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

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Tác giả: Wendong Bai, Xian Du, Ruijing Shi, Kun Wang, Yu Wang, Shuo Yang, Xi Yang

Ngôn ngữ: eng

Ký hiệu phân loại:

Thông tin xuất bản: China : Translational cancer research , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 470114

 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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