Incorporating diffusion-weighted imaging in a diagnostic algorithm for multiparametric MR mammography. (October 2022)

• Record Type: Journal Article
• Authors: Vassiou, Katerina; Fanariotis, Michael; Tsougos, Ioannis; Fezoulidis, Ioannis
• Is Part Of: Acta radiologica. Volume 63:Number 10(2022)
• Page Start: 1332
• Page End: 1343
• Publication Date: 2022-10
• Subjects: Magnetic resonance imaging -- diffusion-weighted imaging -- apparent diffusion coefficient -- BI-RADS -- breast neoplasms; Radiology, Medical -- Periodicals; Radiography, Medical -- Periodicals; Radiotherapy -- Periodicals; 616.0757
• DOI: 10.1177/02841851211041822
• Languages: English
• ISSNs: 0284-1851

Abstract

Background: Apparent diffusion coefficient (ADC) measurements are not incorporated in BI-RADS classification. Purpose: To assess the probability of malignancy of breast lesions at magnetic resonance mammography (MRM) at 3 T, by combining ADC measurements with the BI-RADS score, in order to improve the specificity of MRM. Material and Methods: A total of 296 biopsy-proven breast lesions were included in this prospective study. MRM was performed at 3 T, using a standard protocol with dynamic sequence (DCE-MRI) and an extra echo-planar diffusion-weighted sequence. A freehand region of interest was drawn inside the lesion, and ADC values were calculated. Each lesion was categorized according to the BI-RADS classification. Logistic regression analysis was employed to predict the probability of malignancy of a lesion. The model combined the BI-RADS classification and the ADC value. Sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were calculated. Results: In total, 153 malignant and 143 benign lesions were analyzed; 257 lesions were masses and 39 lesions were non-mass-like enhancements. The sensitivity and specificity of the combined method were 96% and 86%, respectively, in contrast to 95% and 81% with BI-RADS classification alone. Conclusion: We propose a method of assessing the probability of malignancy in breast lesions by combining BI-RADS score and ADC values into a single formula, increasing sensitivity and specificity compared to BI-RADS classification alone.