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Identifying relations between imaging phenotypes and molecular subtypes of breast cancer: Model discovery and external validation
Author(s) -
Wu Jia,
Sun Xiaoli,
Wang Jeff,
Cui Yi,
Kato Fumi,
Shirato Hiroki,
Ikeda Debra M.,
Li Ruijiang
Publication year - 2017
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.25661
Subject(s) - false discovery rate , breast cancer , logistic regression , medicine , magnetic resonance imaging , univariate , cancer , receiver operating characteristic , univariate analysis , oncology , multivariate statistics , pathology , multivariate analysis , radiology , biology , computer science , machine learning , biochemistry , gene
Purpose 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. Materials and Methods 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. Results 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. Conclusion DCE‐MRI characteristics of breast cancer and BPE may potentially be used to distinguish among molecular subtypes of breast cancer. Level of Evidence: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017;46:1017–1027.