Publications by authors named "Hong-Ju Yan"
Background: It is challenging to correctly identify and diagnose breast nonmass lesions. This study aimed to explore the multimodal ultrasound features associated with malignant breast nonmass lesions (NMLs), and evaluate their combined diagnostic performance.
Methods: This retrospective analysis was conducted on 573 breast NMLs, including 309 were benign and 264 were malignant, their multimodal ultrasound features (B-mode, color Doppler and strain elastography) were assessed by two experienced radiologists.
Background: Automated Breast Ultrasound (AB US) has shown good application value and prospects in breast disease screening and diagnosis. The aim of the study was to explore the ability of AB US to detect and diagnose mammographically Breast Imaging Reporting and Data System (BI-RADS) category 4 microcalcifications.
Methods: 575 pathologically confirmed mammographically BI-RADS category 4 microcalcifications from January 2017 to June 2021 were included.
Article Synopsis
- Elastography ultrasound (EUS) is an important imaging method, but it faces challenges like subjective interpretation and hardware limitations that affect the miniaturization of equipment.
- A new deep neural network has been developed to create virtual EUS (V-EUS) from traditional B-mode images, providing a cost-effective solution.
- Analysis of 4580 breast tumor cases shows that V-EUS performs similarly to real EUS in identifying tumors, and it improves the diagnostic accuracy of portable ultrasound devices by approximately 5%.