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

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abstract

  • Background

    While 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 type

    Retrospective.

    Population

    In all, 892 female invasive breast cancer patients.

    Sequence

    Dynamic contrast-enhanced MRI with field strength 1.5 T and 3 T.

    Assessment

    Computer 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 tests

    We 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).

    Results

    The 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 conclusion

    Quantitative 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

    4 Technical Efficacy: Stage 6 J. Magn. Reson. Imaging 2019.

authors

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

publication date

  • 2019

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