ArticleJournal of robotic surgery2024
Predictive model of the surgical difficulty of robot-assisted total mesorectal excision for rectal cancer: a multicenter, retrospective study.
Article in Journal of robotic surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Textbook outcome of robotic rectal resection with primary anastomosis for cancer: a composite quality measure to evaluate functional and midterm outcomes.Journal of robotic surgery · 2026Article
- Global research trends in robotic surgery for rectal cancer: a bibliometric and science-mapping analysis.Journal of robotic surgery · 2026Review
- Comment On: "Robotic versus laparoscopic surgery for mid and low rectal cancer: a propensity score-matched analysis of short-term outcomes and costs".Updates in surgery · 2026Article
- Pain management and early recovery after robot-assisted partial nephrectomy in a short-stay ambulatory pathway: a single-center retrospective cohort study.Journal of robotic surgery · 2026Article
- Interpretable machine learning model for predicting operative difficulty in robotic total mesorectal excision for mid-low rectal cancer.Journal of robotic surgery · 2026Article
- Development of a predictive score for prolonged pelvic operation time in robot-assisted low anterior resection: a single-center, retrospective study in Japan.Surgical endoscopy · 2025Article
- Phase-specific learning curves in robot-assisted rectal surgery: implications for surgical training.International journal of colorectal disease · 2025Article
- Development of a predictive model for surgical difficulty assessment in distal gastrectomy of advanced gastric carcinoma using LASSO-random forest integrated algorithm.Surgical endoscopy · 2025Article
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10 authors.
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Abstract
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. 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. 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 tumor 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. 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.
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