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

Frontiers in Oncology

Oncology

Joshua B. Rubin¹  Sara Ranjbar²  Kristin R. Swanson²  Ariana E. Afshari²  Lee Curtin²  Leland S. Hu³  Kamila M. Bond⁴ 

[1] Departments of Neuroscience and Pediatrics, Washington University School of Medicine, St. Louis, MO, United States;Mathematical Neuro-Oncology Lab, Department of Neurological Surgery, Mayo Clinic, Phoenix, AZ, United States;Mathematical Neuro-Oncology Lab, Department of Neurological Surgery, Mayo Clinic, Phoenix, AZ, United States;Department of Radiology, Mayo Clinic, Phoenix, AZ, United States;Mathematical Neuro-Oncology Lab, Department of Neurological Surgery, Mayo Clinic, Phoenix, AZ, United States;Hospital of University of Pennsylvania, Department of Neurosurgery, Philadelphia, PA, United States;

关键词: glioblastoma;    radiomics;    machine learning;    MRI;    imaging;    CNS tumor;    personalized medicine;    glioma;   

DOI  :  10.3389/fonc.2023.1185738

 received in 2023-03-22, accepted in 2023-08-21,  发布年份 2023

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

Unknown   
Copyright © 2023 Bond, Curtin, Ranjbar, Afshari, Hu, Rubin and Swanson