Artificial Intelligence–Based Classification of Breast Lesions Imaged With a Multiparametric Breast MRI Protocol With Ultrafast DCE-MRI, T2, and DWI

Objectives We investigated artificial intelligence (AI)–based classification of benign and malignant breast lesions imaged with a multiparametric breast magnetic resonance imaging (MRI) protocol with ultrafast dynamic contrast-enhanced MRI, T2-weighted, and diffusion-weighted imaging with apparent diffusion coefficient mapping. Materials and Methods We analyzed 576 lesions imaged with MRI, including a consecutive set of biopsied malignant (368) and benign (149) lesions, and an additional set of 59 benign lesions proven by follow-up. We used deep learning methods to interpret ultrafast dynamic contrast-enhanced MRI and T2-weighted information. A random forests classifier combined the output with patient information (PI; age and BRCA status) and apparent diffusion coefficient values obtained from diffusion-weighted imaging to perform the final lesion classification. We used receiver operating characteristic (ROC) analysis to evaluate our results. Sensitivity and specificity were compared with the results of the prospective clinical evaluation by radiologists. Results The area under the ROC curve was 0.811 when only ultrafast dynamics was used. The final AI system that combined all imaging information with PI resulted in an area under the ROC curve of 0.852, significantly higher than the ultrafast dynamics alone (P = 0.002). When operating at the same sensitivity level of radiologists in this dataset, this system produced 19 less false-positives than the number of biopsied benign lesions in our dataset. Conclusions Use of adjunct imaging and PI has a significant contribution in diagnostic performance of ultrafast breast MRI. The developed AI system for interpretation of multiparametric ultrafast breast MRI may improve specificity. Received for publication October 16, 2018; and accepted for publication, after revision, December 7, 2018. Conflicts of interest and sources of funding: The authors declare no potential conflicts of interest associated with this work. This work received funding from the European Union's Seventh Framework Programme for research, technological development, and demonstration (grant agreement no. 601040). Correspondence to: Mehmet U. Dalmiş, MSc, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein 10, Route 766, 6525 GA Nijmegen, the Netherlands. E-mail: mehmet.dalmis@radboudumc.nl. Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.

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