Development and Validation of a MRI-Based Radiomics Prognostic Classifier in Patients with Primary Glioblastoma Multiforme

doi:10.1016/j.acra.2018.12.016

	Development and Validation of a MRI-Based Radiomics Prognostic Classifier in Patients with Primary Glioblastoma Multiforme
	Chen, Xin 1,2,4; Fang, Mengjie ; Dong, Di ; Liu, Lingling 2; Xu, Xiangdong 2; Wei, Xinhua 2; Jiang, Xinqing 2; Qin, Lei 3,4; Liu, Zaiyi 1
刊名	ACADEMIC RADIOLOGY
	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
DOI	10.1016/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.
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关键词	TUMOR HETEROGENEITY ; SURVIVAL ; INFORMATION ; PSEUDOPROGRESSION ; TEMOZOLOMIDE ; PROGRESSION ; PREDICTION ; FEATURES ; IMAGES ; MODEL
WOS研究方向	Radiology, Nuclear Medicine & Medical Imaging
语种	英语
出版者	ELSEVIER SCIENCE INC
WOS记录号	WOS:000488147800003
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
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/26654]
专题	中国科学院自动化研究所
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.