ArticleScientific reports2023
Three-dimensional visualization of the total mesorectal excision plane for dissection in rectal cancer surgery and its ability to predict surgical difficulty.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Deep Learning-Based Prediction System for Surgical Difficulty in Rectal Cancer Patients Using MRI Pelvimetry.Yonsei medical journal · 2026Article
- Development and validation of a multiparametric magnetic resonance imaging-based nomogram for predicting feasibility of sphincter-preserving surgery in mid-low rectal cancer.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Deep learning-based real-time intraoperative detection of thoracic duct.Journal of thoracic disease · 2026Article
- Golden vision: The potential of yellow enhancement in laparoscopic abdominal surgeries and surgical education.World journal of gastrointestinal endoscopy · 2025Review
- Preoperative sarcopenia negatively impacts short- and long-term outcomes of rectal cancer: A propensity score-matched analysis.Annals of gastroenterological surgery · 2025Article
- Simple pelvimetry predicts the pelvic manipulation time in robot-assisted low and ultra-low anterior resection for rectal cancer.Surgery today · 2024Article
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13 authors.
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Abstract
Total mesorectal excision (TME) for rectal cancer is often technically challenging. We aimed to develop a method for three-dimensional (3D) visualization of the TME dissection plane and to evaluate its ability to predict surgical difficulty. Sixty-six patients with lower rectal cancer who underwent robot-assisted surgery were retrospectively analyzed. A 3D TME dissection plane image for each case was reconstructed using Ziostation2. Subsequently, a novel index that reflects accessibility to the deep pelvis during TME, namely, the TME difficulty index, was defined and measured. Representative bony pelvimetry parameters and clinicopathological factors were also analyzed. The operative time for TME was used as an indicator of surgical difficulty. Univariate regression analysis revealed that sex, body mass index, mesorectal fat area, and TME difficulty index were associated with the operative time for TME, whereas bony pelvimetry parameters were not. Multivariate regression analysis found that TME difficulty index (β = - 0.398, P = 0.0025) and mesorectal fat area (β = 0.223, P = 0.045) had significant predictability for the operative time for TME. Compared with conventional bony pelvimetry parameters, the TME difficulty index and mesorectal fat area might be more useful in predicting the difficulty of rectal cancer surgery.
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