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IMG-17. RADIOMICS CHARACTERIZATION OF FOUR PEDIATRIC BRAIN TUMOR SUBTYPES IN PDX MOUSE MODELS

BACKGROUND: Previously, we have reported on the development of advanced magnetic resonance imaging (MRI) protocols for mouse brain tumors. The goal of this follow-up pre-clinical study was to develop a machine-learning MRI classifier (radiomics) for four subtypes of childhood brain tumor in patient-...

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Autores principales: Serkova, Natalie, Stukova, Marina, Henehan, Samuel, Steiner, Jenna, Pierce, Angela, Griesinger, Andrea, Veo, Bethany, Alimova, Irina, Venkataraman, Sujatha, Green, Adam, Dahl, Nathan, Foreman, Nicholas, Vibhakar, Rajeev
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7715626/
http://dx.doi.org/10.1093/neuonc/noaa222.352
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author Serkova, Natalie
Stukova, Marina
Henehan, Samuel
Steiner, Jenna
Pierce, Angela
Griesinger, Andrea
Veo, Bethany
Alimova, Irina
Venkataraman, Sujatha
Green, Adam
Dahl, Nathan
Foreman, Nicholas
Vibhakar, Rajeev
author_facet Serkova, Natalie
Stukova, Marina
Henehan, Samuel
Steiner, Jenna
Pierce, Angela
Griesinger, Andrea
Veo, Bethany
Alimova, Irina
Venkataraman, Sujatha
Green, Adam
Dahl, Nathan
Foreman, Nicholas
Vibhakar, Rajeev
author_sort Serkova, Natalie
collection PubMed
description BACKGROUND: Previously, we have reported on the development of advanced magnetic resonance imaging (MRI) protocols for mouse brain tumors. The goal of this follow-up pre-clinical study was to develop a machine-learning MRI classifier (radiomics) for four subtypes of childhood brain tumor in patient-derived xenograft (PDX) mice. METHODS: MRI scans on orthotopic medulloblastoma, ependymoma, ATRT and DIPG PDX (each n=12 animals) were performed on the animal 9.4 Tesla scanner with an in-plane resolution of 47 microns. Image segmentation, as well as shape and texture based radiomics descriptors were modeled using a modified COLIAGE software for tumor classification and to characterize tumor habitat of each tumor subtype. RESULTS: The mean tumor volumes were 11.2 mm(3). Each MRI scan was segmented into three regions: (i) well defined tumor (including distant metastases); (ii) peritumoral edema; (iii) tumor necrosis. 360 radiomics features (capturing co-occurrence, grey-level dependence and directional gradients) were obtained for each region. The model classified four subtypes with high accuracy while achieving sufficient segmentation accuracy despite the small lesion size. A subset of fourteen tumoral, six peritumoral and five distant MRI radiomics features were found to be predictive of the tumor sub-type (p=0.0017) independently of tumor anatomical location. CONCLUSIONS: MRI protocols followed by radiomics feature analysis discriminated among specific radiological features for four distinct orthotopic PDX models: medulloblastomas exhibit low ADC values, high angiogenesis and cortical metastases as compared to ependymomas (high levels of edema and olfactory bulb metastases), ATRT (the highest level of necrosis) and DIPG (highest T2 signal intensities and spinal metastases).
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spelling pubmed-77156262020-12-09 IMG-17. RADIOMICS CHARACTERIZATION OF FOUR PEDIATRIC BRAIN TUMOR SUBTYPES IN PDX MOUSE MODELS Serkova, Natalie Stukova, Marina Henehan, Samuel Steiner, Jenna Pierce, Angela Griesinger, Andrea Veo, Bethany Alimova, Irina Venkataraman, Sujatha Green, Adam Dahl, Nathan Foreman, Nicholas Vibhakar, Rajeev Neuro Oncol Imaging BACKGROUND: Previously, we have reported on the development of advanced magnetic resonance imaging (MRI) protocols for mouse brain tumors. The goal of this follow-up pre-clinical study was to develop a machine-learning MRI classifier (radiomics) for four subtypes of childhood brain tumor in patient-derived xenograft (PDX) mice. METHODS: MRI scans on orthotopic medulloblastoma, ependymoma, ATRT and DIPG PDX (each n=12 animals) were performed on the animal 9.4 Tesla scanner with an in-plane resolution of 47 microns. Image segmentation, as well as shape and texture based radiomics descriptors were modeled using a modified COLIAGE software for tumor classification and to characterize tumor habitat of each tumor subtype. RESULTS: The mean tumor volumes were 11.2 mm(3). Each MRI scan was segmented into three regions: (i) well defined tumor (including distant metastases); (ii) peritumoral edema; (iii) tumor necrosis. 360 radiomics features (capturing co-occurrence, grey-level dependence and directional gradients) were obtained for each region. The model classified four subtypes with high accuracy while achieving sufficient segmentation accuracy despite the small lesion size. A subset of fourteen tumoral, six peritumoral and five distant MRI radiomics features were found to be predictive of the tumor sub-type (p=0.0017) independently of tumor anatomical location. CONCLUSIONS: MRI protocols followed by radiomics feature analysis discriminated among specific radiological features for four distinct orthotopic PDX models: medulloblastomas exhibit low ADC values, high angiogenesis and cortical metastases as compared to ependymomas (high levels of edema and olfactory bulb metastases), ATRT (the highest level of necrosis) and DIPG (highest T2 signal intensities and spinal metastases). Oxford University Press 2020-12-04 /pmc/articles/PMC7715626/ http://dx.doi.org/10.1093/neuonc/noaa222.352 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Imaging
Serkova, Natalie
Stukova, Marina
Henehan, Samuel
Steiner, Jenna
Pierce, Angela
Griesinger, Andrea
Veo, Bethany
Alimova, Irina
Venkataraman, Sujatha
Green, Adam
Dahl, Nathan
Foreman, Nicholas
Vibhakar, Rajeev
IMG-17. RADIOMICS CHARACTERIZATION OF FOUR PEDIATRIC BRAIN TUMOR SUBTYPES IN PDX MOUSE MODELS
title IMG-17. RADIOMICS CHARACTERIZATION OF FOUR PEDIATRIC BRAIN TUMOR SUBTYPES IN PDX MOUSE MODELS
title_full IMG-17. RADIOMICS CHARACTERIZATION OF FOUR PEDIATRIC BRAIN TUMOR SUBTYPES IN PDX MOUSE MODELS
title_fullStr IMG-17. RADIOMICS CHARACTERIZATION OF FOUR PEDIATRIC BRAIN TUMOR SUBTYPES IN PDX MOUSE MODELS
title_full_unstemmed IMG-17. RADIOMICS CHARACTERIZATION OF FOUR PEDIATRIC BRAIN TUMOR SUBTYPES IN PDX MOUSE MODELS
title_short IMG-17. RADIOMICS CHARACTERIZATION OF FOUR PEDIATRIC BRAIN TUMOR SUBTYPES IN PDX MOUSE MODELS
title_sort img-17. radiomics characterization of four pediatric brain tumor subtypes in pdx mouse models
topic Imaging
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7715626/
http://dx.doi.org/10.1093/neuonc/noaa222.352
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