Toward robust mammography-based models for breast cancer risk-Reference-Cited by-同舟云学术

Toward robust mammography-based models for breast cancer risk

Published:2021-01-27 Issue:578 Volume:13 Page:
ISSN:1946-6234
Container-title:Science Translational Medicine
language:en
Short-container-title:Sci. Transl. Med.

Author:

Yala Adam¹²^ORCID,Mikhael Peter G.¹²^ORCID,Strand Fredrik³⁴^ORCID,Lin Gigin⁵^ORCID,Smith Kevin⁶⁷,Wan Yung-Liang⁵^ORCID,Lamb Leslie⁸,Hughes Kevin⁹,Lehman Constance⁸^ORCID,Barzilay Regina¹²^ORCID

Affiliation:

1. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

2. Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

3. Breast Radiology Unit, Department of Imaging and Physiology, Karolinska University Hospital, 17164 Solna, Sweden.

4. Department of Oncology-Pathology, Karolinska Institute, 17164 Solna, Sweden.

5. Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan 333, Taiwan.

6. School of Electrical Engineering and Computer, KTH Royal Institute of Technology, 10044 Stockholm, Sweden.

7. Science for Life Laboratory, 17165 Solna, Sweden.

8. Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.

9. Division of Surgical Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.

Abstract

An algorithm to predict breast cancer risk, Mirai, outperforms clinical risk models across test cohorts from the United States, Sweden, and Taiwan.

Funder

Susan G. Komen for the Cure

Breast Cancer Research Foundation

Chang Gung Medical Foundation

Quanta Computing

MIT J-Clinic

Stockholm Läns Landsting HMT

Publisher

American Association for the Advancement of Science (AAAS)

Subject

General Medicine

Reference72 articles.

1. U. S. Food and Drug Administration Mammography Quality Standards Act and Program. Retrieved from https://www.fda.gov/radiation-emitting-products/mqsa-insights/mqsa-national-statistics.

2. Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection

3. The Cost of Breast Cancer Screening in the Medicare Population

4. Mammography service screening and mortality in breast cancer patients: 20-year follow-up before and after introduction of screening

5. Reduction in Breast Cancer Mortality from Organized Service Screening with Mammography: 1. Further Confirmation with Extended Data

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