ArticleFrontiers in oncology2022
An MRI-based pelvimetry nomogram for predicting surgical difficulty of transabdominal resection in patients with middle and low rectal cancer.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 11 citations in OpenAlex.
- Interpretable machine learning model for predicting operative difficulty in robotic total mesorectal excision for mid-low rectal cancer.Journal of robotic surgery · 2026Article
- Machine learning for predicting surgical difficulty of laparoscopic total mesorectal excision for rectal cancer: integrating MR-based pelvimetry and peritoneal reflection.Frontiers in medicine · 2026Article
- Predictive Risk Factors for Low Anterior Resection Syndrome (LARS) in Rectal Cancer-An Observational Cohort Study.Journal of clinical medicine · 2025Article
- Development and validation of machine learning models and nomograms for predicting the surgical difficulty of laparoscopic resection in rectal cancer.World journal of surgical oncology · 2024Article
- Nomogram for predicting the surgical difficulty of laparoscopic total mesorectal excision and exploring the technical advantages of robotic surgery.Frontiers in oncology · 2024Article
- Interpretable machine learning model to predict surgical difficulty in laparoscopic resection for rectal cancer.Frontiers in oncology · 2024Review
- Advances in pelvic imaging parameters predicting surgical difficulty in rectal cancer.World journal of surgical oncology · 2023Review
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: The current work aimed to develop a nomogram comprised of MRI-based pelvimetry and clinical factors for predicting the difficulty of rectal surgery for middle and low rectal cancer (RC). Methods: Consecutive mid to low RC cases who underwent transabdominal resection between June 2020 and August 2021 were retrospectively enrolled. Univariable and multivariable logistic regression analyses were carried out for identifying factors (clinical factors and MRI-based pelvimetry parameters) independently associated with the difficulty level of rectal surgery. A nomogram model was established with the selected parameters for predicting the probability of high surgical difficulty. The predictive ability of the nomogram model was assessed by the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Results: A total of 122 cases were included. BMI (OR = 1.269, Conclusions: The nomogram model is feasible for predicting the difficulty level of rectal surgery, utilizing MRI-based pelvimetry parameters and clinical factors in mid to low RC cases.
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