ArticleEuropean radiology experimental2022
Radiomics in photon-counting dedicated breast CT: potential of texture analysis for breast density classification.
Article in European radiology experimental, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 9 citations in OpenAlex.
- Radiomic Characterization of Breast Tissue from Breast CT Images Obtained with Synchrotron Beams.Tomography (Ann Arbor, Mich.) · 2026Article
- Article
- Photon-counting detector CT: a disrupting innovation in medical imaging.European radiology experimental · 2025Article
- Reporting breast density on chest computed tomography.Translational breast cancer research : a journal focusing on translational research in breast cancer · 2023Review
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Authors and funding
9 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundWe investigated whether features derived from texture analysis (TA) can distinguish breast density (BD) in spiral photon-counting breast computed tomography (PC-BCT).
methodsIn this retrospective single-centre study, we analysed 10,000 images from 400 PC-BCT examinations of 200 patients. Images were categorised into four-level density scale (a-d) using Breast Imaging Reporting and Data System (BI-RADS)-like criteria. After manual definition of representative regions of interest, 19 texture features (TFs) were calculated to analyse the voxel grey-level distribution in the included image area. ANOVA, cluster analysis, and multinomial logistic regression statistics were used. A human readout then was performed on a subset of 60 images to evaluate the reliability of the proposed feature set.
resultsOf the 19 TFs, 4 first-order features and 7 second-order features showed significant correlation with BD and were selected for further analysis. Multinomial logistic regression revealed an overall accuracy of 80% for BD assessment. The majority of TFs systematically increased or decreased with BD. Skewness (rho -0.81), as a first-order feature, and grey-level nonuniformity (GLN, -0.59), as a second-order feature, showed the strongest correlation with BD, independently of other TFs. Mean skewness and GLN decreased linearly from density a to d. Run-length nonuniformity (RLN), as a second-order feature, showed moderate correlation with BD, but resulted in redundant being correlated with GLN. All other TFs showed only weak correlation with BD (range -0.49 to 0.49, p < 0.001) and were neglected.
conclusionTA of PC-BCT images might be a useful approach to assess BD and may serve as an observer-independent tool.
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