Objective:To explore the influence of multidisciplinary treatment (MDT) on clinical staging and diagnosis and treatment strategies for rectal cancer.Methods:A retrospective case series study was conducted. The clinical data of 142 rectal cancer patients who underwent surgical treatment in Shanxi Provincial People's Hospital from March 2021 to December 2021 were retrospectively analyzed. According to whether to implement MDT or not, all patients were divided into MDT group (68 cases) and non-MDT group (74 cases). Relevant clinical data including patients' basic information (gender, age, etc.), TNM staging, whether to receive neoadjuvant radiotherapy and chemotherapy or not, surgical methods, R0 resection rate of both groups were compared. The implementation methods and the effects of MDT for patients were summarized.Results:There were statistically significant differences in the proportion of clinical N staging at initial diagnosis, whether to receive neoadjuvant radiotherapy and chemotherapy or not of both groups (all P < 0.05). The overall agreement rate of clinical T staging at initial diagnosis and pathological T staging was 67.6% (46/68), 50.0% (37/74), respectively in the MDT group and the non-MDT group, and the difference was statistically significant ( χ2 = 4.54, P = 0.033). The overall agreement rate of N staging at initial diagnosis and pathological N staging was 50.0% (34/68), 54.1% (40/74), respectively in the MDT group and the non-MDT group, and the difference was not statistically significant ( χ2 = 0.23, P = 0.629). The treatment rate of neoadjuvant radiotherapy and chemotherapy was 57.4% (39/68) and 4.1% (3/74), respectively in the MDT group and the non-MDT group, and the difference was statistically significant ( χ2 = 48.33, P < 0.001). The R0 resection rate in both the MDT group and non-MDT group was 100.0%, and no tumor tissue was found at the upper, lower, and circumferential margins. Conclusions:MDT could provide more accurate clinical staging and more effective diagnosis and treatment opinions for patients, and provide reliable guidance for the treatment selections.

Objective To investigate the independent risk factors for acute severe cholangitis and related protective factors, and to construct a risk prediction scoring model for acute severe cholangitis. Methods A retrospective analysis was performed for the clinical data of 381 patients with acute cholangitis who were admitted to Department of Hepatobiliary Surgery, The 940th Hospital of Joint Logistics Support Force of Chinese People's Liberation Army, from January 2016 to July 2021, among whom there were 273 patients with non-severe cholangitis and 108 patients with severe cholangitis. Univariate and multivariate logistic regression analyses were used to screen out the independent risk factors for acute severe cholangitis and related protective factors, and then a logistic regression model was established. The receiver operating characteristic (ROC) curve was used to evaluate the discriminatory ability of the model, the calibration curve was used to evaluate the prediction accuracy of the model, and decision curve analysis (DCA) was used to evaluate the clinical value of the model. Moreover, the enhanced Bootstrap method was used to perform internal validation of the model and evaluate the performance of the model in internal validation. The model was visualized by the construction of Web calculator, nomogram, and scoring system. The two-independent-samples t test was used for comparison of normally distributed continuous data between groups, and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between groups; the chi-square test was used for comparison of categorical data between groups. Results The univariate and multivariate logistic regression analyses showed that total bilirubin (TBil) (odds ratio [ OR ]=1.014, 95% confidence interval [ CI ]: 1.009-1.020, P < 0.001), percentage of neutrophils ( OR =1.128, 95% CI : 1.088-1.175, P < 0.001), and age ( OR =1.053, 95% CI : 1.027-1.082, P < 0.001) were independent risk factors, and albumin (Alb) ( OR =0.871, 95% CI : 0.817-0.924, P < 0.001) was a protective factor. The above independent risk factors and protective factor were included in the logistic regression analysis for model fitting, and the predictive model obtained had an area under the ROC curve (AUC) of 0.925 (95% CI : 0.897-0.952), with a specificity of 0.817 and a sensitivity of 0.935 at the optimal cut-off value of 0.245. The calibration curve showed that the predicted probability of the model was approximately equal to the actual probability, with a Brier value of 0.098, and the decision curve analysis showed that the model had a higher net income within the threshold probability interval of 0.1-0.9. Internal validation showed an AUC internal validation of 0.915 and a Brier value internal verification of 0.106. Conclusion TBil, percentage of neutrophils, and age are independent risk factors for acute severe cholangitis, while Alb is a protective factor. The established risk prediction scoring model has good discriminatory ability, calibration, and clinical value and can identify patients with acute severe cholangitis at an early stage, which provides a reference for subsequent diagnosis and treatment.