Automated Triage of Screening Breast MRI Examinations in High-Risk Women Using an Ensemble Deep Learning Model

From the Departments of ∗Radiology

†Epidemiology and Biostatistics

‡Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY.

Received for publication December 5, 2022; and accepted for publication, after revision, February 20, 2023.

Conflicts of interest and sources of funding: K.P. received payment for activities not related to the present article including lectures and service on speakers' bureaus and for travel/accommodations/meeting expenses unrelated to activities listed from the European Society of Breast Imaging (MRI educational course, annual scientific meeting), the IDKD 2019 (educational course), GBCC 10, TIBCS 2021, Olea Medical, Vara Merantix Healthcare GmbH, AURA Health Technologies GmbH, and Siemens Healthineers. She is also the Principal Investigator of a research project sponsored by Grail Inc, and a consultant for Merantix Healthcare, Siemens Healthineers, and Genentech, Inc. The other authors of this manuscript declare no conflicts of interest.

This work was supported in part by NIH/NCI Cancer Center Support Grant (P30 CA008748) and the NIH/NCI UG3 CA239861 grant.

Correspondence to: Sarah Eskreis-Winkler, MD, PhD, Department of Radiology, Memorial Sloan Kettering Cancer Center, 300 E 66th St, New York, NY 10065. E-mail: [email protected].

Supplemental digital contents are available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s Web site (www.investigativeradiology.com).

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