Texture analysis using Non-contrast MRI to predict estrogen receptor status in breast cancer lesions; is it feasible?

Abstract

PurposeWe aimed to evaluate the feasibility of non-contrast enhanced T1 sequence in texture analysis of breast cancer lesions to predict their estrogen receptor status. MethodsThe study included 85 pathologically proven breast cancer lesions in 53 patients. Immuno-histochemical studies were performed to determine the estrogen receptor status (ER). Lesions were divided into two groups: ER + ve status and ER-ve status. Texture analysis using the second-order analysis features [The Co-occurrence matrix (11 features)] was applied on both T1 and dynamic contrast enhanced (DCE) MRI images for each lesion. Texture features gained from both T1 and DCE images were analyzed to obtain cut-off values using ROC curves to sort lesions according to their estrogen receptor status. ResultsAngular second momentum and some of the Entropy based features showed statistically significant cut-off values in differentiation between the two groups [P-values for pre- and post-contrast images for AngSecMom (0.001, 0.008), Sum Entropy (0.003,0.005), and Entropy (0.033,0.019) respectively]. On comparing the AUCs between pre- and post-contrast images, we found that differences were statistically insignificant. Sum of squares, Sum Variance, Sum Average showed statistically significant cut-off points only on pre-contrast images [P-values for Sum of squares (0.018), Sum Variance (0.024), Sum Average (0.039)] ConclusionsTexture analysis features showed promising results in predicting estrogen receptor status of breast cancer lesions on non-contrast T1 images.