Radiomic and deep learning characterization of breast parenchyma on full field digital mammograms and specimen radiographs: a pilot study of a potential cancer field effect-Reference-Cited by-同舟云学术

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Radiomic and deep learning characterization of breast parenchyma on full field digital mammograms and specimen radiographs: a pilot study of a potential cancer field effect

Published:2023-07-08 Issue:04 Volume:10 Page:
ISSN:2329-4302
Container-title:Journal of Medical Imaging
language:
Short-container-title:J. Med. Imag.

Author:

Baughan Natalie¹,Li Hui¹,Lan Li¹,Embury Matthew²,Yim Isaiah²,Whitman Gary J.³,El-Zein Randa⁴,Bedrosian Isabelle²,Giger Maryellen L.¹

Affiliation:

1. The University of Chicago, Department of Radiology, Chicago, Illinois, United States

2. The University of Texas MD Anderson Cancer Center, Department of Breast Surgical Oncology, Houston, Texas, United States

3. The University of Texas MD Anderson Cancer Center, Department of Breast Imaging, Houston, Texas, United States

4. The Houston Methodist Research Institute, Houston, Texas, United States

Publisher

SPIE-Intl Soc Optical Eng

Subject

Radiology, Nuclear Medicine and imaging

Reference23 articles.

1. Cancer statistics, 2023

2. Breast Cancer Screening With Imaging: Recommendations From the Society of Breast Imaging and the ACR on the Use of Mammography, Breast MRI, Breast Ultrasound, and Other Technologies for the Detection of Clinically Occult Breast Cancer

3. Breast Cancer Screening for Women at Average Risk

4. Prevalence of Mammographically Dense Breasts in the United States

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