Modelling the connection between image quality, cancer detection, and overdiagnosis in breast imaging: a new perspective on DM and DBT

Earlier treatment of breast cancer results in better survival. Screening enables this through detection of tumors before they cause symptoms. On the negative side, some tumors if not detected through screening would not cause symptoms, leading to overdiagnosis. Improvements in image quality allow even earlier detection and diagnosis of even smaller tumors at an even earlier stage. This study aims to model the connection between improved image quality and cancer detection in screening, and how earlier detection of tumors affects both mortality and overdiagnosis. A Monte Carlo-based screening model was developed, simulating yearly incidence, progression and detection of breast cancer in a population of screened women from age 30 and up, using clinical data sources. To investigate the effect of increasing image quality, the model was run twice with different setting, each arm including 100 000 women: one with standard image quality and another with increased image quality, modelled as equivalent to digital breast tomosynthesis (DBT) in sensitivity and average detected tumor size. According to the simulations, increasing mammography image quality to a DBT level increases overdiagnosis by 53% in absolute terms and from 3.0% to 3.7% of screen-detected cancers in relative terms. On the other hand, prevented breast cancer deaths increases by 123%, as more patients survive the cancer treatment and die later of natural causes. The fraction of cancer patients that survive longer due to screening increases from 59.4% to 76.1% of those who eventually die from breast cancer. The model suggests that improved image quality results in better screening outcomes, but also increased overdiagnosis. Defining an optimal trade-off is very important for future screening.