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Radiomic features extracted from dynamic contrast-enhanced (DCE) magnetic resonance (MR) images of breast cancers have previously demonstrated merit in the prediction of response to treatment. Radiomic features can be used to support clinical trial studies, as is being conducted in I-SPY2. Our goal was to compare the quantification of features used to predict pathological complete response in the I-SPY2 clinical trial obtained through our automated methods to those obtained via reported semi-manual methods. We used automated methods based on identification of a single seed-point to segment tumors and extract features of tumors from 972 patients. On an overlapped set of patients (381 patients) between the entire set and a previously-published I-SPY2 study, the Pearson correlation coefficient was used to compare features calculated automatically to published features calculated semi-manually, focusing on longest diameter (semi-manual) versus maximum diameter (automated), sphericity (semi-manual versus automated), and functional tumor volume (semimanual) versus most enhancing tumor volume (automated), extracted from pre-treatment DCE-MR image series. The Pearson correlation coefficients were considered significant if p <0.017 (Bonferroni-corrected for three comparisons.) Results showed significant correlation (p < 0.01) between automated and semi-manual extraction for all three features, but differences were observed in the actual extracted feature values. It is interesting to note that with the use of our fully automated methods the tumors from almost all the I-SPY2 patients were able to be analyzed, thus, potentially contributing to more efficient studies of radiomic features in clinical trial assessment.
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