Effects of MRI scanner parameters on breast cancer radiomics

Abstract

Purpose To assess the impact of varying magnetic resonance imaging (MRI) scanner parameters on the extraction of algorithmic features in breast MRI radiomics studies. Methods In this retrospective study, breast imaging data for 272 patients were analyzed with magnetic resonance (MR) images. From the MR images, we assembled and implemented 529 algorithmic features of breast tumors and fibrograndular tissue (FGT). We divided the features into 10 groups based on the type of data used for the feature extraction and the nature of the extracted information. Three scanner parameters were considered: scanner manufacturer, scanner magnetic field strength, and slice thickness. We assessed the impact of each of the scanner parameters on each of the feature by testing whether the feature values are systematically diverse for different values of these scanner parameters. A two-sample t-test has been used to establish whether the impact of a scanner parameter on values of a feature is significant and receiver operating characteristics have been used for to establish the extent of that effect. Results On average, higher proportion (69% FGT versus 20% tumor) of FGT related features were affected by the three scanner parameters. Of all feature groups and scanner parameters, the feature group related to the variation in FGT enhancement was found to be the most sensitive to the scanner manufacturer (AUC = 0.81 ± 0.14). Conclusions Features involving calculations from FGT are particularly sensitive to the scanner parameters.

Breast cancer MRI radiomics: An overview of algorithmic features and impact of inter‐reader variability in annotating tumors

“…The complete list of all features that we extracted, along with the related references and modifications, can be found in the Appendix (Supplementary Material A) of our previous study. 46 Some of these features were based on the algorithms proposed by our group. A majority of the features extraction algorithms were proposed by other researchers.…”

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

“…Please note that since the segmentation algorithm focused on bright regions, the necrotic regions were likely excluded from the tumor segmentation. We also extracted the breast masks and two fibroglandular tissue (FGT) masks (from the first postcontrast and T 1 nonfat‐saturated sequences, respectively) as described previously . The breast mask was obtained by removing the chest cavity by a parametric polynomial curve fitting technique on the T 1 nonfat‐saturated sequence.…”

“…Following segmentation of the breast into different regions, we applied a comprehensive automated analysis of the tumors and their surroundings resulting in 529 features for each patient. The complete list of features can be found in the Supplementary Materials of the previous publication …”

“…We also extracted the breast masks and two fibroglandular tissue (FGT) masks (from the first postcontrast and T 1 nonfat-saturated sequences, respectively) as described previously. 11 The breast mask was obtained by removing the chest cavity by a parametric polynomial curve fitting technique on the T 1 nonfat-saturated sequence. Then, automatic global thresholding and an active contour method were applied to obtain the breast mask.…”

“…The complete list of features can be found in the Supplementary Materials of the previous publication. 11 The feature extraction requires the use of all of or a subset of breast masks, FGT masks, and tumor masks. Some features require the involvement of the mask only and not the intensity values of MRI sequences.…”