Hybridizing Mechanistic Modeling and Deep Learning for Personalized Survival Prediction After Immune Checkpoint Inhibitor Immunotherapy

Authors

Joseph D Butner
Prashant Dogra
Caroline Chung
Eugene J Koay
James W Welsh
David S Hong
Vittorio Cristini
Zhihui Wang

Publication Date

8-14-2024

Journal

NPJ Systems Biology and Applications

Abstract

We present a study where predictive mechanistic modeling is combined with deep learning methods to predict individual patient survival probabilities under immune checkpoint inhibitor (ICI) immunotherapy. This hybrid approach enables prediction based on both measures that are calculable from mechanistic models of key mechanisms underlying ICI therapy that may not be directly measurable in the clinic and easily measurable quantities or patient characteristics that are not always readily incorporated into predictive mechanistic models. A deep learning time-to-event predictive model trained on a hybrid mechanistic + clinical data set from 93 patients achieved higher per-patient predictive accuracy based on event-time concordance, Brier score, and negative binomial log-likelihood-based criteria than when trained on only mechanistic model-derived values or only clinical data. Feature importance analysis revealed that both clinical and model-derived parameters play prominent roles in increasing prediction accuracy, further supporting the advantage of our hybrid approach.

Keywords

Humans, Deep Learning, Immune Checkpoint Inhibitors, Immunotherapy, Precision Medicine, Neoplasms, Male, Survival Analysis, Female

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Supplementary Materials

PMID: 39143136