Prediction of Lymphovascular Invasion in Early-Stage Lung Adenocarcinoma Using Artificial Intelligence-Based Radiomics.

ABSTRACT: Objectives: This study utilized artificial intelligence (AI)-based radiomics analysis of computed tomography (CT) images using a modified U-Net for lung nodule segmentation and convolutional neural network based on VGG-16 to predict lymphovascular invasion (LVI) in stage 0-I lung adenocarcinoma. Additionally, the study investigated whether combining radiomics data with serum microRNA (miR)-30d level as a potential biomarker could enhance predictive performance. Methods: A total of 1265 patients who underwent complete resection between 2008 and 2018 were included. AI-based CT analysis was performed, and logistic regression was applied to predict LVI using 35 imaging features. A risk score (RS) generated from 840 patients in the derivation cohort was used to identify a high-ris

SUBMITTER: Shimada Y 

PROVIDER: S-EPMC12731777 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature