ArticleFrontiers in artificial intelligence2024
Predicting overall survival from tumor dynamics metrics using parametric statistical and machine learning models: application to patients with
Article in Frontiers in artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Building Hybrid Pharmacometric-Machine Learning Models in Oncology Drug Development: Current State and Recommendations.CPT: pharmacometrics & systems pharmacology · 2026Review
- Application of Machine Learning for Predicting Progression-Free and Overall Survival in Patients With Renal Cell Carcinoma.Clinical and translational science · 2025Article
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8 authors.
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Abstract
In oncology drug development, tumor dynamics modeling is widely applied to predict patients' overall survival (OS) via parametric models. However, the current modeling paradigm, which assumes a disease-specific link between tumor dynamics and survival, has its limitations. This is particularly evident in drug development scenarios where the clinical trial under consideration contains patients with tumor types for which there is little to no prior institutional data. In this work, we propose the use of a pan-indication solid tumor machine learning (ML) approach whereby all three tumor metrics (tumor shrinkage rate, tumor regrowth rate and time to tumor growth) are simultaneously used to predict patients' OS in a tumor type independent manner. We demonstrate the utility of this approach in a clinical trial of cancer patients treated with the tyrosine kinase inhibitor, pralsetinib. We compared the parametric and ML models and the results showed that the proposed ML approach is able to adequately predict patient OS across
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