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Zhang ST, Wang SY, Zhang J, Dong D, Mu W, Xia XE, Fu FF, Lu YN, Wang S, Tang ZC, Li P, Qu JR, Wang MY, Tian J, Liu JH. Artificial intelligence-based computer-aided diagnosis system supports diagnosis of lymph node metastasis in esophageal squamous cell carcinoma: A multicenter study. Heliyon 2023; 9:e14030. [PMID: 36923854 PMCID: PMC10009687 DOI: 10.1016/j.heliyon.2023.e14030] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 02/17/2023] [Accepted: 02/20/2023] [Indexed: 03/06/2023] Open
Abstract
Background This study aimed to develop an artificial intelligence-based computer-aided diagnosis system (AI-CAD) emulating the diagnostic logic of radiologists for lymph node metastasis (LNM) in esophageal squamous cell carcinoma (ESCC) patients, which contributed to clinical treatment decision-making. Methods A total of 689 ESCC patients with PET/CT images were enrolled from three hospitals and divided into a training cohort and two external validation cohorts. 452 CT images from three publicly available datasets were also included for pretraining the model. Anatomic information from CT images was first obtained automatically using a U-Net-based multi-organ segmentation model, and metabolic information from PET images was subsequently extracted using a gradient-based approach. AI-CAD was developed in the training cohort and externally validated in two validation cohorts. Results The AI-CAD achieved an accuracy of 0.744 for predicting pathological LNM in the external cohort and a good agreement with a human expert in two external validation cohorts (kappa = 0.674 and 0.587, p < 0.001). With the aid of AI-CAD, the human expert's diagnostic performance for LNM was significantly improved (accuracy [95% confidence interval]: 0.712 [0.669-0.758] vs. 0.833 [0.797-0.865], specificity [95% confidence interval]: 0.697 [0.636-0.753] vs. 0.891 [0.851-0.928]; p < 0.001) among patients underwent lymphadenectomy in the external validation cohorts. Conclusions The AI-CAD could aid in preoperative diagnosis of LNM in ESCC patients and thereby support clinical treatment decision-making.
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Key Words
- 18F-FDG PET/CT, 18-fluorine-fluorodeoxyglucose positron-emission tomography/computed tomography
- AI, Artificial intelligence
- AI-CAD, Artificial intelligence-based computer-aided diagnosis
- Artificial intelligence
- CI, Confidence interval
- CT, Computed tomography
- ESCC, Esophageal squamous cell carcinoma
- Esophageal squamous cell carcinoma
- LNM, Lymph node metastasis
- Lymph node metastasis
- OS, Overall survival
- PET/CT
- PFS, Progression-free survival
- SD, Standard deviation
- SLR, Ratio of the SUV value to liver uptake
- SUV, Standardized uptake value
- cN, Clinical N stage
- nCRT, Neoadjuvant chemoradiotherapy
- pN, Pathological N stage
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Affiliation(s)
- Shuai-Tong Zhang
- School of Medical Technology, Beijing Institute of Technology, Beijing, China.,Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, China.,CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Si-Yun Wang
- Department of PET Center, Guangdong Provincial People's Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Jie Zhang
- Department of Radiology, Zhuhai City People's Hospital/Zhuhai Hospital Affiliated to Jinan University, Zhuhai, Guangdong, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Wei Mu
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, China.,Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Xue-Er Xia
- Department of Gastrointestinal Surgery, General Surgery Center, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Fang-Fang Fu
- Department of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou, Henan, China
| | - Ya-Nan Lu
- Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan, China
| | - Shuo Wang
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, China.,Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Zhen-Chao Tang
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, China.,Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Peng Li
- Department of PET Center, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan, China
| | - Jin-Rong Qu
- Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan, China
| | - Mei-Yun Wang
- Department of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou, Henan, China
| | - Jie Tian
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, China.,CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Jian-Hua Liu
- Department of Oncology, Guangdong Provincial People's Hospital, Southern Medical University, Guangzhou, Guangdong, China
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