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Wang TW, Hong JS, Chiu HY, Chao HS, Chen YM, Wu YT. Standalone deep learning versus experts for diagnosis lung cancer on chest computed tomography: a systematic review. Eur Radiol 2024:10.1007/s00330-024-10804-6. [PMID: 38777902 DOI: 10.1007/s00330-024-10804-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Revised: 03/10/2024] [Accepted: 04/01/2024] [Indexed: 05/25/2024]
Abstract
PURPOSE To compare the diagnostic performance of standalone deep learning (DL) algorithms and human experts in lung cancer detection on chest computed tomography (CT) scans. MATERIALS AND METHODS This study searched for studies on PubMed, Embase, and Web of Science from their inception until November 2023. We focused on adult lung cancer patients and compared the efficacy of DL algorithms and expert radiologists in disease diagnosis on CT scans. Quality assessment was performed using QUADAS-2, QUADAS-C, and CLAIM. Bivariate random-effects and subgroup analyses were performed for tasks (malignancy classification vs invasiveness classification), imaging modalities (CT vs low-dose CT [LDCT] vs high-resolution CT), study region, software used, and publication year. RESULTS We included 20 studies on various aspects of lung cancer diagnosis on CT scans. Quantitatively, DL algorithms exhibited superior sensitivity (82%) and specificity (75%) compared to human experts (sensitivity 81%, specificity 69%). However, the difference in specificity was statistically significant, whereas the difference in sensitivity was not statistically significant. The DL algorithms' performance varied across different imaging modalities and tasks, demonstrating the need for tailored optimization of DL algorithms. Notably, DL algorithms matched experts in sensitivity on standard CT, surpassing them in specificity, but showed higher sensitivity with lower specificity on LDCT scans. CONCLUSION DL algorithms demonstrated improved accuracy over human readers in malignancy and invasiveness classification on CT scans. However, their performance varies by imaging modality, underlining the importance of continued research to fully assess DL algorithms' diagnostic effectiveness in lung cancer. CLINICAL RELEVANCE STATEMENT DL algorithms have the potential to refine lung cancer diagnosis on CT, matching human sensitivity and surpassing in specificity. These findings call for further DL optimization across imaging modalities, aiming to advance clinical diagnostics and patient outcomes. KEY POINTS Lung cancer diagnosis by CT is challenging and can be improved with AI integration. DL shows higher accuracy in lung cancer detection on CT than human experts. Enhanced DL accuracy could lead to improved lung cancer diagnosis and outcomes.
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Affiliation(s)
- Ting-Wei Wang
- Institute of Biophotonics, National Yang-Ming Chiao Tung University, Taipei, Taiwan
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan
| | - Jia-Sheng Hong
- Institute of Biophotonics, National Yang-Ming Chiao Tung University, Taipei, Taiwan
| | - Hwa-Yen Chiu
- Institute of Biophotonics, National Yang-Ming Chiao Tung University, Taipei, Taiwan
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan
- Department of Chest Medicine, Taipei Veteran General Hospital, Taipei, Taiwan
| | - Heng-Sheng Chao
- Department of Chest Medicine, Taipei Veteran General Hospital, Taipei, Taiwan
| | - Yuh-Min Chen
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan
- Department of Chest Medicine, Taipei Veteran General Hospital, Taipei, Taiwan
| | - Yu-Te Wu
- Institute of Biophotonics, National Yang-Ming Chiao Tung University, Taipei, Taiwan.
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Wang H, Zhu H, Ding L, Yang K. Attention pyramid pooling network for artificial diagnosis on pulmonary nodules. PLoS One 2024; 19:e0302641. [PMID: 38753596 PMCID: PMC11098435 DOI: 10.1371/journal.pone.0302641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 04/09/2024] [Indexed: 05/18/2024] Open
Abstract
The development of automated tools using advanced technologies like deep learning holds great promise for improving the accuracy of lung nodule classification in computed tomography (CT) imaging, ultimately reducing lung cancer mortality rates. However, lung nodules can be difficult to detect and classify, from CT images since different imaging modalities may provide varying levels of detail and clarity. Besides, the existing convolutional neural network may struggle to detect nodules that are small or located in difficult-to-detect regions of the lung. Therefore, the attention pyramid pooling network (APPN) is proposed to identify and classify lung nodules. First, a strong feature extractor, named vgg16, is used to obtain features from CT images. Then, the attention primary pyramid module is proposed by combining the attention mechanism and pyramid pooling module, which allows for the fusion of features at different scales and focuses on the most important features for nodule classification. Finally, we use the gated spatial memory technique to decode the general features, which is able to extract more accurate features for classifying lung nodules. The experimental results on the LIDC-IDRI dataset show that the APPN can achieve highly accurate and effective for classifying lung nodules, with sensitivity of 87.59%, specificity of 90.46%, accuracy of 88.47%, positive predictive value of 95.41%, negative predictive value of 76.29% and area under receiver operating characteristic curve of 0.914.
