ArticleThe British journal of surgery2020
Machine learning to predict early recurrence after oesophageal cancer surgery.
Article in The British journal of surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 3 of them syntheses that pooled it.
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Who cites it
33 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine learning applications in predicting pharmacological treatment outcomes and responses in esophageal cancer.Frontiers in pharmacology · 2026Pooled it
- The application of random forest-based models in prognostication of gastrointestinal tract malignancies: a systematic review.Frontiers in artificial intelligence · 2025Pooled it
- Machine learning to guide clinical decision-making in abdominal surgery-a systematic literature review.Langenbeck's archives of surgery · 2022Pooled it
- The Cutting Edge: A Systematic Review of Artificial Intelligence and Machine Learning in Predicting Esophagectomy Outcomes.Annals of thoracic surgery short reports · 2026Article
- Clinical Machine Learning Model for Predicting Pathological Complete Response in Patients with Esophageal and Gastroesophageal Junction Adenocarcinoma After Trimodality Therapy.Annals of surgical oncology · 2026Article
- Recurrence of Basal Cell Carcinoma across Different Treatment Modalities: A Nationwide Study with 8 Years of Follow-up and Modelled Prediction.Acta dermato-venereologica · 2026Article
- Machine learning combined with CT-based radiomics predicts the prognosis of oesophageal squamous cell carcinoma.Insights into imaging · 2025Article
- Machine learning for predicting the prognosis of patients with thymoma and thymic carcinoma.Journal of thoracic disease · 2025Article
- Establishment and validation of a recurrence risk model in early-stage tongue squamous cell carcinoma patients incorporating immune-inflammatory biomarkers and clinicopathological parameters.American journal of cancer research · 2025Article
- Integrated machine learning developed a prognosis-related gene signature to predict prognosis in oesophageal squamous cell carcinoma.Journal of cellular and molecular medicine · 2024Article
- Article
- Design of risk prediction model for esophageal cancer based on machine learning approach.Heliyon · 2024Article
- Advancing Glaucoma Care: Integrating Artificial Intelligence in Diagnosis, Management, and Progression Detection.Bioengineering (Basel, Switzerland) · 2024Review
- Development of a machine learning-based model to predict major adverse events after surgery for type A aortic dissection complicated by malnutrition.Frontiers in nutrition · 2024Article
- A decade of the Oesophageal Cancer Clinical and Molecular Stratification Consortium.Nature medicine · 2024Article
- Machine learning to predict postoperative complications after digestive surgery: a scoping review.The British journal of surgery · 2023Article
- Clinical predictors of early postoperative recurrence after radical esophagectomy for thoracic esophageal cancer.Esophagus : official journal of the Japan Esophageal Society · 2023Article
- Article
- Optical coherence tomography and convolutional neural networks can differentiate colorectal liver metastases from liver parenchyma ex vivo.Journal of cancer research and clinical oncology · 2023Article
- Identifying ureteral stent encrustation using machine learning based on CT radiomics features: a bicentric study.Frontiers in medicine · 2023Article
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Authors and funding
17 authors.
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Abstract
backgroundEarly cancer recurrence after oesophagectomy is a common problem, with an incidence of 20-30 per cent despite the widespread use of neoadjuvant treatment. Quantification of this risk is difficult and existing models perform poorly. This study aimed to develop a predictive model for early recurrence after surgery for oesophageal adenocarcinoma using a large multinational cohort and machine learning approaches.
methodsConsecutive patients who underwent oesophagectomy for adenocarcinoma and had neoadjuvant treatment in one Dutch and six UK oesophagogastric units were analysed. Using clinical characteristics and postoperative histopathology, models were generated using elastic net regression (ELR) and the machine learning methods random forest (RF) and extreme gradient boosting (XGB). Finally, a combined (ensemble) model of these was generated. The relative importance of factors to outcome was calculated as a percentage contribution to the model.
resultsA total of 812 patients were included. The recurrence rate at less than 1 year was 29·1 per cent. All of the models demonstrated good discrimination. Internally validated areas under the receiver operating characteristic (ROC) curve (AUCs) were similar, with the ensemble model performing best (AUC 0·791 for ELR, 0·801 for RF, 0·804 for XGB, 0·805 for ensemble). Performance was similar when internal-external validation was used (validation across sites, AUC 0·804 for ensemble). In the final model, the most important variables were number of positive lymph nodes (25·7 per cent) and lymphovascular invasion (16·9 per cent).
conclusionThe model derived using machine learning approaches and an international data set provided excellent performance in quantifying the risk of early recurrence after surgery, and will be useful in prognostication for clinicians and patients.
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