Abstract P2-03-01: Computer extracted image texture features on T2-weighted MRI appear to correlate with nuclear morphologic descriptors from H&E-stained histopathology in estrogen receptor positive breast cancers

Abstract

Abstract Oncotype DX (ODX) is a 21 panel gene-expression based assay for predicting whether patients with estrogen receptor-positive (ER+) breast cancer (BCa) are candidates for adjuvant chemotherapy. However, the time and expense associated with genomic assays suggests the need for a non-invasive, imaging-based, pre-therapeutic tool for assessment of disease risk and selection of an appropriate treatment regimen. The objective of this research was to determine whether (a) computer extracted image features on T2-weighted (T2w) MRI and H&E stained histopathology are independently able to distinguish ER+ BCa with low and high ODX recurrence scores (RS) and (b) to determine whether there is a correlation between MRI and histologic features identified as being predictive of low and high ODX risk categories. A total of 11 ER+ BCa patients were considered in this study, based on availability of in vivo 1.5 Tesla T2w MRI. For each study, the corresponding formalin-fixed paraffin-embedded H&E stained tissue specimens were digitized at 20x (0.5 μm/pixel) using a whole-slide scanner. Of the 11 patients, 8 were identified in the low ODX (RS < 18) and 3 in the high ODX (RS > 30) risk categories. Each dataset was accompanied by expert annotations of (a) the lesion ROI on MRI and (b) boundaries of epithelial nuclei from a representative field-of-view on the digitized histology slide. For each MRI study, a multi-scale, multi-orientation Gabor filter bank was convolved with the annotated lesion area providing a set of 192 texture features (FMRI). For each corresponding histology image, 471 features (FHIST) were extracted describing both nuclear morphology (NM) and Laws texture (LT) within the nuclear regions. Independent 2-sample t-tests were used to identify salient features in FMRI and FHIST that are able to distinguish low and high ODX risk categories. We found that, for the MRI dataset, Gabor texture features at several scales and orientations yielded salient features (p < 0.05) while on histopathology, nuclear texture and convexity (shape) features were identified as the top discriminative features (p < 0.01). Relationships between significant features were evaluated via Spearman's rank correlation test (see table), where high correlations were observed between lesion texture on T2w MRI and nuclear texture and shape on histology. Correlation of histologic and MRI features able to distinguish low and high ODX RSHistologic feature correlated with ODXMRI feature correlated with ODXCorrelation coefficient (ρ)p-valueLT: 70 Mean HSVGF: Scale 2: Orientation 3: min/max-0.85450.0008NM: ConvexityGF: Scale 5: Orientation 6: mean-0.85450.0008LT: 70 Mean HSVGF: Scale 2: Orientation 3: min/max-0.83640.0013LT: 70 Mean HSVGF: Scale 3: Orientation 8: mean-0.83640.0013LT: 70 Mean HSVGF: Scale 3: Orientation 2: mean-0.81820.0021 Our results suggest that quantitative features extracted on both T2w MRI and histopathology can independently distinguish between low and high risk ODX classes. Moreover, some of these MRI and histologic features appear to be significantly correlated, suggesting that information regarding tumor biology is reflected in both MRI and histologic image features. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P2-03-01.