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TRV-2026-1121Certified recordPeer-reviewed

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 toxicities using real-world data. Research design and methods Data from 542 patients (51 centers, 24 countries) were analyzed. Six outcomes were predicted including grade 3-4 AEs. Four ML algorithms were trained on an 80% split and tested…

Health · The Trace — both readings · certified 2026-09-17 · v1 · article view · machine-readable

Current reading — gain

Machine learning with SHAP interpretability predicted enfortumab vedotin toxicities including grade 3-4 events, diarrhea, and dose skipping in advanced urothelial carcinoma patients using real-world data.

Current reading — problem

The predictive models remain exploratory with small event counts and no external validation, so they cannot yet be used clinically to mitigate adverse events from enfortumab vedotin therapy.

What this doesn’t fix

Findings are preliminary and hypothesis-generating due to single train-test split, small event counts, and lack of external validation, which precludes clinical use and requires prospective validation.

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Truvace Impact Record TRV-2026-1121, v1: “Machine learning integrated explainable artificial intelligence in predicting toxicities of enfortumab vedotin in urothelial carcinoma: an exploratory study.” Truvace, 2026-09-17. /record/TRV-2026-1121 (accessed at citation time). sha256 7eb639dfc4d97d46…

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