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Breast parenchymal patterns in processed versus raw digital mammograms: A large population study toward assessing differences in quantitative measures across image representations
Author(s) -
Gastounioti Aimilia,
Oustimov Andrew,
Keller Brad M.,
Pantalone Lauren,
Hsieh MengKang,
Conant Emily F.,
Kontos Despina
Publication year - 2016
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4963810
Subject(s) - wilcoxon signed rank test , digital mammography , intraclass correlation , breast imaging , breast cancer , artificial intelligence , correlation , nuclear medicine , feature (linguistics) , medicine , mathematics , pattern recognition (psychology) , mammography , statistics , radiology , computer science , reproducibility , cancer , linguistics , philosophy , geometry , mann–whitney u test
Purpose With raw digital mammograms (DMs), which retain the relationship with x‐ray attenuation of the breast tissue, not being routinely available, processed DMs are often the only viable means to acquire imaging measures. The authors investigate differences in quantitative measures of breast density and parenchymal texture, shown to have value in breast cancer risk assessment, between the two DM representations. Methods The authors report data from 8458 pairs of bilateral raw (“FOR PROCESSING”) and processed (“FOR PRESENTATION”) DMs acquired from 4278 women undergoing routine screening evaluation, collected with DM units from two different vendors. Breast dense tissue area and percent density (PD), as well as a range of quantitative descriptors of breast parenchymal texture (statistical, co‐occurrence, run‐length, and structural descriptors), were measured using previously validated, fully automated software. Feature measurements were compared using matched‐pairs Wilcoxon signed‐ranks test, correlation ( r ), and linear‐mixed‐effects (LME) models, where potential interactions with woman‐ and system‐specific factors were also assessed. The authors also compared texture feature correlations with the established risk factors of the Gail lifetime risk score ( r G ) and breast PD ( r PD ), and evaluated the within woman intraclass feature correlation (ICC), a measure of bilateral breast‐tissue symmetry, in raw versus processed images. Results All density measures and most of the texture features were strongly ( r ≥ 0.6) or moderately (0.4 ≤ r < 0.6) correlated between raw and processed images. However, measurements were significantly different between the two imaging formats (Wilcoxon signed‐ranks test, p w < 0.05). The association between measurements varied across features and vendors, and was substantially modified by woman‐ and system‐specific image acquisition factors, such as age, BMI, and mAs/kVp, respectively. The strongest correlation, combined with minimal LME‐model interactions, was observed for structural texture features. Overall, texture measures from either image representation were weakly associated with Gail lifetime risk (−0.2 ≤ r G ≤ 0.2), weakly to moderately associated with breast PD (−0.6 ≤ r PD ≤ 0.6), and had overall strong bilateral symmetry (ICC ≥ 0.6). Conclusions Differences in measures from processed versus raw DM depend highly on the feature, the DM vendor, and image acquisition settings, where structural features appear to be more robust across the different DM settings. The reported findings may serve as a reference in the design of future large‐scale studies on mammographic features and breast cancer risk assessment involving multiple DM representations.