Breast cancer screening using convolutional neural network and follow-up digital mammography

2018 ◽  
Author(s):  
Yufeng Zheng ◽  
Clifford Yang ◽  
Aleksey Merkulov
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Cancer Screening ◽  
Convolutional Neural Network ◽  
Breast Cancer Screening ◽  
Digital Mammography

scholarly journals Cost-Effectiveness of Digital Mammography Breast Cancer Screening

2008 ◽  
Vol 148 (1) ◽  
pp. 1 ◽  
Author(s):  
Anna N.A. Tosteson ◽  
Natasha K. Stout ◽  
Dennis G. Fryback ◽  
Suddhasatta Acharyya ◽  
Benjamin A. Herman ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cost Effectiveness ◽  
Cancer Screening ◽  
Breast Cancer Screening ◽  
Digital Mammography

scholarly journals Effectiveness of diet and physical activity interventions amongst adults attending colorectal and breast cancer screening: a systematic review and meta-analysis

2020 ◽  
Author(s):  
Samuel T. Orange ◽  
Kirsty M. Hicks ◽  
John M. Saxton
Keyword(s):  
Breast Cancer ◽  
Physical Activity ◽  
Cancer Screening ◽  
Breast Cancer Screening ◽  
Vegetable Intake ◽  
Dietary Interventions ◽  
Physical Activity Interventions ◽  
Mean Differences ◽  
Quality Evidence

Abstract Purpose To estimate the effectiveness of tailored physical activity and dietary interventions amongst adults attending colorectal and breast cancer screening. Methods Five literature databases were systematically searched to identify randomised controlled trials (RCTs) of tailored physical activity and/or dietary interventions with follow-up support initiated through colorectal and breast cancer screening programmes. Outcomes included markers of body fatness, physical activity, and dietary intake. Mean differences (MDs) or standardised mean differences (SMDs) with 95% confidence intervals (CIs) were pooled using random effects models. Results Five RCTs met the inclusion criteria encompassing a total of 722 participants. Diet and physical activity interventions led to statistically significant reductions in body mass (MD − 1.6 kg, 95% CI − 2.7 to − 0.39 kg; I2 = 81%; low quality evidence), body mass index (MD − 0.78 kg/m2, 95% CI − 1.1 to − 0.50 kg/m2; I2 = 21%; moderate quality evidence), and waist circumference (MD − 2.9 cm, 95% CI − 3.8 to − 1.91; I2 = 0%; moderate quality evidence), accompanied by an increase in physical activity (SMD 0.31, 95% CI 0.13 to 0.50; I2 = 0%; low quality evidence) and fruit and vegetable intake (SMD 0.33, 95% CI 0.01 to 0.64; I2 = 51%; low quality evidence). Conclusion There is low quality evidence that lifestyle interventions involving follow-up support lead to modest weight loss and increased physical activity and fruit and vegetable intake. Due to the modest intervention effects, low quality of evidence and small number of studies, further rigorously designed RCTs with long-term follow-up of modifiable risk factors and embedded cost–benefit analyses are warranted (PROSPERO ref: CRD42020179960).

Breast cancer overdiagnosis in stop-screen trials: More uncertainty than previously reported

2020 ◽  
pp. 096914132095078
Author(s):  
Stuart G Baker ◽  
Philip C Prorok
Keyword(s):  
Breast Cancer ◽  
Cancer Screening ◽  
Confidence Interval ◽  
Breast Cancer Screening ◽  
Confidence Intervals ◽  
Latent Class ◽  
Screening Program ◽  
Screen Interval ◽  
Binomial Confidence Interval

Objective According to the Independent UK Panel on Breast Cancer Screening, the most reliable estimates of overdiagnosis for breast cancer screening come from stop-screen trials Canada 1, Canada 2, and Malmo. The screen-interval overdiagnosis fraction is the fraction of cancers in a screening program that are overdiagnosed. We used the cumulative incidence method to estimate screen-interval overdiagnosis fraction. Our goal was to derive confidence intervals for estimated screen-interval overdiagnosis fraction and adjust for refusers in these trials. Methods We first show that the UK Panel’s use of a 95% binomial confidence interval for estimated screen-interval overdiagnosis fraction was incorrect. We then derive a correct 95% binomial-Poisson confidence interval. We also use the method of latent-class instrumental variables to adjust for refusers. Results For the Canada 1 trial, the estimated screen-interval overdiagnosis fraction was 0.23 with a 95% binomial confidence interval of (0.18, 0.27) and a 95% binomial-Poisson confidence interval of (0.04, 0.41). For the Canada 2 trial, the estimated screen-interval overdiagnosis fraction was 0.16 with a 95% binomial confidence interval of (0.12, 0.19) and a 95% binomial-Poisson confidence interval of (−0.01, 0.32). For the Malmo trial, the estimated screen-interval overdiagnosis fraction was 0.19 with a 95% binomial confidence interval of (0.15, 0.22). Adjusting for refusers, the estimated screen-interval overdiagnosis fraction was 0.26 with a 95% binomial-Poisson confidence interval of (0.03, 0.50). Conclusion The correct 95% binomial-Poisson confidence interval s for the estimated screen-interval overdiagnosis fraction based on the Canada 1, Canada 2, and Malmo stop-screen trials are much wider than the previously reported incorrect 95% binomial confidence intervals. The 95% binomial-Poisson confidence intervals widen as follow-up time increases, an unappreciated downside of longer follow-up in stop-screen trials.

Comparison of Contrast-Enhanced Mammography With Conventional Digital Mammography in Breast Cancer Screening: A Pilot Study

2019 ◽  
Vol 16 (10) ◽  
pp. 1456-1463 ◽  
Author(s):  
Geunwon Kim ◽  
Jordana Phillips ◽  
Elodia Cole ◽  
Alexander Brook ◽  
Tejas Mehta ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Screening ◽  
Pilot Study ◽  
Breast Cancer Screening ◽  
Digital Mammography ◽  
Contrast Enhanced
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