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

• Published in: PloS one Vol. 10; no. 11; p. e0143308
• Main Authors: Wang, Jeff, Kato, Fumi, Oyama-Manabe, Noriko, Li, Ruijiang, Cui, Yi, Tha, Khin Khin, Yamashita, Hiroko, Kudo, Kohsuke, Shirato, Hiroki
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 24.11.2015; Public Library of Science (PLoS)
• Subjects: Aged; Algorithms; Analysis; Biomarkers, Tumor; Breast cancer; Cancer therapies; Carcinoma; Care and treatment; Classification; Cluster Analysis; Collaboration; Complications and side effects; Contrast Media; Densitometers; Education; Engineering; Feature extraction; Female; Gene expression; Genetic aspects; Heterogeneity; Hospitals; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Image Processing, Computer-Assisted; Invasiveness; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Mammography; Mathematical models; Medical diagnosis; Medicine; Menstruation; Middle Aged; NMR; Nuclear magnetic resonance; Parenchyma; Phenotyping; Pilot Projects; Prediction models; Prognosis; Radiology; Radiomics; Risk Factors; Science; Search algorithms; Support vector machines; Texture; Triple Negative Breast Neoplasms - diagnosis; Triple Negative Breast Neoplasms - pathology; Tumors; University graduates; Womens health; Massachusetts; Japan
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0143308

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. 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. 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.