Application of dual-stream 3D convolutional neural network based on 18F-FDG PET/CT in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules

Xiaonan Shao; Rong Niu; Xiaoliang Shao; Jianxiong Gao; Yunmei Shi; Zhenxing Jiang; Yuetao Wang

doi:10.1186/s40658-021-00423-1

Application of dual-stream 3D convolutional neural network based on ¹⁸F-FDG PET/CT in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules

EJNMMI Phys. 2021 Nov 2;8(1):74. doi: 10.1186/s40658-021-00423-1.

Authors

Xiaonan Shao^#^{1

2}, Rong Niu^#^{1

2}, Xiaoliang Shao^{1

2}, Jianxiong Gao^{1

2}, Yunmei Shi^{1

2}, Zhenxing Jiang³, Yuetao Wang^{4

5}

Affiliations

¹ Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China.
² Changzhou Key Laboratory of Molecular Imaging, Changzhou, 213003, China.
³ Department of Radiology, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China.
⁴ Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China. yuetao-w@163.com.
⁵ Changzhou Key Laboratory of Molecular Imaging, Changzhou, 213003, China. yuetao-w@163.com.

^# Contributed equally.

Abstract

Purpose: This 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).

Methods: We 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).

Results: A 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].

Conclusion: The 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.

Keywords: Deep learning; Differential diagnosis; Fluorodeoxyglucose F18; Lung adenocarcinoma; Positron emission tomography-computed tomography.

Abstract

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