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TRUVACE RECORD VERSION
record: TRV-2026-1121
version: 2
kind: revised
reason: Editorial pass: sharper headline; gain/problem restated as concrete outcomes; refiled trace to p_space (gain activity_only, problem outcome).
timestamp: 2026-11-04T09:55:43.138067Z
status: published
lens: p_space
sector: health
headline: Machine learning integrated explainable artificial intelligence in predicting toxicities of enfortumab vedotin in urothelial carcinoma: an exploratory study
dek: 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…
gain_title: (none)
problem_title: The models are exploratory, built on small event counts with no external validation, so they cannot yet be used in clinics to reduce side effects from enfortumab vedotin.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: The models are exploratory, built on small event counts with no external validation, so they cannot yet be used in clinics to reduce side effects from enfortumab vedotin.
problem_evidence: single train-test split, small event counts, and lack of external validation preclude clinical use | Enfortumab vedotin (EV) therapy for advanced urothelial carcinoma is limited by adverse events (AEs)
quick_read: 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.
limitation: 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.
tag: Evidence-backed problem
key_points: 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.
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.
sources:
- peer_reviewed | Expert Review of Anticancer Therapy | https://doi.org/10.1080/14737140.2026.2733827 | 2026-09-16
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