Machine learning approach for prediction of pT3a upstaging and outcomes of localized RCC (UroCCR-15).

Date publication

janvier 2023

Journal

BJU international

Auteurs

Membres identifiés du Cancéropôle Est :
Pr LANG Hervé

Tous les auteurs :
Boulenger de Hauteclocque A, Ferrer L, Ambrosetti D, Ricard S, Bigot P, Bensalah K, Henon F, Doumerc N, Méjean A, Verkarre V, Dariane C, Larré S, Champy C, de La Taille A, Bruyère F, Rouprêt M, Paparel P, Droupy S, Fontenil A, Patard JJ, Durand X, Waeckel T, Lang H, Lebâcle C, Guy L, Pignot G, Durand M, Long JA, Charles T, Xylinas E, Boissier R, Yacoub M, Colin T, Bernhard JC

Lien Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/36648124

Résumé

To assess the impact of pathological upstaging from clinically localized to locally advanced pT3a on RCC survival, as well as the oncological safety of various surgical approaches in this setting and develop a machine learning-based, contemporary, clinically relevant model for individual preoperative prediction of pT3a upstaging.

Mots clés

Disease-free survival, TNM staging, machine learning, partial nephrectomy, renal cell carcinoma

Référence

BJU Int. 2023 01 17;: