Abstract
PurposeThis work aims to train, validate, and test a dual-stream three-dimensional convolutional neural network (3D-CNN) based on fluorine 18 (18F)-fluorodeoxyglucose (FDG) PET/CT to distinguish benign lesions and invasive adenocarcinoma (IAC) in ground-glass nodules (GGNs).MethodsWe retrospectively analyzed patients with suspicious GGNs who underwent 18F-FDG PET/CT in our hospital from November 2011 to November 2020. The patients with benign lesions or IAC were selected for this study. According to the ratio of 7:3, the data were randomly divided into training data and testing data. Partial image feature extraction software was used to segment PET and CT images, and the training data after using the data augmentation were used for the training and validation (fivefold cross-validation) of the three CNNs (PET, CT, and PET/CT networks).ResultsA total of 23 benign nodules and 92 IAC nodules from 106 patients were included in this study. In the training set, the performance of PET network (accuracy, sensitivity, and specificity of 0.92 ± 0.02, 0.97 ± 0.03, and 0.76 ± 0.15) was better than the CT network (accuracy, sensitivity, and specificity of 0.84 ± 0.03, 0.90 ± 0.07, and 0.62 ± 0.16) (especially accuracy was significant, P-value was 0.001); in the testing set, the performance of both networks declined. However, the accuracy and sensitivity of PET network were still higher than that of CT network (0.76 vs. 0.67; 0.85 vs. 0.70). For dual-stream PET/CT network, its performance was almost the same as PET network in the training set (P-value was 0.372–1.000), while in the testing set, although its performance decreased, the accuracy and sensitivity (0.85 and 0.96) were still higher than both CT and PET networks. Moreover, the accuracy of PET/CT network was higher than two nuclear medicine physicians [physician 1 (3-year experience): 0.70 and physician 2 (10-year experience): 0.73].ConclusionThe 3D-CNN based on 18F-FDG PET/CT can be used to distinguish benign lesions and IAC in GGNs, and the performance is better when both CT and PET images are used together.
Highlights
With the application of low-dose CT (LDCT) and the screening of COVID-19, the detection rate of early lung adenocarcinoma manifested as ground-glass opacity nodule (GGN) has increased rapidly [1, 2]
This study aims to train, validate, and test the 3D-Convolutional neural networks (CNN) based on 18F-FDG PET/CT images and evaluate CT, PET, and PET/CT Three-dimensional convolutional neural networks (3D-CNN) performance in distinguishing benign lesions and invasive adenocarcinoma (IAC)
In this study, we developed a dual-stream 3D-CNN that can distinguish between benign lesions and IAC based on clinical 18F-FDG PET/CT GGN images
Summary
With the application of low-dose CT (LDCT) and the screening of COVID-19, the detection rate of early lung adenocarcinoma manifested as ground-glass opacity nodule (GGN) has increased rapidly [1, 2]. To treat lung GGN, an important early step is to estimate the probability of malignancy. GGNs are associated with various lung diseases, such as inflammatory pseudotumor, tuberculoma, sclerosing hemangioma, lymphoepithelioma, and non-small cell lung cancer (NSCLC) [4]. The imaging features used to determine lesion malignancy include size, density, follow-up stability, edge appearance, wall thickness, and the presence of cavitation and calcification [5,6,7]. The clinical management of GGN is determined based on the assessed risk, which may involve routine CT follow-up, functional imaging, and/or tissue biopsy [5, 8, 9]
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