Development of an individual postoperative prediction model for kidney cancer recurrence using machine learning (UroCCR study 120)

被引：0
|
作者：
Margue, G. [1 ,2 ]
Ferrer, L. [2 ]
Etchepare, G.
Bensalah, K. [3 ]
Mejean, A. [4 ]
Roupret, M. [5 ]
Doumerc, N. [6 ]
Ingels, A. [7 ]
Boissier, R. [8 ]
Pignot, G. [9 ]
Parier, B. [10 ]
Paparel, P. [11 ]
Waeckel, T. [12 ]
Bigot, P. [13 ]
Colin, T. [2 ]
Bernhard, J. C. [1 ]
机构：
[1] Bordeaux Univ Hosp, Dept Urol, Bordeaux, France
[2] Sophia Genet, Multimodal Res Team, Pessac, France
[3] Rennes Univ Hosp, Dept Urol, Rennes, France
[4] HEGP, APHP, Dept Urol, Paris, France
[5] La Pitie APHP, Dept Urol, Paris, France
[6] Toulouse Univ Hosp, Dept Urol, Toulouse, France
[7] Mondor APHP, Dept Urol, Paris, France
[8] APHM, Dept Urol, Marseille, France
[9] IPC, Dept Urol, Marseille, France
[10] Kremlin Bicetre, APHP, Dept Urol, Paris, France
[11] HCL, Dept Urol, Lyon, France
[12] Cean Univ Hosp, Dept Urol, Caen, France
[13] Angers Univ Hosp, Dept Urol, Angers, France
来源：
EUROPEAN UROLOGY | 2023年 / 83卷
关键词：
D O I：
暂无
中图分类号：
R5 [内科学]; R69 [泌尿科学（泌尿生殖系疾病）];
学科分类号：
1002 ; 100201 ;
摘要：
A0473
引用
收藏
页码：S674 / S675
页数：2
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