Abstract #3273

Early prediction of progression free survival and overall survival of patients with glioblastoma using machine learning and multiparametric MRI

Nate Tran^1,2, Tracy Luks¹, Devika Nair¹, Angela Jakary¹, Yan Li¹, Janine Lupo¹, Javier Villanueva-Meyer¹, Nicholas Butowski³, Jennifer Clarke³, and Susan Chang³

¹Department of Radiology & Biomedical Imaging, University of California, San Francisco, SAN FRANCISCO, CA, United States, ²UCSF/UC Berkeley Graduate Program in Bioengineering, SAN FRANCISCO, CA, United States, ³Department of Neurological Surgery, University of California, San Francisco, SAN FRANCISCO, CA, United States

This study evaluates the predictive power of multi-parametric MRI at pre-therapy and mid-RT time points in predicting progression-free and overall survival of patients with glioblastoma (GBM). We trained and tested random forest models using metabolic, perfusion, and diffusion images at both preRT and midRT scans, and found that not confining these metrics to the anatomical lesion boundaries improved outcome prediction. The CEL volume mid-RT and type of treatment were among the most important features in predicting PFS, while the T2L volume and metabolic metrics at pre-RT were more relevant for OS prediction.

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