Cost-effectiveness of using artificial intelligence versus polygenic risk score to guide breast cancer screening

Abstract

Background Current guidelines for mammography screening for breast cancer vary across agencies, especially for women aged 40–49. Using artificial Intelligence (AI) to read mammography images has been shown to predict breast cancer risk with higher accuracy than alternative approaches including polygenic risk scores (PRS), raising the question whether AI-based screening is more cost-effective than screening based on PRS or existing guidelines. This study provides the first evidence to shed light on this important question. Methods This study is a model-based economic evaluation. We used a hybrid decision tree/microsimulation model to compare the cost-effectiveness of eight strategies of mammography screening for women aged 40–49 (screening beyond age 50 follows existing guidelines). Six of these strategies were defined by combinations of risk prediction approaches (AI, PRS or family history) and screening frequency for low-risk women (no screening or biennial screening). The other two strategies involved annual screening for all women and no screening, respectively. Data used to populate the model were sourced from the published literature. Results Risk prediction using AI followed by no screening for low-risk women is the most cost-effective strategy. It dominates (i.e., costs more and generates fewer quality adjusted life years (QALYs)) strategies for risk prediction using PRS followed by no screening or biennial screening for low-risk women, risk prediction using AI or family history followed by biennial screening for low-risk women, and annual screening for all women. It also extendedly dominates (i.e., achieves higher QALYs at a lower incremental cost per QALY) the strategy for risk prediction using family history followed by no screening for low-risk women. Meanwhile, it is cost-effective versus no screening, with an incremental cost-effectiveness ratio of $23,755 per QALY gained. Conclusions Risk prediction using AI followed by no breast cancer screening for low-risk women is the most cost-effective strategy. This finding can be explained by AI’s ability to identify high-risk women more accurately than PRS and family history (which reduces the possibility of delayed breast cancer diagnosis) and fewer false-positive diagnoses from not screening low-risk women.

Equity within AI systems: What can health leaders expect?

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Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID-19: A Narrative Review

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Saliva microRNA signature to diagnose endometriosis: A cost‐effectiveness evaluation of the Endotest®

“…Previous studies have reported an average cost of about $500 per patient for NGS 53 . Moreover, although several studies have analysed the cost‐effectiveness of AI over classic management in various pathologies, 54,55 to date no reports have analysed the cost of AI for a biological diagnostic test in the specific context of endometriosis 56 …”

“…Previous studies have reported an average cost of about $500 per patient for NGS. 53 Moreover, although several studies have analysed the cost-effectiveness of AI over classic management in various pathologies, 54,55 to date no reports have analysed the cost of AI for a biological diagnostic test in the specific context of endometriosis. 56 The present study demonstrates the value of Endotest® from an economic perspective in the French context: when Endotest® was priced at €500 or €750, the strategies integrating the test were dominant over the current French diagnostic algorithm.…”