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AI-based Prognostic Imaging Biomarkers for Precision Neurooncology: the ReSPOND Consortium.

Artificial intelligence (AI) and machine learning (ML) methods have begun to reveal that complex imaging patterns can provide individualized biomarkers for diagnosis and prognosis. However, AI methods have been challenged by insufficient training, heterogeneity of imaging protocols across hospitals, and lack of generalization to new patient data. These challenges prompted the development of the ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium on glioblastoma (GBM). This collaboration of over 10 institutions, across 3 continents, is positioned to pool, harmonize, and analyze brain MRIs from more than 3300 de novo GBM patients who underwent the Stupp protocol, in addition to datasets from The Cancer Imaging Archive (TCIA).1 ReSPOND aims to further develop and test AI-based biomarkers for individualized prediction and prognostication, by moving from single-institution studies to generalized, well-validated predictive biomarkers in the following 4 areas: