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.

【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.

Objective:To examine a survival prognostic model applicable for patients with intrahepatic cholangiocarcinoma (ICC) based on Bayesian network.Methods:The clinical and pathological data of ICC patients who underwent curative intent resection in ten Chinese hepatobiliary surgery centers from January 2010 to December 2018 were collected.A total of 516 patients were included in the study. There were 266 males and 250 females.The median age( M( Q R)) was 58(14) years.One hundred and sixteen cases (22.5%) with intrahepatic bile duct stones,and 143 cases (27.7%) with chronic viral hepatitis.The Kaplan-Meier method was used for survival analysis.The univariate and multivariate analysis were implemented respectively using the Log-rank test and Cox proportional hazard model.One-year survival prediction models based on tree augmented naive Bayesian (TAN) and na?ve Bayesian algorithm were established by Bayesialab software according to different variables,a nomogram model was also developed based on the independent predictors.The receiver operating characteristic curve and the area under curve (AUC) were used to evaluate the prediction effect of the models. Results:The overall median survival time was 25.0 months,and the 1-,3-and 5-year cumulative survival rates was 76.6%,37.9%,and 21.0%,respectively.Univariate analysis showed that gender,preoperative jaundice,pathological differentiation,vascular invasion,microvascular invasion,liver capsule invasion,T staging,N staging,margin,intrahepatic bile duct stones,carcinoembryonic antigen,and CA19-9 affected the prognosis(χ 2=5.858-54.974, all P<0.05).The Cox multivariate model showed that gender,pathological differentiation,liver capsule invasion, T stage,N stage,intrahepatic bile duct stones,and CA19-9 were the independent predictive factors(all P<0.05). The AUC of the TAN model based on all 19 clinicopathological factors was 74.5%,and the AUC of the TAN model based on the 12 prognostic factors derived from univariate analysis was 74.0%,the AUC of the na?ve Bayesian model based on 7 independent prognostic risk factors was 79.5%,the AUC and C-index of the nomogram survival prediction model based on 7 independent prognostic risk factors were 78.8% and 0.73,respectively. Conclusion:The Bayesian network model may provide a relatively accurate prognostic prediction for ICC patients after curative intent resection and performed superior to the nomogram model.

Objective:To evaluate the related factors of gallstones related gallbladder intraepithelial neoplasia(GBIN) and establish the prediction models for gallstones related GBIN.Methods:The clinicopathological data of 750 patients who underwent cholecystectomy for gallstones at Department of Hepatobiliary Surgery of the First Affiliated Hospital of Xi′an Jiaotong University from January 2013 to December 2018 and the postoperative pathological examination showed chronic cholecystitis or GBIN were analyzed retrospectively,including 150 cases of gallstones with GBIN and 600 cases of gallstones with chronic cholecystitis.There were 264 males and 486 females with age of (51.3±14.5) years (range: 18 to 90 years).The related factors for gallstones related GBIN were screened by χ 2 test and Logistic regression model,and the prediction models were established based on independent related factors and internal validation was conducted.The original data were randomly divided into a training cohort(526 cases) and a validation cohort(224 cases) at a ratio of 7∶3,and the nomogram and tree augmented na?ve Bayes were conducted to establish the prediction model for gallstones related GBIN.The consistency index(C-index),calibration chart,area under the receiver operating characteristic curve(AUC) and confusion matrix were used to evaluate the prediction performance of the two models. Results:Univariate analysis showed that age,gallstones history(years),gallbladder size,whether the gallbladder mucosa smooth or not,whether the gallbladder wall thickened or not,gallstones diameter,and number of gallstones were related factors for the occurrence of gallstones related GBIN (χ2=19.957,8.599,9.724,9.301,8.341,15.288,9.169,all P<0.05).Multivariate analysis showed that age ( OR=2.23,95% CI:1.50-3.31, P<0.01),gallbladder size ( OR=2.11,95% CI:1.17-3.80, P=0.013),whether the gallbladder mucosa smooth or not ( OR=1.80,95% CI:1.13-2.88, P=0.014),gallstones diameter( OR=2.98,95% CI:1.71-5.21, P<0.01),and number of gallstones ( OR=2.14,95% CI:1.34-3.42, P<0.01) were independent related factors for the occurrence of gallstones related GBIN; the C-index of the nomogram in training cohort and validation cohort were 0.708 and 0.696,respectively.The AUC of the two models in training cohort were 70.60% and 70.73%,and in validation cohort were 68.14% and 67.47%,respectively.The accuracy of the two models in training cohort were 69.96% and 70.72%,and in validation cohort were 66.96% and 67.41%,respectively. Conclusion:Age,gallbladder size,whether the gallbladder mucosa smooth or not,gallstones diameter and number of gallstones are independent related factors for the occurrence of gallstones related GBIN,and the nomogram and tree augmented na?ve Bayes prediction models based on the above factors can be used to predict the occurrence of GBIN.

