Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study | |
Gu, Dongsheng1,2; Hu, Yabin3,4,5; Ding, Hui5; Wei, Jingwei1,2; Chen, Ke6; Liu, Hao7; Zeng, Mengsu3,4; Tian, Jie1,2,8,9 | |
发表期刊 | EUROPEAN RADIOLOGY |
ISSN | 0938-7994 |
2019-06-21 | |
卷号 | 29期号:12页码:6880-6890 |
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. |
关键词 | Neoplasm grading Pancreas Neuroendocrine tumor Radiomics CT |
DOI | 10.1007/s00330-019-06176-x |
关键词[WOS] | APPARENT DIFFUSION-COEFFICIENT ; PROGNOSTIC-FACTORS ; NEOPLASMS ; FEATURES ; MRI |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China[2017YFC1308700] ; National Key Research and Development Program of China[2017YFA0205200] ; Science and Technology Service Network Initiative of the Chinese Academy of Sciences[KFJ-SW-STS-160] ; National Natural Science Foundation of China[81501616] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[61231004] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[81227901] ; 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] ; Science and Technology Service Network Initiative of the Chinese Academy of Sciences[KFJ-SW-STS-160] ; National Key Research and Development Program of China[2017YFA0205200] ; National Key Research and Development Program of China[2017YFC1308700] |
WOS研究方向 | Radiology, Nuclear Medicine & Medical Imaging |
WOS类目 | Radiology, Nuclear Medicine & Medical Imaging |
WOS记录号 | WOS:000500979400051 |
出版者 | SPRINGER |
七大方向——子方向分类 | 医学影像处理与分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/29406 |
专题 | 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.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, 95 East Zhongguancun Rd, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Fudan Univ, Zhongshan Hosp, Dept Radiol, 180 Fenglin Rd, Shanghai 200032, Peoples R China 4.Shanghai Inst Med Imaging, 180 Fenglin Rd, Shanghai 200032, Peoples R China 5.Qingdao Univ, Dept Radiol, Affiliated Hosp, Laoshan Hosp, Qingdao 266061, Shandong, Peoples R China 6.Fudan Univ, Zhongshan Hosp, Dept Pathol, Shanghai 200032, Peoples R China 7.Cent Hosp ZiBo, Dept Radiol, Zibo 255036, Shandong, Peoples R China 8.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing 100191, Peoples R China 9.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Sch Life Sci & Technol, Minist Educ, Xian 710126, Shanxi, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
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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