Multiparameter MRI Model With DCE-MRI, DWI, and Synthetic MRI Improves the Diagnostic Performance of BI-RADS 4 Lesions

Abstract

ObjectivesTo evaluate the value of synthetic magnetic resonance imaging (syMRI), diffusion-weighted imaging (DWI), DCE-MRI, and clinical features in breast imaging–reporting and data system (BI-RADS) 4 lesions, and develop an efficient method to help patients avoid unnecessary biopsy.MethodsA total of 75 patients with breast diseases classified as BI-RADS 4 (45 with malignant lesions and 30 with benign lesions) were prospectively enrolled in this study. T1-weighted imaging (T1WI), T2WI, DWI, and syMRI were performed at 3.0 T. Relaxation time (T1 and T2), apparent diffusion coefficient (ADC), conventional MRI features, and clinical features were assessed. “T” represents the relaxation time value of the region of interest pre-contrast scanning, and “T+” represents the value post-contrast scanning. The rate of change in the T value between pre- and post-contrast scanning was represented by ΔT%.ResultsΔT1%, T2, ADC, age, body mass index (BMI), menopause, irregular margins, and heterogeneous internal enhancement pattern were significantly associated with a breast cancer diagnosis in the multivariable logistic regression analysis. Based on the above parameters, four models were established: model 1 (BI-RADS model, including all conventional MRI features recommended by BI-RADS lexicon), model 2 (relaxation time model, including ΔT1% and T2), model 3 [multi-parameter (mp)MRI model, including ΔT1%, T2, ADC, margin, and internal enhancement pattern], and model 4 (combined image and clinical model, including ΔT1%, T2, ADC, margin, internal enhancement pattern, age, BMI, and menopausal state). Among these, model 4 has the best diagnostic performance, followed by models 3, 2, and 1.ConclusionsThe mpMRI model with DCE-MRI, DWI, and syMRI is a robust tool for evaluating the malignancies in BI-RADS 4 lesions. The clinical features could further improve the diagnostic performance of the model.

Identification of the Benignity and Malignancy of BI-RADS 4 Breast Lesions Based on a Combined Quantitative Model of Dynamic Contrast-Enhanced MRI and Intravoxel Incoherent Motion

“…The ADC _min value of malignant lesions was lower than that of benign lesions, which was the same as the previous research results [ 26 ]. This might be attributed to the continuous proliferation of tumor cells, the increase in the synthesis of macromolecular substances in the cytoplasm, the release of a large amount of necrotic substances, the reduction of extracellular space, the increase of bound water content, and the restricted diffusion of free water molecules [ 27 ]. A meta-analysis showed that the ADC value for distinguishing between benign and malignant breast lesions range from 0.92 to 1.61 × 10 −3 mm 2 /s [ 28 ].…”

The road to breast cancer screening with diffusion MRI

“…Many groups have also found significant differences of ADC values between benign and metastatic breast lymph nodes ( 62 – 64 ), however, their diagnostic performance in differentiating these lymph nodes still need further investigation ( 64 ) compared to simpler markers such as the lymph node size. Quantitative DWI in addition to DCE‐MRI and other plain MRI such as T1WI and T2WI also leads to improved diagnostic performance, in terms of specificity for BI-RADS (Breast Imaging-Reporting And Data System) 3 and 4 lesions, or evaluating malignancies with BI-RADS 4 lesions ( 65 , 66 ).…”

Combination of ultrafast dynamic contrast-enhanced MRI-based radiomics and artificial neural network in assessing BI-RADS 4 breast lesions: Potential to avoid unnecessary biopsies

“…Therefore, it is necessary to find a sensitive tool to improve the assessment of BI-RADS 4 lesions. In order to avoid false positive BI-RADS 4 category assignments, previous studies utilized either advanced MRI techniques (10)(11)(12)(13) or specific clinical decision rule incorporated morphological and kinetic BI-RADS descriptors (14, 15). Although these approaches showed encouraging results, additional measurements increase the scan time and the decision rule require human image features interpretation that may lead to inter-reader variation (16).…”