Development of fully automated deep-learning-based approach for prediction of sentinel lymph node metastasis in breast cancer patients using ultrasound imaging.

ABSTRACT:

Purpose

This study aimed to develop a novel predicting model based on deep learning (DL) to predict sentinel lymph node (SLN) metastasis in breast cancer (BC) patients using ultrasound (US) imaging.

Methods

A retrospective cohort consisting of 692 female BC patients from two hospitals was analyzed, with data collected from January 2020 to October 2023. Patients from Hospital A were randomly allocated to training (n = 405) and internal validation (n = 174) sets (7:3 ratio), with Hospital B patients (n = 113) serving as the external test set. A post-fusion model integrating the DeepLabV3, U-Net, and U-Net++ segmentation algorithms, respectively, was utilized to automatically delineate regions of interest (ROIs). Furthermore, three convolutional neural netw

SUBMITTER: Liu T 

PROVIDER: S-EPMC12423051 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature