The AUGIS Survival Predictor: Prediction of Long-Term and Conditional Survival After Esophagectomy Using Random Survival Forests

• Published in: Annals of surgery Vol. 277; no. 2; p. 267
• Main Authors: Rahman, Saqib A, Walker, Robert C, Maynard, Nick, Nigel Trudgill, Crosby, Tom, Cromwell, David A, Underwood, Timothy J
• Format: Journal Article
• Language: English
• Published: United States 01.02.2023
• Subjects: Carcinoma, Squamous Cell - surgery; Esophageal Neoplasms; Esophagectomy - methods; Humans; Lymph Node Excision - methods; Neoplasm Staging

The aim of this study was to develop a predictive model for overall survival after esophagectomy using pre/postoperative clinical data and machine learning. For patients with esophageal cancer, accurately predicting long-term survival after esophagectomy is challenging. This study investigated survi...