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

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 charac...

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Autores principales: Bond, Kamila M., Curtin, Lee, Ranjbar, Sara, Afshari, Ariana E., Hu, Leland S., Rubin, Joshua B., Swanson, Kristin R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10578440/
https://www.ncbi.nlm.nih.gov/pubmed/37849813
http://dx.doi.org/10.3389/fonc.2023.1185738
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author Bond, Kamila M.
Curtin, Lee
Ranjbar, Sara
Afshari, Ariana E.
Hu, Leland S.
Rubin, Joshua B.
Swanson, Kristin R.
author_facet Bond, Kamila M.
Curtin, Lee
Ranjbar, Sara
Afshari, Ariana E.
Hu, Leland S.
Rubin, Joshua B.
Swanson, Kristin R.
author_sort Bond, Kamila M.
collection PubMed
description 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.
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spelling pubmed-105784402023-10-17 An image-based modeling framework for predicting spatiotemporal brain cancer biology within individual patients Bond, Kamila M. Curtin, Lee Ranjbar, Sara Afshari, Ariana E. Hu, Leland S. Rubin, Joshua B. Swanson, Kristin R. Front Oncol Oncology 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. Frontiers Media S.A. 2023-10-02 /pmc/articles/PMC10578440/ /pubmed/37849813 http://dx.doi.org/10.3389/fonc.2023.1185738 Text en Copyright © 2023 Bond, Curtin, Ranjbar, Afshari, Hu, Rubin and Swanson https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Bond, Kamila M.
Curtin, Lee
Ranjbar, Sara
Afshari, Ariana E.
Hu, Leland S.
Rubin, Joshua B.
Swanson, Kristin R.
An image-based modeling framework for predicting spatiotemporal brain cancer biology within individual patients
title An image-based modeling framework for predicting spatiotemporal brain cancer biology within individual patients
title_full An image-based modeling framework for predicting spatiotemporal brain cancer biology within individual patients
title_fullStr An image-based modeling framework for predicting spatiotemporal brain cancer biology within individual patients
title_full_unstemmed An image-based modeling framework for predicting spatiotemporal brain cancer biology within individual patients
title_short An image-based modeling framework for predicting spatiotemporal brain cancer biology within individual patients
title_sort image-based modeling framework for predicting spatiotemporal brain cancer biology within individual patients
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10578440/
https://www.ncbi.nlm.nih.gov/pubmed/37849813
http://dx.doi.org/10.3389/fonc.2023.1185738
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