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Sci Rep. 2016 Feb 18;6:21394. doi: 10.1038/srep21394.

A Radio-genomics Approach for Identifying High Risk Estrogen Receptor-positive Breast Cancers on DCE-MRI: Preliminary Results in Predicting OncotypeDX Risk Scores.

Author information

1
School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
2
Dept. of Biomedical Engineering, Case Western Reserve University, OH, USA.
3
Dept. of Radiology, Boston University School of Medicine, MA, USA.
4
Dept. of Radiology, University Hospitals Case Medical Center, OH, USA.
5
Dept. of Nutrition, Case Western Reserve University, OH, USA.
6
Division of Anatomic Pathology, Case Western Reserve University, OH, USA.
7
Division of Hematology and Oncology, Seidman Cancer Center, OH, USA.

Abstract

To identify computer extracted imaging features for estrogen receptor (ER)-positive breast cancers on dynamic contrast enhanced (DCE)-MRI that are correlated with the low and high OncotypeDX risk categories. We collected 96 ER-positive breast lesions with low (< 18, N = 55) and high (> 30, N = 41) OncotypeDX recurrence scores. Each lesion was quantitatively characterize via 6 shape features, 3 pharmacokinetics, 4 enhancement kinetics, 4 intensity kinetics, 148 textural kinetics, 5 dynamic histogram of oriented gradient (DHoG), and 6 dynamic local binary pattern (DLBP) features. The extracted features were evaluated by a linear discriminant analysis (LDA) classifier in terms of their ability to distinguish low and high OncotypeDX risk categories. Classification performance was evaluated by area under the receiver operator characteristic curve (Az). The DHoG and DLBP achieved Az values of 0.84 and 0.80, respectively. The 6 top features identified via feature selection were subsequently combined with the LDA classifier to yield an Az of 0.87. The correlation analysis showed that DHoG (ρ = 0.85, P < 0.001) and DLBP (ρ = 0.83, P < 0.01) were significantly associated with the low and high risk classifications from the OncotypeDX assay. Our results indicated that computer extracted texture features of DCE-MRI were highly correlated with the high and low OncotypeDX risk categories for ER-positive cancers.

PMID:
26887643
PMCID:
PMC4757835
DOI:
10.1038/srep21394
[Indexed for MEDLINE]
Free PMC Article

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