Poster + Paper
3 April 2024 Using an AI-based density prediction method to explore the risk of breast cancer in different ethnic groups
Emma Wylie, Stepan Romanov, D. Gareth Evans, Elaine F. Harkness, Susan M. Astley
Author Affiliations +
Conference Poster
Abstract
Introduction: Breast cancer is the most common female cancer worldwide; however ethnic differences have been observed in both prevalence and prognosis, with Black women often having less favorable outcomes. Increased breast density is an independent risk factor for breast cancer and reduces the efficacy of mammographic screening. We investigate how it relates to ethnicity, to facilitate the provision of appropriate screening and advice to all women. Method: We use data from the UK Predicting Risk of Cancer at Screening (PROCAS) study. This involved completion of a questionnaire to obtain personal risk factor information during routine breast screening. Mammographic density was assessed using Visual Analogue Scales (VAS), and these scores were used to train an AI-based density measure, pVAS, which we applied to raw mammographic data from 41,241 women in PROCAS. Analysis of covariance was used to assess the relationship between ethnicity and breast density after adjusting for age, body mass index (BMI), menopausal status, hormone replacement therapy (HRT) use, parity, alcohol consumption, and family history of breast cancer. Pairwise comparisons for each ethnic group were performed using a Bonferroni correction. Results: 91.0% of the study population were white, 1.6% Asian, 1.1% Black and 1.0% Jewish. Jewish women had higher breast density than all other ethnic groups studied (p<0.001), with a mean pVAS of 34.8% (95% CI 33.6-36.1). Asian women had a mean density of 31.4% (95% CI 30.4-32.4) and significantly denser breasts than White women who had a mean pVAS density of 28.6% (95% CI 28.4-28.7). Conclusion: Previous research has reported mixed results. The relationship between risk factors for breast cancer are complex, and data not always complete, making this a challenging area of research. Our results support published evidence that some groups have increased density, and this relationship should be considered to ensure equity in screening and diagnosis.
© (2024) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Emma Wylie, Stepan Romanov, D. Gareth Evans, Elaine F. Harkness, and Susan M. Astley "Using an AI-based density prediction method to explore the risk of breast cancer in different ethnic groups", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 1292720 (3 April 2024); https://doi.org/10.1117/12.3008487
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KEYWORDS
Breast density

Breast cancer

Mammography

Breast

Cancer

Brain-machine interfaces

Statistical analysis

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