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Cited by 73AI in cancer detectionMRI in cancer diagnosisDigital Radiography and Breast Imaging

The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.

Deep Learning for Automated Triaging of 4581 Breast MRI Examinations from the DENSE Trial

Open access
Verburg, Erik, van Gils, Carla H., van der Velden, Bas H. M., Bakker, Marije F., Pijnappel, Ruud M., Veldhuis, Wouter B., Gilhuijs, Kenneth G. A.·2022·Radiology
DOI 10.1148/radiol.2021203960
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Abstract

Background Supplemental screening with MRI has proved beneficial in women with extremely dense breasts. Most MRI examinations show normal anatomic and physiologic variation that may not require radiologic review. Thus, ways to triage these normal MRI examinations to reduce radiologist workload are needed. Purpose To determine the feasibility of an automated triaging method using deep learning (DL) to dismiss the highest number of MRI examinations without lesions while still identifying malignant disease. Materials and Methods This secondary analysis of data from the Dense Tissue and Early Breast Neoplasm Screening, or DENSE, trial evaluated breast MRI examinations from the first screening round performed in eight hospitals between December 2011 and January 2016. A DL model was developed to differentiate between breasts with lesions and breasts without lesions. The model was trained to dismiss breasts with normal phenotypical variation and to triage lesions (Breast Imaging Reporting and Data System [BI-RADS] categories 2–5) using eightfold internal-external validation. The model was trained on data from seven hospitals and tested on data from the eighth hospital, alternating such that each hospital was used once as an external test set. Performance was assessed using receiver operating characteristic analysis. At 100% sensitivity for malignant disease, the fraction of examinations dismissed from radiologic review was estimated. Results A total of 4581 MRI examinations of extremely dense breasts from 4581women (mean age, 54.3 years; interquartile range, 51.5–59.8 years) were included. Of the 9162 breasts, 838 had at least one lesion (BI-RADS category 2–5, of which 77 were malignant) and 8324 had no lesions. At 100% sensitivity for malignant lesions, the DL model considered 90.7% (95% CI: 86.7, 94.7) of the MRI examinations with lesions to be nonnormal and triaged them to radiologic review. The DL model dismissed 39.7% (95% CI: 30.0, 49.4) of the MRI examinations without lesions. The DL model had an average area under the receiver operating characteristic curve of 0.83 (95% CI: 0.80, 0.85) in the differentiation between normal breast MRI examinations and MRI examinations with lesions. Conclusion Automated analysis of breast MRI examinations in women with dense breasts dismissed nearly 40% of MRI scans without lesions while not missing any cancers. ClinicalTrials.gov: NCT01315015 © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Joe in this issue.

Overview

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Crossref · 10.1148/radiol.2021203960

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27 of 27 references verified0 citation errors · 0 not checkable

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Citations

Cites

Research works printed but not linked: 6

  1. Morris EA, 2013, 2013 ACR BI-RADS Atlas: Breast Imaging Reporting and Data System
  2. Lundberg SM, 2017, Advances in Neural Information Processing Systems 30 (NIPS 2017) [book online]. Proceedings of the 2017 Conference on Neural Information Processing Systems
  3. Dalmış MU, 2018, J Med Imaging (Bellingham), 5, 014502
  4. Bakker M, 2019, Radiological Society of North America 2019 Scientific Assembly and Annual Meeting program, 2019
  5. Simonyan K, arXiv preprint arXiv:1409.1556 · arXiv
  6. Bergstra JS, 2011, Advances in Neural Information Processing Systems [book online]. Proceedings of the 2011 Conference on Neural Information Processing Systems

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Metadata

Authors
Verburg, E., van Gils, C. H., van der Velden, B. H. M., Bakker, M. F., Pijnappel, R. M., Veldhuis, W. B., & Gilhuijs, K. G. A.
Affiliations
  • Utrecht University (NL)
  • From the Image Sciences Institute (E.V., B.H.M.v.d.V., K.G.A.G.), Julius Center for Health Sciences and Primary Care (C.H.v.G., M.F.B.), and Department of Radiology (R.M.P., W.B.V.), University Medical Center Utrecht, Utrecht University, Utrecht 3584 CX, the Netherlands.
ORCID
Venue
Radiology
Publisher
Radiological Society of North America (RSNA)
Published
2022-01
Volume
302
Issue
1
Pages
29-36
ISSN
  • 0033-8419 (ISSN-L)
  • 1527-1315
DOI
10.1148/radiol.2021203960
OpenAlex
W3202477521
Cited by
73
References
27
Citations by year
2026: 4 · 2025: 25 · 2024: 14 · 2023: 18 · 2022: 11
FWCI
3.87
Type
journal article
Language
en
Open access
Locations
  • Radiology (publishedVersion)
  • PubMed (publishedVersion)
  • Socio-Environmental Systems Modeling (acceptedVersion)
  • Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
  • Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
Funders
  • KWF Kankerbestrijding (UU-2014-7151)
  • University Medical Center Utrecht
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › MRI in cancer diagnosis
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Digital Radiography and Breast Imaging
Keywords
breast MRI, extremely dense breasts, deep learning, BI-RADS, breast cancer screening, radiologist workload, breast lesion detection, false positive reduction, external validation
MeSH terms
Breast Density, Deep Learning, Breast, Breast Neoplasms, Feasibility Studies, Female, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Middle Aged, Triage

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