ArticleBMC cancer2025
Interpretable machine learning model for predicting anastomotic leak after esophageal cancer surgery via LightGBM.
Article in BMC cancer, 2025. 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.
- Identification and validation of an explainable machine learning model for early postoperative pulmonary complications after esophagectomy in patients with esophageal cancer.Journal of gastrointestinal oncology · 2026Article
- Longitudinal modeling of Post-COVID-19 condition over three years: A machine learning approach using clinical, neuropsychological, and fluid markers.Scientific reports · 2026Article
- Construction and validation of a machine learning model for predicting anastomotic leak following radical esophagectomy for esophageal cancer.Frontiers in oncology · 2026Article
- Applications of artificial intelligence in postoperative surveillance and management of esophageal squamous cell carcinoma.Frontiers in artificial intelligence · 2026Review
- Interpretable SVM Model for Predicting CMV Infection in Seropositive Kidney Transplant Recipients: A Single-Center Retrospective Study.Infection and drug resistance · 2026Article
- Mechanomedicine in digestive surgery: a theranostic framework integrating mechanical diagnostics and therapeutic modulation across the perioperative continuum.Theranostics · 2026Review
- Nine-year risk stratification and prediction ofFrontiers in public health · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPostoperative anastomotic leakage (AL) is a severe complication following esophageal cancer surgery, that often leads to a poor prognosis. This study aims to develop an interpretable machine learning (ML) model to predict AL occurrence and identify associated risk factors.
methodsA retrospective case‒control study analyzed clinical and laboratory data from esophageal cancer patients obtained via a case management system. Nine machine learning (ML) models were compared to identify the best-performing model and its optimal feature set. The selected LightGBM-based model underwent internal cross-validation and external validation. Performance was evaluated via metrics such as ROC, DCA, and PR curves. To enhance interpretability, the SHapley Additive exPlanations (SHAP) method was applied for feature analysis.
resultsData from a total of 406 esophageal cancer patients were collected, and the LightGBM-based model showed the best performance. The model included the following features: lesion length, McKeown surgery, gastrointestinal decompression drainage (GID) volume on postoperative day 1, and prealbumin difference. SHAP dependence plots were created for each variable to understand their impact on the outcome. The model achieved an AUC of 0.956 (95% CI: 0.934–0.978).
conclusionThis study successfully developed an interpretable ML model based on the LightGBM to predict postoperative AL in patients with esophageal cancer.
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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.