Predictive model of the surgical difficulty of robot-assisted total mesorectal excision for rectal cancer: A multicenter, retrospective study

Background Rectal cancer robotic surgery is becoming more and more common, but evidence for predicting surgical difficulty is scarce. Our goal was to look at the elements that influence the complexity of robot-assisted total mesorectal excision (R-TME) in the medical care of middle and low rectal cancer as well as to establish and validate a predictive model on the basis of these factors. Methods Within this multicenter retrospective investigation, 166 consecutive patients receiving R-TME between January 2021 and December 2022 with middle and low rectal cancer were included and categorized according to the median operation time. A nomogram was created to forecast the procedure's complexity after variables that could affect its difficulty were found using logistic regression analysis. Results Using R software, a total of 166 patients were randomly split into two groups: a test group (48 patients) and a training group (118 patients) at a ratio of 7 to 3. The median operation time of all patients was 207.5 min; patients whose operation time was ≥ 207.5 min were allocated to the difficult surgery group (83 patients), and patients whose operation time was < 207.5 min were allocated to the nondifficult surgery group. Multivariate analysis revealed that body mass index (BMI), the gap between the tumour and the anal verge and the posterior rectal mesenteric thickness were independent predictors of surgical duration. A clinical predictive model was created and assessed employing the above independent predictors. The results of the receiver operating characteristic (ROC) analysis revealed the adequate discriminative ability of the predictive model. Conclusion Our study revealed that it is feasible to predict surgical difficulty by obtaining clinical and magnetic resonance parameters for imaging (the gap between the anal verge and the tumour, and posterior mesorectal thickness), and these predictions could be useful in making clinical decisions.