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19 September 2017 Quantitative texture analysis: robustness of radiomics across two digital mammography manufacturers’ systems
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Abstract
The robustness of radiomic texture analysis across different manufacturers of mammography imaging systems is investigated. We quantified feature robustness across mammography manufacturers using a dataset of 111 women who underwent consecutive screening mammography on both general electric and Hologic systems. In each mammogram, a square region of interest (ROI) directly behind the nipple was manually selected. Radiomic features describing parenchymal patterns were automatically extracted on each ROI. Feature comparisons were conducted between manufacturers (and breast densities) using newly developed robustness metrics descriptive of correlation, equivalence, and variability. By examining the distribution of these metric values, we propose the following selection criteria to guide feature evaluation in this dataset: (1) 0.8<mean of feature ratios <1.2, (2) standard deviation of feature ratios <0.3, (3) correlation of features (ρF)<0.5, and (4) p<0.05. Statistically significant correlation coefficients ranged from 0.13 to 0.68 in comparisons between the two mammographic systems tested. Features describing spatial patterns tended to exhibit high correlation coefficients, while intensity- and directionality-based features had comparatively poor correlation. Our proposed robustness metrics may be used to evaluate other datasets, for which different ranges of metric values may be appropriate.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE) 2329-4302/2017/$25.00 © 2017 SPIE
Kayla R. Mendel, Hui Li, Li Lan, Cathleen M. Cahill, Victoria Rael, Hiroyuki Abe, and Maryellen L. Giger "Quantitative texture analysis: robustness of radiomics across two digital mammography manufacturers’ systems," Journal of Medical Imaging 5(1), 011002 (19 September 2017). https://doi.org/10.1117/1.JMI.5.1.011002
Received: 15 May 2017; Accepted: 10 July 2017; Published: 19 September 2017
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