Presentation (CTI)

OBJECTIVES: Contrast-enhanced mammography (CEM) has emerged as a promising imaging technique for breast cancer (BC) diagnosis. Incipient evidence suggests potential advantages over standard mammography in BC detection in dense breasts. It also shows cost and accessibility advantages over magnetic-resonance imaging (MRI). This study uses Real-World Data (RWD) to explore the economic impact of adopting CEM in the Spanish National Common Benefit Package.
METHODS: Five diagnostic scenarios were defined according to CEM indications: (1) confirmation of suspected BC after inconclusive mammography or ultrasound, (2) locoregional staging, (3) assessment of response to neoadjuvant therapy, (4) BC detection in women with non-characterizable lesions by mammography (primarily dense breasts), and (5) BC detection in women at increased BC risk. A retrospective analysis of RWD from BIGAN, the health data lake of Aragon (Spain), estimated the potential volume and cost of CEM tests in each scenario. Subsequently, an economic impact assessment modeled the substitution of current reference tests (mammography, ultrasound and/or MRI) with CEM across these scenarios.
RESULTS: Between 2015 and 2023, 30123 tests were performed in scenarios 1-5 in the Aragón region (Aragón represents around 3% of Spain’s total population), with most tests conducted in scenarios 4 and 5. Under the analysis assumptions, adopting CEM would generate net cost savings across all scenarios, though with varying magnitudes. Specifically, replacing MRI with CEM in scenarios 1-3 would yield an annual saving of €43927, while replacing first-line detection techniques (mammography and/or ultrasound) in scenarios 4-5 would save €21303 annually.
CONCLUSIONS: This analysis offers real-world evidence on the impact of adopting CEM for BC diagnosis. While CEM use following adoption would be more extensive in screening scenarios (4-5), the greatest economic impact would occur where MRI is currently performed (1-3). Broader considerations around RWD use in decision-making will also be addressed in the presentation, including challenges around generalisability of locally-sourced data.