ArticleTranslational cancer research2025
A diagnostic test of two-dimensional ultrasonic feature extraction based on artificial intelligence combined with blood flow Adler classification and contrast-enhanced ultrasound for predicting
Article in Translational cancer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Diagnostic performance of multimodal ultrasonography for molecular subtyping of breast cancer: a retrospective study.Gland surgery · 2026Article
- The value of multimodal ultrasound imaging in differentiating HER2-positive breast cancer.Frontiers in oncology · 2026Article
- Ultrasound elastography: advances and challenges in early detection of breast cancer.Frontiers in oncology · 2025Review
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7 authors.
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Abstract
Background: Human epidermal growth factor receptor 2 ( Methods: A retrospective analysis was performed on 140 patients (88 Results: Long diameter direction, Adler grade of blood flow, contrast agent distribution characteristics, and nodule boundary after CEUS were statistically significant different between the positive and negative groups in internal test and external validation samples (P<0.05). The sensitivity, specificity, accuracy of the combined diagnosis model were significantly higher than single-parameter diagnosis method both in internal test and external validation samples, and the kappa values of combined diagnosis model were highest. The AUC of the combined diagnosis model of internal test and external validation samples was 0.861 and 0.969, which was significantly higher (P<0.05) than that in the long diameter direction (0.717 and 0.732), blood flow Adler grade (0.674 and 0.786), CEUS distribution characteristics (0.666 and 0.750), and the nodule boundary after CEUS (0.684 and 0.786). Conclusions: The combined diagnosis model based on two-dimensional ultrasonic feature extraction, blood flow, and CEUS can effectively predict the expression of
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