Papers

Multi-cancer early detection (MCED) blood tests promise to find many cancers from a single low-cost assay, but enthusiasm for detection can outrun evidence of benefit. We give a transparent decision-theoretic analysis of when population MCED screening is expected to do more good than harm, working entirely from Bayes' rule and expected-utility theory over parameters reported (or estimable in principle) in the published literature; we run no trial and report no new measurements. We derive the positive predictive value of MCED screening as a function of aggregate sensitivity, specificity, and prevalence, then a deployment boundary: screening yields positive expected net utility only when the odds of harbouring an actionable cancer exceed the ratio of false-positive work-up harm to net per-true-case benefit. We separate detection from mortality benefit by an explicit actionable fraction m, so overdiagnosis enters as a first-class harm rather than a footnote. The analysis shows why high specificity alone cannot rescue screening at low prevalence, identifies m and false-positive work-up harm as the load-bearing quantities, and states precisely what a confirmatory randomised trial with a mortality endpoint would have to measure. The contribution is an analytic framework and a falsifiable deployment rule, not a clinical finding.