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CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study

文献类型：期刊论文

作者Gu, Dongsheng8,9; Hu, Yabin2,3,4; Ding, Hui4; Wei, Jingwei8,9; Chen, Ke5; Liu, Hao6; Zeng, Mengsu2,3; Tian, Jie1,7,8,9
刊名EUROPEAN RADIOLOGY
出版日期2019-12-01
卷号29期号:12页码:6880-6890
关键词Neoplasm grading Pancreas Neuroendocrine tumor Radiomics CT
ISSN号0938-7994
DOI10.1007/s00330-019-06176-x
通讯作者Zeng, Mengsu(zengmengsu@outlook.com) ; Tian, Jie(tian@ieee.org)
英文摘要Objective To develop and validate a radiomics-based nomogram for preoperatively predicting grade 1 and grade 2/3 tumors in patients with pancreatic neuroendocrine tumors (PNETs). Methods One hundred thirty-eight patients derived from two institutions with pathologically confirmed PNETs (104 in the training cohort and 34 in the validation cohort) were included in this retrospective study. A total of 853 radiomic features were extracted from arterial and portal venous phase CT images respectively. Minimum redundancy maximum relevance and random forest methods were adopted for the significant radiomic feature selection and radiomic signature construction. A fusion radiomic signature was generated by combining both the single-phase signatures. The nomogram based on a comprehensive model incorporating the clinical risk factors and the fusion radiomic signature was established, and decision curve analysis was applied for clinical use. Results The fusion radiomic signature has significant association with histologic grade (p<0.001). The nomogram integrating independent clinical risk factor tumor margin and fusion radiomic signature showed strong discrimination with an area under the curve (AUC) of 0.974 (95% CI 0.950-0.998) in the training cohort and 0.902 (95% CI 0.798-1.000) in the validation cohort with good calibration. Decision curve analysis verified the clinical usefulness of the predictive nomogram. Conclusion We proposed a comprehensive nomogram consisting of tumor margin and fusion radiomic signature as a powerful tool to predict grade 1 and grade 2/3 PNET preoperatively and assist the clinical decision-making for PNET patients.
WOS关键词APPARENT DIFFUSION-COEFFICIENT ; PROGNOSTIC-FACTORS ; NEOPLASMS ; FEATURES ; MRI
资助项目National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[61231004] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[81501616] ; National Key Research and Development Program of China[2017YFA0205200] ; National Key Research and Development Program of China[2017YFC1308700] ; Science and Technology Service Network Initiative of the Chinese Academy of Sciences[KFJ-SW-STS-160]
WOS研究方向Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:000500979400051
出版者SPRINGER
资助机构National Natural Science Foundation of China ; National Key Research and Development Program of China ; Science and Technology Service Network Initiative of the Chinese Academy of Sciences
源URL[http://ir.ia.ac.cn/handle/173211/29405]  
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Zeng, Mengsu; Tian, Jie
作者单位1.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing 100191, Peoples R China
2.Fudan Univ, Zhongshan Hosp, Dept Radiol, 180 Fenglin Rd, Shanghai 200032, Peoples R China
3.Shanghai Inst Med Imaging, 180 Fenglin Rd, Shanghai 200032, Peoples R China
4.Qingdao Univ, Dept Radiol, Affiliated Hosp, Laoshan Hosp, Qingdao 266061, Shandong, Peoples R China
5.Fudan Univ, Zhongshan Hosp, Dept Pathol, Shanghai 200032, Peoples R China
6.Cent Hosp ZiBo, Dept Radiol, Zibo 255036, Shandong, Peoples R China
7.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Sch Life Sci & Technol, Minist Educ, Xian 710126, Shanxi, Peoples R China
8.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, 95 East Zhongguancun Rd, Beijing 100190, Peoples R China
9.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Gu, Dongsheng,Hu, Yabin,Ding, Hui,et al. CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study[J]. EUROPEAN RADIOLOGY,2019,29(12):6880-6890.
APA Gu, Dongsheng.,Hu, Yabin.,Ding, Hui.,Wei, Jingwei.,Chen, Ke.,...&Tian, Jie.(2019).CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study.EUROPEAN RADIOLOGY,29(12),6880-6890.
MLA Gu, Dongsheng,et al."CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study".EUROPEAN RADIOLOGY 29.12(2019):6880-6890.

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来源：自动化研究所

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