Identifying relations between imaging phenotypes and molecular subtypes of breast cancer: Model discovery and external validation.
To determine whether dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) characteristics of the breast tumor and background parenchyma can distinguish molecular subtypes (ie, luminal A/B or basal) of breast cancer.



In all, 84 patients from one institution and 126 patients from The Cancer Genome Atlas (TCGA) were used for discovery and external validation, respectively. Thirty-five quantitative image features were extracted from DCE-MRI (1.5 or 3T) including morphology, texture, and volumetric features, which capture both tumor and background parenchymal enhancement (BPE) characteristics. Multiple testing was corrected using the Benjamini-Hochberg method to control the false-discovery rate (FDR). Sparse logistic regression models were built using the discovery cohort to distinguish each of the three studied molecular subtypes versus the rest, and the models were evaluated in the validation cohort.



On univariate analysis in discovery and validation cohorts, two features characterizing tumor and two characterizing BPE were statistically significant in separating luminal A versus nonluminal A cancers; two features characterizing tumor were statistically significant for separating luminal B; one feature characterizing tumor and one characterizing BPE reached statistical significance for distinguishing basal (Wilcoxon P < 0.05, FDR < 0.25). In discovery and validation cohorts, multivariate logistic regression models achieved an area under the receiver operator characteristic curve (AUC) of 0.71 and 0.73 for luminal A cancer, 0.67 and 0.69 for luminal B cancer, and 0.66 and 0.79 for basal cancer, respectively.



DCE-MRI characteristics of breast cancer and BPE may potentially be used to distinguish among molecular subtypes of breast cancer.



3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017;46:1017-1027.
Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California, USA.
Jia
J
Wu
Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California, USA.|Radiotherapy Department, First Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, P.R. China.
Xiaoli
X
Sun
Department of Radiation Medicine, Hokkaido University Graduate School of Medicine, Sapporo, Hokkaido, Japan.|Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education (GI-CoRE), Hokkaido University, Proton Beam Therapy Center, Sapporo, Hokkaido, Japan.
Jeff
J
Wang
Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California, USA.|Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education (GI-CoRE), Hokkaido University, Proton Beam Therapy Center, Sapporo, Hokkaido, Japan.
Yi
Y
Cui
Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan.
Fumi
F
Kato
Department of Radiation Medicine, Hokkaido University Graduate School of Medicine, Sapporo, Hokkaido, Japan.|Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education (GI-CoRE), Hokkaido University, Proton Beam Therapy Center, Sapporo, Hokkaido, Japan.
Hiroki
H
Shirato
Department of Radiology, Stanford University School of Medicine, Advanced Medicine Center, Stanford, California, USA.
Debra M
DM
Ikeda
Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California, USA.|Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California, USA.
Ruijiang
R
Li
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001940:Breast / Q000000981:diagnostic imaging; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D003287:Contrast Media; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D010641:Phenotype; D015203:Reproducibility of Results
D003287:Contrast Media
breast cancer; classification; dynamic contrast enhanced MRI; imaging genomics; molecular subtype