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Radiomic features extracted from dynamic contrast-enhanced magnetic resonance (DCE-MR) images of breast lesions can be used in computer-aided diagnosis. However, some features depend upon field strength of acquisition. This motivates investigation of harmonizing the features using a method to address this “batch” effect of field strength of image acquisition. In this study, thirty-two radiomic features were extracted from DCE-MR images of 1,164 lesions (264 benign, 900 cancers) of the breast acquired at 1.5 T or 3.0 T, after segmentation using a fuzzy C-means method. ComBat harmonization was applied in terms of feature categories of morphology, enhancement texture, and most kinetic curve features, due to their potential intrinsic dependence upon field strength of image acquisition. The covariate was status of lesions as benign or malignant. Changes to features were investigated with the Kolmogorov-Smirnov test statistic and the Davies-Bouldin index for degree of clustering using features reduced from 32 to 2 via t-SNE. Classification performance in the task of distinguishing lesions as benign or malignant was evaluated using ten-fold cross-validation and a random forest classifier. The area under the receiver operating characteristic curve (AUC) was used as figure of merit, and classification performance using features in their raw form and in a set using some harmonized features was deemed to be statistically significantly different if p < 0.05. The Kolmogorov-Smirnov test statistic demonstrated that feature value distributions changed the most in features extracted from images acquired at 3.0 T. The Davies-Bouldin index was 6% and 7% respective for benign lesions and cancers, showing that the features became more similar as a result of harmonization. AUC pre- and post-harmonization [95% CI] was 0.84 [0.81, 0.87] and 0.86 [0.83, 0.88] respectively (p = 0.0012). These results suggest that harmonization of radiomic features across field strength of image acquisition may improve the classification performance of computer-aided diagnosis using datasets acquired at different imaging field strengths.
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