Association of distant recurrence-free survival with algorithmically extracted MRI characteristics in breast cancer Journal Articles uri icon

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  • BackgroundWhile important in diagnosis of breast cancer, the scientific assessment of the role of imaging in prognosis of outcomes and treatment planning is limited.Purpose: To evaluate the potential of using quantitative imaging variables for stratifying risk of distant recurrence in breast cancer patients.Study TypeRetrospective.PopulationIn all, 892 female invasive breast cancer patients.SequenceDynamic contrast‐enhanced MRI with field strength 1.5 T and 3 T.AssessmentComputer vision algorithms were applied to extract a comprehensive set of 529 imaging features quantifying size, shape, enhancement patterns, and heterogeneity of the tumors and the surrounding tissue. Using a development set with 446 cases, we selected 20 imaging features with high prognostic value.Statistical TestsWe evaluated the imaging features using an independent test set with 446 cases. The principal statistical measure was a concordance index between individual imaging features and patient distant recurrence‐free survival (DRFS).ResultsThe strongest association with DRFS that persisted after controlling for known prognostic clinical and pathology variables was found for signal enhancement ratio (SER) partial tumor volume (concordance index [C] = 0.768, 95% confidence interval [CI]: 0.679–0.856), tumor major axis length (C = 0.742, 95% CI: 0.650–0.834), kurtosis of the SER map within tumor (C = 0.640, 95% CI: 0.521–0.760), tumor cluster shade (C = 0.313, 95% CI: 0.216–0.410), and washin rate information measure of correlation (C = 0.702, 95% CI: 0.601–0.803).Data ConclusionQuantitative assessment of breast cancer features seen in a routine breast MRI might be able to be used for assessment of risk of distant recurrence.Level of Evidence: 4Technical Efficacy: Stage 6J. Magn. Reson. Imaging 2019.


  • Mazurowski, Maciej A
  • Saha, Ashirbani
  • Harowicz, Michael R
  • Cain, Elizabeth Hope
  • Marks, Jeffrey R
  • Marcom, P Kelly

publication date

  • 2019

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