ArticleFrontiers in medicine2026
Biomarker-integrated machine learning models for predicting treatment-related bowel dysfunction after rectal cancer surgery: a multicenter development and external validation study.
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
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Low anterior resection syndrome (LARS) is a prevalent treatment-related complication following sphincter-preserving surgery for rectal cancer that severely impairs survivors' quality of life. In the era of biomarker-driven personalized cancer therapy, reliable tools integrating clinicopathological biomarkers for individualized risk stratification remain lacking. Machine learning (ML) approaches offer the potential to integrate heterogeneous biomarkers-including nutritional, inflammatory, and treatment-related indicators-for precision prediction, yet their comparative performance against conventional nomograms in LARS prediction has not been rigorously evaluated in multicenter settings. Methods: This multicenter retrospective cohort included 906 patients undergoing laparoscopic low anterior resection for rectal adenocarcinoma, with 506 in the training cohort and 400 from four hospitals for external validation. Major LARS was defined as a 6-month LARS score ≥ 21. LASSO selected features from 24 candidate variables. Five machine-learning models and a logistic regression nomogram were evaluated using AUC, calibration, decision curve analysis, and SHAP interpretation. Results: The incidence of major LARS was 48.0% (243/506) in the training cohort and 52.2% (209/400) in the validation cohort. LASSO regression selected 14 predictive biomarkers, including preoperative serum albumin, neoadjuvant chemotherapy, and preoperative radiotherapy. In external validation, the random forest model achieved the highest AUC of 0.903 (sensitivity 0.871, specificity 0.796), followed by the logistic regression nomogram (AUC 0.888). Multivariate analysis identified tumor-anal verge distance (OR = 0.733, 95% CI 0.679-0.792, Conclusion: The random forest model integrating clinicopathological biomarkers and treatment-related variables demonstrated robust predictive performance for postoperative or post-treatment risk stratification of treatment-related bowel dysfunction (LARS). The model is intended to support postoperative monitoring, survivorship counseling, and rehabilitation planning rather than purely preoperative treatment selection. These biomarker-driven prediction tools can facilitate individualized functional-risk assessment in rectal cancer patients undergoing multimodal therapy.
Indexed as
Identifiers
What Socratic 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 Socratic graph.