Artificial intelligence-based computer-aided diagnosis system supports diagnosis of lymph node metastasis in esophageal squamous cell carcinoma: A multicenter study
Shuai-Tong Zhang,
Si-Yun Wang,
Jie Zhang,
Di Dong,
Wei Mu,
Xue-er Xia,
Fang-Fang Fu,
Ya-Nan Lu,
Shuo Wang,
Zhen-Chao Tang,
Peng Li,
Jin-Rong Qu,
Mei-Yun Wang,
Jie Tian,
Jian-Hua Liu
Affiliations
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; Corresponding author. Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China No. 127 Dongming Road, Jinshui District, Zhengzhou, 450008, E-, China.
Mei-Yun Wang
Department of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou, Henan, China; Corresponding author. No. 7 Weiwu Road, Jinshui District, Zhengzhou, 450003, 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; Corresponding author. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, China. No.37 Xueyuan Road, Haidian District, Beijing, 100191 China.
Jian-Hua Liu
Department of Oncology, Guangdong Provincial People's Hospital, Southern Medical University, Guangzhou, Guangdong, China; Corresponding author. Department of Oncology, Guangdong Provincial People's Hospital, Southern Medical University, Guangzhou, Guangdong, China No. 123 Huifu Road West, Yuexiu District, Guangzhou, 510180, China.
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.
