The impact of using weight estimated from mammographic images vs. self-reported weight on breast cancer risk calculation

Kalyani P. Nair; Elaine F. Harkness; Soujanye Gadde; Yit Y. Lim; Anthony J. Maxwell; Emmanouil Moschidis; Philip Foden; Jack Cuzick; Adam Brentnall; D. Gareth Evans; Anthony Howell; Susan M. Astley

doi:10.1117/12.2255619

3 March 2017 The impact of using weight estimated from mammographic images vs. self-reported weight on breast cancer risk calculation

Kalyani P. Nair, Elaine F. Harkness, Soujanye Gadde, Yit Y. Lim, Anthony J. Maxwell, Emmanouil Moschidis, Philip Foden, Jack Cuzick, Adam Brentnall, D. Gareth Evans, Anthony Howell, Susan M. Astley

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Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 101342V (2017) https://doi.org/10.1117/12.2255619
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Personalised breast screening requires assessment of individual risk of breast cancer, of which one contributory factor is weight. Self-reported weight has been used for this purpose, but may be unreliable. We explore the use of volume of fat in the breast, measured from digital mammograms. Volumetric breast density measurements were used to determine the volume of fat in the breasts of 40,431 women taking part in the Predicting Risk Of Cancer At Screening (PROCAS) study. Tyrer-Cuzick risk using self-reported weight was calculated for each woman. Weight was also estimated from the relationship between self-reported weight and breast fat volume in the cohort, and used to re-calculate Tyrer-Cuzick risk. Women were assigned to risk categories according to 10 year risk (below average <2%, average 2-3.49%, above average 3.5-4.99%, moderate 5-7.99%, high ≥8%) and the original and re-calculated Tyrer-Cuzick risks were compared. Of the 716 women diagnosed with breast cancer during the study, 15 (2.1%) moved into a lower risk category, and 37 (5.2%) moved into a higher category when using weight estimated from breast fat volume. Of the 39,715 women without a cancer diagnosis, 1009 (2.5%) moved into a lower risk category, and 1721 (4.3%) into a higher risk category. The majority of changes were between below average and average risk categories (38.5% of those with a cancer diagnosis, and 34.6% of those without). No individual moved more than one risk group. Automated breast fat measures may provide a suitable alternative to self-reported weight for risk assessment in personalized screening.

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Kalyani P. Nair, Elaine F. Harkness, Soujanye Gadde, Yit Y. Lim, Anthony J. Maxwell, Emmanouil Moschidis, Philip Foden, Jack Cuzick, Adam Brentnall, D. Gareth Evans, Anthony Howell, and Susan M. Astley "The impact of using weight estimated from mammographic images vs. self-reported weight on breast cancer risk calculation", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101342V (3 March 2017); https://doi.org/10.1117/12.2255619

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KEYWORDS

Breast

Cancer

Breast cancer

Mammography

Image analysis

Tumor growth modeling

Data modeling

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