Novel study on the prediction of BI-RADS 4A positive lesions in mammography using deep learning technology and clinical factors.

ABSTRACT:

Background

The classification of Breast Imaging Reporting and Data System (BI-RADS) category 4A lesions in mammography is complicated by subjective interpretations and unclear criteria, which can lead to potential misclassifications and unnecessary biopsies. Thus, more accurate assessment methods need to be developed. This study aimed to improve the classification prediction of BI-RADS 4A positive lesions in mammography by combining deep learning (DL) technology with relevant clinical factors.

Methods

A retrospective analysis of 590 patients diagnosed with BI-RADS 4A at Shenzhen People's Hospital and Shenzhen Luohu People's Hospital was conducted, and a multi-faceted approach was employed to construct a robust predictive model. The patients were divided into training, valida

SUBMITTER: Ouyang R 

PROVIDER: S-EPMC11652064 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature