Machine‐learning approach for prediction of pT3a upstaging and outcomes of localized renal cell carcinoma (UroCCR‐15)

Objectives To assess the impact of pathological upstaging from clinically localized to locally advanced pT3a on survival in patients with renal cell carcinoma (RCC), as well as the oncological safety of various surgical approaches in this setting, and to develop a machine‐learning‐based, contemporar...

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Veröffentlicht in:	BJU international 2023-08, Vol.132 (2), p.160-169
Hauptverfasser:	Boulenger de Hauteclocque, Astrid, Ferrer, Loïc, Ambrosetti, Damien, Ricard, Solene, Bigot, Pierre, Bensalah, Karim, Henon, François, Doumerc, Nicolas, Méjean, Arnaud, Verkarre, Virginie, Dariane, Charles, Larré, Stéphane, Champy, Cécile, Taille, Alexandre, Bruyère, Franck, Rouprêt, Morgan, Paparel, Philippe, Droupy, Stéphane, Fontenil, Alexis, Patard, Jean‐Jacques, Durand, Xavier, Waeckel, Thibaut, Lang, Herve, Lebâcle, Cédric, Guy, Laurent, Pignot, Geraldine, Durand, Matthieu, Long, Jean‐Alexandre, Charles, Thomas, Xylinas, Evanguelos, Boissier, Romain, Yacoub, Mokrane, Colin, Thierry, Bernhard, Jean‐Christophe
Format:	Artikel
Sprache:	eng
Schlagworte:	Cancer disease‐free survival Kidney cancer Learning algorithms Life Sciences Machine learning Medical prognosis Nephrectomy partial nephrectomy Prediction models Renal cell carcinoma Survival analysis TNM staging Tumors
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