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    intensity of a collection of pixel values (22). At least two tex-ture features were chosen in all folds, whether features related to size were considered for feature selection or not. The tex-ture feature of Suramin hexasodium salt was selected in every fold, in each fea-ture selection scenario. In all, the increased classification performance compared to using solely a prominent feature alone identified through a stepwise feature selection, irregu-larity, emphasizes the utility of using stepwise feature selec-tion and LDA with a collection of radiomic features.
    The present study does not separate cases according to their imaging status as pre- or post-biopsy, but previous work by our laboratory (32) found that AUC performance in this classification question compared by biopsy status failed to show a significant difference across all radiomic features.
    One limitation of the present study was that, although a large number of clinical cases were used, the cases may not truly represent the full population of luminal A breast cancer cases. However, because all images were acquired at the same medical center, the imaging protocol was likely more consistently administered than if images were collected from multiple institutions, reducing one source of variability in image acquisition. Second, the images used in the study were acquired at two different magnetic
    Academic Radiology, Vol 26, No 2, February 2019 RADIOMICS IN DIAGNOSING LUMINAL A CANCERS
    field strengths. Our group is currently investigating the impact of field strength difference on this particular classi-fication task (33). At the same time, our inclusion of fea-tures extracted from images at both field strengths enabled us to maximize statistical power, and our aim for this work was to describe classification performance for clinical populations, for which imaging can be conducted at the two field strengths used here. Third, in addition to quantitative measurements of lesion size that are produced during case workup, radiologists use the BI-RADS4 lexi-con descriptions of lesion appearance, such as margin or shape, in their evaluation of lesions. In this work, we chose to focus on comparing the AUC performance of radiomics of maximum lesion size against feature selection methods, and our work did not compare the performance of radiomics against readings by radiologists for this classi-fication task, nor did it investigate how the availability of radiomic information may affect radiologist performance for this classification task. Such a comparison will be the focus of future studies. Fourth, the 10-fold cross-valida-tion method can, by nature, result in some variability of feature selection, but in our experience, the variability did not show notable differences in the selection of features for classification, particularly in the selection of irregular-ity. Finally, the present study focused on the issue of clas-sification of benign lesions vs luminal A breast cancers. Although a majority of breast cancers are of subtype lumi-nal A, it would certainly be useful to investigate radiomic signatures for other subtypes and to extend the analysis to make use of deep learning techniques.
    This work demonstrated that in the clinical task of distin-guishing between benign lesions and luminal A breast cancers, a radiomic signature using the features described here, quantitatively extracted from MR images, signifi-cantly improved the ability to classify the lesions. The radiomic features of irregularity and entropy appear to be particularly useful in classifying lesions as benign or sub-type luminal A. Furthermore, excluding features related to size from classification resulted in a radiomic signature that was statistically equivalent in terms of AUC to the radiomic signature using all features. This finding is nota-ble, given the importance of size in the routine visual assessment of lesions on clinical images.
    2. Giger ML, Chan H-P, Boone J. Anniversary paper: history and status of
    3. Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014; 5:4006. 
    5. Rahbar H, McDonald ES, Lee JM, et al. How can advanced imaging be used to mitigate potential breast cancer overdiagnosis? Acad Radiol 2016; 23:1–6. 6. Giger ML. Computer-aided detection/computer-aided diagnosis. In: Wolbarst AB, Mossman KL, Hendee WR, eds. Advances in medical physics: 2008, Madison, WI: Medical Physics Publishing; 2008:143–168.
    7. Chen W, Giger ML, Bick U, et al. Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI. Med Phys 2006; 33:2878–2887.
    8. Bickelhaupt S, Paech D, Kickingereder P, et al. Prediction of malignancy by a radiomic signature from contrast agent-free diffusion MRI in suspicious breast lesions found on screening mammography. J Magn Reson Imaging 2017; 46:604–616.