Metastatic renal cell carcinoma remains clinically challenging because of heterogeneous outcomes and limited predictive biomarkers for immunotherapy. We performed an explainable machine learning analysis using data from the multicenter retrospective Meet-URO 15 study, including 571 patients with metastatic renal cell carcinoma treated with second-line or later nivolumab. Clinical and inflammatory variables were used to develop classification models for disease control rate, progression-free survival at 3 and 9 months, and overall survival at 6, 18 and 24 months, as well as survival models for continuous progression-free and overall survival. Model performance was assessed using weighted F1-score for classification and concordance index for survival analysis, with interpretability provided through Shapley additive explanations. The best classification performance was observed for 6-month overall survival using a support vector machine model combined with minimum redundancy maximum relevance feature selection, achieving an F1-score of 0.81 on the test set and 0.77 in external validation. In survival analysis, random survival forest achieved a test-set concordance index of 0.68 for overall survival. Inflammatory indices, IMDC score, hemoglobin, lymphocytes and platelets consistently emerged as relevant prognostic features. These findings support explainable machine learning as a transparent approach to refine outcome prediction in immunotherapy-treated metastatic renal cell carcinoma.
NPJ precision oncology. 2026 Jul 02 [Epub ahead of print]
Sara Elena Rebuzzi, Vanja Miskovic, Giuseppe Fornarini, Sara Ferri, Sebastiano Buti, Matteo Piceni, Alessio Signori, Leonardo Provenzano, Giuseppe Luigi Banna, Pasquale Rescigno, Marco Maruzzo, Davide Bimbatti, Beatrice Ramella Pollone, Umberto Basso, Ugo De Giorgi, Paolo Pedrazzoli, Luca Galli, Paolo Andrea Zucali, Fabrizio Di Costanzo, Aruni Ghose, Alessandra Laura Giulia Pedrocchi, Arsela Prelaj
Medical Oncology Unit, Ospedale San Paolo, Savona, Italy., Department of Electronics, Information and Bioengineering, Polytechnic University of Milan, Milan, Italy., Medical Oncology Unit, IRCCS Azienda Ospedaliera Metropolitana (IRCCS AOM), IRCCS Ospedale Policlinico San Martino, Genova, Italy. ., Medical Oncology Unit, University Hospital of Parma, Parma, Italy., Department of Health Sciences (DISSAL), Section of Biostatistics, University of Genova, Genova, Italy., Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy., Department of Oncology, Portsmouth Hospitals University NHS Trust, Portsmouth, UK., Translational and Clinical Research Institute, Centre for Cancer, Newcastle University, Newcastle Upon Tyne, UK., Oncology Unit 3, Istituto Oncologico Veneto-IOV-IRCCS, Padua, Italy., Oncology Unit 1, Istituto Oncologico Veneto-IOV-IRCCS, Padua, Italy., Medical Oncology Unit, IRCCS Azienda Ospedaliera Metropolitana (IRCCS AOM), IRCCS Ospedale Policlinico San Martino, Genova, Italy., Department of Medical Oncology, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, Italy., Medical Oncology Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy., Medical Oncology Unit 2, Azienda Ospedaliera Universitaria Pisana, Pisa, Italy., Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy., Barts Cancer Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, UK.