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. [ABSTRACT FROM AUTHOR]