A multiparametric classification system for lesions detected by breast magnetic resonance imaging

Background: To validate a new categorisation scheme for suspicious breast lesions according to the well-defined Breast Imaging Reporting and Data System (BI-RADS) magnetic resonance imaging (MRI) lexicon descriptors, apparent diffusion coefficients (ADC), T2-weighted signal intensity (SI), and kinetic curve assessment categories.

Methods: The MRI descriptors and ADC were analysed in 697 lesions detected in 499 subjects. The descriptors were classified into Minor, Intermediate, and Major findings, and were divided into the BI-RADS subcategories 3, 4A, 4B, 4C, and 5 according to the number of descriptors. Positive predictive values (PPV) were calculated for each descriptor. The descriptors were then fitted into a multinomial logistic regression model to determine the odds ratio for a malignant diagnosis. The PPV were measured for the new categories and compared with the assigned PPV of the BI-RADS descriptors.

Results: The PPV for MRI descriptors ranged from 17.9%-100%. Of the 697 lesions assessed, 19 (2.7 %) were categorized as BI-RADS 3, 27 (3.9 %) as 4A, 53 (7.6 %) as 4B, 174 (25.0 %) as 4C, and 424 (60.8 %) as 5. None of the subjects in BI-RADS category 3 had a malignant diagnosis. The PPV for malignancy increased progressively with increasing BI-RADS category (4A, 11.1 %; 4B, 28.3 %; 4C, 64.4 %; 5, 94.8 %). All descriptor groups were significant in the logistic regression model.

Conclusions: This study shows that using BI-RADS MRI descriptors together with ADC and T2-weighted SI in a multiparametric classification system can yield an applicable categorisation of lesions with PPV values within the recommended ranges for BI-RADS categories.