Background and purpose: This meta-analysis assesses the surgical outcomes between robot-assisted minimally-invasive McKeown esophagectomy and conventional one. Method: This meta-analysis searched the Web of Science, PUBMED, and EMBASE from the database’s inception to January 2022. Altogether, 1073 records were identified in the literature search. Studies that evaluated the outcomes between robot-assisted minimally-invasive McKeown esophagectomy and conventional one among postoperative patients with oesophageal neoplasms were included. The assessed outcomes involved complications and clinical outcomes. In addition, heterogeneity was analyzed, and evidence quality was evaluated. Result: Evidence indicated that RAMIE (minimally-invasive esophagectomy assisted with robot) decreased incidences of lung complications and hospital stay as well as increased harvested lymph nodes. Conclusions: There was currently little evidence from randomized studies depicting that robot surgery manifested a clear overall advantage, but there was growing evidence regarding the clinical benefits of robot-assisted minimally invasive McKeown esophagectomy over conventional one.

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BackgroundPrediction of prognosis for patients with esophageal cancer(EC) is beneficial for their postoperative clinical decision-making. This study’s goal was to create a dependable machine learning (ML) model for predicting the prognosis of patients with EC after surgery.MethodsThe files of patients with esophageal squamous cell carcinoma (ESCC) of the thoracic segment from China who received radical surgery for EC were analyzed. The data were separated into training and test sets, and prognostic risk variables were identified in the training set using univariate and multifactor COX regression. Based on the screened features, training and validation of five ML models were carried out through nested cross-validation (nCV). The performance of each model was evaluated using Area under the curve (AUC), accuracy(ACC), and F1-Score, and the optimum model was chosen as the final model for risk stratification and survival analysis in order to build a valid model for predicting the prognosis of patients with EC after surgery.ResultsThis study enrolled 810 patients with thoracic ESCC. 6 variables were ultimately included for modeling. Five ML models were trained and validated. The XGBoost model was selected as the optimum for final modeling. The XGBoost model was trained, optimized, and tested (AUC = 0.855; 95% CI, 0.808-0.902). Patients were separated into three risk groups. Statistically significant differences (p < 0.001) were found among all three groups for both the training and test sets.ConclusionsA ML model that was highly practical and reliable for predicting the prognosis of patients with EC after surgery was established, and an application to facilitate clinical utility was developed.