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Heterogeneity Wavelet Kinetics from DCE-MRI for Classifying Gene Expression Based Breast Cancer Recurrence Risk

  • Majid Mahrooghy
  • Ahmed B. Ashraf
  • Danya Daye
  • Carolyn Mies
  • Michael Feldman
  • Mark Rosen
  • Despina Kontos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8150)

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.

Keywords

Breast DCE-MRI breast tumor heterogeneity partitioning tumor feature extraction breast cancer recurrence prediction 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Majid Mahrooghy
    • 1
  • Ahmed B. Ashraf
    • 1
  • Danya Daye
    • 1
  • Carolyn Mies
    • 1
  • Michael Feldman
    • 1
  • Mark Rosen
    • 1
  • Despina Kontos
    • 1
  1. 1.The University of PennsylvaniaPhiladelphiaUSA

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