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Computed tomography-based radiomic model at node level for the prediction of normal-sized lymph node metastasis in cervical cancer 期刊论文
TRANSLATIONAL ONCOLOGY, 2021, 卷号: 14, 期号: 8, 页码: 7
作者:  Liu, Yujia;  Fan, Huijian;  Dong, Di;  Liu, Ping;  He, Bingxi;  Meng, Lingwei;  Chen, Jiaming;  Chen, Chunlin;  Lang, Jinghe;  Tian, Jie
收藏  |  浏览/下载：237/0  |  提交时间：2021/11/03
Cervical cancer  Lymph node metastasis  Radiomics  Preoperative prediction  Classifiers  
Building CT Radiomics-Based Models for Preoperatively Predicting Malignant Potential and Mitotic Count of Gastrointestinal Stromal Tumors 期刊论文
TRANSLATIONAL ONCOLOGY, 2019, 卷号: 12, 期号: 9, 页码: 1229-1236
作者:  Wang, Chao;  Li, Hailin;  Jiaerken, Yeerfan;  Huang, Peiyu;  Sun, Lifeng;  Dong, Fei;  Huang, Yajing;  Dong, Di;  Tian, Jie;  Zhang, Minming
收藏  |  浏览/下载：207/0  |  提交时间：2019/12/16
Diagnosis of Distant Metastasis of Lung Cancer: Based on Clinical and Radiomic Features 期刊论文
TRANSLATIONAL ONCOLOGY, 2018, 卷号: 11, 期号: 1, 页码: 31-36
作者:  Zhou, Hongyu;  Dong, Di;  Chen, Bojiang;  Fang, Mengjie;  Cheng, Yue;  Gan, Yuncun;  Zhang, Rui;  Zhang, Liwen;  Zang, Yali;  Liu, Zhenyu;  Zheng, Hairong;  Li, Weimin;  Tian, Jie
浏览  |  Adobe PDF(626Kb)  |  收藏  |  浏览/下载：339/60  |  提交时间：2018/01/09
Radiomics  
Quantitative Biomarkers for Prediction of Epidermal Growth Factor Receptor Mutation in Non-Small Cell Lung Cancer 期刊论文
TRANSLATIONAL ONCOLOGY, 2018, 卷号: 11, 期号: 1, 页码: 94-101
作者:  Zhang, Liwen;  Chen, Bojiang;  Liu, Xia;  Song, Jiangdian;  Fang, Mengjie;  Hu, Chaoen;  Dong, Di;  Li, Weimin;  Tian, Jie
浏览  |  Adobe PDF(1030Kb)  |  收藏  |  浏览/下载：427/121  |  提交时间：2018/01/09
Radiomics  
2D and 3D CT Radiomics Features Prognostic Performance Comparison in Non-Small Cell Lung Cancer 期刊论文
TRANSLATIONAL ONCOLOGY, 2017, 卷号: 10, 期号: 6, 页码: 886-894
作者:  Shen, Chen;  Liu, Zhenyu;  Guan, Min;  Song, Jiangdian;  Lian, Yucheng;  Wang, Shuo;  Tang, Zhenchao;  Dong, Di;  Kong, Lingfei;  Wang, Meiyun;  Shi, Dapeng;  Tian, Jie
浏览  |  Adobe PDF(859Kb)  |  收藏  |  浏览/下载：477/106  |  提交时间：2018/01/13
Radiomics