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Predicting efficacy in patients with locally advanced/metastatic urothelial carcinoma (mUC) treated with immunotherapy using explainable machine learning approaches: the SamUR-AI trial on behalf of the Meet-URO group

Background: Immune checkpoint inhibitors (ICIs) have reshaped the treatment landscape for metastatic urothelial carcinoma (mUC), yet reliable predictive biomarkers remain limited. The SamUR-AI study was designed to evaluate whether machine learning (ML) and explainable artificial intelligence (XAI)...

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Autori principali: A. Rametta, S. Ferri, M. Maruzzo, F. Pierantoni, L. Antonuzzo, V. Rossi, S. Buti, I. Testi, P. Zucali, C. Mucciarini, M. Stellato, L. Provenzano, D. Raggi, E. Farè, A.L.G. Pedrocchi, A. Prelaj, A. Necchi, G. Procopio, V. Miskovic, P. Giannatempo
Natura: Artigo
Lingua:Inglês
Pubblicazione: Elsevier 2026-03-01
Serie:ESMO Real World Data and Digital Oncology
Soggetti:
artificial intelligence
machine learning
urothelial carcinoma
immunotherapy
predictive factors
real-world data
Accesso online:http://www.sciencedirect.com/science/article/pii/S2949820125005703
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