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, 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.