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基于CT影像组学的胃癌TNM分期预测算法研究 学位论文
, 中国科学院自动化研究所: 中国科学院自动化研究所, 2022
Authors:  方梦捷
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影像组学  深度学习  胃癌  TNM 分期  计算机断层扫描  
The potential of prostate gland radiomic features in identifying the Gleason score 期刊论文
COMPUTERS IN BIOLOGY AND MEDICINE, 2022, 卷号: 144, 页码: 11
Authors:  Gong, Lixin;  Xu, Min;  Fang, Mengjie;  He, Bingxi;  Li, Hailin;  Fang, Xiangming;  Dong, Di;  Tian, Jie
Favorite  |  View/Download：78/0  |  Submit date：2022/07/25
MRI  Radiomics  Prostate cancer  Gleason score  Gland segmentation  
Deep learning for predicting immunotherapeutic efficacy in advanced non-small cell lung cancer patients: a retrospective study combining progression-free survival risk and overall survival risk 期刊论文
TRANSLATIONAL LUNG CANCER RESEARCH, 2022, 页码: 23
Authors:  He, Bing-Xi;  Zhong, Yi-Fan;  Zhu, Yong-Bei;  Deng, Jia-Jun;  Fang, Meng-Jie;  She, Yun-Lang;  Wang, Ting-Ting;  Yang, Yang;  Sun, Xi-Wen;  Belluomini, Lorenzo;  Watanabe, Satoshi;  Dong, Di;  Tian, Jie;  Xie, Dong
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Tumor biomarkers  immunotherapy  lung neoplasms  programmed cell death 1 receptor (PD-1 receptor)  biostatistics  
Deep learning-based AI model for signet-ring cell carcinoma diagnosis and chemotherapy response prediction in gastric cancer 期刊论文
MEDICAL PHYSICS, 2022, 页码: 12
Authors:  Li, Cong;  Qin, Yun;  Zhang, Wei-Han;  Jiang, Hanyu;  Song, Bin;  Bashir, Mustafa R.;  Xu, Heng;  Duan, Ting;  Fang, Mengjie;  Zhong, Lianzhen;  Meng, Lingwei;  Dong, Di;  Hu, Zhenhua;  Tian, Jie;  Hu, Jian-Kun
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chemotherapy  deep learning  diagnosis  signet-ring cell carcinoma  survival  
Artificial intelligence in gastric cancer: applications and challenges 期刊论文
GASTROENTEROLOGY REPORT, 2022, 卷号: 10, 页码: 16
Authors:  Cao, Runnan;  Tang, Lei;  Fang, Mengjie;  Zhong, Lianzhen;  Wang, Siwen;  Gong, Lixin;  Li, Jiazheng;  Dong, Di;  Tian, Jie
Favorite  |  View/Download：22/0  |  Submit date：2023/01/09
gastric cancer  artificial intelligence  radiomics  endoscopy  computed tomography  pathology  
A deep learning-based radiomic nomogram for prognosis and treatment decision in advanced nasopharyngeal carcinoma: A multicentre study 期刊论文
EBIOMEDICINE, 2021, 卷号: 70, 页码: 10
Authors:  Zhong, Lianzhen;  Dong, Di;  Fang, Xueliang;  Zhang, Fan;  Zhang, Ning;  Zhang, Liwen;  Fang, Mengjie;  Jiang, Wei;  Liang, Shaobo;  Li, Cong;  Liu, Yujia;  Zhao, Xun;  Cao, Runnan;  Shan, Hong;  Hu, Zhenhua;  Ma, Jun;  Tang, Linglong;  Tian, Jie
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Multi-task deep learning  Radiomic nomogram  Survival analysis  Treatment decision  Advanced nasopharyngeal carcinoma  
Identifying early gastric cancer under magnifying narrow-band images with deep learning: a multicenter study 期刊论文
GASTROINTESTINAL ENDOSCOPY, 2021, 卷号: 93, 期号: 6, 页码: 1333-+
Authors:  Hu, Hao;  Gong, Lixin;  Dong, Di;  Zhu, Liang;  Wang, Min;  He, Jie;  Shu, Lei;  Cai, Yiling;  Cai, Shilun;  Su, Wei;  Zhong, Yunshi;  Li, Cong;  Zhu, Yongbei;  Fang, Mengjie;  Zhong, Lianzhen;  Yang, Xin;  Zhou, Pinghong;  Tian, Jie
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Exploring the predictive value of additional peritumoral regions based on deep learning and radiomics: A multicenter study 期刊论文
MEDICAL PHYSICS, 2021, 页码: 12
Authors:  Wu, Xiangjun;  Dong, Di;  Zhang, Lu;  Fang, Mengjie;  Zhu, Yongbei;  He, Bingxi;  Ye, Zhaoxiang;  Zhang, Minming;  Zhang, Shuixing;  Tian, Jie
Adobe PDF(3862Kb)  |  Favorite  |  View/Download：125/5  |  Submit date：2021/05/06
deep learning  peritumor  radiomics  
Integrating No.3 lymph nodes and primary tumor radiomics to predict lymph node metastasis in T1-2 gastric cancer 期刊论文
BMC Medical Imaging, 2021, 卷号: 21, 期号: 1, 页码: 10
Authors:  Wang,Xiaoxiao;  Li,Cong;  Fang,Mengjie;  Zhang,Liwen;  Zhong,Lianzhen;  Dong,Di;  Tian,Jie;  Shan,Xiuhong
Adobe PDF(2269Kb)  |  Favorite  |  View/Download：174/14  |  Submit date：2021/04/06
Stomach cancer  Lymph nodes  Nomogram  
CT-based deep learning radiomics analysis for evaluation of serosa invasion in advanced gastric cancer 期刊论文
EUROPEAN JOURNAL OF RADIOLOGY, 2020, 卷号: 132, 页码: 8
Authors:  Sun, Rui-Jia;  Fang, Meng-Jie;  Tang, Lei;  Li, Xiao-Ting;  Lu, Qiao-Yuan;  Dong, Di;  Tian, Jie;  Sun, Ying-Shi
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Stomach neoplasms  Multi-detector computed tomography  Radiomics  Deep learning