ORIGINAL RESEARCH article
Front. Med.
Sec. Pulmonary Medicine
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1603472
CT radiomics combined with neural networks predict the malignant degree of pulmonary grinding glass nodules
Provisionally accepted
- The First Affiliated Hospital of Bengbu Medical College, Handan, China
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This study investigates the use of CT radiomics combined with convolutional neural networks (CNN) to predict the malignancy of lung ground glass nodules (GGN), which are challenging to diagnose due to their ambiguous boundaries. The goal is to improve diagnostic accuracy and support personalized treatment planning.Retrospective data from 670 patients with pulmonary nodules (2019-2023) were analyzed. CT images were preprocessed using Gaussian filtering and manually segmented to define regions of interest (ROI). A CNN model was trained using MATLAB's Deep Learning Toolbox, and its performance was compared to the Mayo and Brock models.Key predictors of malignancy included nodule diameter, volume, mean CT value, and consolidationto-tumor ratio (CTR). The CNN-based model achieved an AUC of 0.887, with 82.4% sensitivity and 75.5% specificity, outperforming existing models (Mayo: AUC=0.655; Brock: AUC=0.574). Validation accuracy reached 85.07%.In this single-center retrospective study, integrating CT radiomics with CNN depicted promising potential for GGN malignancy prediction, though external validation remains necessary. These findings warrant verification in multicenter prospective cohorts.
Keywords: Convolutional Neural Network, lung ground glass nodules, CT radiomics, Infiltration, predictive modelling
Received: 31 Mar 2025; Accepted: 22 May 2025.
Copyright: © 2025 Chen, Gong, Zhang and Geng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yang Geng, The First Affiliated Hospital of Bengbu Medical College, Handan, China
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