Majid Mahrooghy, Presenter: Nothing to Disclose

Ahmed Bilal Ashraf PhD, Abstract Co-Author: Nothing to Disclose

Dania Daye MD, PhD, Abstract Co-Author: Nothing to Disclose

Mark Alan Rosen MD, PhD, Abstract Co-Author: Nothing to Disclose

Carolyn J. Mies MD, Abstract Co-Author: Advisory Board, Genomic Health, Inc

Michael D. Feldman MD, PhD, Abstract Co-Author: Nothing to Disclose

Despina Kontos PhD, Abstract Co-Author: Nothing to Disclose

Breast cancer tumors have been shown to be heterogeneous, and this presents challenges in targeted therapeutics. We investigate tissue permeability heterogeneity information of breast cancer tumors using DCE-MRI as a prognostic biomarker for assessing the risk of breast cancer recurrence as determined by a validated tumor gene expression assay. 

Breast DCE-MRI scans were retrospectively analyzed from 56 women with estrogen receptor positive/node negative invasive breast cancer. The women had previously undergone Oncotype Dx (Genomic Health Inc.) profiling of their tumor, a gene expression assay that provides a score for 10-year risk of recurrence (risk: low/medium ≤ 30, high > 31). Using the “compartment modeling based on convex analysis of mixtures” (CM-CAM) technique, we estimate pharmacokinetic parameters of the local volume transfer constants for tissue types (Ktrans) and plasma volume (Vp) for each pixel. Fuzzy c-means clustering is applied to the pharmacokinetic parameter maps to group pixels into intra-tumor heterogeneity partitions and wavelet coefficients are extracted within each partition to measure spatial frequencies. Multivariable logistic regression is performed with leave-one-out cross-validation and feature selection to classify tumors as high vs. low/medium risk for recurrence based on the extracted features. We compare our proposed DCE-MRI heterogeneity features against standard MR descriptors including kinetic, textural, and morphologic features. Area under the curve (AUC) of the receiver operating characteristic (ROC) is used to evaluate classification performance.

DCE-MRI features based on pharmacokinetic heterogeneity have ROC AUC of 0.88, outperforming standard features (AUC=0.65). Performance is improved when heterogeneity features are combined with standard features (AUC=0.94). Both standard and pharmacokinetic heterogeneity features are selected by the model, including Enhancement Ratio, Enhancement at First Post-contrast, Peak Enhancement, Curve Shape Index, and high frequency wavelet information.

DCE-MRI features of pharmacokinetic heterogeneity could be used as prognostic markers for assessing risk of breast cancer recurrence.

Breast DCE-MRI pharmacokinetic heterogeneity features could be used to assess risk of recurrence and ultimately help guide treatment decisions. Larger studies are needed to validate our findings.

Mahrooghy, M, Ashraf, A, Daye, D, Rosen, M, Mies, C, Feldman, M, Kontos, D, Breast DCE-MRI Pharmacokinetic Heterogeneity as Prognostic Biomarker for Breast Cancer Recurrence.  Radiological Society of North America 2014 Scientific Assembly and Annual Meeting, - ,Chicago IL. http://archive.rsna.org/2014/14045697.html