Ultrasound-based deep learning radiomics for multi-stage assisted diagnosis in reducing unnecessary biopsies of BI-RADS 4A lesions.

ABSTRACT:

Background

Even with the Breast Imaging Reporting and Data System (BI-RADS) guiding risk stratification on ultrasound (US) images, inconsistencies in diagnostic accuracy still exist, leading patients being subjected to unnecessary biopsies in clinical practice. This study investigated the construction of deep learning radiomics (DLR) models to improve the diagnostic consistency and reduce the unnecessary biopsies for BI-RADS 4A lesions.

Methods

A total of 746 patients with breast lesions were enrolled in this retrospective study. Two DLR models based on US images and clinical variables were developed to conduct breast lesion risk re-stratification as BI-RADS 3 or lower and BI-RADS 4A or higher (DLR_LH), while simultaneously identifying BI-RADS 4A lesions with low malignancy

SUBMITTER: Lu X 

PROVIDER: S-EPMC11948369 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature