Abstract P2-02-12: Computer derived image features on DCE-MRI appear to distinguish estrogen receptor-positive breast cancers with low and high oncotype DX recurrence scores

Abstract

Abstract The Oncotype DX (ODX) is a 21 panel gene-expression based assay for identifying which Estrogen Receptor-positive (ER+) breast cancer (BCa) patients are candidates for adjuvant chemotherapy. The objective of this research was to identify whether computerized texture features on a staging DCE-MRI can distinguish ER+ BCa with low and high ODX recurrence scores (RS) (i.e. to distinguish which ER+ BCa patients are more likely to benefit from adjuvant hormonal therapy from those who require chemotherapy). This would provide a non-invasive, imaging based, pre-therapeutic assessment tool for predicting the appropriate treatment regimen. This work, to the best of our knowledge, is the first attempt to quantitatively correlate low versus high risk stratification via computer derived MRI measurements to corresponding risk stratification via the ODX assay. 52 ER+ BCa patient studies with high (>30, N = 28) and low (<18, N = 24) ODX RS were available for this study from two sites; 16 breast MRIs from the Boston Medical Center using a Phillips 1.5T magnet with a 7-channel breast coil, and 36 MRIs from the Case Medical Center using a Siemens 1.5T magnet with a 8-channel breast coil. All datasets included T1w images obtained prior to, during, and after administration of 0.1 mmol/kg of Gd-DTPA and corresponding ODX RS. For each study a radiologist picked a representative slice showing the tumor and then manually segmented the region of interest (ROI) containing the lesion. Computerized image analysis tools developed in-house via the MATLAB© programming platform were applied to the manually segmented lesion ROI for each of the 52 MRI studies to quantitatively characterize the lesion via a set of (a) 6 shape, (b) 3 pharmacokinetic (Ktrans, ve, kep) based on Tofts model (PK), (c) 12 enhancement kinetic (EK), (d) 12 intensity kinetic (IK), (e) 312 textural kinetic (TK), (f) 6 dynamic local binary pattern (DLBP), and (g) 5 dynamic histogram of oriented gradient (DHoG) features. The computer extracted features were evaluated via a linear discriminant analysis (LDA) classifier in terms of their ability to distinguish ER+ BCa as having a low or high ODX RS via a 2-fold randomized cross validation scheme. At each iteration, half of the studies were randomly selected from the 52 cases and used for training the LDA classifier and the remaining 26 studies were used for independent testing. This process was repeated 200 times. Classification performance was evaluated by area under the ROC curve (AUC). Higher AUC values suggest a stronger relationship between risk stratification via MRI attributes and ODX. Table 1Feature classAccuracy (μ±Δ)AUC (μ±Δ)DHoG87.07%±5.66%0.89±0.04DLBP85.86%±7.82%0.83±0.07EK82.36%±8.46%0.80±0.06PK81.14%±7.55%0.78±0.07TK75.93%±6.65%0.76±0.08IK76.43%±7.23%0.75±0.12Shape71.04%±6.81%0.70±0.06 Table 1 illustrates the mean and standard deviation in accuracy and AUC values over 200 runs of randomized cross validation. DHoG, DBLP and EK features yielded the highest classification accuracy and AUC. Although lesion shape has been shown to be important for discriminating benign and malignant lesions on MRI, shape appears to be less useful in distinguishing between ER+ BCa lesions with low and high ODX RS. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P2-02-12.