Reducing Bias in the Evaluation of Robotic Surgery for Lung Cancer Through Machine Learning.

Date publication

octobre 2025

Journal

Cancers

Auteurs

Membres identifiés du Cancéropôle Est :
Pr BERNARD Alain , Pr QUANTIN Catherine

Tous les auteurs :
Bernard A, Cottenet J, Tubert-Bitter P, Quantin C

Lien Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/41154403

Résumé

Robot-assisted surgery (RAS) is a major innovation in the treatment of lung cancer, offering advantages in surgical precision and reducing postoperative complications. However, its impact on 90-day mortality remains controversial due to methodological biases in comparative studies. This study uses machine learning methods to improve propensity score estimation and reduce selection bias.

Mots clés

90-day mortality, lung cancer, machine learning, propensity score, robotic surgery

Référence

Cancers (Basel). 2025 10 17;17(20):