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J Oncol. 2019 Apr 10;2019:2087983. doi: 10.1155/2019/2087983. eCollection 2019.

Imaging Biomarkers as Predictors for Breast Cancer Death.

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Department of Radiation Sciences, Oncology, UmeƄ University, Sweden.
Department of Mammography, County Hospital Falun, Falun, Sweden.
Department of Pathology, Laboratory Medicine Dalarna, County Hospital Falun, Falun, Sweden.
Kainan University, Taiwan.
Taipei Medical University, Taiwan.
Chang Gung University, Taiwan.
National Taiwan University, Taipei, Taiwan.
University of South Australia, Adelaide, Australia.
American Cancer Society, Atlanta, GA, USA.
Queen Mary University of London, UK.



To differentiate the risk of breast cancer death in a longitudinal cohort using imaging biomarkers of tumor extent and biology, specifically, the mammographic appearance, basal phenotype, histologic tumor distribution, and conventional tumor attributes.


Using a prospective cohort study design, 498 invasive breast cancer patients diagnosed between 1996 and 1998 were used as the test cohort to assess the independent effects of the imaging biomarkers and other predictors on the risk of breast cancer death. External validation was performed with a cohort of 848 patients diagnosed between 2006 and 2010.


Mammographic tumor appearance was an independent predictor of risk of breast cancer death (P=0.0003) when conventional tumor attributes and treatment modalities were controlled. The casting type calcifications and architectural distortion were associated with 3.13-fold and 3.19-fold risks of breast cancer death, respectively. The basal phenotype independently conferred a 2.68-fold risk compared with nonbasal phenotype. The observed deaths did not differ significantly from expected deaths in the validation cohort. The application of imaging biomarkers together with other predictors classified twelve categories of risk for breast cancer death.


Combining imaging biomarkers such as the mammographic appearance of the tumor with the histopathologic distribution and basal phenotype, accurately predicted long-term risk of breast cancer death. The information may be relevant for determining the need for molecular testing, planning treatment, and determining the most appropriate clinical surveillance schedule for breast cancer patients.

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