Towards risk‐stratified population breast cancer screening: more than mammographic density

John L Hopper and Tuong Linh Nguyen
Med J Aust 2021; 215 (8): . || doi: 10.5694/mja2.51268
Published online: 18 October 2021

Powerful new automated tools are being developed to identify the women most likely to have an existing or future cancer

The article by Noguchi and colleagues in this issue of the MJA1 is timely and motivated by an important aim: to improve breast screening for both women and its funders. The authors conducted a comprehensive analysis of routinely collected data for all screening mammograms by BreastScreen WA over the ten years from July 2007. Although they studied screening episodes rather than individual women, they found evidence that key performance indicators — screen‐detected and interval cancer rates — differed by age, family history, hormone replacement therapy use, benign breast disease, and breast density. Importantly, the strengths of the relationships between some factors and performance varied by age group.1

  • Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC



We acknowledge the generous support for our work in this area over many years from the National Breast Cancer Foundation, the Cancer Council Victoria, Cancer Australia, the National Health and Medical Research Council, and the National Institutes of Health (USA).

Competing interests:

No relevant disclosures.

  • 1. Noguchi N, Marinovich ML, Wylie EJ, et al. Screening outcomes by risk factor and age: evidence from BreastScreen WA for discussions of risk‐stratified population screening. Med J Aust 2021; 215: 359–365.
  • 2. Allweis TM, Hermann N, Bernstein‐Molho R, Guindy M. Personalized screening for breast cancer: rationale, present practices, and future directions. Ann Surg Oncol 2021; 28: 4306–4317.
  • 3. Eklund M, Broglio K, Yau C, Connor JT, et al. The WISDOM personalized breast cancer screening trial: simulation study to assess potential bias and analytic approaches. JNCI Cancer Spectr 2018; 2: pky067.
  • 4. Hopper JL, Nguyen TL, Schmidt DF, et al. Going beyond conventional mammographic density to discover novel mammogram‐based predictors of breast cancer risk. J Clin Med 2020; 9: 627.
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  • 7. Nguyen TL, Aung YK, Evans CF, et al. Mammographic density defined by higher than conventional brightness thresholds better predicts breast cancer risk. Int J Epidemiol 2017; 46: 652–661.
  • 8. Schmidt DF, Makalic E, Goudey B, et al. Cirrus: an automated mammography‐based measure of breast cancer risk based on textural features. JNCI Cancer Spectr 2018; 2: pky057.
  • 9. Nguyen TL, Schmidt DF, Makalic E, et al. Novel mammogram‐based measures improve breast cancer risk prediction beyond an established mammographic density measure. Int J Cancer 2021; 148: 2193–2202.
  • 10. Hopper JL. Genetics for population and public health. Int J Epidemiol 2017; 46: 8–11.
  • 11. Freeman K, Geppert J, Stinton C, et al. Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy. BMJ 2021; 374: n1872.


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