Abstract P2-02-04: Kinetic heterogeneity features on breast DCE-MRI as prognostic markers of breast cancer recurrence
Authors: Mahrooghy M , Ashraf AB , Gavenonis SC , Daye D , Mies C , Feldman M , Rosen M , Kontos D 
Published: August 2014

PURPOSE

Breast tumors have been shown to be heterogeneous lesions, and tumor heterogeneity is one of the major difficulties in the way of an effective cancer diagnosis and successful treatment. We evaluate the predictive capacity of DCE-MRI kinetic heterogeneity features for assessing the risk of breast cancer recurrence as determined by a validated tumor gene expression assay.

METHOD AND MATERIALS

Bilateral Breast DCE-MRI scans were retrospectively analyzed from 57 women with estrogen receptor positive/node negative invasive breast cancer. The ages of the women at the time of the imaging ranged from 37 to 74 years with a mean age of 55.5 years. The women were imaged prone in a 1.5T scanner (GE LX echo, GE Healthcare, or Siemens Sonata, Siemens); matrix size: 512 × 512; slice thickness: 2.4-4.4 mm; flip angle: 25° or 30°. The images were collected before and after the administration of gadodiamide (Omniscan) or gadobenate dimeglumine (MultiHance) contrast agents. Dynamic contrast enhanced images were acquired at 90 second intervals for 3 post contrast time points. The women had previously undergone Oncotype Dx (Genomic Health Inc.) profiling of their tumor. The Oncotype DX assay provides the likelihood of 10-year breast cancer recurrence, using a score stratified into 3 risk categories (risk: low ≤17, medium = 18-30, high ≥ 31). Pixel-wise relative enhancement curves were computed using three post-contrast time points. Fuzzy C-means clustering was applied to partition the tumor pixels according to the variance of their relative enhancement. To capture kinetic heterogeneity, wavelet features were extracted within each tumor partition as a measure of spatial variation. Mean and variance of these features were further estimated within each region. Using these features, multivariable logistic regression was performed with leave-one-out cross-validation and feature selection to classify the tumors as high or low/medium risk. We compared our kinetic heterogeneity features against standard kinetics and texture features. Area under the curve (AUC) of the receiver operating characteristic (ROC) was used to evaluate classification performance.

RESULTS

Feature selection indicated an optimal set of 7 kinetic heterogeneity features (out of 54). The classifier based on these features had an AUC = 0.82 in classifying high versus low/medium risk tumors. Classifiers based on standard kinetics and texture features performed with AUCs of 0.69 and 0.64 respectively.

CONCLUSION

Wavelet kinetic features from breast DCE-MRI could be used to capture the spatial pattern of kinetic heterogeneity of and potentially serve as prognostic markers for the risk of recurrence. In addition, breast DCE-MRI kinetic heterogeneity features could be used to assess likelihood of recurrence and ultimately help guide therapeutic decisions. Larger studies are needed to validate these findings.

Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P2-02-04.