Machine‐learning approach for prediction of pT3a upstaging and outcomes of localized renal cell carcinoma ( UroCCR ‐15)
Astrid Boulenger de Hauteclocque
,
Loïc Ferrer
(1)
,
Damien Ambrosetti
(2)
,
Solene Ricard
(3, 4)
,
Pierre Bigot
(5)
,
Karim Bensalah
(6)
,
François Henon
(7)
,
Nicolas Doumerc
(8)
,
Arnaud Méjean
(9)
,
Virginie Verkarre
(9)
,
Charles Dariane
(10)
,
Stéphane Larré
(11)
,
Cécile Champy
,
Alexandre de La Taille
(12)
,
Franck Bruyère
(13)
,
Morgan Rouprêt
(14)
,
Philippe Paparel
(15)
,
Stéphane Droupy
(16, 17)
,
Alexis Fontenil
,
Jean‐jacques Patard
,
Xavier Durand
,
Thibaut Waeckel
,
Herve Lang
,
Cédric Lebâcle
,
Laurent Guy
,
Geraldine Pignot
,
Matthieu Durand
,
Jean‐alexandre Long
,
Thomas Charles
,
Evanguelos Xylinas
,
Romain Boissier
,
Mokrane Yacoub
,
Thierry Colin
,
Jean‐christophe Bernhard
1
BPH -
Bordeaux population health
2 CHU - Hôpital Pasteur [Nice]
3 CHU Bordeaux
4 UroCCR - French Network for Research on Kidney Cancer
5 CHU Angers - Centre Hospitalier Universitaire d'Angers
6 Centre Hospitalier Universitaire de Rennes [CHU Rennes] = Rennes University Hospital [Pontchaillou]
7 CHRU Lille - Centre Hospitalier Régional Universitaire [CHU Lille]
8 Service des Explorations Fonctionnelles Physiologiques [CHU Toulouse]
9 HEGP - Hôpital Européen Georges Pompidou [APHP]
10 INEM - UM 111 (UMR 8253 / U1151) - Institut Necker Enfants-Malades
11 CHU Reims - Hôpital universitaire Robert Debré [Reims]
12 TRePCa - Résistances Thérapeutiques du Cancer de la Prostate
13 Service d'urologie [Tours]
14 CHU Pitié-Salpêtrière [AP-HP]
15 Institut de Cancérologie [Hospices civils de Lyon]
16 CHU Nîmes - Centre Hospitalier Universitaire de Nîmes
17 UM - Université de Montpellier
2 CHU - Hôpital Pasteur [Nice]
3 CHU Bordeaux
4 UroCCR - French Network for Research on Kidney Cancer
5 CHU Angers - Centre Hospitalier Universitaire d'Angers
6 Centre Hospitalier Universitaire de Rennes [CHU Rennes] = Rennes University Hospital [Pontchaillou]
7 CHRU Lille - Centre Hospitalier Régional Universitaire [CHU Lille]
8 Service des Explorations Fonctionnelles Physiologiques [CHU Toulouse]
9 HEGP - Hôpital Européen Georges Pompidou [APHP]
10 INEM - UM 111 (UMR 8253 / U1151) - Institut Necker Enfants-Malades
11 CHU Reims - Hôpital universitaire Robert Debré [Reims]
12 TRePCa - Résistances Thérapeutiques du Cancer de la Prostate
13 Service d'urologie [Tours]
14 CHU Pitié-Salpêtrière [AP-HP]
15 Institut de Cancérologie [Hospices civils de Lyon]
16 CHU Nîmes - Centre Hospitalier Universitaire de Nîmes
17 UM - Université de Montpellier
Astrid Boulenger de Hauteclocque
- Function : Author
- PersonId : 1283670
- ORCID : 0000-0002-0082-5104
Damien Ambrosetti
- Function : Author
- PersonId : 758598
- ORCID : 0000-0001-8665-0546
- IdRef : 143452592
Cécile Champy
- Function : Author
Franck Bruyère
- Function : Author
- PersonId : 1245846
- ORCID : 0000-0002-9281-9421
Stéphane Droupy
- Function : Author
- PersonId : 1248441
- ORCID : 0000-0002-9444-6964
Alexis Fontenil
- Function : Author
Jean‐jacques Patard
- Function : Author
Xavier Durand
- Function : Author
Thibaut Waeckel
- Function : Author
Herve Lang
- Function : Author
Cédric Lebâcle
- Function : Author
- PersonId : 1198483
- ORCID : 0000-0002-8215-2918
Laurent Guy
- Function : Author
Geraldine Pignot
- Function : Author
Matthieu Durand
- Function : Author
- PersonId : 763798
- ORCID : 0000-0001-6335-8067
Jean‐alexandre Long
- Function : Author
Thomas Charles
- Function : Author
Evanguelos Xylinas
- Function : Author
Romain Boissier
- Function : Author
- PersonId : 1283671
- ORCID : 0000-0001-6428-0273
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.