Identifying Triple-Negative Breast Cancer Using Background Parenchymal Enhancement Heterogeneity on Dynamic Contrast-Enhanced MRI: A Pilot Radiomics Study.
发表日期： 2015.11.24 来源：PLoS One. 2015 Nov 24;10(11):e0143308.
Wang J1,2, Kato F3, Oyama-Manabe N2,3, Li R2,4, Cui Y2, Tha KK1,2, Yamashita H5, Kudo K2,3, Shirato H1,2.
1. Department of Radiation Medicine, Hokkaido University Graduate School of Medicine, North 15 West 7 Kita-ku, Sapporo, Hokkaido, 060-8638, Japan.
2. Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education (GI-CoRE), Hokkaido University, Proton Beam Therapy Center, North 14 West 5 Kita-ku, Sapporo, Hokkaido, 060-8648, Japan.
3. Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, North 14 West 5 Kita-ku, Sapporo, Hokkaido, 060-8648, Japan.
4. Department of Radiation Oncology, Stanford University School of Medicine, 291 Campus Drive, Li Ka Shing Building, Stanford, CA 94305, United States of America.
5. Department of Breast Surgery, Hokkaido University Hospital, North 14 West 5 Kita-ku, Sapporo, Hokkaido, 060-8648, Japan.
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.
Material 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 subtypes, 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.
根据现有的现有技术，基于肿瘤区域的成像特征在区别其他三阴性癌症时，AUC达到了0.782。当包括背景实质增强特征时，AUC显着增加至0.878（p < 0.01）。在几乎所有进行的亚型分类任务中都看到类似的改进。值得注意的是，预测三阴性癌症的最具特色的特征是背景实质增强的纹理。
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.
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.