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 HER-2-positive breast cancer
Kun Wang, Xi Yang, Shuo Yang, Xian Du, Ruijing Shi, Wendong Bai, Yu Wang · Translational Cancer Research · 2025
Background: -positive breast cancer, we compared the value of the area under the receiver operating characteristic (ROC) curve (AUC) of the combined diagnosis model and single-factor models. Methods: -positive. The ultrasonography characteristics in different ultrasonography methods were analyzed, the parameters with statistically significant differences between groups of internal test samples were incorporated to establish a joint diagnosis model. The sensitivity, specificity and accuracy of the combined diagnosis model and single-factor models were calculated, the ROC curve was drawn to evaluate the diagnostic efficacy of the combined diagnosis model. 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: in breast cancer.