High-grade patterns, visceral pleural invasion, lymphovascular invasion, spread through air spaces, and lymph node metastasis are high-risk factors and associated with poor prognosis in lung adenocarcinomas (LUADs). This study aimed to construct and validate a radiomic model and a radiographic model derived from low-dose CT (LDCT) for predicting high-risk LUADs in solid and part-solid nodules.This study retrospectively enrolled 658 pathologically confirmed LUADs from July 2018 to December 2022 from four centers, which were divided into training set (n=411), internal validation set (n=139), and external validation set (n=108). Radiomic features and radiographic features including maximal diameter, consolidation/tumor ratio (CTR), and semantic features, were obtained to construct a radiomic model and a radiographic model through multivariable logistic regression. Area under receiver operating characteristic curve (AUC) was utilized to assess the diagnostic performance of the models.Three radiomic features (GLCM_Correlation, GLSZM_SmallAreaEmphasis, and GLDM_LargeDependenceHighGrayLevelEmphasis) and four radiographic features (maximal diameter, CTR, spiculation, and pleural indentation) were selected to build models. The radiomic model yielded AUCs of 0.916 in the internal validation set and 0.938 in the external validation set, which were significantly higher than the AUCs of the radiographic model (0.916 vs. 0.868, P=0.014 and 0.938 vs. 0.880, P=0.002).Our LDCT-based radiomic model enabled non-invasive identification of high-risk LUADs in solid and part-solid nodules with good diagnostic performance and might assist in case-specific decision-making in lung cancer screening.Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.