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Shao X, Niu R, Shao X, Gao J, Shi Y, Jiang Z, Wang Y. 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; 8:74. [PMID: 34727258 PMCID: PMC8561359 DOI: 10.1186/s40658-021-00423-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Accepted: 10/25/2021] [Indexed: 12/31/2022] Open
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. Supplementary Information The online version contains supplementary material available at 10.1186/s40658-021-00423-1.
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Affiliation(s)
- Xiaonan Shao
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, 213003, China
| | - Rong Niu
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, 213003, China
| | - Xiaoliang Shao
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, 213003, China
| | - Jianxiong Gao
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, 213003, China
| | - Yunmei Shi
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, 213003, China
| | - Zhenxing Jiang
- Department of Radiology, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China
| | - Yuetao Wang
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China. .,Changzhou Key Laboratory of Molecular Imaging, Changzhou, 213003, China.
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Niu R, Shao X, Shao X, Jiang Z, Wang J, Wang Y. Establishment and verification of a prediction model based on clinical characteristics and positron emission tomography/computed tomography (PET/CT) parameters for distinguishing malignant from benign ground-glass nodules. Quant Imaging Med Surg 2021; 11:1710-1722. [PMID: 33936959 DOI: 10.21037/qims-20-840] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Background To develop and verify a prediction model for distinguishing malignant from benign ground-glass nodules (GGNs) combined with clinical characteristics and 18F-fluorodeoxyglucose (FDG) positron emission tomography-computed tomography (PET/CT) parameters. Methods We retrospectively analyzed 170 patients (56 males and 114 females) with GGNs who underwent PET/CT and high-resolution CT examination in our hospital from November 2011 to December 2019. The clinical and imaging data of all patients were collected, and the nodules were randomly divided into a derivation set and a validation set. For the derivation set, we used multivariate logistic regression to develop a prediction model for distinguishing benign from malignant GGNs. A receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of the model, and the data in the validation set were used to verify the prediction model. Results Among the 170 patients, 197 GGNs were confirmed via postoperative pathological examination or clinical follow-up. There were 21 patients with 27 GGNs in the benign group and 149 patients with 170 GGNs in the adenocarcinoma group. A total of five parameters, including the patient's sex, nodule location, margin, pleural indentation, and standardized uptake value (SUV) index (the ratio of nodule SUVmax to liver SUVmean), were selected to develop a prediction model for distinguishing benign from malignant GGNs. The area under the curve (AUC) of the model was 0.875 in the derivation set, with a sensitivity of 0.702 and a specificity of 0.923. The positive likelihood ratio was 9.131, and the negative likelihood ratio was 0.322. In the validation set, the AUC of the model was 0.874, which was not significantly different from the derivation set (P=0.989). Conclusions This study developed and validated a prediction model based on 18F-FDG PET/CT imaging and clinical characteristics for distinguishing malignant from benign GGNs. The model showed good diagnostic efficacy and high specificity, which can improve the preoperative diagnosis of high-risk GGNs.
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Affiliation(s)
- Rong Niu
- Department of Nuclear Medicine, the Third Affiliated Hospital of Soochow University, Changzhou, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, China
| | - Xiaonan Shao
- Department of Nuclear Medicine, the Third Affiliated Hospital of Soochow University, Changzhou, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, China
| | - Xiaoliang Shao
- Department of Nuclear Medicine, the Third Affiliated Hospital of Soochow University, Changzhou, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, China
| | - Zhenxing Jiang
- Department of Radiology, the Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Jianfeng Wang
- Department of Nuclear Medicine, the Third Affiliated Hospital of Soochow University, Changzhou, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, China
| | - Yuetao Wang
- Department of Nuclear Medicine, the Third Affiliated Hospital of Soochow University, Changzhou, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, China
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Abstract
Pulmonary nodules are often detected during the clinical course of several diseases or through routine screening. Various guidelines have proposed management algorithms for suspicious solitary nodules in lung cancer. Generally, solitary pulmonary nodules are managed according to nodule appearance and risk of lung cancer using low-dose, thin section computed tomography (CT). Liquid biopsy is promising for diagnosis, therapeutic-monitoring and follow-up in lung cancer; however, diagnosis and management pathways based on genetic examination alone have not been established. Management of solitary pulmonary nodules should be carried out by a multidisciplinary team and tissue biopsy is necessary for the diagnosis of lung cancer. Genetic analysis via liquid biopsy is warranted in addition to more established techniques in pulmonary nodule management.
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Affiliation(s)
- Masaoki Ito
- Department of Surgical Oncology, Research Institute for Radiation Biology and Medicine, Hiroshima University, Hiroshima, Japan
| | - Yoshihiro Miyata
- Department of Surgical Oncology, Research Institute for Radiation Biology and Medicine, Hiroshima University, Hiroshima, Japan
| | - Morihito Okada
- Department of Surgical Oncology, Research Institute for Radiation Biology and Medicine, Hiroshima University, Hiroshima, Japan
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Zhou Z, Zhan P, Jin J, Liu Y, Li Q, Ma C, Miao Y, Zhu Q, Tian P, Lv T, Song Y. The imaging of small pulmonary nodules. Transl Lung Cancer Res 2017; 6:62-67. [PMID: 28331825 DOI: 10.21037/tlcr.2017.02.02] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Lung cancer is the leading cause of cancer death worldwide. The major goal in lung cancer research is the improvement of long-term survival. Pulmonary nodules have high clinical importance, they may not only prove to be an early manifestation of lung cancer, but decide to choose the right therapy. This review will introduce the development and current situation of several imaging examination methods: computed tomography (CT), positron emission tomography/computed tomography (PET/CT), endobronchial ultrasound (EBUS).
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Affiliation(s)
- Zejun Zhou
- Department of Respiratory Medicine, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, China
| | - Ping Zhan
- Department of Respiratory Medicine, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, China
| | - Jiajia Jin
- Department of Respiratory Medicine, Jinling Hospital, Southeast University School of Medicine, Nanjing 210002, China
| | - Yafang Liu
- Department of Respiratory Medicine, Jinling Hospital, Southern Medical University, Nanjing 210002, China
| | - Qian Li
- Department of Respiratory Medicine, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, China
| | - Chenhui Ma
- Department of Respiratory Medicine, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, China
| | - Yingying Miao
- Department of Respiratory Medicine, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, China
| | - Qingqing Zhu
- Department of Respiratory Medicine, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, China
| | - Panwen Tian
- Department of Respiratory and Critical Care Medicine, Lung Cancer Treatment Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Tangfeng Lv
- Department of Respiratory Medicine, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, China;; Department of Respiratory Medicine, Jinling Hospital, Southeast University School of Medicine, Nanjing 210002, China;; Department of Respiratory Medicine, Jinling Hospital, Southern Medical University, Nanjing 210002, China
| | - Yong Song
- Department of Respiratory Medicine, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, China;; Department of Respiratory Medicine, Jinling Hospital, Southeast University School of Medicine, Nanjing 210002, China;; Department of Respiratory Medicine, Jinling Hospital, Southern Medical University, Nanjing 210002, China
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