Ultrasound derived deep learning features for predicting axillary lymph node metastasis in breast cancer using graph convolutional networks in a multicenter study

Ultrasound derived deep learning features for predicting axillary lymph node metastasis in breast cancer using graph convolutional networks in a multicenter study

Abstract The purpose of this study was to create and validate an ultrasound-based graph convolutional network (US-based GCN) model for the prediction of axillary lymph node metastasis (ALNM) in patients with breast cancer. A total of 820 eligible patients with breast cancer who underwent preoperativ...

Full description

Saved in:
Bibliographic Details
Main Authors: Enock Adjei Agyekum, Wentao Kong, Doris Nti Agyekum, Eliasu Issaka, Xian Wang, Yong-zhen Ren, Gongxun Tan, Xuan Jiang, Xiangjun Shen, Xiaoqin Qian
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Axillary lymph node metastasis
Breast cancer
Deep learning features
Recursive feature elimination
Graph convolutional network
Online Access:https://doi.org/10.1038/s41598-025-13086-0
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1038/s41598-025-13086-0

Similar Items