Abstract
This paper aims to explore the application value of SonoVue contrast-enhanced ultrasonography based on deep unsupervised learning (DNS) in the diagnosis of nipple discharge. In this paper, a new model (ODNS) is proposed based on the unsupervised learning model and stack self-coding network. The ultrasonic images of 1,725 patients with breast lesions in the shared database are used as the test data of the model. The differences in accuracy (Acc), recall (RE), sensitivity (Sen), and running time between the two models before and after optimization and other algorithms are compared. A total of 48 female patients with nipple discharge are enrolled. The differences in SE, specificity (SP), positive predictive value (PPV), and negative predictive value (NPV) of conventional ultrasound and contrast-enhanced ultrasonography are analyzed based on pathological examination results. The results showed that when the number of network layers is 5, the classification accuracies of DNS and ODNS model data reached the highest values, which were 91.45% and 98.64%, respectively.
Highlights
Nipple discharge is one of the three common symptoms of female breast diseases
Since patients with nipple discharge are often not accompanied by breast masses or masses are too small, the results of molybdenum target and color Doppler ultrasound are often negative [4]. e exfoliated cells of nipple discharge are commonly used in clinical examination of breast diseases, but its sensitivity is low [5]. e operation of galactography is simple and completely noninvasive and can directly observe the morphology of mammary duct
Contrast-enhanced ultrasound examination is performed on the basis of ultrasound. e results showed that 26 patients (54.17%) had serous nipple discharge, 12 patients (25%) had bloody overflow, 8 patients (16.67%) had milk-white overflow, and 2 patients (4.17%) had clear water-like overflow. is indicated that most patients with nipple discharge had serous nipple discharge, which is consistent with the results of Jung et al (2019) [29]. e results in this study showed that the SE value in the diagnosis of nipple discharge by SonoVue galactography is significantly higher than that by conventional ultrasound (P < 0.05), and the negative predictive value (NPV) value in the diagnosis of nipple discharge by SonoVue galactography is higher than that by conventional ultrasound (P < 0.01)
Summary
Nipple discharge is one of the three common symptoms of female breast diseases. Pathological nipple discharge is often manifested as spontaneous and serous discharge, mainly caused by trauma, inflammation, intraductal papilloma, ductal dilatation, and papillomatosis. Breast molybdenum target, breast color Doppler ultrasound, galactography, and cytology are often used for the diagnosis of nipple discharge. Since patients with nipple discharge are often not accompanied by breast masses or masses are too small, the results of molybdenum target and color Doppler ultrasound are often negative [4]. E exfoliated cells of nipple discharge are commonly used in clinical examination of breast diseases, but its sensitivity is low [5]. High-frequency ultrasound can display the morphology and relationship of dilated breast duct, duct, and surrounding glands, and the sensitivity of pathological nipple discharge diagnosis can reach 97% [7]
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