Development and validation of an integrated model combining deep learning, radiomics, and clinical and breast ultrasound features for Breast Imaging Reporting and Data System 4A lesion malignancy classification.

ABSTRACT:

Background

The subjective assessment of Breast Imaging Reporting and Data System (BI-RADS) 4A lesions leads to a high number of unnecessary biopsies, highlighting the need for more objective and accurate diagnostic tools. This study aimed to construct and validate a novel multimodal framework that integrates deep learning (DL), radiomics, and clinical and breast ultrasound (US) features to distinguish between benign and malignant breast lesions classified as BI-RADS 4A.

Methods

A total of 935 patients with pathologically confirmed BI-RADS 4A lesions were retrospectively enrolled and randomly divided into a training cohort (n=654) and an internal validation cohort (n=281). An additional 488 patients were enrolled as the external validation cohort. DL and radiomics models were

SUBMITTER: Ye J 

PROVIDER: S-EPMC12682498 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature