Hybridizing mechanistic modeling and deep learning for personalized survival prediction after immune checkpoint inhibitor immunotherapy.

Autor: Butner JD; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. jdbutner@mdanderson.org.; Institute for Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. jdbutner@mdanderson.org.; The Cameron School of Business, University of St. Thomas, Houston, TX, USA. jdbutner@mdanderson.org., Dogra P; Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA.; Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA., Chung C; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.; Institute for Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA., Koay EJ; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA., Welsh JW; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA., Hong DS; Department of Investigational Cancer Therapeutics, University of Texas MD Anderson Cancer Center, Houston, TX, USA., Cristini V; Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA.; Neal Cancer Center, Houston Methodist Research Institute, Houston, TX, USA.; Physiology, Biophysics, and Systems Biology Program, Graduate School of Medical Sciences, Weill Cornell Medicine, New York, NY, USA.; Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA., Wang Z; Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA. zwang@houstonmethodist.org.; Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA. zwang@houstonmethodist.org.; Neal Cancer Center, Houston Methodist Research Institute, Houston, TX, USA. zwang@houstonmethodist.org.; Department of Medical Education, Texas A&M University School of Medicine, Bryan, TX, USA. zwang@houstonmethodist.org.
Jazyk: angličtina
Zdroj: NPJ systems biology and applications [NPJ Syst Biol Appl] 2024 Aug 14; Vol. 10 (1), pp. 88. Date of Electronic Publication: 2024 Aug 14.
DOI: 10.1038/s41540-024-00415-8
Abstrakt: 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
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