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