Artificial intelligence-based computer-aided diagnosis system supports diagnosis of lymph node metastasis in esophageal squamous cell carcinoma: A multicenter study. Issue 3 (March 2023)

• Record Type: Journal Article
• Authors: Zhang, Shuai-Tong; Wang, Si-Yun; Zhang, Jie; Dong, Di; Mu, Wei; Xia, Xue-er; Fu, Fang-Fang; Lu, Ya-Nan; Wang, Shuo; Tang, Zhen-Chao; Li, Peng; Qu, Jin-Rong; Wang, Mei-Yun; Tian, Jie; Liu, Jian-Hua
• Is Part Of: Heliyon. Volume 9:Issue 3(2023)
• Publication Date: 2023-03
• Subjects: Esophageal squamous cell carcinoma -- PET/CT -- Lymph node metastasis -- Artificial intelligence; ESCC Esophageal squamous cell carcinoma -- LNM Lymph node metastasis -- nCRT Neoadjuvant chemoradiotherapy -- CT Computed tomography -- 18F-FDG PET/CT 18-fluorine-fluorodeoxyglucose positron-emission tomography/computed tomography -- cN Clinical N stage -- AI Artificial intelligence -- AI-CAD Artificial intelligence-based computer-aided diagnosis -- pN Pathological N stage -- SUV Standardized uptake value -- SLR Ratio of the SUV value to liver uptake -- SD Standard deviation -- OS Overall survival -- PFS Progression-free survival -- CI Confidence interval; Research -- Periodicals; Medical sciences -- Periodicals; Natural history -- Periodicals; Social sciences -- Periodicals; Earth sciences -- Periodicals; Physical sciences -- Periodicals; 507.2
• DOI: 10.1016/j.heliyon.2023.e14030
• Languages: English
• ISSNs: 2405-8440

Abstract

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.