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Deep learning of magnetic resonance fingerprints for the quantitative imaging of apoptosis following oncolytic virotherapy
Oncolytic virotherapy is a promising anticancer strategy, however, non-invasive imaging methods that detect intratumoral viral spread and an individual’s response to therapy are either slow, lack specificity or require the use of radioactive or metal-based contrast agents, hindering their clinical t...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9091056/ https://www.ncbi.nlm.nih.gov/pubmed/34764440 http://dx.doi.org/10.1038/s41551-021-00809-7 |
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author | Perlman, Or Ito, Hirotaka Herz, Kai Shono, Naoyuki Nakashima, Hiroshi Zaiss, Moritz Chiocca, E. Antonio Cohen, Ouri Rosen, Matthew S. Farrar, Christian T. |
author_facet | Perlman, Or Ito, Hirotaka Herz, Kai Shono, Naoyuki Nakashima, Hiroshi Zaiss, Moritz Chiocca, E. Antonio Cohen, Ouri Rosen, Matthew S. Farrar, Christian T. |
author_sort | Perlman, Or |
collection | PubMed |
description | Oncolytic virotherapy is a promising anticancer strategy, however, non-invasive imaging methods that detect intratumoral viral spread and an individual’s response to therapy are either slow, lack specificity or require the use of radioactive or metal-based contrast agents, hindering their clinical translation. Here, we describe a fast chemical-exchange saturation transfer magnetic resonance fingerprinting (CEST-MRF) approach that selectively labels the exchangeable amide protons of endogenous proteins and the exchangeable macromolecule protons of lipids, without requiring an exogenous contrast agent. A deep neural network, previously trained with simulated MR-fingerprints, then generates, using CEST-MRF image data, quantitative maps of intratumoral pH, and protein and lipid concentrations. In a mouse model of glioblastoma multiforme, the maps enabled the detection of the early apoptotic response to oncolytic virotherapy, characterized by decreased cytosolic pH and reduced protein synthesis. Imaging of a healthy human volunteer yields molecular parameters in good agreement with literature values, demonstrating the clinical potential of artificial intelligence-based CEST-MRF, which may be further applicable to the imaging of a wide range of pathologies, including stroke, cancer, and neurological disorders. |
format | Online Article Text |
id | pubmed-9091056 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
record_format | MEDLINE/PubMed |
spelling | pubmed-90910562022-05-30 Deep learning of magnetic resonance fingerprints for the quantitative imaging of apoptosis following oncolytic virotherapy Perlman, Or Ito, Hirotaka Herz, Kai Shono, Naoyuki Nakashima, Hiroshi Zaiss, Moritz Chiocca, E. Antonio Cohen, Ouri Rosen, Matthew S. Farrar, Christian T. Nat Biomed Eng Article Oncolytic virotherapy is a promising anticancer strategy, however, non-invasive imaging methods that detect intratumoral viral spread and an individual’s response to therapy are either slow, lack specificity or require the use of radioactive or metal-based contrast agents, hindering their clinical translation. Here, we describe a fast chemical-exchange saturation transfer magnetic resonance fingerprinting (CEST-MRF) approach that selectively labels the exchangeable amide protons of endogenous proteins and the exchangeable macromolecule protons of lipids, without requiring an exogenous contrast agent. A deep neural network, previously trained with simulated MR-fingerprints, then generates, using CEST-MRF image data, quantitative maps of intratumoral pH, and protein and lipid concentrations. In a mouse model of glioblastoma multiforme, the maps enabled the detection of the early apoptotic response to oncolytic virotherapy, characterized by decreased cytosolic pH and reduced protein synthesis. Imaging of a healthy human volunteer yields molecular parameters in good agreement with literature values, demonstrating the clinical potential of artificial intelligence-based CEST-MRF, which may be further applicable to the imaging of a wide range of pathologies, including stroke, cancer, and neurological disorders. 2022-05 2021-11-11 /pmc/articles/PMC9091056/ /pubmed/34764440 http://dx.doi.org/10.1038/s41551-021-00809-7 Text en Reprints and permissions information is available at www.nature.com/reprints (http://www.nature.com/reprints) . Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms |
spellingShingle | Article Perlman, Or Ito, Hirotaka Herz, Kai Shono, Naoyuki Nakashima, Hiroshi Zaiss, Moritz Chiocca, E. Antonio Cohen, Ouri Rosen, Matthew S. Farrar, Christian T. Deep learning of magnetic resonance fingerprints for the quantitative imaging of apoptosis following oncolytic virotherapy |
title | Deep learning of magnetic resonance fingerprints for the quantitative
imaging of apoptosis following oncolytic virotherapy |
title_full | Deep learning of magnetic resonance fingerprints for the quantitative
imaging of apoptosis following oncolytic virotherapy |
title_fullStr | Deep learning of magnetic resonance fingerprints for the quantitative
imaging of apoptosis following oncolytic virotherapy |
title_full_unstemmed | Deep learning of magnetic resonance fingerprints for the quantitative
imaging of apoptosis following oncolytic virotherapy |
title_short | Deep learning of magnetic resonance fingerprints for the quantitative
imaging of apoptosis following oncolytic virotherapy |
title_sort | deep learning of magnetic resonance fingerprints for the quantitative
imaging of apoptosis following oncolytic virotherapy |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9091056/ https://www.ncbi.nlm.nih.gov/pubmed/34764440 http://dx.doi.org/10.1038/s41551-021-00809-7 |
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