Multiparametric MRI model with dynamic contrast-enhanced and diffusion-weighted imaging enables breast cancer diagnosis with high accuracy Journal Article
| Authors: | Zhang, M.; Horvat, J. V.; Bernard-Davila, B.; Marino, M. A.; Leithner, D.; Ochoa-Albiztegui, R. E.; Helbich, T. H.; Morris, E. A.; Thakur, S.; Pinker, K. |
| Article Title: | Multiparametric MRI model with dynamic contrast-enhanced and diffusion-weighted imaging enables breast cancer diagnosis with high accuracy |
| Abstract: | Background: The MRI Breast Imaging-Reporting and Data System (BI-RADS) lexicon recommends that a breast MRI protocol contain T 2 -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 2 -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 2 -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 2 -weighted imaging according to BI-RADS, as well as DWI descriptors. Two separate models were analyzed, using DCE-MRI BI-RADS descriptors, T 2 -weighted imagines, and ADCmean as either a continuous or binary form using a previously published ADC cutoff value of ≤1.25 × 10 −3 mm 2 /sec for differentiation between benign and malignant lesions. Histopathology was the standard of reference. Statistical Tests: χ 2 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 2 -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 2 -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. © 2018 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. |
| Keywords: | breast cancer; bi-rads; dynamic contrast-enhanced mri; diffusion-weighted imaging; t2-weighted imaging |
| Journal Title: | Journal of Magnetic Resonance Imaging |
| Volume: | 49 |
| Issue: | 3 |
| ISSN: | 1053-1807 |
| Publisher: | Wiley Blackwell |
| Date Published: | 2019-03-01 |
| Start Page: | 864 |
| End Page: | 874 |
| Language: | English |
| DOI: | 10.1002/jmri.26285 |
| PUBMED: | 30375702 |
| PROVIDER: | scopus |
| PMCID: | PMC6375760 |
| DOI/URL: | |
| Notes: | Article -- Export Date: 1 March 2019 -- Source: Scopus |
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