Special Issue "Artificial Intelligence in Breast Cancer Screening"

A special issue of Tomography (ISSN 2379-139X). This special issue belongs to the section "Cancer Imaging".

Deadline for manuscript submissions: 31 October 2022.

Special Issue Editors

Dr. Aimilia Gastounioti
E-Mail Website
Guest Editor
Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA
Interests: computational imaging phenotypes; artificial intelligence; radiomics; deep learning; breast cancer risk
Dr. Debbie Bennett
E-Mail Website
Guest Editor
Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA
Interests: breast cancer screening and diagnosis; mammography; breast ultrasound; breast intervention; clinical trials for breast cancer

Special Issue Information

Dear Colleagues,

Most developed healthcare systems have implemented breast cancer screening programs, initially using analog screen-film-based mammography systems and, over the last 20 years, transitioning to the use of fully digital systems (digital mammography and digital breast tomosynthesis). Much of the effort to improve breast cancer screening outcomes has focused on intensifying screening, e.g., double-reading instead of single-reading and more frequent or supplemental screening (with breast ultrasound or MRI), which entail increased resources and often come at a cost of higher false-positive rates. Furthermore, personalized breast cancer screening regimens tailored to an individual’s breast cancer risk are increasingly being advocated. The artificial intelligence (AI) revolution in computational imaging, driven by radiomic machine learning and more recently by deep learning, has also pervaded this complex landscape of breast cancer screening, including AI models for breast density evaluation, breast cancer risk assessment, breast cancer detection and prognosis, as well as enhancing efficiency in breast cancer care.

Therefore, it is with pleasure that we invite investigators to contribute to this Special Issue with original research articles, review articles, and meta-analysis articles addressing these topics, with special regard to their clinical and radiological implications for breast cancer screening.

Dr. Aimilia Gastounioti
Dr. Debbie Bennett
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Tomography is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • artificial intelligence
  • deep learning
  • radiomics
  • machine learning
  • breast cancer
  • breast cancer risk
  • digital mammography
  • breast tomosynthesis
  • breast MRI
  • breast ultrasound

Published Papers

This special issue is now open for submission.
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