Assessing the reproducibility,stability,and biological interpretability of multimodal computed tomography image features for prognosis in advanced non-small cell lung cancerOA
Assessing the reproducibility,stability,and biological interpretability of multimodal computed tomography image features for prognosis in advanced non-small cell lung cancer
Jiajun Wang;Gang Dai;Xiufang Ren;Ruichuan Shi;Ruibang Luo;Jianhua Liu;Kexue Deng;Jiangdian Song
Department of Thoracic Surgery,The First Affiliated Hospital of China Medical University,Shenyang,Liaoning,ChinaDepartment of Radiology,The First Affiliated Hospital of University of Science and Technology of China(USTC),Division of Life Sciences and Medicine,USTC,Hefei,Anhui,ChinaDepartment of Pathology,Shengjing Hospital of China Medical University,Shenyang,Liaoning,ChinaDepartment of Medical Oncology,The First Hospital of China Medical University,Shenyang,Liaoning,ChinaDepartment of Computer Science,The University of Hong Kong,Hong Kong,ChinaDepartment of Oncology,Cancer Center,Guangdong Provincial People's Hospital,Guangdong Academy of Medical Sciences,and Southern Medical University,Guangzhou,Guangdong,ChinaDepartment of Radiology,The First Affiliated Hospital of University of Science and Technology of China(USTC),Division of Life Sciences and Medicine,USTC,Hefei,Anhui,ChinaSchool of Health Management,China Medical University,Shenyang,Liaoning,China
artificial intelligencecomputed tomographycritical pathwaysnon-small cell lung cancerx-ray
artificial intelligencecomputed tomographycritical pathwaysnon-small cell lung cancerx-ray
《智能影像学（英文）》 2024 (1)
3-16,14
National Natural Science Foundation of China,Grant/Award Numbers:92259104,82001904
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