A Markov model was used to assess the impact of two interventions often proposed as enhancements to breast cancer screening: computer aided detection and double reading of mammograms.
Bibliographic databases were searched for evaluations of CAD and of double reading. A meta-analysis identified a range of values for the impact of both CAD and double reading on both the cancer detection rate and recall rate of screening. A Markov model was then constructed to examine the implications for the effectiveness of screening.
Age-specific figures were taken from national data for: screening uptake, cancers detected at screening, non-screening detected cancer and other cause mortality. Audits were used to identify stage profiles for screen-detected and non-screen detected cancers. Different survival curves for the stages of cancer are calculated from published data. The model is used to assess the impact on various outcomes, including mortality, of changes in the screen detected rate by running the simulation and varying the figures for cancer detection based on the results of the meta-analysis described above.
Eight studies were found comparing single reading to single reading with CAD and fifteen comparing single reading with double reading. Where double reading is performed with arbitration it shows a significant increase in detection rate (odds ratio: 1.08, 95% CI:1.02-1.15) and a significant decrease in recall rate (odds ratio:0.94, 95% CI:0.92-0.96). CAD studies do not show a significant increase in cancer detection rate (odds ratio: 1.05, 95% CI: 0.95-1.17) and show an increased recall rate (odds ratio: 1.14, 95% CI: 1.11-1.16).
The simulation was run with data from
The model shows a clear benefit from double reading with arbitration, with lowered recall rates, higher cancer detection rates and fewer deaths from cancer. CAD also shows improved cancer detection rates and fewer deaths, but the improvement is smaller and at the cost of an increased recall rate.