Objective:To examine a survival prognostic model applicable for patients with intrahepatic cholangiocarcinoma (ICC) based on Bayesian network.Methods:The clinical and pathological data of ICC patients who underwent curative intent resection in ten Chinese hepatobiliary surgery centers from January 2010 to December 2018 were collected.A total of 516 patients were included in the study. There were 266 males and 250 females.The median age( M( Q R)) was 58(14) years.One hundred and sixteen cases (22.5%) with intrahepatic bile duct stones,and 143 cases (27.7%) with chronic viral hepatitis.The Kaplan-Meier method was used for survival analysis.The univariate and multivariate analysis were implemented respectively using the Log-rank test and Cox proportional hazard model.One-year survival prediction models based on tree augmented naive Bayesian (TAN) and na?ve Bayesian algorithm were established by Bayesialab software according to different variables,a nomogram model was also developed based on the independent predictors.The receiver operating characteristic curve and the area under curve (AUC) were used to evaluate the prediction effect of the models. Results:The overall median survival time was 25.0 months,and the 1-,3-and 5-year cumulative survival rates was 76.6%,37.9%,and 21.0%,respectively.Univariate analysis showed that gender,preoperative jaundice,pathological differentiation,vascular invasion,microvascular invasion,liver capsule invasion,T staging,N staging,margin,intrahepatic bile duct stones,carcinoembryonic antigen,and CA19-9 affected the prognosis(χ 2=5.858-54.974, all P<0.05).The Cox multivariate model showed that gender,pathological differentiation,liver capsule invasion, T stage,N stage,intrahepatic bile duct stones,and CA19-9 were the independent predictive factors(all P<0.05). The AUC of the TAN model based on all 19 clinicopathological factors was 74.5%,and the AUC of the TAN model based on the 12 prognostic factors derived from univariate analysis was 74.0%,the AUC of the na?ve Bayesian model based on 7 independent prognostic risk factors was 79.5%,the AUC and C-index of the nomogram survival prediction model based on 7 independent prognostic risk factors were 78.8% and 0.73,respectively. Conclusion:The Bayesian network model may provide a relatively accurate prognostic prediction for ICC patients after curative intent resection and performed superior to the nomogram model.

