Simple Summary Muscle-invasive bladder cancer is an aggressive disease in which many patients remain at risk of recurrence despite surgery and standard treatments. There is an urgent need for better tools to identify patients who may benefit from additional therapies or, conversely, avoid unnecessary treatment-related toxicity. Circulating tumor DNA, a small amount of tumor-derived genetic material detectable in blood, has emerged as a promising marker for monitoring residual disease and predicting treatment response. In this review, we summarize the current evidence on the clinical applications of circulating tumor DNA in muscle-invasive bladder cancer, including its role in prognosis, treatment selection, and surveillance. We also discuss future perspectives for integrating molecular monitoring into personalized cancer care.
M. Polito, L. Sisca, D. Caruso et al.· Cancers· 0 citations
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 on 20%. Performance was evaluated via standard metrics with SHAP for interpretability.
RESULTS
In this exploratory analyses, Random Forest achieved highest overall performance, yielding best AUC for diarrhea, severe AEs, and dose skipping. XGBoost led for cutaneous toxicity and diabetes; LASSO led for neuropathy (differences modest). Age was the most important associated variable, followed by prior immunotherapy and ECOG status. Liver metastases influenced diabetes and cutaneous toxicity; lung metastases impacted diarrhea, neuropathy, and skin toxicity. SHAP showed atezolizumab/nivolumab linked to lower cutaneous risk, and female sex to higher risk.
CONCLUSION
These preliminary, hypothesis-generating findings suggest ML may predict EV-related toxicities, but single train-test split, small event counts, and lack of external validation preclude clinical use. Prospective validation is essential.
K. Sridharan, Mattia Alberto Di Civita, G. Sivaramakrishnan et al.· Expert Review of Anticancer...· 0 citations
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