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Yang X, Chu XP, Huang S, Xiao Y, Li D, Su X, Qi YF, Qiu ZB, Wang Y, Tang WF, Wu YL, Zhu Q, Liang H, Zhong WZ. A novel image deep learning-based sub-centimeter pulmonary nodule management algorithm to expedite resection of the malignant and avoid over-diagnosis of the benign. Eur Radiol 2024; 34:2048-2061. [PMID: 37658883 DOI: 10.1007/s00330-023-10026-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 05/08/2023] [Accepted: 06/26/2023] [Indexed: 09/05/2023]
Abstract
OBJECTIVES With the popularization of chest computed tomography (CT) screening, there are more sub-centimeter (≤ 1 cm) pulmonary nodules (SCPNs) requiring further diagnostic workup. This area represents an important opportunity to optimize the SCPN management algorithm avoiding "one-size fits all" approach. One critical problem is how to learn the discriminative multi-view characteristics and the unique context of each SCPN. METHODS Here, we propose a multi-view coupled self-attention module (MVCS) to capture the global spatial context of the CT image through modeling the association order of space and dimension. Compared with existing self-attention methods, MVCS uses less memory consumption and computational complexity, unearths dimension correlations that previous methods have not found, and is easy to integrate with other frameworks. RESULTS In total, a public dataset LUNA16 from LIDC-IDRI, 1319 SCPNs from 1069 patients presenting to a major referral center, and 160 SCPNs from 137 patients from three other major centers were analyzed to pre-train, train, and validate the model. Experimental results showed that performance outperforms the state-of-the-art models in terms of accuracy and stability and is comparable to that of human experts in classifying precancerous lesions and invasive adenocarcinoma. We also provide a fusion MVCS network (MVCSN) by combining the CT image with the clinical characteristics and radiographic features of patients. CONCLUSION This tool may ultimately aid in expediting resection of the malignant SCPNs and avoid over-diagnosis of the benign ones, resulting in improved management outcomes. CLINICAL RELEVANCE STATEMENT In the diagnosis of sub-centimeter lung adenocarcinoma, fusion MVCSN can help doctors improve work efficiency and guide their treatment decisions to a certain extent. KEY POINTS • Advances in computed tomography (CT) not only increase the number of nodules detected, but also the nodules that are identified are smaller, such as sub-centimeter pulmonary nodules (SCPNs). • We propose a multi-view coupled self-attention module (MVCS), which could model spatial and dimensional correlations sequentially for learning global spatial contexts, which is better than other attention mechanisms. • MVCS uses fewer huge memory consumption and computational complexity than the existing self-attention methods when dealing with 3D medical image data. Additionally, it reaches promising accuracy for SCPNs' malignancy evaluation and has lower training cost than other models.
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Affiliation(s)
- Xiongwen Yang
- School of Medicine, South China University of Technology, Guangzhou, China
- Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, 106 Zhongshan Er Rd, Guangzhou, 510080, China
| | - Xiang-Peng Chu
- School of Medicine, South China University of Technology, Guangzhou, China
- Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, 106 Zhongshan Er Rd, Guangzhou, 510080, China
| | - Shaohong Huang
- Department of Cardio-Thoracic Surgery, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Yi Xiao
- Department of Cardio-Thoracic Surgery, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Dantong Li
- Medical Big Data Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China
- Guangdong Cardiovascular Institute, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
| | - Xiaoyang Su
- Department of Thoracic Surgery, Maoming City People's Hospital, Maoming, China
| | - Yi-Fan Qi
- School of Medicine, South China University of Technology, Guangzhou, China
- Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, 106 Zhongshan Er Rd, Guangzhou, 510080, China
| | - Zhen-Bin Qiu
- School of Medicine, South China University of Technology, Guangzhou, China
- Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, 106 Zhongshan Er Rd, Guangzhou, 510080, China
| | - Yanqing Wang
- Department of Gynecology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Wen-Fang Tang
- Department of Cardio-Thoracic Surgery, Zhongshan City People's Hospital, Zhongshan, China
| | - Yi-Long Wu
- Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, 106 Zhongshan Er Rd, Guangzhou, 510080, China
| | - Qikui Zhu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA.
| | - Huiying Liang
- Medical Big Data Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China.
- Guangdong Cardiovascular Institute, Guangzhou, Guangdong, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China.
| | - Wen-Zhao Zhong
- School of Medicine, South China University of Technology, Guangzhou, China.
- Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, 106 Zhongshan Er Rd, Guangzhou, 510080, China.
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