Association of distant recurrence-free survival with algorithmically extracted MRI characteristics in breast cancer.

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.

SUBMITTER: Mazurowski MA 

PROVIDER: S-EPMC6551277 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature