Feasibility and acceptability of offering breast cancer risk assessment to general population women aged 30–39 years: a mixed-methods study protocol

Hindmarch, Sarah; Howell, Sacha J; Usher-Smith, Juliet A; Gorman, Louise; Evans, D Gareth; French, David P

doi:10.1136/bmjopen-2023-078555

Cited by 5 publications

(2 citation statements)

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“…focuses on mammographic density’s relation to risk in women aged 40 to 74. Lastly ( 46 ), emphasizes age-specific preventive measures for women aged 30–39.…”

Section: Resultsmentioning

confidence: 99%

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A contemporary review of breast cancer risk factors and the role of artificial intelligence

Nicolis,

De Los Angeles,

Taramasco

2024

Front. Oncol.

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BackgroundBreast cancer continues to be a significant global health issue, necessitating advancements in prevention and early detection strategies. This review aims to assess and synthesize research conducted from 2020 to the present, focusing on breast cancer risk factors, including genetic, lifestyle, and environmental aspects, as well as the innovative role of artificial intelligence (AI) in prediction and diagnostics.MethodsA comprehensive literature search, covering studies from 2020 to the present, was conducted to evaluate the diversity of breast cancer risk factors and the latest advances in Artificial Intelligence (AI) in this field. The review prioritized high-quality peer-reviewed research articles and meta-analyses.ResultsOur analysis reveals a complex interplay of genetic, lifestyle, and environmental risk factors for breast cancer, with significant variability across different populations. Furthermore, AI has emerged as a promising tool in enhancing the accuracy of breast cancer risk prediction and the personalization of prevention strategies.ConclusionThe review highlights the necessity for personalized breast cancer prevention and detection approaches that account for individual risk factor profiles. It underscores the potential of AI to revolutionize these strategies, offering clear recommendations for future research directions and clinical practice improvements.

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“…focuses on mammographic density’s relation to risk in women aged 40 to 74. Lastly ( 46 ), emphasizes age-specific preventive measures for women aged 30–39.…”

Section: Resultsmentioning

confidence: 99%

“…investigated this relationship across a cohort of 21,150 women, confirming the effectiveness of automated density assessments in predicting breast cancer risk. Similarly ( 69 ) emphasizes higher risk in younger women with lower BMI ( 46 ). explores mammography-based risk assessment for early screening.…”

Section: Resultsmentioning

confidence: 99%

A contemporary review of breast cancer risk factors and the role of artificial intelligence

Nicolis,

De Los Angeles,

Taramasco

2024

Front. Oncol.

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Deep learning models to predict mammographic density jointly on standard dose and low dose images

Squires,

Mackenzie,

Evans

et al. 2024

Preprint

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Objectives: Mammographic density is associated with increased risk of developing breast cancer. Automated estimation of density in women below normal screening age would enable earlier risk stratification. We are piloting the use of low dose mammograms combined with models that can make accurate mammographic density estimates. Methods: Three models were trained on a joint set (107,619) of standard dose mammograms with associated density scores and their simulated low dose counterparts such that the models made predictions on standard and low dose mammograms. A second set of models was trained separately on the standard and simulated low dose mammograms. All models were tested on a held-out set from the training data and an independent dataset with 294 pairs of standard and real low dose mammograms. Results: The root mean squared errors (RMSE) between the model predictions and density scores on standard and simulated low dose images were 8.26 (8.16-8.36) and 8.27 (8.17-8.38) respectively. The RMSE between predictions on standard and simulated low dose images for the jointly trained models was 1.91 (1.88-1.96). The RMSE of the predictions on the real low dose images compared to the standard dose images is 3.79 (2.75-4.99). Conclusions: Deep learning models make density predictions on low dose images with similar quality as on standard dose images. Such automated analysis of low dose mammograms could contribute to accurate breast cancer risk estimation in younger women enabling stratification for further monitoring and preventative therapy. Advances in knowledge: Mammographic density can be estimated in low dose mammograms with similar quality to standard dose mammograms.

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

Squires,

Mackenzie,

Evans

et al. 2024

J. Med. Imag.

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Breast density is associated with the risk of developing cancer and can be automatically estimated using deep learning models from digital mammograms. Our aim is to evaluate the capacity and reliability of such models to predict density from low-dose mammograms taken to enable risk estimates for younger women.Approach: We trained deep learning models on standard-dose and simulated low-dose mammograms. The models were then tested on a mammography dataset with paired standard-and low-dose images. The effect of different factors (including age, density, and dose ratio) on the differences between predictions on standard and low doses is analyzed. Methods to improve performance are assessed, and factors that reduce the model quality are demonstrated.Results: We showed that, although many factors have no significant effect on the quality of low-dose density prediction, both density and breast area have an impact. The correlation between density predictions on low-and standard-dose images of breasts with the largest breast area is 0.985 (0.949 to 0.995), whereas that with the smallest is 0.882 (0.697 to 0.961). We also demonstrated that averaging across craniocaudal-mediolateral oblique (CC-MLO) images and across repeatedly trained models can improve predictive performance.Conclusions: Low-dose mammography can be used to produce density and risk estimates that are comparable to standard-dose images. Averaging across CC-MLO and model predictions should improve this performance. The model quality is reduced when making predictions on denser and smaller breasts.

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Feasibility and acceptability of offering breast cancer risk assessment to general population women aged 30–39 years: a mixed-methods study protocol

Cited by 5 publications

References 36 publications

A contemporary review of breast cancer risk factors and the role of artificial intelligence

A contemporary review of breast cancer risk factors and the role of artificial intelligence

Deep learning models to predict mammographic density jointly on standard dose and low dose images

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

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