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Machine learning-integrated explainable artificial intelligence for survival prediction in urothelial carcinoma with enfortumab vedotin: an exploratory real-world analysis

Background Enfortumab vedotin (EV) has transformed treatment for advanced urothelial carcinoma (aUC), but outcomes vary. Machine learning (ML) with explainable artificial intelligence (XAI) may improve survival prediction. Methods Data from 544 aUC patients receiving EV after platinum chemotherapy and immunotherapy (51 centers, 24 countries) were analyzed. Four machine learning (ML) algorithms (Random Survival Forest, XGBoost, Elastic Net-regularized Cox, Support Vector Machine) were trained (80%) and tested (20…

By the Truvace desk1 min read1 sourcePeer-reviewedTRV-2026-0978· cite & verify
Machine learning-integrated explainable artificial intelligence for survival prediction in urothelial carcinoma with enfortumab vedotin: an exploratory real-world analysis
Requirements analysis and architectural design of a web-based integrated weapons of mass destruction toolset by Jones, Richard B.. Public domain

In brief

Background Enfortumab vedotin (EV) has transformed treatment for advanced urothelial carcinoma (aUC), but outcomes vary. Machine learning (ML) with explainable artificial intelligence (XAI) may improve survival prediction.

Main points

  1. Background Enfortumab vedotin (EV) has transformed treatment for advanced urothelial carcinoma (aUC), but outcomes vary.
  2. Methods Data from 544 aUC patients receiving EV after platinum chemotherapy and immunotherapy (51 centers, 24 countries) were analyzed.
  3. Four machine learning (ML) algorithms (Random Survival Forest, XGBoost, Elastic Net-regularized Cox, Support Vector Machine) were trained (80%) and tested (20%) to predict overall survival (OS).

The problem

Four machine learning (ML) algorithms (Random Survival Forest, XGBoost, Elastic Net-regularized Cox, Support Vector Machine) were trained (80%) and tested (20%) to predict overall survival (OS).

The rundown

Methods Data from 544 aUC patients receiving EV after platinum chemotherapy and immunotherapy (51 centers, 24 countries) were analyzed. Four machine learning (ML) algorithms (Random Survival Forest, XGBoost, Elastic Net-regularized Cox, Support Vector Machine) were trained (80%) and tested (20%) to predict overall survival (OS).

Sources

  1. Peer-reviewedExpert Opinion on Biological Therapy2026-09-03

The debate