ArticleWorld journal of gastroenterology2023
Machine learning model for prediction of low anterior resection syndrome following laparoscopic anterior resection of rectal cancer: A multicenter study.
Article in World journal of gastroenterology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 11 citations in OpenAlex.
- Machine learning model-guided selective use of temporary diverting ileostomy in rectal cancer surgery: a randomized controlled trial.Nature communications · 2026Trial
- Incremental Predictive Value of Conventional MRI Pelvimetry for Major Low Anterior Resection Syndrome After Rectal Cancer Surgery.Journal of clinical medicine · 2026Article
- Distinct trajectories of low anterior resection syndrome following ileostomy reversal.Frontiers in medicine · 2026Article
- Biomarker-integrated machine learning models for predicting treatment-related bowel dysfunction after rectal cancer surgery: a multicenter development and external validation study.Frontiers in medicine · 2026Article
- Ethical Pitfalls in AI-Based Predictive Models in Surgery.World journal of surgery · 2025Review
- Application and challenges of artificial intelligence in predicting perioperative complications of colorectal cancer.World journal of gastrointestinal surgery · 2025Article
- Sphincter-preserving surgical techniques in low rectal cancer management: A systematic review of contemporary evidence.World journal of gastrointestinal surgery · 2025Review
- Predicting pathological response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer with two step feature selection and ensemble learning.Scientific reports · 2025Article
- Risk factors of the low anterior resection syndrome (LARS) after ileostomy reversal in rectal cancer patient.Scientific reports · 2024Article
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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
backgroundLow anterior resection syndrome (LARS) severely impairs patient postoperative quality of life, especially major LARS. However, there are few tools that can accurately predict major LARS in clinical practice.
aimTo develop a machine learning model using preoperative and intraoperative factors for predicting major LARS following laparoscopic surgery of rectal cancer in Chinese populations.
methodsClinical data and follow-up information of patients who received laparoscopic anterior resection for rectal cancer from two medical centers (one discovery cohort and one external validation cohort) were included in this retrospective study. For the discovery cohort, the machine learning prediction algorithms were developed and internally validated. In the external validation cohort, we evaluated the trained model using various performance metrics. Further, the clinical utility of the model was tested by decision curve analysis.
resultsOverall, 1651 patients were included in the present study. Anastomotic height, neoadjuvant therapy, diverting stoma, body mass index, clinical stage, specimen length, tumor size, and age were the risk factors associated with major LARS. They were used to construct the machine learning model to predict major LARS. The trained random forest (RF) model performed with an area under the curve of 0.852 and a sensitivity of 0.795 (95%CI: 0.681-0.877), a specificity of 0.758 (95%CI: 0.671-0.828), and Brier score of 0.166 in the external validation set. Compared to the previous preoperative LARS score model, the current model exhibited superior predictive performance in predicting major LARS in our cohort (accuracy of 0.772 for the RF model
conclusionWe developed and validated a robust tool for predicting major LARS. This model could potentially be used in the clinic to identify patients with a high risk of developing major LARS and then improve the quality of life.
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