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Portrait of Sophia Zackrisson. Photo

Sophia Zackrisson

Research group manager, Principal investigator, Professor, MD

Portrait of Sophia Zackrisson. Photo

The effect of breast density on the performance of deep learning-based breast cancer detection methods for mammography

Author

  • Magnus Dustler
  • Victor Dahlblom
  • Anders Tingberg
  • Sophia Zackrisson

Editor

  • Hilde Bosmans
  • Nicholas Marshall
  • Chantal Van Ongeval

Summary, in English

Mammographic sensitivity in breasts with higher density has been questioned. Higher breast density is also linked to an increased risk for breast cancer. Even though digital breast tomosynthesis (DBT) offers an attractive solution, for varied reasons it has not yet been widely adopted in screening. An alternative could be to boost the performance of standard mammography by using computer-aided detection based on deep learning, but it remains to be proven how such methods are affected by density. A deep-learning based computer-aided detection program was used to score the suspicion of cancer on a scale of 1 to 10. A set of 13838 mammography screening exams were used. All cases had BIRADS density values available. The set included 2304 exams (11 cancers) in BIRADS 1, 5310 (51 cancers) in BIRADS 2, 4844 (73 cancers) in BIRADS 3 and 1223 (22 cancers) in BIRADS 4. A Kruskal-Wallis analysis of variance showed no statistically significant differences between the cancer risk scores of the density categories for cases diagnosed with cancer (P=0.9225). An identical analysis for cases without cancer, showed significant differences between the density categories (P<0.0001). The results suggest that the risk categorization of the deep-learning software is not affected by density, as though some density categories receive higher risk assessments in general, this does not hold for cancer cases, which show uniformly high risk values despite density. This shows the potential for deep-learning to improve screening sensitivity even for women with high density breasts.

Department/s

  • LUCC: Lund University Cancer Centre
  • Radiology Diagnostics, Malmö
  • Medical Radiation Physics, Malmö
  • EpiHealth: Epidemiology for Health

Publishing year

2020

Language

English

Publication/Series

Proceedings of SPIE - The International Society for Optical Engineering

Volume

11513

Document type

Conference paper

Publisher

SPIE

Topic

  • Cancer and Oncology

Keywords

  • Breast
  • Breast density
  • Computer aided detection
  • Deep learning
  • Mammography
  • Screening

Conference name

15th International Workshop on Breast Imaging, IWBI 2020

Conference date

2020-05-25 - 2020-05-27

Conference place

Leuven, Belgium

Status

Published

Project

  • Can breast cancer screening be improved with artificial intelligence?

Research group

  • Radiology Diagnostics, Malmö
  • Medical Radiation Physics, Malmö

ISBN/ISSN/Other

  • ISSN: 0277-786X
  • ISSN: 1996-756X
  • ISBN: 9781510638310