Multiparametric MRI analysis for the identification of high intensity focused ultrasound-treated tumor tissue.

Multiparametric MRI analysis for the identification of high intensity focused ultrasound-treated tumor tissue.

<h4>Purpose</h4>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.<h4>Methods</h4>Multiparametric M...

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Autores principales: Stefanie J C G Hectors, Igor Jacobs, Gustav J Strijkers, Klaas Nicolay
Formato: article
Lenguaje:EN
Publicado: Public Library of Science (PLoS) 2014
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Acceso en línea:https://doaj.org/article/d8087782835e4d68881a92b461278c04
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spelling oai:doaj.org-article:d8087782835e4d68881a92b461278c042021-11-18T08:15:46ZMultiparametric MRI analysis for the identification of high intensity focused ultrasound-treated tumor tissue.1932-620310.1371/journal.pone.0099936https://doaj.org/article/d8087782835e4d68881a92b461278c042014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24927280/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Purpose</h4>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.<h4>Methods</h4>Multiparametric MRI, consisting of quantitative T1, T2, 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.<h4>Results</h4>The highest agreement between the ISODATA-derived and histology-derived non-viable tumor fractions was observed for feature vector {T1, T2, ADC}. R1 (1/T1), R2 (1/T2), 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. R1, ADC and MTR were also significantly increased at 72 h after HIFU.<h4>Conclusions</h4>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.Stefanie J C G HectorsIgor JacobsGustav J StrijkersKlaas NicolayPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 6, p e99936 (2014)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Stefanie J C G Hectors
Igor Jacobs
Gustav J Strijkers
Klaas Nicolay
Multiparametric MRI analysis for the identification of high intensity focused ultrasound-treated tumor tissue.
description <h4>Purpose</h4>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.<h4>Methods</h4>Multiparametric MRI, consisting of quantitative T1, T2, 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.<h4>Results</h4>The highest agreement between the ISODATA-derived and histology-derived non-viable tumor fractions was observed for feature vector {T1, T2, ADC}. R1 (1/T1), R2 (1/T2), 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. R1, ADC and MTR were also significantly increased at 72 h after HIFU.<h4>Conclusions</h4>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 article
author Stefanie J C G Hectors
Igor Jacobs
Gustav J Strijkers
Klaas Nicolay
author_facet Stefanie J C G Hectors
Igor Jacobs
Gustav J Strijkers
Klaas Nicolay
author_sort Stefanie J C G Hectors
title 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_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_sort multiparametric mri analysis for the identification of high intensity focused ultrasound-treated tumor tissue.
publisher Public Library of Science (PLoS)
publishDate 2014
url https://doaj.org/article/d8087782835e4d68881a92b461278c04
work_keys_str_mv AT stefaniejcghectors multiparametricmrianalysisfortheidentificationofhighintensityfocusedultrasoundtreatedtumortissue
AT igorjacobs multiparametricmrianalysisfortheidentificationofhighintensityfocusedultrasoundtreatedtumortissue
AT gustavjstrijkers multiparametricmrianalysisfortheidentificationofhighintensityfocusedultrasoundtreatedtumortissue
AT klaasnicolay multiparametricmrianalysisfortheidentificationofhighintensityfocusedultrasoundtreatedtumortissue
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