ArticleFrontiers in medicine2026
Machine learning for predicting surgical difficulty of laparoscopic total mesorectal excision for rectal cancer: integrating MR-based pelvimetry and peritoneal reflection.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: To investigate the potential of a machine learning (ML) model integrating MR-based pelvimetry and the peritoneal reflection (PR) in predicting the surgical difficulty of laparoscopic total mesorectal excision (LaTME) for rectal cancer (RC). Methods: RC patients who underwent rectal magnetic resonance imaging (MRI) before LaTME between December 2020 and December 2022 were included in this retrospective study. The duration of surgery, blood loss, postoperative hospital stay, and postoperative complications were selected to evaluate the surgical difficulty. Using unsupervised clustering analysis, all patients were divided into surgical difficulty group and non-surgical difficulty group based on four indicators of surgical difficulty. Least absolute shrinkage and selection operator (LASSO) regression and logistic regression were used to identify factors influencing the surgical difficulty of LaTME, and logistic regression (LR) and the extreme-gradient boosting (XGB) models were constructed. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were utilized to compare the performance of the two models. Results: Data from 283 patients (60.98 ± 11.17 years old, 204 males and 79 females) were evaluated. The LASSO regression suggested that pelvic depth, the distance from the lower tumor margin to PR (T-PR), and gender were associated with the operative difficulty. The XGB model with an area under curve (AUC) of 0.809 (95% CI: 0.757-0.862) demonstrated a better performance than the LR model with an AUC of 0.623 (95%CI: 0.553-0.694). DCA confirmed that the XGB model was superior to the LR approach. Conclusion: The ML model integrating the pelvic depth, T-PR, and gender can evaluate the surgical difficulty of LaTME preoperatively and non-invasively, thus facilitating the predictive, preventive, and personalized treatment process in RC treatment.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.