K. Sridharan

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 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.

Genetically predicted vitamin D levels and risk of head and neck cancer: a mendelian randomization study

Seroconversion Rates Following COVID-19 Vaccination in People Living with HIV: A Systematic Review and Meta-Analysis

Background: People living with human immunodeficiency virus (HIV) (PLWH) remain at increased risk of severe COVID-19 outcomes; however, conflicting evidence exists regarding the seroconversion rates of COVID-19 vaccines in this population. Methods: A systematic review and meta-analysis were conducted on studies reporting seroconversion outcomes following COVID-19 vaccination in PLWH. Results: Forty-four studies (5391 PLWH) were included in the meta-analysis. The overall pooled seroconversion proportion was 93.5%. Bootstrap analysis confirmed robustness (92.8%). Subgroup analyses revealed significantly higher seroconversion rates for mRNA vaccines (98.2%) compared to non-mRNA vaccines (80.5%). CD4 count demonstrated a graded association: <200 cells/mm3 (53.8%), 200–500 cells/mm3 (86.2%), and >500 cells/mm3 (93.5%). Prior COVID-19 infection (99.1% vs. 89.5%) and antiretroviral therapy (ART) status (93.5% vs. 49.6%) were significant determinants. Safety data demonstrated a favorable profile, with predominantly mild-to-moderate local (injection-site pain: 23.8%) and systemic (headache: 12.95%, fatigue: 6.4%) adverse events; serious adverse events were rare and no consistent association with HIV disease progression was observed. Conclusions: COVID-19 vaccination induces high seroconversion rates in PLWH, particularly among those receiving mRNA vaccines, with preserved CD4 counts, on ART, or with prior infection.