Dr. Dania Daye Profile

Dr. Dania Daye

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Proceedings Article | 20 March 2014 Paper

Sparse representation of multi parametric DCE-MRI features using K-SVD for classifying gene expression based breast cancer recurrence risk

Majid Mahrooghy, Ahmed Ashraf, Dania Daye, Carolyn Mies, Mark Rosen, Michael Feldman, Despina Kontos

Proceedings Volume 9035, 90350X (2014) https://doi.org/10.1117/12.2043719

KEYWORDS: Associative arrays, Feature extraction, Tumors, Breast cancer, Breast, Feature selection, Medicine, Magnetic resonance imaging, Cancer, Genetic algorithms

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We evaluate the prognostic value of sparse representation-based features by applying the K-SVD algorithm on multiparametric kinetic, textural, and morphologic features in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). K-SVD is an iterative dimensionality reduction method that optimally reduces the initial feature space by updating the dictionary columns jointly with the sparse representation coefficients. Therefore, by using K-SVD, we not only provide sparse representation of the features and condense the information in a few coefficients but also we reduce the dimensionality. The extracted K-SVD features are evaluated by a machine learning algorithm including a logistic regression classifier for the task of classifying high versus low breast cancer recurrence risk as determined by a validated gene expression assay. The features are evaluated using ROC curve analysis and leave one-out cross validation for different sparse representation and dimensionality reduction numbers. Optimal sparse representation is obtained when the number of dictionary elements is 4 (K=4) and maximum non-zero coefficients is 2 (L=2). We compare K-SVD with ANOVA based feature selection for the same prognostic features. The ROC results show that the AUC of the K-SVD based (K=4, L=2), the ANOVA based, and the original features (i.e., no dimensionality reduction) are 0.78, 0.71. and 0.68, respectively. From the results, it can be inferred that by using sparse representation of the originally extracted multi-parametric, high-dimensional data, we can condense the information on a few coefficients with the highest predictive value. In addition, the dimensionality reduction introduced by K-SVD can prevent models from over-fitting.

Proceedings Article | 9 March 2011 Paper

Mammographic parenchymal texture as an imaging marker of hormonal activity: a comparative study between pre- and post-menopausal women

Dania Daye, Ezra Bobo, Bethany Baumann, Antonios Ioannou, Emily Conant, Andrew D. Maidment, Despina Kontos

Proceedings Volume 7963, 79631N (2011) https://doi.org/10.1117/12.878924

KEYWORDS: Breast cancer, Breast, Tissues, Image segmentation, Performance modeling, Fractal analysis, Receivers, Image classification, Cancer, Tumor growth modeling

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Mammographic parenchymal texture patterns have been shown to be related to breast cancer risk. Yet, little is known about the biological basis underlying this association. Here, we investigate the potential of mammographic parenchymal texture patterns as an inherent phenotypic imaging marker of endogenous hormonal exposure of the breast tissue. Digital mammographic (DM) images in the cranio-caudal (CC) view of the unaffected breast from 138 women diagnosed with unilateral breast cancer were retrospectively analyzed. Menopause status was used as a surrogate marker of endogenous hormonal activity. Retroareolar 2.5cm² ROIs were segmented from the post-processed DM images using an automated algorithm. Parenchymal texture features of skewness, coarseness, contrast, energy, homogeneity, grey-level spatial correlation, and fractal dimension were computed. Receiver operating characteristic (ROC) curve analysis was performed to evaluate feature classification performance in distinguishing between 72 pre- and 66 post-menopausal women. Logistic regression was performed to assess the independent effect of each texture feature in predicting menopause status. ROC analysis showed that texture features have inherent capacity to distinguish between pre- and post-menopausal statuses (AUC>0.5, p<0.05). Logistic regression including all texture features yielded an ROC curve with an AUC of 0.76. Addition of age at menarche, ethnicity, contraception use and hormonal replacement therapy (HRT) use lead to a modest model improvement (AUC=0.78) while texture features maintained significant contribution (p<0.05). The observed differences in parenchymal texture features between pre- and post- menopausal women suggest that mammographic texture can potentially serve as a surrogate imaging marker of endogenous hormonal activity.

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