year survival rates were 67.4%, 40.8%, 32.0%, respectively.For the Bayesian model, the number of correctly predicted cases was 121(≤23.77 months) and 115(>23.77 months) respectively, leading to a 74.86% accuracy of this model.The prior probability of survival time was 0.503 2(≤23.77 months) and 0.496 8(>23.77 months), the importance ranking showed that NMLN(0.366 6), margin(0.350 1), T stage(0.319 2) and pathological grade(0.258 9) were the top 4 prognosis factors influencing the postoperative MST.These four factors were taken as observation variables to get the probability of patients in different survival periods.Basing on these results, a survival prediction score system including NMLN, margin, T stage and pathological grade was designed, the median survival time(month) of 4-9 points were 66.8, 42.4, 26.0, 9.0, 7.5 and 2.3, respectively, there was a statistically significant difference in the different points(P<0.01).@*Conclusions@#The survival prediction model of GBC based on Bayesian network has high accuracy.NMLN, margin, T staging and pathological grade are the top 4 risk factors affecting the survival of patients with advanced GBC who underwent curative resection.The survival prediction score system based on these four factors could be used to predict the survival and to guide the decision making of patients with advanced GBC.