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Following are recent publications that are significant to the CRE, illustrating relevant innovation and findings.

  1. Nguyen, T.L., et al., Predicting interval and screen-detected breast cancers from mammographic density defined by different brightness thresholds. Breast Cancer Res, 2018. 20(1): p. 152.
  2. Schmidt, D.F., et al., Cirrus: An automated mammography-based measure of breast cancer risk based on textural features. JNCI Cancer Spectrum, 2018. 2(4): p. pky057.
  3. Joo J, et al., Heritable DNA methylation marks associated with susceptibility to breast cancer. Nature Communications, 2018.
  4. Stacy M. Carter, et al., The ethical, legal and social implications of using artificial intelligence systems in breast cancer care. The Breast 2020. 49: p. 25-32.
  5. Saya, S., et al., The Impact of a Comprehensive Risk Prediction Model for Colorectal Cancer on a Population Screening Program. JNCI Cancer Spectrum, 2020. 4(5).
  6. Tran, Q., et al., Carbon-track localisation as an adjunct to wire-guided excision of impalpable breast lesions: A retrospective cohort study. International Journal of Surgery Open, 2019. 21: p. 7-11.
  7. Bromley, H., et al., Valuing preferences for treating screen detected ductal carcinoma in situ. European Journal of Cancer, 2019. 123: p. 130-137.
  8. Michailidou, K., et al., Association analysis identifies 65 new breast cancer risk loci. Nature, 2017. 551(7678): p. 92-94.
  9. Huo, C.W., et al., Mammographically dense human breast tissue stimulates progression to invasive lesions and metastasis. Breast Cancer Research, 2016. 18: p. 1-13.
  10. Terry, M.B., et al., 10-year performance of four models of breast cancer risk: a validation study. Lancet Oncol, 2019. 20(4): p. 504-517.