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Affiliation(s)
- Hongfeng Wang
- School of Network Engineering, Zhoukou Normal University, Zhoukou, China
| | - Hai Zhu
- School of Network Engineering, Zhoukou Normal University, Zhoukou, China
| | - Lihua Ding
- College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Kaili Yang
- Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Henan University People’s Hospital, Zhengzhou, China
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Huang W, Deng H, Li Z, Xiong Z, Zhou T, Ge Y, Zhang J, Jing W, Geng Y, Wang X, Tu W, Dong P, Liu S, Fan L. Baseline whole-lung CT features deriving from deep learning and radiomics: prediction of benign and malignant pulmonary ground-glass nodules. Front Oncol 2023; 13:1255007. [PMID: 37664069 PMCID: PMC10470826 DOI: 10.3389/fonc.2023.1255007] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 07/28/2023] [Indexed: 09/05/2023] Open
Abstract
Objective To develop and validate the model for predicting benign and malignant ground-glass nodules (GGNs) based on the whole-lung baseline CT features deriving from deep learning and radiomics. Methods This retrospective study included 385 GGNs from 3 hospitals, confirmed by pathology. We used 239 GGNs from Hospital 1 as the training and internal validation set; 115 and 31 GGNs from Hospital 2 and Hospital 3 as the external test sets 1 and 2, respectively. An additional 32 stable GGNs from Hospital 3 with more than five years of follow-up were used as the external test set 3. We evaluated clinical and morphological features of GGNs at baseline chest CT and extracted the whole-lung radiomics features simultaneously. Besides, baseline whole-lung CT image features are further assisted and extracted using the convolutional neural network. We used the back-propagation neural network to construct five prediction models based on different collocations of the features used for training. The area under the receiver operator characteristic curve (AUC) was used to compare the prediction performance among the five models. The Delong test was used to compare the differences in AUC between models pairwise. Results The model integrated clinical-morphological features, whole-lung radiomic features, and whole-lung image features (CMRI) performed best among the five models, and achieved the highest AUC in the internal validation set, external test set 1, and external test set 2, which were 0.886 (95% CI: 0.841-0.921), 0.830 (95%CI: 0.749-0.893) and 0.879 (95%CI: 0.712-0.968), respectively. In the above three sets, the differences in AUC between the CMRI model and other models were significant (all P < 0.05). Moreover, the accuracy of the CMRI model in the external test set 3 was 96.88%. Conclusion The baseline whole-lung CT features were feasible to predict the benign and malignant of GGNs, which is helpful for more refined management of GGNs.
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Affiliation(s)
- Wenjun Huang
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China
- School of Medical Imaging, Weifang Medical University, Weifang, Shandong, China
- Department of Radiology, The Second People’s hospital of Deyang, Deyang, Sichuan, China
| | - Heng Deng
- School of Medicine, Shanghai University, Shanghai, China
| | - Zhaobin Li
- Department of Radiation Oncology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Zhanda Xiong
- Department of Artificial Intelligence Medical Imaging, Tron Technology, Shanghai, China
| | - Taohu Zhou
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China
- School of Medical Imaging, Weifang Medical University, Weifang, Shandong, China
| | - Yanming Ge
- School of Medical Imaging, Weifang Medical University, Weifang, Shandong, China
- Medical Imaging Center, Affiliated Hospital of Weifang Medical University, Weifang, Shandong, China
| | - Jing Zhang
- Department of Radiology, The Second People’s hospital of Deyang, Deyang, Sichuan, China
| | - Wenbin Jing
- Department of Radiology, The Second People’s hospital of Deyang, Deyang, Sichuan, China
| | - Yayuan Geng
- Clinical Research Institute, Shukun (Beijing) Technology Co., Ltd., Beijing, China
| | - Xiang Wang
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Wenting Tu
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Peng Dong
- School of Medical Imaging, Weifang Medical University, Weifang, Shandong, China
| | - Shiyuan Liu
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Li Fan
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China
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Wang B, Zhang H, Li W, Fu S, Li Y, Gao X, Wang D, Yang X, Xu S, Wang J, Hou D. Neural network-based model for evaluating inert nodules and volume doubling time in T1 lung adenocarcinoma: a nested case-control study. Front Oncol 2023; 13:1037052. [PMID: 37293594 PMCID: PMC10244560 DOI: 10.3389/fonc.2023.1037052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 05/09/2023] [Indexed: 06/10/2023] Open
Abstract
