2022
DOI: 10.1186/s41747-022-00285-x
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Radiomics in photon-counting dedicated breast CT: potential of texture analysis for breast density classification

Abstract: Background We investigated whether features derived from texture analysis (TA) can distinguish breast density (BD) in spiral photon-counting breast computed tomography (PC-BCT). Methods In 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 defini… Show more

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Cited by 6 publications
(3 citation statements)
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References 41 publications
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“…High breast density on mammography increases a woman’s risk of breast cancer by 2–6 times compared to less dense breast tissue ( 1 ). Dense breast tissue also reduces the sensitivity of two-dimensional mammography ( 2 ).…”
Section: Introductionmentioning
confidence: 99%
“…High breast density on mammography increases a woman’s risk of breast cancer by 2–6 times compared to less dense breast tissue ( 1 ). Dense breast tissue also reduces the sensitivity of two-dimensional mammography ( 2 ).…”
Section: Introductionmentioning
confidence: 99%
“…3 Previous studies have demonstrated the effectiveness of AI models and radiomics features in reducing inter-reader variability. [4][5][6] By automating the interpretation process and providing consistent assessments, AI has the potential to significantly improve the accuracy and efficiency of breast density identification.…”
mentioning
confidence: 99%
“…Leveraging the power of artificial intelligence (AI), particularly machine learning (ML), and deep learning (DL) algorithms, offers a promising solution to address these challenges and enhance breast cancer screening and risk assessment 3 . Previous studies have demonstrated the effectiveness of AI models and radiomics features in reducing inter‐reader variability 4–6 . By automating the interpretation process and providing consistent assessments, AI has the potential to significantly improve the accuracy and efficiency of breast density identification.…”
mentioning
confidence: 99%