title: Pharmacokinetic Tumor Heterogeneity as a Prognostic Biomarker for Classifying Breast Cancer Recurrence Risk.
abstract: OBJECTIVE
  Heterogeneity in cancer can affect response to therapy and patient prognosis. Histologic measures have classically been used to measure heterogeneity, although a reliable noninvasive measurement is needed both to establish baseline risk of recurrence and monitor response to treatment. Here, we propose using spatiotemporal wavelet kinetic features from dynamic contrast-enhanced magnetic resonance imaging to quantify intratumor heterogeneity in breast cancer.
  
  
  METHODS
  Tumor pixels are first partitioned into homogeneous subregions using pharmacokinetic measures. Heterogeneity wavelet kinetic (HetWave) features are then extracted from these partitions to obtain spatiotemporal patterns of the wavelet coefficients and the contrast agent uptake. The HetWave features are evaluated in terms of their prognostic value using a logistic regression classifier with genetic algorithm wrapper-based feature selection to classify breast cancer recurrence risk as determined by a validated gene expression assay.
  
  
  RESULTS
  Receiver operating characteristic analysis and area under the curve (AUC) are computed to assess classifier performance using leave-one-out cross validation. The HetWave features outperform other commonly used features (AUC = 0.88 HetWave versus 0.70 standard features). The combination of HetWave and standard features further increases classifier performance (AUCs 0.94).
  
  
  CONCLUSIONS
  The rate of the spatial frequency pattern over the pharmacokinetic partitions can provide valuable prognostic information.
  
  
  CONCLUSIONS
  HetWave could be a powerful feature extraction approach for characterizing tumor heterogeneity, providing valuable prognostic information.
authors.forename: Majid
authors.initials: M
authors.lastname: Mahrooghy
authors.forename: Ahmed B
authors.initials: AB
authors.lastname: Ashraf
authors.forename: Dania
authors.initials: D
authors.lastname: Daye
authors.forename: Elizabeth S
authors.initials: ES
authors.lastname: McDonald
authors.forename: Mark
authors.initials: M
authors.lastname: Rosen
authors.forename: Carolyn
authors.initials: C
authors.lastname: Mies
authors.forename: Michael
authors.initials: M
authors.lastname: Feldman
authors.forename: Despina
authors.initials: D
authors.lastname: Kontos
mesh_terms: D000328:Adult; D000368:Aged; D015415:Biomarkers / Q000378:metabolism*; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000378:metabolism / Q000473:pathology*; D003287:Contrast Media / Q000493:pharmacokinetics*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D015233:Models, Statistical; D011379:Prognosis; D012008:Recurrence
chemical_list: D015415:Biomarkers; D003287:Contrast Media