Prof. Dafydd Gareth Evans Profile

Prof. Dafydd Gareth Evans

at Univ of Manchester

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Publications (5)

SPIE Journal Paper | 6 August 2024

Capability and reliability of deep learning models to make density predictions on low-dose mammograms

Steven Squires, Alistair Mackenzie, Dafydd Gareth Evans, Sacha Howell, Susan Astley

JMI, Vol. 11, Issue 04, 044506, (August 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.4.044506

KEYWORDS: Breast, Breast density, Mammography, Data modeling, Education and training, Performance modeling, Deep learning, Reliability, Histograms, Cancer

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Proceedings Article | 3 April 2024 Presentation + Paper

AI-based density prediction for breast cancer prevention: Can we measure mammographic density in just one breast?

Megan Perry, Stepan Romanov, Sacha Howell, D. Gareth Evans, Elaine Harkness, Susan Astley

Proceedings Volume 12927, 129270D (2024) https://doi.org/10.1117/12.3006745

KEYWORDS: Breast density, Breast, Mammography, Breast cancer, Cancer, Cancer detection, Risk assessment, Tissues

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Proceedings Article | 3 April 2024 Poster + Paper

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 Harkness, Susan Astley

Proceedings Volume 12927, 1292720 (2024) https://doi.org/10.1117/12.3008487

KEYWORDS: Breast density, Breast cancer, Mammography, Breast, Cancer, Brain-machine interfaces, Statistical analysis, Education and training, Visualization, Ovarian cancer

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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.

Proceedings Article | 3 April 2024 Poster + Paper

Maintaining high resolution information in AI-based breast cancer risk prediction

Stepan Romanov, Sacha Howell, Elaine Harkness, D. Gareth Evans, Steven Squires, Martin Fergie, Sue Astley

Proceedings Volume 12927, 129271X (2024) https://doi.org/10.1117/12.3006522

KEYWORDS: Cancer, Mammography, Breast cancer, Deep learning, Breast density, Breast

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SPIE Journal Paper | 5 April 2023 Open Access

Automatic assessment of mammographic density using a deep transfer learning method

Steven Squires, Elaine Harkness, Dafydd Gareth Evans, Susan Astley

JMI, Vol. 10, Issue 02, 024502, (April 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.2.024502

KEYWORDS: Education and training, Breast density, Deep learning, Data modeling, Mammography, Feature extraction, Linear regression, Performance modeling, Cancer, Image processing

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