Development and internal validation of a mammography-based model fusing clinical, radiomics, and deep learning models for sentinel lymph node metastasis prediction in breast cancer.

ABSTRACT:

Objective

To develop a mammography (MG)-based post-fusion model combined with Clinical, Radiomics, and Deep Learning Models to evaluate the status of sentinel lymph node (SLN) in patients with breast cancer.

Methods

A total of 290 breast cancer patients who underwent MG were randomly divided into a training set (n = 203) and an internal validation set (n = 87), with an additional 82 patients included in the test set for independent validation. From the MG images of mediolateral oblique (MLO) and craniocaudal (CC) views, 1726 radiomic (Rad) features and 1,024 deep learning (DL) features were extracted for each patient. After the feature fusion and selection, the single-modal models and pre-fusion models were established by stochastic gradient descent (SGD). Usin

SUBMITTER: Liu X 

PROVIDER: S-EPMC12454431 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature