ArticleAnnals of medicine2026
Development and validation of an interpretable Random Forest model for predicting recurrence after endoscopic submucosal dissection in superficial oesophageal squamous cell carcinoma.
Article in Annals of medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Methodological considerations for validating a machine learning model predicting recurrence after ESD in superficial esophageal cancer.Annals of medicine · 2026Article
- Development and validation of prediction model for intrapulmonary metastasis in lung adenocarcinoma based on machine learning.Journal of thoracic disease · 2026Article
- Mapping Two Decades of Research on Endoscopic Submucosal Dissection in Esophageal Cancer: A Bibliometric and Visual Analysis.Medical science monitor : international medical journal of experimental and clinical research · 2026Article
- Building and Validating an Explainable Machine Learning Model for Predicting Health-Promoting Behaviors in Older Adults: A Multicenter Study.Prevention science : the official journal of the Society for Prevention Research · 2026Article
- An interpretable machine learning model for predicting brain metastasis in breast cancer.Frontiers in medicine · 2026Article
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9 authors.
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Abstract
backgroundCurrently, endoscopic submucosal dissection (ESD) has become the preferred treatment for superficial oesophageal squamous cell carcinoma (SESCC). However, due to the residual background mucosa, some patients are still at risk of postoperative recurrence. This study aimed to develop and validate an explainable machine learning model to predict recurrence risk in SESCC patients undergoing ESD.
methodsWe enrolled SESCC patients treated with ESD between 1 January 2016 and 31 December 2023. Patients from 2016 to 2021 were allocated to the training set, further split into a 7:3 ratio for model training and internal validation. Patients from 2022 to 2023 constituted the temporal validation set. Seven machine learning algorithms - logistic regression, extreme gradient boosting, light gradient boosting machine, random forest (RF), gradient boosting decision tree, Gaussian Naïve Bayes and Multilayer Perceptron - were employed to construct risk models. Shapley Additive exPlanations (SHAP) method was applied to interpret the optimal model's feature importance.
resultsEight predictors were identified for model construction: platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, low-density lipoprotein level, biological age, mid-oesophageal location, multiple tumour lesions, poor differentiation and infiltration depth (muscularis mucosa or superficial submucosa). Among the seven models, the RF algorithm demonstrated superior performance, achieving an area under the receiver operating characteristic curve of 0.892 in internal validation and 0.761 in temporal validation. An online prediction platform (https://zzhapp.shinyapps.io/shiny/) was developed for clinical application.
conclusionThis study successfully developed and validated an explainable RF-based machine learning model to accurately predict recurrence risk in SESCC patients after ESD.
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