Abstract
BACKGROUND: Accurately predicting the malignant risk of ground-glass nodules (GGOs) is crucial for precise treatment planning. This study aims to utilize convolutional neural networks based on dual-time-point (18)F-FDG PET/CT to predict the malignant risk of GGOs. METHODS: Retrospectively analyzing 311 patients with 397 GGOs, this study identified 118 low-risk GGOs and 279 high-risk GGOs through pathology and follow-up according to the new WHO classification. The dataset was randomly divided into a training set comprising 239 patients (318 lesions) and a testing set comprising 72 patients (79 lesions), we employed a self-configuring 3D nnU-net convolutional neural network with majority voting method to segment GGOs and predict malignant risk of GGOs. Three independent segmentation prediction models were developed based on thin-section lung CT, early-phase (18)F-FDG PET/CT, and dual-time-point (18)F-FDG PET/CT, respectively. Simultaneously, the results of the dual-time-point (18)F-FDG PET/CT model on the testing set were compared with the diagnostic of nuclear medicine physicians. RESULTS: The dual-time-point (18)F-FDG PET/CT model achieving a Dice coefficient of 0.84 ± 0.02 for GGOs segmentation and demonstrating high accuracy (84.81%), specificity (84.62%), sensitivity (84.91%), and AUC (0.85) in predicting malignant risk. The accuracy of the thin-section CT model is 73.42%, and the accuracy of the early-phase (18)F-FDG PET/CT model is 78.48%, both of which are lower than the accuracy of the dual-time-point (18)F-FDG PET/CT model. The diagnostic accuracy for resident, junior and expert physicians were 67.09%, 74.68%, and 78.48%, respectively. The accuracy (84.81%) of the dual-time-point (18)F-FDG PET/CT model was significantly higher than that of nuclear medicine physicians. CONCLUSIONS: Based on dual-time-point (18)F-FDG PET/CT images, the 3D nnU-net with a majority voting method, demonstrates excellent performance in predicting the malignant risk of GGOs. This methodology serves as a valuable adjunct for physicians in the risk prediction and assessment of GGOs.