Cost-effectiveness analysis of tumor molecular classification in high-risk early-stage endometrial cancer

Tumor molecular analyses in endometrial cancer (EC) includes 4 distinct subtypes: (1) POLE-mutated, (2) mismatch repair protein (MMR) deficient, (3) p53 mutant, and (4) no specific molecular profile. Recently, a sub-analysis of PORTEC-3 demonstrated notable differences in treatment response between molecular classification (MC) groups. Cost of testing is one barrier to widespread adoption of MC. Therefore, we sought to determine the cost-effectiveness of MC in patients with stage I and II high-risk EC. A Markov decision model was developed to compare tumor molecular classification (TMC) vs. no testing (NT). A healthcare payor's perspective and 5-year time horizon were used. Base case data were abstracted from PORTEC-3 and the molecular sub-analysis. Cost and utility data were derived from public databases, peer-reviewed literature, and expert input. Strategies were compared using the incremental cost-effectiveness ratio (ICER) with effectiveness in quality-adjusted life years (QALYs) and evaluated with a willingness-to-pay threshold of $100,000 per QALY gained. Sensitivity analyses were performed to test model robustness. When compared to NT, TMC was cost effective with an ICER of $25,578 per QALY gained; incremental cost was $1780 and incremental effectiveness was 0.070 QALYs. In one-way sensitivity analyses, results were most sensitive to the cost of POLE testing, but TMC remained cost-effective over all parameter ranges. TMC in early-stage high-risk EC is cost-effective, and the model results were robust over a range of parameters. Given that MC can be used to guide adjuvant treatment decisions, these findings support adoption of TMC into routine practice. •Tumor molecular testing is a rapidly emerging area of research in endometrial cancer and may guide adjuvant treatment.•A Markov decision model was developed to compare tumor molecular classification versus no molecular testing.•Compared to no testing, tumor molecular classification is a highly cost-effective strategy.