Objectives: When evaluating potential new cancer screening modalities, estimating sensitivity, especially for early-stage cases, is critical. There are methods to approximate stage-specific sensitivity in asymptomatic populations, both in the prospective (active screening) and retrospective (stored specimens) scenarios. We explored their validity via a simulation study.
Methods: We fit natural history models to lung and ovarian cancer screening data that permitted estimation of stage-specific (early/late) true sensitivity, defined as the probability subjects screened in the given stage had positive tests. We then ran simulations, using the fitted models, of the prospective and retrospective scenarios. Prospective sensitivity by stage was estimated as screen-detected divided by screen-plus interval-detected cancers, where stage is defined as stage at detection. Retrospective sensitivity by stage was estimated based on cancers detected within specified windows before clinical diagnosis with stage defined as stage at clinical diagnosis.
Results: Stage-specific true sensitivities estimated by the lung cancer natural history model were 47% (early) and 63% (late). Simulation results for the prospective setting gave estimated sensitivities of 81% (early) versus 62% (late). In the retrospective scenario, early/late sensitivity estimates were 35%/57% (1-year window) and 27%/49% (2-year window). In the prospective scenario, most subjects with negative early-stage screens presented as other than early-stage interval cases. Results were similar for ovarian cancer, with estimated prospective sensitivity much greater than true sensitivity for early stage, 84% versus 25%.
Conclusions: Existing methods for approximating stage-specific sensitivity in both prospective and retrospective scenarios are unsatisfactory; improvements are needed before they can be considered to be reliable.
Keywords: Cancer stage; modeling; screening; sensitivity.