A 18F-FDG PET/CT-based deep learning-radiomics-clinical model for prediction of cervical lymph node metastasis in esophageal squamous cell carcinoma

A 18F-FDG PET/CT-based deep learning-radiomics-clinical model for prediction of cervical lymph node metastasis in esophageal squamous cell carcinoma

Abstract Background To develop an artificial intelligence (AI)-based model using Radiomics, deep learning (DL) features extracted from 18F-fluorodeoxyglucose (18F-FDG) Positron emission tomography/Computed Tomography (PET/CT) images of tumor and cervical lymph node with clinical feature for predicti...

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Main Authors: Ping Yuan, Zhen-Hao Huang, Yun-Hai Yang, Fei-Chao Bao, Ke Sun, Fang-Fang Chao, Ting-Ting Liu, Jing-Jing Zhang, Jin-Ming Xu, Xiang-Nan Li, Feng Li, Tao Ma, Hao Li, Zi-Hao Li, Shan-Feng Zhang, Jian Hu, Yu Qi
Format: Article
Language:English
Published: BMC 2024-11-01
Series:Cancer Imaging
Subjects:
Radiomics
Deep learning
Medical images analysis
PET/CT
Esophageal squamous cell carcinoma
Cervical lymph node metastasis
Online Access:https://doi.org/10.1186/s40644-024-00799-0
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https://doi.org/10.1186/s40644-024-00799-0

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