scholarly journals Computer-Aided Detection for Breast Cancer Screening in Clinical Settings: Scoping Review

10.2196/12660 ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. e12660 ◽  
Author(s):  
Rafia Masud ◽  
Mona Al-Rei ◽  
Cynthia Lokker
Keyword(s):  
Breast Cancer ◽  
Cancer Screening ◽  
Breast Cancer Screening ◽  
Scoping Review ◽  
Clinical Settings ◽  
Computer Aided Detection ◽  
Computer Aided

Improving computer-aided detection assistance in breast cancer screening by removal of obviously false-positive findings

2017 ◽  
Vol 44 (4) ◽  
pp. 1390-1401 ◽  
Author(s):  
Jan-Jurre Mordang ◽  
Albert Gubern-Mérida ◽  
Alessandro Bria ◽  
Francesco Tortorella ◽  
Gerard den Heeten ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Screening ◽  
Breast Cancer Screening ◽  
False Positive ◽  
Computer Aided Detection ◽  
Computer Aided

Computational Radiology in Breast Cancer Screening and Diagnosis Using Artificial Intelligence

2020 ◽  
pp. 084653712094997
Author(s):  
William T. Tran ◽  
Ali Sadeghi-Naini ◽  
Fang-I Lu ◽  
Sonal Gandhi ◽  
Nicholas Meti ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Cancer Screening ◽  
Diagnostic Accuracy ◽  
Breast Cancer Screening ◽  
Current Status ◽  
Computer Aided Detection ◽  
Computer Aided ◽  
Detection And Diagnosis ◽  
Screening And Diagnosis

Breast cancer screening has been shown to significantly reduce mortality in women. The increased utilization of screening examinations has led to growing demands for rapid and accurate diagnostic reporting. In modern breast imaging centers, full-field digital mammography (FFDM) has replaced traditional analog mammography, and this has opened new opportunities for developing computational frameworks to automate detection and diagnosis. Artificial intelligence (AI), and its subdomain of deep learning, is showing promising results and improvements on diagnostic accuracy, compared to previous computer-based methods, known as computer-aided detection and diagnosis. In this commentary, we review the current status of computational radiology, with a focus on deep neural networks used in breast cancer screening and diagnosis. Recent studies are developing a new generation of computer-aided detection and diagnosis systems, as well as leveraging AI-driven tools to efficiently interpret digital mammograms, and breast tomosynthesis imaging. The use of AI in computational radiology necessitates transparency and rigorous testing. However, the overall impact of AI to radiology workflows will potentially yield more efficient and standardized processes as well as improve the level of care to patients with high diagnostic accuracy.

scholarly journals Computer-Aided Detection for Breast Cancer Screening in Clinical Settings: Scoping Review (Preprint)

2018 ◽  
Author(s):  
Rafia Masud ◽  
Mona Al-Rei ◽  
Cynthia Lokker
Keyword(s):  
Breast Cancer ◽  
Cancer Screening ◽  
Diagnostic Accuracy ◽  
Breast Cancer Screening ◽  
Patient Outcomes ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Clinical Settings ◽  
Computer Aided Detection ◽  
Detection Rates

BACKGROUND With the growth of machine learning applications, the practice of medicine is evolving. Computer-aided detection (CAD) is a software technology that has become widespread in radiology practices, particularly in breast cancer screening for improving detection rates at earlier stages. Many studies have investigated the diagnostic accuracy of CAD, but its implementation in clinical settings has been largely overlooked. OBJECTIVE The aim of this scoping review was to summarize recent literature on the adoption and implementation of CAD during breast cancer screening by radiologists and to describe barriers and facilitators for CAD use. METHODS The MEDLINE database was searched for English, peer-reviewed articles that described CAD implementation, including barriers or facilitators, in breast cancer screening and were published between January 2010 and March 2018. Articles describing the diagnostic accuracy of CAD for breast cancer detection were excluded. The search returned 526 citations, which were reviewed in duplicate through abstract and full-text screening. Reference lists and cited references in the included studies were reviewed. RESULTS A total of nine articles met the inclusion criteria. The included articles showed that there is a tradeoff between the facilitators and barriers for CAD use. Facilitators for CAD use were improved breast cancer detection rates, increased profitability of breast imaging, and time saved by replacing double reading. Identified barriers were less favorable perceptions of CAD compared to double reading by radiologists, an increase in recall rates of patients for further testing, increased costs, and unclear effect on patient outcomes. CONCLUSIONS There is a gap in the literature between CAD’s well-established diagnostic accuracy and its implementation and use by radiologists. Generally, the perceptions of radiologists have not been considered and details of implementation approaches for adoption of CAD have not been reported. The cost-effectiveness of CAD has not been well established for breast cancer screening in various populations. Further research is needed on how to best facilitate CAD in radiology practices in order to optimize patient outcomes, and the views of radiologists need to be better considered when advancing CAD use.

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