title: Heterogeneity wavelet kinetics from DCE-MRI for classifying gene expression based breast cancer recurrence risk.
abstract: Breast tumors are heterogeneous lesions. Intra-tumor heterogeneity presents a major challenge for cancer diagnosis and treatment. Few studies have worked on capturing tumor heterogeneity from imaging. Most studies to date consider aggregate measures for tumor characterization. In this work we capture tumor heterogeneity by partitioning tumor pixels into subregions and extracting heterogeneity wavelet kinetic (HetWave) features from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to obtain the spatiotemporal patterns of the wavelet coefficients and contrast agent uptake from each partition. Using a genetic algorithm for feature selection, and a logistic regression classifier with leave one-out cross validation, we tested our proposed HetWave features for the task of classifying breast cancer recurrence risk. The classifier based on our features gave an ROC AUC of 0.78, outperforming previously proposed kinetic, texture, and spatial enhancement variance features which give AUCs of 0.69, 0.64, and 0.65, respectively.
authors.affiliation: The University of Pennsylvania, Philadelphia, PA 19104, USA. Majid.Mahrooghy@uphs.upenn.edu
authors.forename: Majid
authors.initials: M
authors.lastname: Mahrooghy
authors.affiliation: The University of Pennsylvania, Philadelphia, PA 19104, USA.
authors.forename: Ahmed B
authors.initials: AB
authors.lastname: Ashraf
authors.affiliation: The University of Pennsylvania, Philadelphia, PA 19104, USA.
authors.forename: Dania
authors.initials: D
authors.lastname: Daye
authors.affiliation: The University of Pennsylvania, Philadelphia, PA 19104, USA.
authors.forename: Carolyn
authors.initials: C
authors.lastname: Mies
authors.affiliation: The University of Pennsylvania, Philadelphia, PA 19104, USA.
authors.forename: Michael
authors.initials: M
authors.lastname: Feldman
authors.affiliation: The University of Pennsylvania, Philadelphia, PA 19104, USA.
authors.forename: Mark
authors.initials: M
authors.lastname: Rosen
authors.affiliation: The University of Pennsylvania, Philadelphia, PA 19104, USA.
authors.forename: Despina
authors.initials: D
authors.lastname: Kontos
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001943:Breast Neoplasms / Q000473:pathology*; D003198:Computer Simulation; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D008957:Models, Genetic; D009364:Neoplasm Recurrence, Local / Q000473:pathology*; D015203:Reproducibility of Results; D012307:Risk Factors; D012680:Sensitivity and Specificity; D058067:Wavelet Analysis