A Radio-genomics Approach for Identifying High Risk Estrogen Receptor-positive Breast Cancers on DCE-MRI: Preliminary Results in Predicting OncotypeDX Risk Scores
| Autor: | Carl Jaffe, Chery I L Thompson, B. Nicolas Bloch, Lyndsay Harris, Hannah Gilmore, Anant Madabhushi, Donna Plecha, Tao Wan |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Adult
Local binary patterns Estrogen receptor Feature selection Breast Neoplasms Risk Assessment Article 030218 nuclear medicine & medical imaging Risk category 03 medical and health sciences 0302 clinical medicine Medicine Humans Aged Multidisciplinary Receiver operating characteristic business.industry Pattern recognition Genomics Middle Aged Linear discriminant analysis Magnetic Resonance Imaging 3. Good health Dynamic contrast Receptors Estrogen 030220 oncology & carcinogenesis Correlation analysis Female Artificial intelligence business |
| Zdroj: | Scientific Reports |
| ISSN: | 2045-2322 |
| Popis: | To identify computer extracted imaging features for estrogen receptor (ER)-positive breast cancers on dynamic contrast en-hanced (DCE)-MRI that are correlated with the low and high OncotypeDX risk categories. We collected 96 ER-positivebreast lesions with low (N = 55) and high (>30, N = 41) OncotypeDX recurrence scores. Each lesion was quantitatively charac-terize 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 ρ = 0.83, P typeDX 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. |
| Databáze: | OpenAIRE |
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