Assessing Breast Cancer Risk by Combining AI for Lesion Detection and Mammographic Texture-Reference-Cited by-同舟云学术

Assessing Breast Cancer Risk by Combining AI for Lesion Detection and Mammographic Texture

Published:2023-08-01 Issue:2 Volume:308 Page:
ISSN:0033-8419
Container-title:Radiology
language:en
Short-container-title:Radiology

Author:

Lauritzen Andreas D.¹^ORCID,von Euler-Chelpin My C.¹^ORCID,Lynge Elsebeth¹^ORCID,Vejborg Ilse¹^ORCID,Nielsen Mads¹^ORCID,Karssemeijer Nico¹^ORCID,Lillholm Martin¹

Affiliation:

1. From the Departments of Computer Science (A.D.L., M.N., M.L.) and Public Health (M.C.v.E.C., E.L.), University of Copenhagen, Universitetsparken 1, 2100 Copenhagen Ø, Denmark; Department of Breast Examinations, Gentofte Hospital, Gentofte, Denmark (I.V.); and Department of Radiology and Nuclear Medicine, Radboud University Medical Centre and ScreenPoint Medical, Nijmegen, the Netherlands (N.K.).

Funder

Eurostars

Publisher

Radiological Society of North America (RSNA)

Subject

Radiology, Nuclear Medicine and imaging

Link

http://pubs.rsna.org/doi/pdf/10.1148/radiol.230227

Reference27 articles.

1. Mammographic Parenchymal Patterns

2. Risk for breast cancer development determined by mammographic parenchymal pattern

3. Quantitative Classification of Mammographic Densities and Breast Cancer Risk: Results From the Canadian National Breast Screening Study

4. The Royal College of Radiologists. Clinical Radiology UK workforce census 2018. London, UK. https://www.rcr.ac.uk/publication/clinical-radiology-uk-workforce-census-report-2018. Published April 2019. Accessed March 22, 2023.

5. World Health Organization: Regional Office for Europe. World Cancer Report: Cancer research for cancer development. Lyon, France. http://publications.iarc.fr/586. Published 2020. Accessed March 22, 2023.

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