机构:[1]Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan Er Road, Guangzhou 510080, China[2]School of Medicine, South China University of Technology, Guangzhou 510006, China[3]Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China[4]Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, China[5]Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou 511400, China[6]Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, 1 Panfu Road, Guangzhou 510180, China
This study was supported by the Regional Innovation and
Development Joint Fund of National Natural Science Foundation of
China (grant number U22A20345), the National Science Fund for Distinguished
Young Scholars (grant number 81925023), the National Natural
Scientific Foundation of China (grant number 82072090), Guangdong
Provincial Key Laboratory of Artificial Intelligence in Medical
Image Analysis and Application (grant number 2022B1212010011),
High-level Hospital Construction Project (DFJHBF202105), and
Science and Technology Projects in Guangzhou (grant numbers
202201020001 and 202201010513).
第一作者机构:[1]Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan Er Road, Guangzhou 510080, China[2]School of Medicine, South China University of Technology, Guangzhou 510006, China[3]Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
共同第一作者:
通讯作者:
通讯机构:[1]Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan Er Road, Guangzhou 510080, China[2]School of Medicine, South China University of Technology, Guangzhou 510006, China[3]Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
推荐引用方式(GB/T 7714):
Li Suyun,Li Zhenhui,Wang Li,et al.CT morphological features for predicting the risk of lymph node metastasis in T1 colorectal cancer[J].EUROPEAN RADIOLOGY.2023,33(10):6861-6871.doi:10.1007/s00330-023-09688-9.
APA:
Li Suyun,Li Zhenhui,Wang Li,Wu Mimi,Chen Xiaobo...&Liu Zaiyi.(2023).CT morphological features for predicting the risk of lymph node metastasis in T1 colorectal cancer.EUROPEAN RADIOLOGY,33,(10)
MLA:
Li Suyun,et al."CT morphological features for predicting the risk of lymph node metastasis in T1 colorectal cancer".EUROPEAN RADIOLOGY 33..10(2023):6861-6871