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Identifying Triple-Negative Breast Cancer Using Background Parenchymal Enhancement Heterogeneity on Dynamic Contrast-Enhanced MRI : A Pilot Radiomics Study

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Title: Identifying Triple-Negative Breast Cancer Using Background Parenchymal Enhancement Heterogeneity on Dynamic Contrast-Enhanced MRI : A Pilot Radiomics Study
Authors: Wang, Jeff Browse this author
Kato, Fumi Browse this author →KAKEN DB
Oyama-Manabe, Noriko Browse this author →KAKEN DB
Li, Ruijiang Browse this author
Cui, Yi Browse this author
Tha, Khin Khin Browse this author →KAKEN DB
Yamashita, Hiroko Browse this author →KAKEN DB
Kudo, Kohsuke Browse this author →KAKEN DB
Shirato, Hiroki Browse this author →KAKEN DB
Issue Date: 24-Nov-2015
Publisher: PLOS
Journal Title: PLoS ONE
Volume: 10
Issue: 11
Start Page: e0143308
Publisher DOI: 10.1371/journal.pone.0143308
Abstract: Objectives: To determine the added discriminative value of detailed quantitative characterization of background parenchymal enhancement in addition to the tumor itself on dynamic contrast-enhanced (DCE) MRI at 3.0 Tesla in identifying "triple-negative" breast cancers. Materials and Methods: In this Institutional Review Board-approved retrospective study, DCE-MRI of 84 women presenting 88 invasive carcinomas were evaluated by a radiologist and analyzed using quantitative computer-aided techniques. Each tumor and its surrounding parenchyma were segmented semi-automatically in 3-D. A total of 85 imaging features were extracted from the two regions, including morphologic, densitometric, and statistical texture measures of enhancement. A small subset of optimal features was selected using an efficient sequential forward floating search algorithm. To distinguish triple-negative cancers from other sub-types, we built predictive models based on support vector machines. Their classification performance was assessed with the area under receiver operating characteristic curve (AUC) using cross-validation. Results: Imaging features based on the tumor region achieved an AUC of 0.782 in differentiating triple-negative cancers from others, in line with the current state of the art. When background parenchymal enhancement features were included, the AUC increased significantly to 0.878 (p<0.01). Similar improvements were seen in nearly all subtype classification tasks undertaken. Notably, amongst the most discriminating features for predicting triple-negative cancers were textures of background parenchymal enhancement. Conclusions: Considering the tumor as well as its surrounding parenchyma on DCE-MRI for radiomic image phenotyping provides useful information for identifying triple-negative breast cancers. Heterogeneity of background parenchymal enhancement, characterized by quantitative texture features on DCE-MRI, adds value to such differentiation models as they are strongly associated with the triple-negative subtype. Prospective validation studies are warranted to confirm these findings and determine potential implications.
Rights: http://creativecommons.org/licenses/by/4.0/
Type: article
URI: http://hdl.handle.net/2115/60653
Appears in Collections:国際連携研究教育局 : GI-CoRE (Global Institution for Collaborative Research and Education : GI-CoRE) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
北海道大学病院 (Hokkaido University Hospital) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 加藤 扶美

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