Open Access

Michelle Zhang, Joao V. Horvat, Blanca Bernard-Davila, Maria Adele Marino, Doris Leithner, Elena Ochoa‐Albiztegui, Thomas H. Helbich, Elizabeth A. Morris, Sunitha Thakur, Katja Pinker

• Background: The MRI Breast Imaging-Reporting and Data System (BI-RADS) lexicon recommends that a breast MRI proto-col contain T2-weighted and dynamic contrast-enhanced (DCE) MRI sequences. The addition of diffusion-weighted imag-ing (DWI) significantly improves diagnostic accuracy. This study aims to clarify which descriptors from DCE-MRI, DWI, andT2-weighted imaging are most strongly associated with a breast cancer diagnosis.Purpose/Hypothesis: To develop a multiparametric MRI (mpMRI) model for breast cancer diagnosis incorporating Ameri-can College of Radiology (ACR) BI-RADS recommended descriptors for breast MRI with DCE, T2-weighted imaging, andDWI with apparent diffusion coefficient (ADC) mapping.Study Type: Retrospective.Subjects: In all, 188 patients (mean 51.6 years) with 210 breast tumors (136 malignant and 74 benign) who underwentmpMRI from December 2010 to September 2014.Field Strength/Sequence: IR inversion recovert DCE-MRI dynamic contrast-enhanced magnetic resonance imaging VIBEVolume-Interpolated-Breathhold-Examination FLASH turbo fast-low-angle-shot TWIST Time-resolved angiography withstochastic Trajectories.Assessment: Two radiologists in consensus and another radiologist independently evaluated the mpMRI data. Charac-teristics for mass (n = 182) and nonmass (n = 28) lesions were recorded on DCE and T2-weighted imaging accordingto BI-RADS, as well as DWI descriptors. Two separate models were analyzed, using DCE-MRI BI-RADS descriptors, T2-weighted imagines, and ADCmean as either a continuous or binary form using a previously published ADC cutoffvalue of ≤1.25 × 10−3mm2/sec for differentiation between benign and malignant lesions. Histopathology was the stan-dard of reference.Statistical Tests: χ2test, Fisher’s exact test, Kruskal–Wallis test, Pearson correlation coefficient, multivariate logistic regres-sion analysis, Hosmer–Lemeshow test of goodness-of-fit, receiver operating characteristics analysis.Results: In Model 1, ADCmean (P = 0.0031), mass margins with DCE (P = 0.0016), and delayed enhancement with DCE(P = 0.0016) were significantly and independently associated with breast cancer diagnosis; Model 2 identified ADCmean(P = 0.0031), mass margins with DCE (P = 0.0012), initial enhancement (P = 0.0422), and delayed enhancement with DCE(P = 0.0065) to be significantly independently associated with breast cancer diagnosis. T2-weighted imaging variables werenot included in the final models