Differentiation between malignant and benign breast masses: combination of semi-quantitative analysis on DCE-MRI and...

To investigate the performance of combined semi-quantitative analysis on dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) and histogram analysis of diffusion-weighted imaging (DWI) for distinguishing malignant from benign breast masses.

This study included 178 patients with breast masses (benign:malignant=88:9) who underwent both DCE-MRI and DWI. The semi-quantitative parameters, derived from DCE-MRI, included maximum slope of increase (MSI), signal intensity slope (SI_slope), initial percentage of enhancement (E_initial), percentage of peak enhancement (E_peak), early signal enhancement ratio (ESER), and second enhancement percentage (SEP). Histogram parameters derived from apparent diffusion coefficient (ADC) maps included ADC_min, ADC_max, ADC_mean, ADC₁₀, ADC₂₅, ADC₅₀, ADC₇₅, ADC₉₀, skewness, and kurtosis. All parameters were compared between malignant and benign groups, and their differences were tested using independent-samples t-test or Mann-Whitney test. Receiver operating characteristic (ROC) curves were used to determine the diagnostic value of each significant parameter.

Among semi-quantitative parameters, SI_slope exhibited the best diagnostic performance in predicting malignancy (cut-off value, 0.096; ROC, 0.756; sensitivity, 86.7%; specificity, 61.4%). Among histogram parameters, ADC₁₀ exhibited the best diagnostic performance in predicting malignancy (cut-off value, 1.051; ROC, 0.885; sensitivity, 86.7%; specificity, 84.1%). The optimal diagnostic performance of combined ADC₁₀ and SI_slope (area under curve [AUC], 0.888; sensitivity, 82.2%; specificity, 95.5%) was significantly better than SI_slope alone (p<0.001). Moreover, the combination showed higher AUC (0.888 versus 0.885) than ADC₁₀ alone, but the difference was not statistically significant (p=0.914).

SI_slope and ADC₁₀ are significant predictors for breast malignancy. The combination of DCE-MRI and DWI improves differentiating performance.