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 MRI-Based Radiomics Prognostic Classifier in Patients with Primary Glioblastoma Multiforme

文献类型：期刊论文

作者Chen, Xin1,2,4; Fang, Mengjie; Dong, Di; Liu, Lingling2; Xu, Xiangdong2; Wei, Xinhua2; Jiang, Xinqing2; Qin, Lei3,4; Liu, Zaiyi1
刊名ACADEMIC RADIOLOGY
出版日期2019-10-01
卷号26期号:10页码:1292-1300
关键词Glioblastoma multiform Survival analyses Magnetic resonance imaging Radiomics
ISSN号1076-6332
DOI10.1016/j.acra.2018.12.016
通讯作者Qin, Lei(lqin2@partner.org) ; Liu, Zaiyi(zyliu@163.com)
英文摘要Rationale and Objectives: Glioblastoma multiforme (GBM) is the most common and deadly type of primary malignant tumor of the central nervous system. Accurate risk stratification is vital for a more personalized approach in GBM management. The purpose of this study is to develop and validate a MRI-based prognostic quantitative radiomics classifier in patients with newly diagnosed GBM and to evaluate whether the classifier allows stratification with improved accuracy over the clinical and qualitative imaging features risk models. Methods: Clinical and MR imaging data of 127 GBM patients were obtained from the Cancer Genome Atlas and the Cancer Imaging Archive. Regions of interest corresponding to high signal intensity portions of tumor were drawn on postcontrast T1-weighted imaging (post-T1WI) on the 127 patients (allocated in a 2:1 ratio into a training [n = 85] or validation [n = 42] set), then 3824 radiomics features per patient were extracted. The dimension of these radiomics features were reduced using the minimum redundancy maximum relevance algorithm, then Cox proportional hazard regression model was used to build a radiomics classifier for predicting overall survival (OS). The value of the radiomics classifier beyond clinical (gender, age, Karnofsky performance status, radiation therapy, chemotherapy, and type of resection) and VASARI features for OS was assessed with multivariate Cox proportional hazards model. Time-dependent receiver operating characteristic curve analysis was used to assess the predictive accuracy. Results: A classifier using four post-T1WI-MRI radiomics features built on the training dataset could successfully separate GBM patients into low- or high-risk group with a significantly different OS in training (HR, 6.307 [95% CI, 3.475-11.446]; p < 0.001) and validation set (HR, 3.646 [95% CI, 1.709-7.779]; p < 0.001). The area under receiver operating characteristic curve of radiomics classifier (training, 0.799; validation, 0.815 for 12-month) was higher compared to that of the clinical risk model (Karnofsky performance status, radiation therapy; training, 0.749; validation, 0.670 for 12-month), and none of the qualitative imaging features was associated with OS. The predictive accuracy was further improved when combined the radiomics classifier with clinical data (training, 0.819; validation: 0.851 for 12-month). Conclusion: A classifier using radiomics features allows preoperative prediction of survival and risk stratification of patients with GBM, and it shows improved performance compared to that of clinical and qualitative imaging features models.
WOS关键词TUMOR HETEROGENEITY ; SURVIVAL ; INFORMATION ; PSEUDOPROGRESSION ; TEMOZOLOMIDE ; PROGRESSION ; PREDICTION ; FEATURES ; IMAGES ; MODEL
资助项目National Key Research and Development Program of China[2017YFC130910002] ; National Natural Scientific Foundation of China[81601469] ; National Natural Scientific Foundation of China[81771912] ; National Natural Scientific Foundation of China[81671854] ; Science and Technology Planning Project of Guangdong Province[2014A020212341] ; China Scholarship Council[201808440033]
WOS研究方向Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:000488147800003
出版者ELSEVIER SCIENCE INC
资助机构National Key Research and Development Program of China ; National Natural Scientific Foundation of China ; Science and Technology Planning Project of Guangdong Province ; China Scholarship Council
源URL[http://ir.ia.ac.cn/handle/173211/26654]  
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Qin, Lei; Liu, Zaiyi
作者单位1.Guangdong Acad Med Sci, Guangdong Prov Peoples Hosp, Dept Radiol, Guangzhou 510080, Guangdong, Peoples R China
2.South China Univ Technol, Sch Med, Guangzhou Peoples Hosp 1, Dept Radiol, Guangzhou, Guangdong, Peoples R China
3.Dana Farber Canc Inst, Dept Imaging, Boston, MA 02115 USA
4.Harvard Med Sch, Dept Radiol, Boston, MA 02115 USA
5.Univ Chinese Acad Sci, Chinese Acad Sci, Key Lab Mol Imaging, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Chen, Xin,Fang, Mengjie,Dong, Di,et al. Development and Validation of a MRI-Based Radiomics Prognostic Classifier in Patients with Primary Glioblastoma Multiforme[J]. ACADEMIC RADIOLOGY,2019,26(10):1292-1300.
APA Chen, Xin.,Fang, Mengjie.,Dong, Di.,Liu, Lingling.,Xu, Xiangdong.,...&Liu, Zaiyi.(2019).Development and Validation of a MRI-Based Radiomics Prognostic Classifier in Patients with Primary Glioblastoma Multiforme.ACADEMIC RADIOLOGY,26(10),1292-1300.
MLA Chen, Xin,et al."Development and Validation of a MRI-Based Radiomics Prognostic Classifier in Patients with Primary Glioblastoma Multiforme".ACADEMIC RADIOLOGY 26.10(2019):1292-1300.

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。