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Multiparametric MRI Analysis for the Identification of High Intensity Focused Ultrasound-Treated Tumor Tissue
PURPOSE: In this study endogenous magnetic resonance imaging (MRI) biomarkers for accurate segmentation of High Intensity Focused Ultrasound (HIFU)-treated tumor tissue and residual or recurring non-treated tumor tissue were identified. METHODS: Multiparametric MRI, consisting of quantitative T(1),...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4057317/ https://www.ncbi.nlm.nih.gov/pubmed/24927280 http://dx.doi.org/10.1371/journal.pone.0099936 |
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author | Hectors, Stefanie J. C. G. Jacobs, Igor Strijkers, Gustav J. Nicolay, Klaas |
author_facet | Hectors, Stefanie J. C. G. Jacobs, Igor Strijkers, Gustav J. Nicolay, Klaas |
author_sort | Hectors, Stefanie J. C. G. |
collection | PubMed |
description | PURPOSE: In this study endogenous magnetic resonance imaging (MRI) biomarkers for accurate segmentation of High Intensity Focused Ultrasound (HIFU)-treated tumor tissue and residual or recurring non-treated tumor tissue were identified. METHODS: Multiparametric MRI, consisting of quantitative T(1), T(2), Apparent Diffusion Coefficient (ADC) and Magnetization Transfer Ratio (MTR) mapping, was performed in tumor-bearing mice before (n = 14), 1 h after (n = 14) and 72 h (n = 7) after HIFU treatment. A non-treated control group was included (n = 7). Cluster analysis using the Iterative Self Organizing Data Analysis (ISODATA) technique was performed on subsets of MRI parameters (feature vectors). The clusters resulting from the ISODATA segmentation were divided into a viable and non-viable class based on the fraction of pixels assigned to the clusters at the different experimental time points. ISODATA-derived non-viable tumor fractions were quantitatively compared to histology-derived non-viable tumor volume fractions. RESULTS: The highest agreement between the ISODATA-derived and histology-derived non-viable tumor fractions was observed for feature vector {T(1), T(2), ADC}. R(1) (1/T(1)), R(2) (1/T(2)), ADC and MTR each were significantly increased in the ISODATA-defined non-viable tumor tissue at 1 h after HIFU treatment compared to viable, non-treated tumor tissue. R(1), ADC and MTR were also significantly increased at 72 h after HIFU. CONCLUSIONS: This study demonstrates that non-viable, HIFU-treated tumor tissue can be distinguished from viable, non-treated tumor tissue using multiparametric MRI analysis. Clinical application of the presented methodology may allow for automated, accurate and objective evaluation of HIFU treatment. |
format | Online Article Text |
id | pubmed-4057317 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-40573172014-06-18 Multiparametric MRI Analysis for the Identification of High Intensity Focused Ultrasound-Treated Tumor Tissue Hectors, Stefanie J. C. G. Jacobs, Igor Strijkers, Gustav J. Nicolay, Klaas PLoS One Research Article PURPOSE: In this study endogenous magnetic resonance imaging (MRI) biomarkers for accurate segmentation of High Intensity Focused Ultrasound (HIFU)-treated tumor tissue and residual or recurring non-treated tumor tissue were identified. METHODS: Multiparametric MRI, consisting of quantitative T(1), T(2), Apparent Diffusion Coefficient (ADC) and Magnetization Transfer Ratio (MTR) mapping, was performed in tumor-bearing mice before (n = 14), 1 h after (n = 14) and 72 h (n = 7) after HIFU treatment. A non-treated control group was included (n = 7). Cluster analysis using the Iterative Self Organizing Data Analysis (ISODATA) technique was performed on subsets of MRI parameters (feature vectors). The clusters resulting from the ISODATA segmentation were divided into a viable and non-viable class based on the fraction of pixels assigned to the clusters at the different experimental time points. ISODATA-derived non-viable tumor fractions were quantitatively compared to histology-derived non-viable tumor volume fractions. RESULTS: The highest agreement between the ISODATA-derived and histology-derived non-viable tumor fractions was observed for feature vector {T(1), T(2), ADC}. R(1) (1/T(1)), R(2) (1/T(2)), ADC and MTR each were significantly increased in the ISODATA-defined non-viable tumor tissue at 1 h after HIFU treatment compared to viable, non-treated tumor tissue. R(1), ADC and MTR were also significantly increased at 72 h after HIFU. CONCLUSIONS: This study demonstrates that non-viable, HIFU-treated tumor tissue can be distinguished from viable, non-treated tumor tissue using multiparametric MRI analysis. Clinical application of the presented methodology may allow for automated, accurate and objective evaluation of HIFU treatment. Public Library of Science 2014-06-13 /pmc/articles/PMC4057317/ /pubmed/24927280 http://dx.doi.org/10.1371/journal.pone.0099936 Text en © 2014 Hectors et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Hectors, Stefanie J. C. G. Jacobs, Igor Strijkers, Gustav J. Nicolay, Klaas Multiparametric MRI Analysis for the Identification of High Intensity Focused Ultrasound-Treated Tumor Tissue |
title | Multiparametric MRI Analysis for the Identification of High Intensity Focused Ultrasound-Treated Tumor Tissue |
title_full | Multiparametric MRI Analysis for the Identification of High Intensity Focused Ultrasound-Treated Tumor Tissue |
title_fullStr | Multiparametric MRI Analysis for the Identification of High Intensity Focused Ultrasound-Treated Tumor Tissue |
title_full_unstemmed | Multiparametric MRI Analysis for the Identification of High Intensity Focused Ultrasound-Treated Tumor Tissue |
title_short | Multiparametric MRI Analysis for the Identification of High Intensity Focused Ultrasound-Treated Tumor Tissue |
title_sort | multiparametric mri analysis for the identification of high intensity focused ultrasound-treated tumor tissue |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4057317/ https://www.ncbi.nlm.nih.gov/pubmed/24927280 http://dx.doi.org/10.1371/journal.pone.0099936 |
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