scholarly journals Breast cancer screening: false positive rate is lower in older women

BMJ ◽  
1998 ◽  
Vol 317 (7158) ◽  
pp. 599-599
Author(s):  
G Rubin ◽  
L. Garvican
BMJ Open ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. e028766 ◽  
Author(s):  
Yan Yuan ◽  
Khanh Vu ◽  
Ye Shen ◽  
James Dickinson ◽  
Marcy Winget

ObjectivesRegular breast cancer screening is a widely used cancer prevention strategy. Important quality indicators of screening include cancer detection rate, false positive rate, benign biopsy rate and post-screen invasive cancer rate. We compared quality indicators of community radiology clinics to those of ‘Screen Test’, which feature centralised batch reading and quality control processes. Both types of providers operated under a single provincial Breast Cancer Screening Programme.SettingCommunity radiology clinics are operated by independent fee-for-service radiologists serving large and small communities throughout the Canadian province of Alberta. Launched by the provincial cancer agency, the Screen Test operates two physical clinics serving metropolises and mobile units serving remote regions. Eligible women may self-refer to any provider for screening mammography.ParticipantsWomen aged 50 to 69 years who had at least one screening mammogram between July 2006 and June 2010 in Alberta were included. Women with missing health region information or prior breast cancer diagnosis were excluded.ResultsA total of 389 788 screening mammograms were analysed, of which 12.7% were performed by Screen Test. Compared with Screen Test during 2006 to 2008, community radiology clinics had a lower cancer detection rate (3.6 vs 4.6 per 1000 screens, risk ratio (RR): 0.81, 95% CI: 0.67 to 0.98) and a much higher false positive rate (9.4% vs 3.4%, RR: 2.72, 95% CI: 2.55 to 2.90). Most other performance indicators were also better in Screen Test overall and across all health regions. These performance indicators were similar during 2008 to 2010, showing no improvement with time.ConclusionsScreen Test has a quality assurance process in place and performed significantly better. This provides empirical evidence of the effectiveness of a quality assurance process and may explain some of the large differences in breast cancer screening indicators between provinces and countries with formal programmes and those without.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lorena Squillace ◽  
Lorenzo Pizzi ◽  
Flavia Rallo ◽  
Carmen Bazzani ◽  
Gianni Saguatti ◽  
...  

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.


2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Gabriele Valvano ◽  
Gianmarco Santini ◽  
Nicola Martini ◽  
Andrea Ripoli ◽  
Chiara Iacconi ◽  
...  

Cluster of microcalcifications can be an early sign of breast cancer. In this paper, we propose a novel approach based on convolutional neural networks for the detection and segmentation of microcalcification clusters. In this work, we used 283 mammograms to train and validate our model, obtaining an accuracy of 99.99% on microcalcification detection and a false positive rate of 0.005%. Our results show how deep learning could be an effective tool to effectively support radiologists during mammograms examination.


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