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

Abstract

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 MethodsClinical 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. ResultsA 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). ConclusionsOur 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.

Machine learning in renal cell carcinoma research: the promise and pitfalls of ‘renal‐izing’ the potential of artificial intelligence

“…However, despite some promising results, recent studies show that the benefits of ML are not ubiquitous, especially when deployed on imperfect and non-granular datasets [2].In this issue of the BJUI, Boulenger de Hauteclocque employ data from the French multi-institutional kidney cancer database UroCCR (ClinicalTrials.gov Identifier: NCT03293563), to investigate the ability of seven ML algorithms to predict pT3a upstaging in a cohort of patients who underwent surgery for cT1/cT2a RCC. Using supervised ML algorithms, they reported a prediction accuracy (measured by the area under the receiver-operating curve) of 0.77 for their best model [3]. While the results are intriguing, the design of the study raises questions that should be considered by clinicians when interpreting the results.…”

The BJUI Editorial Team's view on artificial intelligence and machine learning

“…Boulenger de Hauteclocque et al. [1] tested different ML algorithms to predict upstaging to pathological tumour stage pT3a in patients undergoing surgery for clinical tumour stage cT1/cT2a renal cell carcinoma. The best prediction model achieved an area under the receiver‐operating characteristic curve of 0.77.…”

Reply to: machine learning in renal cell carcinoma research: promise and pitfalls of ‘renal‐izing’ the potential of artificial intelligence

“…Astrid Boulenger de Hauteclocque 1 , Lo€ ıc Ferrer 2 , Ga€ elle Margue 1 , Guillaume Etchepare 2 , Thierry Colin 2 and Jean-Christophe Bernhard 1…”

“…A plethora of studies has focused on the key determinants of postoperative functional outcomes [1]. If renal ischaemia has historically been one of the most important drivers of early functional impairment, growing evidence has suggested that type and duration of ischaemia have far less long-term influence than other factors [2].…”

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We wish to thank Khene et al [1] for their comments on our article [2], thus offering us the opportunity to clarify important methodological points.

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