P1.139. Advancing Risk Prediction After Esophagectomy: A Systematic Review of Machine Learning Models

Clicks: 13
ID: 326196
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #452 of 453 articles by views in diseases of the esophagus : official journal of the international society for diseases of the esophagus

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 453 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract Topic Esophageal Cancer: Other Background Esophagectomy is associated with substantial morbidity, mortality, and resource utilization. Traditional regression-based risk tools may inadequately capture complex nonlinear interactions. Contemporary evidence on machine learning (ML) models predicting postoperative outcomes after esophagectomy was synthesized, focusing on discrimination, validation, and comparison with conventional regression approaches. Methods A PRISMA-guided systematic review was conducted using Embase, MEDLINE, PubMed, and the Cochrane Library in January 2026. A total of 196 studies were identified. After title and abstract screening, 32 studies underwent full-text review, of which 10 met final inclusion criteria as ML-focused prediction models in esophagectomy populations. Extracted data included study design, cohort size, procedure type, predicted outcome, modeling approach (ML versus regression), validation strategy (internal or external), performance metrics (e.g., area under the receiver operating characteristic curve [AUROC]), and reporting elements such as calibration and decision-curve analysis. ML-focused studies were defined as those applying algorithms including gradient boosting, support vector machines, neural networks, or survival forests to postoperative outcome prediction. Results Ten studies applying ML models to esophagectomy outcomes were included (median cohort size 700; range 200–4700). Anastomotic leak was the most frequently predicted outcome (4/10), followed by mortality, major complications, readmission, strictures, and recurrence or survival. Common algorithms included gradient boosting (XGBoost, LightGBM, GBM), support vector machines, neural networks, and survival forests. Reported discrimination ranged from moderate to high (AUROC 0.64 for 90-day mortality and 0.65–0.70 for major complications, increasing to 0.79–0.90 for anastomotic leak prediction; some internally validated models reported AUROC >0.95). Three studies performed independent external validation, and performance generally declined in external cohorts. Comparative analyses demonstrated that ML often matched but did not consistently outperform regression-based models. Conclusion Machine learning models for postoperative risk prediction after esophagectomy demonstrate promising discrimination, particularly for anastomotic leak. Although external validation remains limited, ML approaches are still in early stages of clinical translation. With prospective data integration and robust multicenter validation, ML has the potential to enhance individualized risk stratification, support shared decision-making, guide perioperative planning, and improve allocation of postoperative resources in esophageal surgery.
Reference Key
openalex_W7203976652 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Tim Hsu-Han Wang, Otari Beldishevski‐Shotadze, Nick Evennett
Are you one of these authors? Claim your profile
Journal diseases of the esophagus : official journal of the international society for diseases of the esophagus
Year 2026
DOI
10.1093/dote/doag077.287
URL
https://doi.org/10.1093/dote/doag077.287
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.