Morphological Assessment of Breast Lesions With Type 2 Dynamic Curves Using DWI and T2WI Based on Breast Imaging Reporting and Data System Lexicon Descriptors

基于乳腺影像报告和数据系统词汇描述符,利用DWI和T2WI对具有2型动态曲线的乳腺病变进行形态学评估

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Abstract

Purpose: This study aimed to qualitatively assess the added diagnostic value of diffusion-weighted imaging (DWI) and T2-weighted imaging (T2WI), using Breast Imaging Reporting and Data System (BI-RADS) lexicon descriptors, in evaluating breast lesions with type 2 dynamic curves. Materials and Methods: We retrospectively reviewed 181 breast lesions with type 2 dynamic curves in 181 consecutive patients who underwent 3-Tesla (3-T) magnetic resonance imaging (MRI). Trained radiologists assessed the morphological features of the lesions on dynamic contrast-enhanced (DCE) MRI, DWI, and T2WI using BI-RADS lexicon descriptors and measured the apparent diffusion coefficient (ADC). Statistical analysis was performed to compare variables in lesion type groups (mass-like group vs. nonmass-like group). Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC) and the DeLong test, with statistical significance at p < 0.05. Results: In mass-like lesions, all morphological parameters significantly distinguished benign from malignant lesions on DCE, DWI, and T2WI (all p < 0.05). ADC values also showed significant differences (p < 0.05). The combined approach (DCE + DWI + T2WI) yielded the highest AUC (0.895), significantly outperforming the individual methods (all p < 0.05). In nonmass-like lesions, no parameter significantly predicted malignancy (all p > 0.05). Conclusion: The addition of DWI and T2WI, interpreted using the BI-RADS lexicon descriptors, enhances the differential diagnosis of breast lesions with type 2 dynamic curves.

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