An image-based modeling framework for predicting spatiotemporal brain cancer biology within individual patients

Abstract

Imaging is central to the clinical surveillance of brain tumors yet it provides limited insight into a tumor’s underlying biology. Machine learning and other mathematical modeling approaches can leverage paired magnetic resonance images and image-localized tissue samples to predict almost any characteristic of a tumor. Image-based modeling takes advantage of the spatial resolution of routine clinical scans and can be applied to measure biological differences within a tumor, changes over time, as well as the variance between patients. This approach is non-invasive and circumvents the intrinsic challenges of inter- and intratumoral heterogeneity that have historically hindered the complete assessment of tumor biology and treatment responsiveness. It can also reveal tumor characteristics that may guide both surgical and medical decision-making in real-time. Here we describe a general framework for the acquisition of image-localized biopsies and the construction of spatiotemporal radiomics models, as well as case examples of how this approach may be used to address clinically relevant questions.

Image-localized biopsy mapping of brain tumor heterogeneity: A single-center study protocol

“…Rather than alter clinical workflow, we must find ways (such as normalization techniques) to bring these images together into a comparable space for radiomics models. We have provided a framework for such radiomic model generation in recent work [ 80 ].…”