Predicthor: AI-Powered Predictive Risk Model for 30-Day Mortality and 30-Day Complications in Patients Undergoing Thoracic Surgery for Lung Cancer.

Date publication

juin 2025

Journal

Annals of surgery open : perspectives of surgical history, education, and clinical approaches

Auteurs

Membres identifiés du Cancéropôle Est :
Pr FALCOZ Pierre-Emmanuel , Dr PAGES Pierre-Benoit

Tous les auteurs :
Durand X, Hédou J, Bellan G, Thomas PA, Pages PB, D'Journo XB, Brouchet L, Rivera C, Falcoz PE, Gillibert A, Baste JM

Lien Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/40557354

Résumé

To assess the predictive performance of Predicthor, an artificial intelligence model, for 30-day mortality and complications following major pulmonary resections.

Mots clés

Clavien–Dindo, artificial intelligence, bilobectomy, complications, data, data curation, epithor, lobectomy, lung, machine learning, mortality, predicthor, surgery, thoracic surgery, thoracoscore, validation

Référence

Ann Surg Open. 2025 06;6(2):e578