ArticleJournal of gastrointestinal oncology2021
Machine learning-based random forest predicts anastomotic leakage after anterior resection for rectal cancer.
Article in Journal of gastrointestinal oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 34 citations in OpenAlex.
- Machine Learning Algorithms for Predicting Surgical Outcomes after Colorectal Surgery: A Systematic Review.World journal of surgery · 2022Pooled it
- Ostomy Does Not Lead to Worse Outcomes After Bowel Resection With Ovarian Cancer: A Systematic Review.Frontiers in oncology · 2022Pooled it
- Machine learning model-guided selective use of temporary diverting ileostomy in rectal cancer surgery: a randomized controlled trial.Nature communications · 2026Trial
- AI Trustworthiness in the Perioperative Period for Patients with Serious Illness: Scoping Narrative Review.JMIR perioperative medicine · 2026Article
- Artificial Intelligence in Minimally Invasive and Robotic Gastrointestinal Surgery: Major Applications and Recent Advances.Journal of personalized medicine · 2026Review
- Development of a nomogram for predicting anastomotic leakage after rectal cancer surgery incorporating inflammatory indices.Frontiers in oncology · 2026Article
- A risk prediction model for overall and Grade C anastomotic leakage after rectal cancer surgery.Frontiers in oncology · 2026Article
- Evidence-based leakage management in cancer-preventive ileostomy care: A Delphi consensus integrating systematic review and clinical expertise.Asia-Pacific journal of oncology nursing · 2025Article
- Machine Learning to Predict Early Death Despite Pancreaticoduodenectomy.The Journal of surgical research · 2025Article
- Article
- Predicting autoimmune thyroiditis in primary Sjogren's syndrome patients using a random forest classifier: a retrospective study.Arthritis research & therapy · 2025Article
- Colorectal cancer prognosis based on dietary pattern using synthetic minority oversampling technique with K-nearest neighbors approach.Scientific reports · 2024Article
- Explainable Machine Learning Model to Preoperatively Predict Postoperative Complications in Inpatients With Cancer Undergoing Major Operations.JCO clinical cancer informatics · 2024Article
- Machine learning-based classifiers to predict metastasis in colorectal cancer patients.Frontiers in artificial intelligence · 2024Article
- Machine learning to predict postoperative complications after digestive surgery: a scoping review.The British journal of surgery · 2023Article
- Article
- Predictive Factors for Anastomotic Leakage Following Colorectal Cancer Surgery: Where Are We and Where Are We Going?Current oncology (Toronto, Ont.) · 2023Review
- Construction and evaluation of prognostic models of ECMO in elderly patients with cardiogenic shock based on BP neural network, random forest, and decision tree.American journal of translational research · 2023Article
- Clinical analysis of anastomotic fistula after anal preservation in 358 cases of rectal cancer and construction of risk prediction model: a single-center retrospective study.American journal of translational research · 2023Article
- A Predictive Model for Qualitative Evaluation of PG-SGA in Tumor Patients Through Machine Learning.Cancer management and research · 2022Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAnastomotic leakage (AL) is one of the commonest and most serious complications after rectal cancer surgery. The previous analyses on predictors for AL included small-scale patients, and their prediction models performed unsatisfactorily.
methodsClinical data of 5,220 patients who underwent anterior resection for rectal cancer were scrutinized to create a prediction model via random forest classifier. Additionally, data of 836 patients served as the test dataset. Patients diagnosed with AL within 6 months' follow-up were recorded. A total of 20 candidate factors were included. Receiver operating characteristic (ROC) curve was conducted to determine the clinical efficacy of our model, and compare the predictive performance of different models.
resultsThe incidence of AL was 6.2% (326/5,220). A multivariate logistic regression analysis and the random forest classifier indicated that sex, distance of tumor from the anal verge, bowel stenosis or obstruction, preoperative hemoglobin, surgeon volume, diabetes, neoadjuvant chemoradiotherapy, and surgical approach were significantly associated with AL. After propensity score matching, the temporary stoma was not identified as a protective factor for AL (P=0.58). Contrastingly, the first year of performing laparoscopic surgery was a predictor (P=0.009). We created a predictive random forest classifier based on the above predictors that demonstrated satisfactory prediction efficacy. The area under the curve (AUC) showed that the random forest had higher efficiency (AUC =0.87) than the nomogram (AUC =0.724).
conclusionsOur findings suggest that eight factors may affect the incidence of AL. Our random forest classifier is an innovative and practical model to effectively predict AL, and could provide rational advice on whether to perform a temporary stoma, which might reduce the rate of stoma and avoid the ensuing complications.
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