Objective The purpose of this study is to establish model for assessing inert nodules predicting nodule volume-doubling. Methods A total of 201 patients with T1 lung adenocarcinoma were analysed retrospectively pulmonary nodule information was predicted by an AI pulmonary nodule auxiliary diagnosis system. The nodules were classified into two groups: inert nodules (volume-doubling time (VDT)>600 days n=152) noninert nodules (VDT<600 days n=49). Then taking the clinical imaging features obtained at the first examination as predictive variables the inert nodule judgement model <sn</sn>>(INM) volume-doubling time estimation model (VDTM) were constructed based on a deep learning-based neural network. The performance of the INM was evaluated by the area under the curve (AUC) obtained from receiver operating characteristic (ROC) analysis the performance of the VDTM was evaluated by R2(determination coefficient). Results The accuracy of the INM in the training and testing cohorts was 81.13% and 77.50%, respectively. The AUC of the INM in the training and testing cohorts was 0.7707 (95% CI 0.6779-0.8636) and 0.7700 (95% CI 0.5988-0.9412), respectively. The INM was effective in identifying inert pulmonary nodules; additionally, the R2 of the VDTM in the training cohort was 0.8008, and that in the testing cohort was 0.6268. The VDTM showed moderate performance in estimating the VDT, which can provide some reference during a patients' first examination and consultation. Conclusion The INM and the VDTM based on deep learning can help radiologists and clinicians distinguish among inert nodules and predict the nodule volume-doubling time to accurately treat patients with pulmonary nodules.
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Affiliation(s)
- Bing Wang
- Department of Radiology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Hui Zhang
- Department of Medical Oncology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Wei Li
- Cancer Research Center, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
| | - Siyun Fu
- Department of Radiology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Ye Li
- Department of Radiology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Xiang Gao
- Cancer Research Center, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
| | - Dongpo Wang
- Department of Radiology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Xinjie Yang
- Department of Medical Oncology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Shaofa Xu
- Cancer Research Center, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
| | - Jinghui Wang
- Department of Medical Oncology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Dailun Hou
- Department of Radiology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing, China
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Kim BG, Um SW. A narrative review of the clinical approach to subsolid pulmonary nodules. ANNALS OF TRANSLATIONAL MEDICINE 2023; 11:217. [PMID: 37007560 PMCID: PMC10061480 DOI: 10.21037/atm-22-5246] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Accepted: 02/19/2023] [Indexed: 03/14/2023]
Abstract
Background and Objective The widespread use of chest computed tomography (CT) for lung cancer screening has led to increased detection of subsolid pulmonary nodules. The management of subsolid nodules (SSNs) is challenging since they are likely to grow slowly and a long-term follow-up is needed. In this review, we discuss the characteristics, natural history, genetic features, surveillance, and management of SSNs. Methods PubMed and Google Scholar were searched to identify relevant articles published in English between January 1998 and December 2022 using the following keywords: "subsolid nodule", "ground-glass nodule (GGN)", and "part-solid nodule (PSN)". Key Content and Findings The differential diagnosis of SSNs includes transient inflammatory lesions, focal fibrosis, and premalignant or malignant lesions. Long-term CT surveillance follow-up is needed to manage SSNs that persist for >3 months. Although most SSNs have an indolent clinical course, PSNs may have a more aggressive clinical course than pure GGNs. The proportion of growth and the time to grow is higher and shorter in PSN than pure GGN. In lung adenocarcinoma manifesting as SSNs, EGFR mutations were the major driver mutations. Guidelines are available for the management of incidentally detected and screening-detected SSNs. The size, solidity, location, and number of SSNs are important factors in determining the need for surveillance and surgical resection, as well as the interval of follow-up. Positron emission tomography/CT and brain magnetic resonance imaging (MRI) are not recommended for the diagnosis of SSNs, especially for pure GGNs. Periodic CT surveillance and lung-sparing surgery are the main strategies for the management of persistent SSNs. Nonsurgical treatment options for persistent SSNs include stereotactic body radiotherapy (SBRT) and radiofrequency ablation (RFA). For multifocal SSNs, the timing of repeated CT scans and the need for surgical treatment are decided based on the most dominant SSN(s). Conclusions The SSN is a heterogeneous disease and a personalized medicine approach is required in the future. Future studies of SSNs should focus on their natural history, optimal follow-up duration, genetic features, and surgical and nonsurgical treatments to improve the corresponding clinical management. All these efforts will lead to the personalized medicine approach for the SSNs.
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Affiliation(s)
- Bo-Guen Kim
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Sang-Won Um
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
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