scholarly journals Radiation-Induced Breast Cancer Incidence and Mortality From Digital Mammography Screening

2016 ◽  
Vol 164 (4) ◽  
pp. 205 ◽  
Author(s):  
Diana L. Miglioretti ◽  
Jane Lange ◽  
Jeroen J. van den Broek ◽  
Christoph I. Lee ◽  
Nicolien T. van Ravesteyn ◽  
...  
2006 ◽  
Vol 45 (5) ◽  
pp. 528-535 ◽  
Author(s):  
Sven Törnberg ◽  
Levent Kemetli ◽  
Elsebeth Lynge ◽  
Anne Helene Olsen ◽  
Solveig Hofvind ◽  
...  

2010 ◽  
Vol 10 (1) ◽  
Author(s):  
Willi Oberaigner ◽  
Wolfgang Buchberger ◽  
Thomas Frede ◽  
Rudolf Knapp ◽  
Christian Marth ◽  
...  

2020 ◽  
Author(s):  
Thuy T. T. Le ◽  
Frederick R. Adler

AbstractBACKGROUNDThe benefits of mammography screening have been controversial, with conflicting findings from various studies.METHODSWe hypothesize that unmeasured heterogeneity in tumor aggressiveness underlies these conflicting results. Based on published data from the Canadian National Breast Screening Study (CNBSS), we develop and parameterize an individual-based mechanistic model for breast cancer incidence and mortality that tracks five stages of breast cancer progression and incorporates the effects of age on breast cancer incidence and all-cause mortality.RESULTSThe model accurately reproduces the reported outcomes of the CNBSS. By varying parameters, we predict that the benefits of mammography depend on the effectiveness of cancer treatment and tumor.CONCLUSIONSIn particular, patients with the most rapidly growing or potentially largest tumors have the highest benefit and least harm from the screening, with only a relatively small effect of age. However, the model predicts that confining mammography populations with a high risk of acquiring breast cancer increases the screening benefit only slightly compared with the full population.


2018 ◽  
Vol 38 (1_suppl) ◽  
pp. 140S-150S ◽  
Author(s):  
Jeroen J. van den Broek ◽  
Nicolien T. van Ravesteyn ◽  
Jeanne S. Mandelblatt ◽  
Hui Huang ◽  
Mehmet Ali Ergun ◽  
...  

Background. The UK Age trial compared annual mammography screening of women ages 40 to 49 years with no screening and found a statistically significant breast cancer mortality reduction at the 10-year follow-up but not at the 17-year follow-up. The objective of this study was to compare the observed Age trial results with the Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer model predicted results. Methods. Five established CISNET breast cancer models used data on population demographics, screening attendance, and mammography performance from the Age trial together with extant natural history parameters to project breast cancer incidence and mortality in the control and intervention arm of the trial. Results. The models closely reproduced the effect of annual screening from ages 40 to 49 years on breast cancer incidence. Restricted to breast cancer deaths originating from cancers diagnosed during the intervention phase, the models estimated an average 15% (range across models, 13% to 17%) breast cancer mortality reduction at the 10-year follow-up compared with 25% (95% CI, 3% to 42%) observed in the trial. At the 17-year follow-up, the models predicted 13% (range, 10% to 17%) reduction in breast cancer mortality compared with the non-significant 12% (95% CI, -4% to 26%) in the trial. Conclusions. The models underestimated the effect of screening on breast cancer mortality at the 10-year follow-up. Overall, the models captured the observed long-term effect of screening from age 40 to 49 years on breast cancer incidence and mortality in the UK Age trial, suggesting that the model structures, input parameters, and assumptions about breast cancer natural history are reasonable for estimating the impact of screening on mortality in this age group.


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