biostudies-literatureUnknown9(3)Zhang ST<h4>Background</h4>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.<h4>Methods</h4>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 approaHeliyone14030https://www.ebi.ac.uk/biostudies/studies/S-EPMC10009687biostudies-literatureArtificial intelligence-based computer-aided diagnosis system supports diagnosis of lymph node metastasis in esophageal squamous cell carcinoma: A multicenter study.PMC10009687Zhang STTian JFu FFWang SYXia XEZhang JLu YNWang MYLi PLiu JHTang ZCQu JRDong DWang SMu WfalseArtificial intelligence-based computer-aided diagnosis system supports diagnosis of lymph node metastasis in esophageal squamous cell carcinoma: A multicenter study.<h4>Background</h4>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.<h4>Methods</h4>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 approa2023-01-01T00:00:00Z2023 Mar2025-04-19T02:01:08.077Z2025-04-07T12:40:43.561ZS-EPMC100096873692385410.1016/j.heliyon.2023.e14030