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Radiomics of liver MRI predict metastases in mice

BACKGROUND: The purpose of this study was to investigate whether any texture features show a correlation with intrahepatic tumor growth before the metastasis is visible to the human eye. METHODS: Eight male C57BL6 mice (age 8–10 weeks) were injected intraportally with syngeneic MC-38 colon cancer ce...

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Autores principales: Becker, Anton S., Schneider, Marcel A., Wurnig, Moritz C., Wagner, Matthias, Clavien, Pierre A., Boss, Andreas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5971192/
https://www.ncbi.nlm.nih.gov/pubmed/29882527
http://dx.doi.org/10.1186/s41747-018-0044-7
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author Becker, Anton S.
Schneider, Marcel A.
Wurnig, Moritz C.
Wagner, Matthias
Clavien, Pierre A.
Boss, Andreas
author_facet Becker, Anton S.
Schneider, Marcel A.
Wurnig, Moritz C.
Wagner, Matthias
Clavien, Pierre A.
Boss, Andreas
author_sort Becker, Anton S.
collection PubMed
description BACKGROUND: The purpose of this study was to investigate whether any texture features show a correlation with intrahepatic tumor growth before the metastasis is visible to the human eye. METHODS: Eight male C57BL6 mice (age 8–10 weeks) were injected intraportally with syngeneic MC-38 colon cancer cells and two mice were injected with phosphate-buffered saline (sham controls). Small animal magnetic resonance imaging (MRI) at 4.7 T was performed at baseline and days 4, 8, 12, 16, and 20 after injection applying a T2-weighted spin-echo sequence. Texture analysis was performed on the images yielding 32 texture features derived from histogram, gray-level co-occurrence matrix, gray-level run-length matrix, and gray-level size-zone matrix. The features were examined with a linear regression model/Pearson correlation test and hierarchical cluster analysis. From each cluster, the feature with the lowest variance was selected. RESULTS: Tumors were visible on MRI after 20 days. Eighteen features from histogram and the gray-level-matrices exhibited statistically significant correlations before day 20 in the experiment group, but not in the control animals. Cluster analysis revealed three distinct clusters of independent features. The features with the lowest variance were Energy, Short Run Emphasis, and Gray Level Non-Uniformity. CONCLUSIONS: Texture features may quantitatively detect liver metastases before they become visually detectable by the radiologist.
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spelling pubmed-59711922018-06-05 Radiomics of liver MRI predict metastases in mice Becker, Anton S. Schneider, Marcel A. Wurnig, Moritz C. Wagner, Matthias Clavien, Pierre A. Boss, Andreas Eur Radiol Exp Original Article BACKGROUND: The purpose of this study was to investigate whether any texture features show a correlation with intrahepatic tumor growth before the metastasis is visible to the human eye. METHODS: Eight male C57BL6 mice (age 8–10 weeks) were injected intraportally with syngeneic MC-38 colon cancer cells and two mice were injected with phosphate-buffered saline (sham controls). Small animal magnetic resonance imaging (MRI) at 4.7 T was performed at baseline and days 4, 8, 12, 16, and 20 after injection applying a T2-weighted spin-echo sequence. Texture analysis was performed on the images yielding 32 texture features derived from histogram, gray-level co-occurrence matrix, gray-level run-length matrix, and gray-level size-zone matrix. The features were examined with a linear regression model/Pearson correlation test and hierarchical cluster analysis. From each cluster, the feature with the lowest variance was selected. RESULTS: Tumors were visible on MRI after 20 days. Eighteen features from histogram and the gray-level-matrices exhibited statistically significant correlations before day 20 in the experiment group, but not in the control animals. Cluster analysis revealed three distinct clusters of independent features. The features with the lowest variance were Energy, Short Run Emphasis, and Gray Level Non-Uniformity. CONCLUSIONS: Texture features may quantitatively detect liver metastases before they become visually detectable by the radiologist. Springer International Publishing 2018-05-28 /pmc/articles/PMC5971192/ /pubmed/29882527 http://dx.doi.org/10.1186/s41747-018-0044-7 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Original Article
Becker, Anton S.
Schneider, Marcel A.
Wurnig, Moritz C.
Wagner, Matthias
Clavien, Pierre A.
Boss, Andreas
Radiomics of liver MRI predict metastases in mice
title Radiomics of liver MRI predict metastases in mice
title_full Radiomics of liver MRI predict metastases in mice
title_fullStr Radiomics of liver MRI predict metastases in mice
title_full_unstemmed Radiomics of liver MRI predict metastases in mice
title_short Radiomics of liver MRI predict metastases in mice
title_sort radiomics of liver mri predict metastases in mice
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5971192/
https://www.ncbi.nlm.nih.gov/pubmed/29882527
http://dx.doi.org/10.1186/s41747-018-0044-7
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