Deep learning radiomics for prediction of axillary lymph node metastasis in patients with clinical stage T1-2 breast cancer.

ABSTRACT:

Background

This study investigates whether deep learning radiomics of conventional ultrasound images can predict preoperative axillary lymph node (ALN) status in patients with clinical stages T1-2 breast cancer (BC).

Methods

This study retrospectively analyzed the preoperative ultrasound data of 892 patients with BC, who were classified into training (n=535), validation (n=178), and test (n=179) cohorts. Linear combinations of the selected features were weighted by their coefficients to obtain the predicted score. Then, deep learning radiomic features were extracted from the ultrasound images to evaluate the ALN status. Receiver-operating characteristic curves were drawn, followed by the calculation of the area under the curve (AUC) to assess the accuracy of the prediction m

SUBMITTER: Wei W 

PROVIDER: S-EPMC10423344 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature