Abstract #1050
Self-Organizing Map Kinetic Features as Prognostic Markers for Classifying Gene Expression Risk for Breast Cancer Recurrence
Majid Mahrooghy 1 , Ahmed B. Ashraf 1 , Dania Daye 1 , Carolyn Mies 2 , Mark Rosen 1 , Michael Feldman 2 , and Despina Kontos 1
1
Department of Radiology, University of
Pennsylvania, Philadelphia, PA, United States,
2
Department
of Pathology and Laboratory Medicine, University of
Pennsylvania, Philadelphia, PA, United States
We developed DCE-MRI kinetic heterogeneity features
using self-organizing map (SOM) neural networks. We use
SOM to cluster tumor pixels based on kinetics and
extract features including variance and entropy of
cluster size, variance of cluster kinetic features, mean
and variance of weighted cluster kinetics, and the
kinetic features of the cluster having maximum peak
enhancement. We evaluated these features for classifying
tumor recurrence risk as determined by a validated gene
expression assay, and compared their performance to
current standard kinetics. Our features have ROC AUC=0.80
for classifying tumors at low- versus high- risk of
recurrence, outperforming standard kinetics with AUC=0.65.
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