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Radiogenomics of magnetic resonance imaging and a new multi-gene classifier for predicting recurrence prognosis in estrogen receptor-positive breast cancer: A preliminary study

To examine the correlation of qualitative and quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) results with 95-gene classifier or Curebest(TM) 95-gene classifier Breast (95GC) results for recurrence prediction in estrogen receptor-positive breast cancer (ERPBC). This retro...

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Autores principales: Tokuda, Yukiko, Yanagawa, Masahiro, Minamitani, Kaori, Naoi, Yasuto, Noguchi, Shinzaburo, Tomiyama, Noriyuki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer Health 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7220792/
https://www.ncbi.nlm.nih.gov/pubmed/32311939
http://dx.doi.org/10.1097/MD.0000000000019664
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author Tokuda, Yukiko
Yanagawa, Masahiro
Minamitani, Kaori
Naoi, Yasuto
Noguchi, Shinzaburo
Tomiyama, Noriyuki
author_facet Tokuda, Yukiko
Yanagawa, Masahiro
Minamitani, Kaori
Naoi, Yasuto
Noguchi, Shinzaburo
Tomiyama, Noriyuki
author_sort Tokuda, Yukiko
collection PubMed
description To examine the correlation of qualitative and quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) results with 95-gene classifier or Curebest(TM) 95-gene classifier Breast (95GC) results for recurrence prediction in estrogen receptor-positive breast cancer (ERPBC). This retrospective study included 78 ERPBC patients (age range, 24–74 years) classified into high- (n = 33) and low- (n = 45) risk groups for recurrence based on 95GC and who underwent DCE-MRI between July 2006 and November 2012. For qualitative evaluation, mass shape, margin, and internal enhancement based on BI-RADS MRI lexicon and multiplicity were determined by consensus interpretation by 2 breast radiologists. For quantitative evaluation, mass size, volume ratios of the DCE-MRI kinetics, and both the kurtosis and the skewness of the intensity histogram for the whole mass in the initial and delayed phases were determined. Differences between the 2 risk-groups were analyzed using univariate logistic regression analyses and multiple logistic regression analyses. Receiver-operating characteristic curve cut-off values were used to define the groups. As for the qualitative findings, the difference between the 2 groups was not significant. For the quantitative data, the volume ratio of “medium” in the initial phase differed significantly between the 2 groups (P = .049). The volume ratio of “medium” (P = .006) and of “slow-persistent” (P = .005), and the delayed phase kurtosis (P = .012) in the univariate logistic regression analyses, and in the multiple logistic regression, volume ratio of “medium” >38.9% and delayed phase kurtosis >3.31 were identified as significant high-risk indicators (odds ratio, 5.83 and 3.55; 95% confidence interval, 1.58 to 21.42 and 1.24 to 10.15; P = .008 and P = .018, respectively). A high volume ratio of “medium” in the initial phase and/or high kurtosis in the delayed phase for quantitative evaluation could predict high ERPBC recurrence risk based on 95GC.
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spelling pubmed-72207922020-06-15 Radiogenomics of magnetic resonance imaging and a new multi-gene classifier for predicting recurrence prognosis in estrogen receptor-positive breast cancer: A preliminary study Tokuda, Yukiko Yanagawa, Masahiro Minamitani, Kaori Naoi, Yasuto Noguchi, Shinzaburo Tomiyama, Noriyuki Medicine (Baltimore) 6800 To examine the correlation of qualitative and quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) results with 95-gene classifier or Curebest(TM) 95-gene classifier Breast (95GC) results for recurrence prediction in estrogen receptor-positive breast cancer (ERPBC). This retrospective study included 78 ERPBC patients (age range, 24–74 years) classified into high- (n = 33) and low- (n = 45) risk groups for recurrence based on 95GC and who underwent DCE-MRI between July 2006 and November 2012. For qualitative evaluation, mass shape, margin, and internal enhancement based on BI-RADS MRI lexicon and multiplicity were determined by consensus interpretation by 2 breast radiologists. For quantitative evaluation, mass size, volume ratios of the DCE-MRI kinetics, and both the kurtosis and the skewness of the intensity histogram for the whole mass in the initial and delayed phases were determined. Differences between the 2 risk-groups were analyzed using univariate logistic regression analyses and multiple logistic regression analyses. Receiver-operating characteristic curve cut-off values were used to define the groups. As for the qualitative findings, the difference between the 2 groups was not significant. For the quantitative data, the volume ratio of “medium” in the initial phase differed significantly between the 2 groups (P = .049). The volume ratio of “medium” (P = .006) and of “slow-persistent” (P = .005), and the delayed phase kurtosis (P = .012) in the univariate logistic regression analyses, and in the multiple logistic regression, volume ratio of “medium” >38.9% and delayed phase kurtosis >3.31 were identified as significant high-risk indicators (odds ratio, 5.83 and 3.55; 95% confidence interval, 1.58 to 21.42 and 1.24 to 10.15; P = .008 and P = .018, respectively). A high volume ratio of “medium” in the initial phase and/or high kurtosis in the delayed phase for quantitative evaluation could predict high ERPBC recurrence risk based on 95GC. Wolters Kluwer Health 2020-04-17 /pmc/articles/PMC7220792/ /pubmed/32311939 http://dx.doi.org/10.1097/MD.0000000000019664 Text en Copyright © 2020 the Author(s). Published by Wolters Kluwer Health, Inc. 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 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc/4.0
spellingShingle 6800
Tokuda, Yukiko
Yanagawa, Masahiro
Minamitani, Kaori
Naoi, Yasuto
Noguchi, Shinzaburo
Tomiyama, Noriyuki
Radiogenomics of magnetic resonance imaging and a new multi-gene classifier for predicting recurrence prognosis in estrogen receptor-positive breast cancer: A preliminary study
title Radiogenomics of magnetic resonance imaging and a new multi-gene classifier for predicting recurrence prognosis in estrogen receptor-positive breast cancer: A preliminary study
title_full Radiogenomics of magnetic resonance imaging and a new multi-gene classifier for predicting recurrence prognosis in estrogen receptor-positive breast cancer: A preliminary study
title_fullStr Radiogenomics of magnetic resonance imaging and a new multi-gene classifier for predicting recurrence prognosis in estrogen receptor-positive breast cancer: A preliminary study
title_full_unstemmed Radiogenomics of magnetic resonance imaging and a new multi-gene classifier for predicting recurrence prognosis in estrogen receptor-positive breast cancer: A preliminary study
title_short Radiogenomics of magnetic resonance imaging and a new multi-gene classifier for predicting recurrence prognosis in estrogen receptor-positive breast cancer: A preliminary study
title_sort radiogenomics of magnetic resonance imaging and a new multi-gene classifier for predicting recurrence prognosis in estrogen receptor-positive breast cancer: a preliminary study
topic 6800
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7220792/
https://www.ncbi.nlm.nih.gov/pubmed/32311939
http://dx.doi.org/10.1097/MD.0000000000019664
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