Cost-effectiveness analysis of artificial intelligence (AI) for response prediction of neoadjuvant radio(chemo)therapy in locally advanced rectal cancer (LARC) in the Netherlands.

BACKGROUND

Given the potential of artificial intelligence (AI) and the increasing importance of understanding AI's economic impact, this study aims to provide insights into the potential cost-effectiveness of an AI tool in the response prediction to neoadjuvant chemoradiotherapy (nCRT) of Stage II-III LARC patients in comparison to usual care (UC).

METHODS

This study included a state-transition Markov model from a Dutch societal perspective. Quality-adjusted life years (QALY) and costs were simulated over a 10-year horizon. Sensitivity analyses and a threshold analysis were performed. Results were presented as incremental cost-effectiveness ratios.

RESULTS

With incremental cost savings of -€2,530,000 per QALY gained per 1000 patients, the AI is dominant in the base-case. Main drivers of cost-effectiveness were the clinical complete response incidence and specificity of the tool. Cost-effectiveness was maintained at a cost of €1,100 and €2,100 for an AI performance of 0.85 and 0.90.

DISCUSSION: Findings of this study present the economic impact of a hypothetical AI-based approach to treatment response prediction in Stage II-III LARC patients who receive nCRT and are eligible for consecutive surgery. The results of this study highlight the complexity of healthcare decision-making in tools that could be cost-saving yet yield lower effectiveness when parameters are uncertain.