acepredicting enfortumab vedotin toxicities in advanced urothelial carcinoma patients using machine learning
Source article: Machine learning integrated explainable artificial intelligence in predicting toxicities of enfortumab vedotin in urothelial carcinoma: an exploratory study
Abstract: 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…
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"Barriers to conducting replications of experiment in cancer research" by Authors of the study: Timothy M Errington, Alexandria Denis, Nicole Perfito, Elizabeth Iorns, Brian A Nosek is licensed under CC BY 4.0. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/.
In a proof-of-concept study published September 16, 2026, investigators used real-world data from 542 patients with advanced urothelial carcinoma to train machine learning models to predict toxicities from enfortumab vedotin, including severe adverse events and specific events like diarrhea and cutaneous toxicity.
The work matters because early identification of high-risk patients could help manage a therapy currently limited by adverse events, but the authors emphasize the results are exploratory and not ready for clinical use without prospective validation and larger event counts.
- Analysis included 542 patients from 51 centers across 24 countries with 80% training and 20% testing split.
- Six toxicity outcomes were modeled; Random Forest led for diarrhea, severe AEs and dose skipping, XGBoost for cutaneous toxicity and diabetes, LASSO for neuropathy.
- SHAP interpretability identified age as most important variable, followed by prior immunotherapy and ECOG performance status, with liver and lung metastases influencing specific toxicities.
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.
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.
The rundown
Researchers trained four algorithms on data from 542 patients treated at 51 centers in 24 countries, evaluating performance on a 20% holdout for six outcomes including grade 3-4 AEs, diarrhea, cutaneous toxicity, neuropathy, diabetes, and dose skipping.
Interpretability analysis highlighted age, prior immunotherapy, and ECOG performance status as top associated variables, with SHAP showing atezolizumab/nivolumab linked to lower cutaneous risk and female sex to higher risk, while liver metastases influenced diabetes and cutaneous toxicity.
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.
Sources
- Peer-reviewedExpert Review of Anticancer Therapy2026-09-16
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