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 Subsequent attendance in a breast cancer screening program after a false-positive result in the Local Health Authority of Bologna (Italy)

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lorena Squillace ◽  
Lorenzo Pizzi ◽  
Flavia Rallo ◽  
Carmen Bazzani ◽  
Gianni Saguatti ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Screening ◽  
Positive Result ◽  
Breast Cancer Screening ◽  
Health Authority ◽  
False Positive ◽  
Screening Program ◽  
Local Health Authority ◽  
Local Health ◽  
False Positive Result

AbstractWe conducted a cross-sectional study to assess the likelihood of returning for routine breast cancer screening among women who have experienced a false-positive result (FPR) and to describe the possible individual and organizational factors that could influence subsequent attendance to the screening program. Several information were collected on demographic and clinical characteristics data. Electronic data from 2014 to 2016 related to breast screening program of the Local Health Authority (LHA) of Bologna (Italy) of women between 45 and 74 years old were reviewed. A total of 4847 women experienced an FPR during mammographic screening and were recalled to subsequent round; 80.2% adhered to the screening. Mean age was 54.2 ± 8.4 years old. Women resulted to be less likely to adhere to screening if they were not-Italian (p = 0.001), if they lived in the Bologna district (p < 0.001), if they had to wait more than 5 days from II level test to end of diagnostic procedures (p = 0.001), if the diagnostic tests were performed in a hospital with the less volume of activity and higher recall rate (RR) (p < 0.001) and if they had no previous participation to screening tests (p < 0.001). Our results are consistent with previous studies, and encourages the implementation and innovation of the organizational characteristics for breast cancer screening. The success of screening programs requires an efficient indicators monitoring strategy to develop and evaluate continuous improvement processes.

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