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

Kamila M. Bond, Lee Curtin, Sara Ranjbar, Ariana E. Afshari, Leland S. Hu, Joshua B. Rubin, Kristin R. Swanson

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number1185738
JournalFrontiers in Oncology
Volume13
DOIs
StatePublished - 2023

Keywords

  • CNS tumor
  • MRI
  • glioblastoma
  • glioma
  • imaging
  • machine learning
  • personalized medicine
  • radiomics

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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