Cost-effectiveness of an artificial intelligence predictive model for guiding androgen deprivation therapy in intermediate-risk prostate cancer

人工智能预测模型指导中危前列腺癌雄激素剥夺疗法的成本效益分析

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Abstract

The ArteraAI Prostate Test (ArteraAI Inc.) is the first predictive biomarker for the benefit of adding short-term androgen deprivation therapy (ADT) to radiotherapy for intermediate-risk prostate cancer. We evaluated the cost-effectiveness of ArteraAI to guide short-term ADT using a Markov model simulating 15-year outcomes for 71-year-old patients with intermediate-risk prostate cancer receiving radiotherapy using NRG/RTOG 9408 data on which ArteraAI was validated. Three strategies were compared: (1) all patients receive ADT (ADT-for-all), (2) only patients with unfavorable intermediate-risk prostate cancer receive ADT (National Comprehensive Cancer Network [NCCN]), and (3) only ArteraAI-positive patients receive ADT (ArteraAI). Costs and utilities obtained from Medicare claims and published literature were used to calculate incremental cost-effectiveness ratios. A willingness-to-pay threshold of $100 000/quality-adjusted life year (QALY) was chosen. The ADT-for-all strategy was dominated by the NCCN strategy. Compared with the NCCN strategy, the ArteraAI strategy lowered costs by $12 296 and improved effectiveness by 0.01 QALYs making it dominant.

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