Objective To systematically review the efficacy of robotic, laparoscopic-assisted, and open total mesorectal excision (TME) for the treatment of rectal cancer. Methods The PubMed, EMbase, The Cochrane Library, and ClinicalTrials.gov databases were electronically searched to identify cohort studies on robotic, laparoscopic-assisted, and open TME for rectal cancer published from January 2016 to January 2022. Two reviewers independently screened the literature, extracted data, and evaluated the risk of bias of the included studies. Subsequently, network meta-analysis was performed using RevMan 5.4 software and R software. Results A total of 24 studies involving 12 348 patients were included. The results indicated that among the three types of surgical procedures, robotic TME showed the best outcomes by shortening the length of hospital stay, reducing the incidence of postoperative anastomotic fistula and intestinal obstruction, and lowering the overall postoperative complication rate. However, differences in the number of dissected peritumoural lymph nodes were not statistically significant. Conclusion Robotic TME shows better outcomes in terms of the radicality of excision and postoperative short-term outcomes in the treatment of rectal cancer. However, clinicians should consider the patients’ actual condition for the selection of surgical methods to achieve individualised treatment for patients with rectal cancer.
ObjectiveTo develop a machine learning model to identify preoperative, intraoperative, and postoperative high-risk factors of laparoscopic inguinal hernia repair (LHR) and to predict recurrent hernia. Methods The patients after LHR from 2010 to 2018 were included. Twenty-nine characteristic variables were collected, including patient demographic characteristics, chronic medical history, laboratory test characteristics, surgical information, and postoperative status of the patients. Four machine learning algorithms, including extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and k-nearest neighbor algorithm (KNN), were used to construct the model. We also applied Shapley additive explanation (SHAP) for visual interpretation of the model and evaluated the model using the k-fold cross-validation method, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). ResultsA total of 1 178 patients with inguinal hernias were included in the study, including 114 patients with recurrent hernias. The XGBoost algorithm showed the best performance among the four prediction models. The ROC curve results showed that the area under the curve (AUC) value of XGBoost was 0.985 in the training set and 0.917 in the validation set, which showed high prediction accuracy. The K-fold cross-validation method, calibration curve, and DCA curve showed that the XGBoost model was stable and clinically useful. The AUC value in the independent validation set was 0.86, indicating that the XGBoost prediction model has good extrapolation. The results of SHAP analysis showed that mesh size, mesh fixtion, diabetes, hypoproteinemia, obesity, smoking history, low intraoperative percutaneous arterial oxygen saturation (SpO2), and low intraoperative body temperature were strongly associated with recurrent hernia. ConclusionThe predictive model of recurrent hernia after LHR in patients derived from the XGBoost machine learning algorithm in this study can assist clinicians in clinical decision making.