Objective:To evaluate the related factors of gallstones related gallbladder intraepithelial neoplasia(GBIN) and establish the prediction models for gallstones related GBIN.Methods:The clinicopathological data of 750 patients who underwent cholecystectomy for gallstones at Department of Hepatobiliary Surgery of the First Affiliated Hospital of Xi′an Jiaotong University from January 2013 to December 2018 and the postoperative pathological examination showed chronic cholecystitis or GBIN were analyzed retrospectively,including 150 cases of gallstones with GBIN and 600 cases of gallstones with chronic cholecystitis.There were 264 males and 486 females with age of (51.3±14.5) years (range: 18 to 90 years).The related factors for gallstones related GBIN were screened by χ 2 test and Logistic regression model,and the prediction models were established based on independent related factors and internal validation was conducted.The original data were randomly divided into a training cohort(526 cases) and a validation cohort(224 cases) at a ratio of 7∶3,and the nomogram and tree augmented na?ve Bayes were conducted to establish the prediction model for gallstones related GBIN.The consistency index(C-index),calibration chart,area under the receiver operating characteristic curve(AUC) and confusion matrix were used to evaluate the prediction performance of the two models. Results:Univariate analysis showed that age,gallstones history(years),gallbladder size,whether the gallbladder mucosa smooth or not,whether the gallbladder wall thickened or not,gallstones diameter,and number of gallstones were related factors for the occurrence of gallstones related GBIN (χ2=19.957,8.599,9.724,9.301,8.341,15.288,9.169,all P<0.05).Multivariate analysis showed that age ( OR=2.23,95% CI:1.50-3.31, P<0.01),gallbladder size ( OR=2.11,95% CI:1.17-3.80, P=0.013),whether the gallbladder mucosa smooth or not ( OR=1.80,95% CI:1.13-2.88, P=0.014),gallstones diameter( OR=2.98,95% CI:1.71-5.21, P<0.01),and number of gallstones ( OR=2.14,95% CI:1.34-3.42, P<0.01) were independent related factors for the occurrence of gallstones related GBIN; the C-index of the nomogram in training cohort and validation cohort were 0.708 and 0.696,respectively.The AUC of the two models in training cohort were 70.60% and 70.73%,and in validation cohort were 68.14% and 67.47%,respectively.The accuracy of the two models in training cohort were 69.96% and 70.72%,and in validation cohort were 66.96% and 67.41%,respectively. Conclusion:Age,gallbladder size,whether the gallbladder mucosa smooth or not,gallstones diameter and number of gallstones are independent related factors for the occurrence of gallstones related GBIN,and the nomogram and tree augmented na?ve Bayes prediction models based on the above factors can be used to predict the occurrence of GBIN.

Objective:To examine the role of the number of lymph nodes examined(NLNE) on the prognosis of patients with curatively resected gallbladder carcinoma(GBC).Methods:The clinicopathological data and prognosis of 401 patients with GBC who underwent radical surgery from six institutions of China from January 2013 to December 2017 were analyzed retrospectively. There were 153 males(38.2%) and 248 females(61.8%), with age of (62.0±10.5) years (range: 30-88 years). Fifty-three patients(22.2%) were accompanied by jaundice. All patients underwent radical resection+regional lymphadenectomy.R0 or R1 resection was confirmed by postoperative pathological examination.The different cut-off values of NLNE were determined by the X-tile software, the optimal cut-off values were identified by analyzing the relationship between different cut-off values of NLNE with survival rate. Kaplan-Meier method was used for survival analysis. Univariate and multivariate analysis were implemented respectively using the Log-rank test and Cox proportional hazard model.Results:Among the 401 patients enrolled, 135 cases (33.6%) had lymphatic metastasis, of which 98 cases were in N1 stage(24.4%) and 37 cases were in N2 stage(9.2%).A total of 2 794 NLNE were retrieved, with a median count of 6 (5).The median positive lymph nodes count was 0 (1), and the median positive lymph nodes ratio was 0 (IQR, 0-0.2). Since the 12 and 15 were determined as the cut-off values by X-tile, all patients were divided into three groups of 1-11, 12-15 and ≥16.The 3-year survival rate of the three groups was 45.2%, 74.5%, 12.0% respectively, with statistically significant difference between three groups (χ 2=10.94, P<0.01). The results of multivariate analysis showed that NLNE was an independent prognostic factor for overall survival ( P<0.05). Further analysis was performed specifically for subgroup of T stages. For T1b patients, the prognosis of the NLNE with 1-7 group was significantly better than that of the ≥8 group(χ 2=4.610, P<0.05). For T2 patients, the prognosis of the TLNE ≥7 group was significantly better than that of 1 -6 group (χ 2=4.287, P<0.05). For T3 and T4 patients, the prognosis of the TLNE with 12 - 15 group was significantly better than that of 1 -11 group (χ 2=5.007, P<0.01) and ≥16 group (χ 2=10.158, P<0.01). Conclusions:The NLNE is an independent factor affecting the prognosis of patients with GBC.For patients with stage T1b,8 lymph nodes should be retrieved; for patients with stage T2,extensive dissection of more than 6 lymph nodes can significantly improve the prognosis.For advanced patients (stages T3 and T4), extensive dissection with 12-15 lymph nodes is recommended. However, it fails to get more survival benefits by dissecting more than 16 lymph nodes.

