Projected impact of population-level multi-cancer early detection screening: a discrete event simulation analysis

Cancer screening for multiple cancers simultaneously, rather than one cancer at a time, has emerged as a promising strategy for population-level cancer screening, largely driven by the advent and rapid development of liquid biopsy-based multi-cancer early detection (MCED) tests. However, their potential impact on mortality across cancer types remains incompletely characterized. We developed a patient-level, cross-cohort discrete event simulation model calibrated to SEER age-specific incidence for ages 18-80 across 1939-2001 birth cohorts for ten cancers, stratified by sex and race (non-Hispanic White and Black). Early detection benefit was modeled by shifting cancers to localized disease and advancing detection by literature-derived sojourn times, under detection sensitivities from 10% to 100%. The multicancer natural history model achieved calibration for all ten cancers, with modeled incidence closely approximating SEER-observed incidence. Under ideal early detection, pooled cancer mortality fell by 41.0%, from 6.42% to 3.79% of the simulated population, with similar proportional reductions across sex-race strata (39.9% to 43.9%). The largest site-specific reductions occurred for prostate cancer in males and ovarian cancer in females, while liver and bladder cancers benefited least. Overdiagnosis ranged from 10.9% to 28.9%, with lung cancer highest. Our MCED framework projects population-level mortality reduction from early multi-cancer detection. Under ideal screening conditions, cancer mortality benefits varied by cancer type, reflecting differences in both the length of the preclinical detectable window and survival at localized stage. This calibrated multicancer model provides a flexible framework for evaluating MCED blood tests as clinical trial data on test performance become available.