ArticleQuantitative imaging in medicine and surgery2024
Multimodal ultrasound imaging for diagnostic differentiation of sclerosing adenosis from invasive ductal carcinoma.
Article in Quantitative imaging in medicine and surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 5 citations in OpenAlex.
- Developing ultrasound-based machine learning models for accurate differentiation between sclerosing adenosis and invasive ductal carcinoma.European radiology · 2026Article
- Diagnostic accuracy of automated breast volume scanning, hand-held ultrasound and molybdenum-target mammography for breast lesions: a systematic review and meta-analysis.Gland surgery · 2025Article
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Authors and funding
8 authors at 5 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Background: Sclerosing adenosis (SA) is a common proliferative benign lesion without atypia in the breast that may mimic invasive ductal carcinoma (IDC) on medical imaging, leading to it often being misdiagnosed and mistreated. Consequently, the purpose of this study was to assess the diagnostic value of multimodal ultrasound imaging in distinguishing SA from IDC. Methods: Multimodal ultrasound imaging, including automated breast volume scan (ABVS), elasticity imaging (EI), and color Doppler flow imaging (CDFI), were performed on 120 consecutive patients comprising 122 breast lesions (54 SA, 68 IDC). All lesions were pathologically confirmed. Multimodal ultrasound imaging features were compared between the two groups. Binary logistic regression analysis based on ABVS, EI, and CDFI was conducted to formulate a logistic regression equation for differentiating SA from IDC. The diagnostic performances of ABVS, EI, CDFI, and their combination were compared by the receiver operating characteristic (ROC) curve analysis. Results: The sensitivity, specificity, and accuracy of ABVS, EI, CDFI, and their combination in differentiating SA from IDC were, respectively, 75.00%, 72.22%, and 73.77%; 86.76%, 72.22%, and 80.33%; 73.53%, 64.81%, and 69.67%; and 88.24%, 74.07%, and 81.97%. Combining multimodal ultrasound imaging yielded an area under the curve (AUC) of 0.895 (95% confidence interval: 0.827-0.943), which was higher than that of ABVS, EI, and CDFI, with AUC values of 0.736, 0.795, and 0.692, respectively, and the difference was statistically significant (ABVS Conclusions: The preliminary results suggested that multimodal ultrasound imaging can improve the diagnostic accuracy of SA and provide additional information for differential diagnosis of SA and IDC.
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