Gallbladder carcinoma (GBC) is the most common malignancy of the biliary tract, radical resection is the only effective treatment for GBC at present. However, the postoperative effect is still poor. Therefore, identifying the key prognostic factors and establishing an individual and accurate survival prediction model for GBC are critical to prognosis assessment, treatment options and clinical decision support in patients with GBC. The prediction value of current commonly used TNM staging system is limited. Cox regression model is the most commonly used classical survival analysis method, but it is difficult to establish the association between prognostic variables. Nomogram and machine learning techniques including Bayesian network have been used to establish survival prediction model of GBC in recent years, which representing a certain degree of advancement, however, the model precision and clinical application still need to be further verified. The establishment of more accurate survival prediction models for GBC based on machine learning algorithm from Chinese multicenter large sample database to guide the clinical decision-making is the main research direction in the future.

Objective:To examine the role of the number of lymph nodes examined(NLNE) on the prognosis of patients with curatively resected gallbladder carcinoma(GBC).Methods:The clinicopathological data and prognosis of 401 patients with GBC who underwent radical surgery from six institutions of China from January 2013 to December 2017 were analyzed retrospectively. There were 153 males(38.2%) and 248 females(61.8%), with age of (62.0±10.5) years (range: 30-88 years). Fifty-three patients(22.2%) were accompanied by jaundice. All patients underwent radical resection+regional lymphadenectomy.R0 or R1 resection was confirmed by postoperative pathological examination.The different cut-off values of NLNE were determined by the X-tile software, the optimal cut-off values were identified by analyzing the relationship between different cut-off values of NLNE with survival rate. Kaplan-Meier method was used for survival analysis. Univariate and multivariate analysis were implemented respectively using the Log-rank test and Cox proportional hazard model.Results:Among the 401 patients enrolled, 135 cases (33.6%) had lymphatic metastasis, of which 98 cases were in N1 stage(24.4%) and 37 cases were in N2 stage(9.2%).A total of 2 794 NLNE were retrieved, with a median count of 6 (5).The median positive lymph nodes count was 0 (1), and the median positive lymph nodes ratio was 0 (IQR, 0-0.2). Since the 12 and 15 were determined as the cut-off values by X-tile, all patients were divided into three groups of 1-11, 12-15 and ≥16.The 3-year survival rate of the three groups was 45.2%, 74.5%, 12.0% respectively, with statistically significant difference between three groups (χ 2=10.94, P<0.01). The results of multivariate analysis showed that NLNE was an independent prognostic factor for overall survival ( P<0.05). Further analysis was performed specifically for subgroup of T stages. For T1b patients, the prognosis of the NLNE with 1-7 group was significantly better than that of the ≥8 group(χ 2=4.610, P<0.05). For T2 patients, the prognosis of the TLNE ≥7 group was significantly better than that of 1 -6 group (χ 2=4.287, P<0.05). For T3 and T4 patients, the prognosis of the TLNE with 12 - 15 group was significantly better than that of 1 -11 group (χ 2=5.007, P<0.01) and ≥16 group (χ 2=10.158, P<0.01). Conclusions:The NLNE is an independent factor affecting the prognosis of patients with GBC.For patients with stage T1b,8 lymph nodes should be retrieved; for patients with stage T2,extensive dissection of more than 6 lymph nodes can significantly improve the prognosis.For advanced patients (stages T3 and T4), extensive dissection with 12-15 lymph nodes is recommended. However, it fails to get more survival benefits by dissecting more than 16 lymph nodes.

Gallbladder carcinoma (GBC) is the most common malignancy of the biliary tract, radical resection is the only effective treatment for GBC at present. However, the postoperative effect is still poor. Therefore, identifying the key prognostic factors and establishing an individual and accurate survival prediction model for GBC are critical to prognosis assessment, treatment options and clinical decision support in patients with GBC. The prediction value of current commonly used TNM staging system is limited. Cox regression model is the most commonly used classical survival analysis method, but it is difficult to establish the association between prognostic variables. Nomogram and machine learning techniques including Bayesian network have been used to establish survival prediction model of GBC in recent years, which representing a certain degree of advancement, however, the model precision and clinical application still need to be further verified. The establishment of more accurate survival prediction models for GBC based on machine learning algorithm from Chinese multicenter large sample database to guide the clinical decision-making is the main research direction in the future.