Interpretable Machine Learning Model for Locoregional Relapse Prediction in Oropharyngeal Cancers.
Fiche publication
Date publication
décembre 2020
Journal
Cancers
Auteurs
Membres identifiés du Cancéropôle Est :
Pr NOEL Georges
Tous les auteurs :
Giraud P, Giraud P, Nicolas E, Boisselier P, Alfonsi M, Rives M, Bardet E, Calugaru V, Noel G, Chajon E, Pommier P, Morelle M, Perrier L, Liem X, Burgun A, Bibault JE
Lien Pubmed
Résumé
There is no evidence to support surgery or radiotherapy as the best treatment for resectable oropharyngeal cancers with a negative HPV status. Predictive algorithms may help to decide which strategy to choose, but they will only be accepted by caregivers and European authorities if they are interpretable. As a proof of concept, we developed a predictive and interpretable algorithm to predict locoregional relapse at 18 months for oropharyngeal cancers as a first step towards that goal.
Mots clés
XGBoost, head and neck, machine learning, oropharyngeal cancer, radiomics
Référence
Cancers (Basel). 2020 Dec 28;13(1):