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
智能影像学(英文)2024,Vol.2Issue(1):3-16,14.
智能影像学(英文)2024,Vol.2Issue(1):3-16,14.DOI:10.1002/ird3.56
Assessing the reproducibility,stability,and biological interpretability of multimodal computed tomography image features for prognosis in advanced non-small cell lung cancer
Assessing the reproducibility,stability,and biological interpretability of multimodal computed tomography image features for prognosis in advanced non-small cell lung cancer
摘要
关键词
artificial intelligence/computed tomography/critical pathways/non-small cell lung cancer/x-rayKey words
artificial intelligence/computed tomography/critical pathways/non-small cell lung cancer/x-ray引用本文复制引用
Jiajun Wang,Gang Dai,Xiufang Ren,Ruichuan Shi,Ruibang Luo,Jianhua Liu,Kexue Deng,Jiangdian Song..Assessing the reproducibility,stability,and biological interpretability of multimodal computed tomography image features for prognosis in advanced non-small cell lung cancer[J].智能影像学(英文),2024,2(1):3-16,14.基金项目
National Natural Science Foundation of China,Grant/Award Numbers:92259104,82001904 ()