中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 中国科学院分子影像重点实验室
Development and validation of a CT-based radiomic nomogram for preoperative prediction of early recurrence in advanced gastric cancer

文献类型：期刊论文

作者Zhang, Wenjuan4,5,6,7; Fang, Mengjie7,8; Dong, Di7,8; Wang, Xiaoxiao1; Ke, Xiaoai4,5,6; Zhang, Liwen7,8; Hu, Chaoen7,8; Guo, Lingyun5,9; Guan, Xiaoying2,5; Zhou, Junlin4,5,6
刊名RADIOTHERAPY AND ONCOLOGY
出版日期2020-04-01
卷号145页码:13-20
关键词Gastric cancer Computed tomography Radiomics Deep learning Prognosis
ISSN号0167-8140
DOI10.1016/j.radonc.2019.11.023
通讯作者Zhou, Junlin(ery_zhoujl@lzu.edu.cn) ; Shan, Xiuhong(xhongshan@hotmail.com) ; Tian, Jie(tian@ieee.org)
英文摘要Background: In the clinical management of advanced gastric cancer (AGC), preoperative identification of early recurrence after curative resection is essential. Thus, we aimed to create a CT-based radiomic model to predict early recurrence in AGC patients preoperatively. Materials and methods: We enrolled 669 consecutive patients (302 in the training set, 219 in the internal test set and 148 in the external test set) with clinicopathologically confirmed AGC from two centers. Radiomic features were extracted from preoperative diagnostic CT images. Machine learning methods were applied to shrink feature size and build a predictive radiomic signature. We incorporated the radiomic signature and clinical risk factors into a nomogram using multivariable logistic regression analysis. The area under the curve (AUC) of operating characteristics (ROC), accuracy, and calibration curves were assessed to evaluate the nomogram's performance in discriminating early recurrence. Results: A radiomic signature, including three hand crafted features and six deep learning features, was significantly associated with early recurrence (p-value <0.0001 for all sets). In addition, clinical N stage, carbohydrate antigen 199 levels, carcinoembryonic antigen levels, and Borrmann type were considered useful predictors for early recurrence. The nomogram, combining all these predictors, showed powerful prognostic ability in the training set and two test sets with AUCs of 0.831 (95% CI, 0.786-0.876), 0.826 (0.772-0.880) and 0.806 (0.732-0.881), respectively. The predicted risk yielded good agreement with the observed recurrence probability. Conclusions: By incorporating a radiomic signature and clinical risk factors, we created a radiomic nomogram to predict early recurrence in patients with AGC, preoperatively, which may serve as a potential tool to guide personalized treatment. (C) 2019 Elsevier B.V. All rights reserved.
WOS关键词PHASE-III TRIAL ; SERUM TUMOR-MARKERS ; NODE-METASTASIS ; PLUS CISPLATIN ; GASTRECTOMY ; CAPECITABINE ; CHEMOTHERAPY ; INTERGROUP ; CARCINOMA ; PATTERNS
资助项目National Key R&D Program of China[2017YFC1308700] ; National Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2017YFC1309100] ; National Key R&D Program of China[2017YFA0700401] ; National Natural Science Foundation of China[81772006] ; National Natural Science Foundation of China[91959130] ; National Natural Science Foundation of China[81971776] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[81501616] ; National Natural Science Foundation of China[81227901] ; Jiangsu Provincial Research Foundation for Basic Research of China[BK20151334] ; Zhenjiang Innovation Capacity Building Program (technological infrastructure) -R&D project of China[SS2015023] ; Jiangsu Provincial Key RD Special Fund[BE2015666] ; Beijing Natural Science Foundation[L182061] ; Key Research Program of the Chinese Academy of Sciences[KGZD-EW-T03] ; Youth Innovation Promotion Association of the Chinese Academy of Sciences[2017175]
WOS研究方向Oncology ; Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:000531474700003
出版者ELSEVIER IRELAND LTD
资助机构National Key R&D Program of China ; National Natural Science Foundation of China ; Jiangsu Provincial Research Foundation for Basic Research of China ; Zhenjiang Innovation Capacity Building Program (technological infrastructure) -R&D project of China ; Jiangsu Provincial Key RD Special Fund ; Beijing Natural Science Foundation ; Key Research Program of the Chinese Academy of Sciences ; Youth Innovation Promotion Association of the Chinese Academy of Sciences
源URL[http://ir.ia.ac.cn/handle/173211/39458]  
专题自动化研究所_中国科学院分子影像重点实验室
通讯作者Zhou, Junlin; Shan, Xiuhong; Tian, Jie
作者单位1.Jiangsu Univ, Dept Radiol, Affiliated Peoples Hosp, Zhenjiang 212002, Jiangsu, Peoples R China
2.Lanzhou Univ, Dept Pathol, Hosp 2, Lanzhou, Peoples R China
3.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing, Peoples R China
4.Lanzhou Univ, Dept Radiol, Hosp 2, Lanzhou 730030, Peoples R China
5.Lanzhou Univ, Clin Sch 2, Lanzhou, Peoples R China
6.Key Lab Med Imaging Gansu Prov, Lanzhou, Peoples R China
7.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
8.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
9.Lanzhou Univ, Dept Gen Surg, Hosp 2, Lanzhou, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Wenjuan,Fang, Mengjie,Dong, Di,et al. Development and validation of a CT-based radiomic nomogram for preoperative prediction of early recurrence in advanced gastric cancer[J]. RADIOTHERAPY AND ONCOLOGY,2020,145:13-20.
APA Zhang, Wenjuan.,Fang, Mengjie.,Dong, Di.,Wang, Xiaoxiao.,Ke, Xiaoai.,...&Tian, Jie.(2020).Development and validation of a CT-based radiomic nomogram for preoperative prediction of early recurrence in advanced gastric cancer.RADIOTHERAPY AND ONCOLOGY,145,13-20.
MLA Zhang, Wenjuan,et al."Development and validation of a CT-based radiomic nomogram for preoperative prediction of early recurrence in advanced gastric cancer".RADIOTHERAPY AND ONCOLOGY 145(2020):13-20.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。