Machine‐learning approach for prediction of pT3a upstaging and outcomes of localized renal cell carcinoma ( UroCCR ‐15) - Université Paris-Est-Créteil-Val-de-Marne Access content directly
Journal Articles BJU International Year : 2023

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

Astrid Boulenger de Hauteclocque
Damien Ambrosetti
François Henon
  • Function : Author
Cécile Champy
  • Function : Author
Franck Bruyère
Alexis Fontenil
  • Function : Author
Jean‐jacques Patard
  • Function : Author
Xavier Durand
  • Function : Author
Thibaut Waeckel
  • Function : Author
Herve Lang
  • Function : Author
Cédric Lebâcle
Laurent Guy
  • Function : Author
Geraldine Pignot
  • Function : Author
Matthieu Durand
Jean‐alexandre Long
  • Function : Author
Thomas Charles
  • Function : Author
Evanguelos Xylinas
  • Function : Author
Romain Boissier
Mokrane Yacoub
  • Function : Author
Thierry Colin
  • Function : Author
Jean‐christophe Bernhard
  • Function : Author

Abstract

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, contemporary, clinically relevant model for individual preoperative prediction of pT3a upstaging. Materials and Methods Clinical data from patients treated with either partial nephrectomy (PN) or radical nephrectomy (RN) for cT1/cT2a RCC from 2000 to 2019, included in the French multi‐institutional kidney cancer database UroCCR, were retrospectively analysed. Seven machine‐learning algorithms were applied to the cohort after a training/testing split to develop a predictive model for upstaging to pT3a. Survival curves for disease‐free survival (DFS) and overall survival (OS) rates were compared between PN and RN after G‐computation for pT3a tumours. Results A total of 4395 patients were included, among whom 667 patients (15%, 337 PN and 330 RN) had a pT3a‐upstaged RCC. The UroCCR‐15 predictive model presented an area under the receiver‐operating characteristic curve of 0.77. Survival analysis after adjustment for confounders showed no difference in DFS or OS for PN vs RN in pT3a tumours (DFS: hazard ratio [HR] 1.08, P = 0.7; OS: HR 1.03, P > 0.9). Conclusions Our study shows that machine‐learning technology can play a useful role in the evaluation and prognosis of upstaged RCC. In the context of incidental upstaging, PN does not compromise oncological outcomes, even for large tumour sizes.

Dates and versions

hal-04240504 , version 1 (13-10-2023)

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Astrid Boulenger de Hauteclocque, Loïc Ferrer, Damien Ambrosetti, Solene Ricard, Pierre Bigot, et al.. Machine‐learning approach for prediction of pT3a upstaging and outcomes of localized renal cell carcinoma ( UroCCR ‐15). BJU International, 2023, 132 (2), pp.160-169. ⟨10.1111/bju.15959⟩. ⟨hal-04240504⟩
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