Abstract
The current state of magnetic resonance imaging (MRI) methods in neurooncology offers great
potential for providing rich characterizations of structural, physiological, and metabolic character-
istics of brain tumors, especially gliomas, which are complex and highly heterogeneous cancers.
Glioblastoma (GBM), in particular, has a grim prognosis, with median overall survival (OS) less than
15 months with relatively little improvement in the past 15 years since the Stupp protocol was
introduced. Many experimental treatments are being pursued; however, OS has largely remained
stagnant. Some of the obstacles in improving this outcome have been 1) disease heterogeneity,
which both renders it difficult to detect treatment effects in Phase 1 or even Phase 2 trials, and calls
for personalized, rather than one-size fits-all, treatment strategies; 2) methods used for tumor
characterization based on size, enhancement, perfusion and diffusion properties are relatively
crude and don't fully leverage the richness of imaging data or their spatial heterogeneity. Quanti-
tative imaging and machine learning (QIML) methods developed in the past decade have shown
great potential for dissecting the spatial, temporal and inter-patient heterogeneity of GBM; for
discoveringrelationships between imaging and molecular characteristics ; foroffering personalized
predictions of clinical outcome; and for leveraging subtle multi-parametric relationships in the data
to detect peri-tumoral infiltration or distinguish treatment related changes, i.e., pseudo-progression
(PsP), from true tumor recurrence. Our group has been at the forefront of QIML, with emphasis on
a) obtaining rich imaging phenotypes relying on multi-parametric signals, texture parameters, shape
properties, spatial patterns derived from atlas registration, and biophysical models of tumor growth,
and b) integrating such imaging signatures using machine learning into predictors of clinical
outcome, early recurrence from peri-tumoral infiltration, PsP, and radiologic subtypes of GBM.
Despite their promise, QIML methods have a notorious limitation: they might overfit specific
datasets from which they are derived, and might display poor reproducibility under real-life
conditions of variable scanner types and imaging protocols. In this proposal we aim to leverage the
recently formed ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium, to
integrate, harmonize, and analyze 4,578 datasets from 14 centers around the world, and hence
more appropriately train and cross-validate QIML tools for a wider generalizability. This consortium
will generate an unprecedented database of diverse and carefully harmonized sets of MRI and
clinical measures, and aims to provide the community with robust and reproducible QIML models
contributing to precision diagnostics and personalize treatment for this dreaded brain cancer.
