Gian Paolo Spinelli

Machine learning integrated explainable artificial intelligence in predicting toxicities of enfortumab vedotin in urothelial carcinoma: an exploratory study

Background Enfortumab vedotin (EV) therapy for advanced urothelial carcinoma is limited by adverse events (AEs). Early identification of high-risk patients is needed. This proof-of-concept study evaluated whether machine learning (ML) with explainable AI (SHAP) could predict EV t...

Prognostic Role of Adverse Events in a Worldwide Population of Patients with Biliary Tract Cancer Treated with Chemo-Immunotherapy

Background: Biliary tract cancers (BTCs) are aggressive malignancies with limited treatment options. The addition of durvalumab or pembrolizumab to cisplatin–gemcitabine improves overall survival (OS), but validated biomarkers of benefit remain limited. Immune-mediated adverse ev...