OBJECTIVES: To determine the added discriminative value of detailed quantitative characterization of background parenchymal enhancement in addition to the @entity5 itself on dynamic contrast-enhanced (DCE) MRI at 3.0 Tesla in identifying "triple-negative" @entity0 . MATERIALS AND METHODS: In this Institutional Review Board-approved retrospective study, DCE-MRI of 84 @entity1 presenting 88 invasive @entity189 were evaluated by a radiologist and analyzed using quantitative computer-aided techniques. Each @entity5 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 @entity5 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. RESULTS: Imaging features based on the @entity5 region achieved an AUC of 0.782 in differentiating @entity5 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 @entity5 were textures of background parenchymal enhancement. CONCLUSIONS: Considering the @entity5 as well as its surrounding parenchyma on DCE-MRI for radiomic image phenotyping provides useful information for identifying triple-negative @entity0 . 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.

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

@entity5 :: ('MESH:D009369', 'Disease') :: ['tumor', 'triple-negative cancers'] @entity1 :: ('9606', 'Species') :: ['women'] @entity189 :: ('MESH:D002277', 'Disease') :: ['carcinomas'] @entity0 :: ('MESH:D001943', 'Disease') :: ['breast cancers']
@entity0 :: (MESH:D001943,Disease) :: ['Breast Cancer']
