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A preoperative CT-based deep learning radiomics model in predicting the Stage, Size, Grade and Necrosis score and outcome in localized clear cell renal cell carcinoma: a multicenter study

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成果类型:
期刊论文
作者:
Nie, Pei;Liu, Shihe;Zhou, Ruizhi;Li, Xiaoli;Zhi, Kaiyue;...
通讯作者:
Yang, GJ;Xu, YC
作者机构:
[Zhi, Kaiyue; Nie, Pei; Li, Xiaoli; Zhou, Ruizhi; Liu, Shihe] Qingdao Univ, Affiliated Hosp, Dept Radiol, Qingdao, Shandong, Peoples R China.
[Wang, Yanmei] GE Healthcare, Shanghai, Peoples R China.
[Dai, Zhengjun] Huiying Med Technol Co Ltd, Sci Res Dept, Beijing, Peoples R China.
[Zhao, Lianzi] Fudan Univ, Shanghai Canc Ctr, Dept Radiat Oncol, Shanghai, Peoples R China.
[Wang, Ning] Shandong First Med Univ, Shandong Prov Hosp, Dept Radiol, Jinan, Shandong, Peoples R China.
通讯机构:
[Yang, GJ ] Q
[Xu, YC ] U
Qingdao Univ, Affiliated Hosp, Dept Nucl Med, 59 Haier Rd, Qingdao 266061, Shandong, Peoples R China.
Univ South China, Sch Nucl Sci & Technol, 28 West Changsheng Rd, Hengyang 421001, Hunan, Peoples R China.
语种:
英文
关键词:
CT;Clear cell renal cell carcinoma;Deep learning;Radiomics;The Stage, Size, Grade and Necrosis score
期刊:
European Journal of Radiology
ISSN:
0720-048X
年:
2023
卷:
166
页码:
111018
基金类别:
This work was supported by the Postdoctoral Science Foundation of China [ 2021M701811 , 2018M642617 ].
机构署名:
本校为通讯机构
院系归属:
核科学技术学院
摘要:
Background and purpose: The Stage, Size, Grade and Necrosis (SSIGN) score is the most commonly used prognostic model in clear cell renal cell carcinoma (ccRCC) patients. It is a great challenge to preoperatively predict SSIGN score and outcome of ccRCC patients. The aim of this study was to develop and validate a CT-based deep learning radiomics model (DLRM) for predicting SSIGN score and outcome in localized ccRCC. Methods: A multicenter 784 (training cohort/ test 1 cohort / test 2 cohort, 475/204/105) localized ccRCC patients were enrolled. R...

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