Multiparametric MRI model with dynamic contrast-enhanced and diffusion-weighted imaging enables breast cancer diagnosis with high accuracy.

BACKGROUND

The MRI Breast Imaging-Reporting and Data System (BI-RADS) lexicon recommends that a breast MRI protocol contain T -weighted and dynamic contrast-enhanced (DCE) MRI sequences. The addition of diffusion-weighted imaging (DWI) significantly improves diagnostic accuracy. This study aims to clarify which descriptors from DCE-MRI, DWI, and T -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 American College of Radiology (ACR) BI-RADS recommended descriptors for breast MRI with DCE, T -weighted imaging, and DWI 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 underwent mpMRI from December 2010 to September 2014.

FIELD STRENGTH/SEQUENCE: IR inversion recovert DCE-MRI dynamic contrast-enhanced magnetic resonance imaging VIBE Volume-Interpolated-Breathhold-Examination FLASH turbo fast-low-angle-shot TWIST Time-resolved angiography with stochastic Trajectories.

ASSESSMENT

Two radiologists in consensus and another radiologist independently evaluated the mpMRI data. Characteristics for mass (n = 182) and nonmass (n = 28) lesions were recorded on DCE and T -weighted imaging according to BI-RADS, as well as DWI descriptors. Two separate models were analyzed, using DCE-MRI BI-RADS descriptors, T -weighted imagines, and ADCmean as either a continuous or binary form using a previously published ADC cutoff value of ≤1.25 × 10 mm /sec for differentiation between benign and malignant lesions. Histopathology was the standard of reference.

STATISTICAL TESTS

χ test, Fisher's exact test, Kruskal-Wallis test, Pearson correlation coefficient, multivariate logistic regression 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. T -weighted imaging variables were not included in the final models. DATA CONCLUSION: mpMRI with DCE-MRI and DWI with ADC mapping enables accurate breast cancer diagnosis. A model using quantitative and qualitative descriptors from DCE-MRI and DWI identifies breast cancer with a high diagnostic accuracy. T -weighted imaging does not significantly contribute to breast cancer diagnosis.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:864-874.