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
Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas

文献类型：期刊论文

作者Han, Yuqi1,2,4; Xie, Zhen3; Zang, Yali2,4,8; Zhang, Shuaitong2,4,8; Gu, Dongsheng2,4,8; Zhou, Mu5; Gevaert, Olivier5; Wei, Jingwei2,4,8; Li, Chao3; Chen, Hongyan6
刊名JOURNAL OF NEURO-ONCOLOGY
出版日期2018-11-01
卷号140期号:2页码:297-306
关键词Lower-grade glioma 1p 19q Co-deletion Prediction Radiomics Magnetic resonance imaging
ISSN号0167-594X
DOI10.1007/s11060-018-2953-y
通讯作者Dong, Di(di.dong@ia.ac.cn) ; Tian, Jie(tian@ieee.org) ; Zhou, Dabiao(dabiaozhou@163.com)
英文摘要PurposeTo perform radiomics analysis for non-invasively predicting chromosome 1p/19q co-deletion in World Health Organization grade II and III (lower-grade) gliomas.MethodsThis retrospective study included 277 patients histopathologically diagnosed with lower-grade glioma. Clinical parameters were recorded for each patient. We performed a radiomics analysis by extracting 647 MRI-based features and applied the random forest algorithm to generate a radiomics signature for predicting 1p/19q co-deletion in the training cohort (n=184). The clinical model consisted of pertinent clinical factors, and was built using a logistic regression algorithm. A combined model, incorporating both the radiomics signature and related clinical factors, was also constructed. The receiver operating characteristics curve was used to evaluate the predictive performance. We further validated the predictability of the three developed models using a time-independent validation cohort (n=93).ResultsThe radiomics signature was constructed as an independent predictor for differentiating 1p/19q co-deletion genotypes, which demonstrated superior performance on both the training and validation cohorts with areas under curve (AUCs) of 0.887 and 0.760, respectively. These results outperformed the clinical model (AUCs of 0.580 and 0.627 on training and validation cohorts). The AUCs of the combined model were 0.885 and 0.753 on training and validation cohorts, respectively, which indicated that clinical factors did not present additional improvement for the prediction.ConclusionOur study highlighted that an MRI-based radiomics signature can effectively identify the 1p/19q co-deletion in histopathologically diagnosed lower-grade gliomas, thereby offering the potential to facilitate non-invasive molecular subtype prediction of gliomas.
WOS关键词OLIGODENDROGLIAL TUMORS ; GLIOBLASTOMA-MULTIFORME ; SURVIVAL ; 1P ; CLASSIFICATION ; RADIOTHERAPY ; TEMOZOLOMIDE ; MANAGEMENT ; IMPACT ; 19Q
资助项目National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[81501616] ; National Natural Science Foundation of China[81771924] ; National Key Research and Development Program of China[2106YFC0103702] ; National Key Research and Development Program of China[2017YFA0205200] ; National Institute of Biomedical Imaging And Bioengineering of the National Institutes of Health[R01EB020527]
WOS研究方向Oncology ; Neurosciences & Neurology
语种英语
WOS记录号WOS:000450472400011
出版者SPRINGER
资助机构National Natural Science Foundation of China ; National Key Research and Development Program of China ; National Institute of Biomedical Imaging And Bioengineering of the National Institutes of Health
源URL[http://ir.ia.ac.cn/handle/173211/25721]  
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Dong, Di; Tian, Jie; Zhou, Dabiao
作者单位1.Xidian Univ, Sch Life Sci & Technol, Xian 710126, Shaanxi, Peoples R China
2.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, Beijing 100190, Peoples R China
3.Capital Med Univ, Beijing Tiantan Hosp, Dept Neurosurg, Beijing 100050, Peoples R China
4.Beijing Key Lab Mol Imaging, Beijing 100190, Peoples R China
5.Stanford Univ, Stanford Ctr Biomed Informat Res, Palo Alto, CA 94304 USA
6.Capital Med Univ, Beijing Neurosurg Inst, Dept Neuroradiol, 6 Tiantanxili, Beijing 100050, Peoples R China
7.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
8.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
9.China Natl Clin Res Ctr Neurol Dis, Beijing 100050, Peoples R China
推荐引用方式
GB/T 7714		 Han, Yuqi,Xie, Zhen,Zang, Yali,et al. Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas[J]. JOURNAL OF NEURO-ONCOLOGY,2018,140(2):297-306.
APA Han, Yuqi.,Xie, Zhen.,Zang, Yali.,Zhang, Shuaitong.,Gu, Dongsheng.,...&Zhou, Dabiao.(2018).Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas.JOURNAL OF NEURO-ONCOLOGY,140(2),297-306.
MLA Han, Yuqi,et al."Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas".JOURNAL OF NEURO-ONCOLOGY 140.2(2018):297-306.

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。