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Abstract
Objectives
To evaluate at which sensitivity digital breast tomosynthesis (DBT) would become cost-effective compared to digital mammography (DM) in a population breast cancer screening program, given a constant estimate of specificity.
Methods
In a microsimulation model, the cost-effectiveness of biennial screening for women aged 50–75 was simulated for three scenarios: DBT for women with dense breasts and DM for women with fatty breasts (scenario 1), DBT for the whole population (scenario 2) or maintaining DM screening (reference). For DM, sensitivity was varied depending on breast density from 65 to 87%, and for DBT from 65 to 100%. The specificity was set at 96.5% for both DM and DBT. Direct medical costs were considered, including screening, biopsy and treatment costs. Scenarios were considered to be cost-effective if the incremental cost-effectiveness ratio (ICER) was below €20,000 per life year gain (LYG).
Results
For both scenarios, the ICER was more favourable at increasing DBT sensitivity. Compared with DM screening, 0.8–10.2% more LYGs were found when DBT sensitivity was at least 75% for scenario 1, and 4.7–18.7% when DBT sensitivity was at least 80% for scenario 2. At €96 per DBT, scenario 1 was cost-effective at a DBT sensitivity of at least 90%, and at least 95% for scenario 2. At €80 per DBT, these values decreased to 80% and 90%, respectively.
Conclusion
DBT is more likely to be a cost-effective alternative to mammography in women with dense breasts. Whether DBT could be cost-effective in a general population highly depends on DBT costs.
Key Points
• DBT could be a cost-effective screening modality for women with dense breasts when its sensitivity is at least 90% at a maximum cost per screen of €96.
• DBT has the potential to be cost-effective for screening all women when sensitivity is at least 90% at a maximum cost per screen of €80.
• Whether DBT could be used as an alternative to mammography for screening all women is highly dependent on the cost of DBT per screen.
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Details
1 University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, The Netherlands (GRID:grid.4494.d) (ISNI:0000 0000 9558 4598)
2 University of Groningen, University Medical Center Groningen, Department of Radiology, Groningen, The Netherlands (GRID:grid.4494.d) (ISNI:0000 0000 9558 4598)
3 University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, The Netherlands (GRID:grid.4494.d) (ISNI:0000 0000 9558 4598); Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Department of Radiology, Brussels, Belgium (GRID:grid.4494.d)
4 University Medical Center Utrecht, Utrecht University, Department of Radiology, Utrecht, The Netherlands (GRID:grid.4494.d)
5 Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Department of Radiology, Brussels, Belgium (GRID:grid.4494.d)
6 University of Sydney, Sydney School of Public Health, Faculty of Medicine and Health, Sydney, Australia (GRID:grid.1013.3) (ISNI:0000 0004 1936 834X)
7 Tianjin Medical University, Department of Epidemiology and Health Statistics, Tianjin, China (GRID:grid.265021.2) (ISNI:0000 0000 9792 1228)