CT-based radiomics-deep learning model predicts occult lymph node metastasis in early-stage lung adenocarcinoma patients: A multicenter study.

ABSTRACT:

Objective

The neglect of occult lymph nodes metastasis (OLNM) is one of the pivotal causes of early non-small cell lung cancer (NSCLC) recurrence after local treatments such as stereotactic body radiotherapy (SBRT) or surgery. This study aimed to develop and validate a computed tomography (CT)-based radiomics and deep learning (DL) fusion model for predicting non-invasive OLNM.

Methods

Patients with radiologically node-negative lung adenocarcinoma from two centers were retrospectively analyzed. We developed clinical, radiomics, and radiomics-clinical models using logistic regression. A DL model was established using a three-dimensional squeeze-and-excitation residual network-34 (3D SE-ResNet34) and a fusion model was created by integrating seleted clinical, radiomics feature

SUBMITTER: Yin X 

PROVIDER: S-EPMC11893343 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature