title: Identifying Triple-Negative Breast Cancer Using Background Parenchymal Enhancement Heterogeneity on Dynamic Contrast-Enhanced MRI: A Pilot Radiomics Study.
abstract: OBJECTIVE
  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.
  
  
  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.
  
  
  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.
authors.affiliation: Department of Radiation Medicine, Hokkaido University Graduate School of Medicine, North 15 West 7 Kita-ku, Sapporo, Hokkaido, 060-8638, Japan.|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.
authors.forename: Jeff
authors.initials: J
authors.lastname: Wang
authors.affiliation: Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, North 14 West 5 Kita-ku, Sapporo, Hokkaido, 060-8648, Japan.
authors.forename: Fumi
authors.initials: F
authors.lastname: Kato
authors.affiliation: Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, North 14 West 5 Kita-ku, Sapporo, Hokkaido, 060-8648, Japan.|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.
authors.forename: Noriko
authors.initials: N
authors.lastname: Oyama-Manabe
authors.affiliation: Department of Radiation Oncology, Stanford University School of Medicine, 291 Campus Drive, Li Ka Shing Building, Stanford, CA 94305, United States of America.|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.
authors.forename: Ruijiang
authors.initials: R
authors.lastname: Li
authors.affiliation: 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.
authors.forename: Yi
authors.initials: Y
authors.lastname: Cui
authors.affiliation: Department of Radiation Medicine, Hokkaido University Graduate School of Medicine, North 15 West 7 Kita-ku, Sapporo, Hokkaido, 060-8638, Japan.|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.
authors.forename: Khin Khin
authors.initials: KK
authors.lastname: Tha
authors.affiliation: Department of Breast Surgery, Hokkaido University Hospital, North 14 West 5 Kita-ku, Sapporo, Hokkaido, 060-8648, Japan.
authors.forename: Hiroko
authors.initials: H
authors.lastname: Yamashita
authors.affiliation: Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, North 14 West 5 Kita-ku, Sapporo, Hokkaido, 060-8648, Japan.|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.
authors.forename: Kohsuke
authors.initials: K
authors.lastname: Kudo
authors.affiliation: Department of Radiation Medicine, Hokkaido University Graduate School of Medicine, North 15 West 7 Kita-ku, Sapporo, Hokkaido, 060-8638, Japan.|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.
authors.forename: Hiroki
authors.initials: H
authors.lastname: Shirato
mesh_terms: D000368:Aged; D014408:Biomarkers, Tumor; D016000:Cluster Analysis; D003287:Contrast Media; D005260:Female; D006801:Humans; D007089:Image Enhancement; D007090:Image Interpretation, Computer-Assisted; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D010865:Pilot Projects; D011379:Prognosis; D012307:Risk Factors; D064726:Triple Negative Breast Neoplasms / Q000175:diagnosis* / Q000473:pathology
chemical_list: D014408:Biomarkers, Tumor; D003287:Contrast Media