Prediction of Prognosis in Glioblastoma Using Radiomics Features of Dynamic Contrast-Enhanced MRI

被引:25
|
作者
Pak, Elena [1 ]
Choi, Kyu Sung [1 ]
Choi, Seung Hong [1 ,4 ,5 ]
Park, Chul-Kee [6 ,7 ]
Kim, Tae Min [8 ]
Park, Sung-Hye [2 ]
Lee, Joo Ho [9 ]
Lee, Soon-Tae [3 ]
Hwang, Inpyeong [1 ]
Yoo, Roh-Eul [1 ]
Kang, Koung Mi [1 ]
Yun, Tae Jin [1 ]
Kim, Ji-Hoon [1 ]
Sohn, Chul-Ho [1 ]
机构
[1] Seoul Natl Univ Hosp, Dept Radiol, 101 Daehak Ro, Seoul 03080, South Korea
[2] Seoul Natl Univ Hosp, Dept Pathol, Seoul, South Korea
[3] Seoul Natl Univ Hosp, Dept Neurol, Seoul, South Korea
[4] Seoul Natl Univ, Ctr Nanoparticle Res, Inst Basic Sci, Seoul, South Korea
[5] Seoul Natl Univ, Sch Chem & Biol Engn, Seoul, South Korea
[6] Seoul Natl Univ Hosp, Dept Neurosurg, Seoul, South Korea
[7] Seoul Natl Univ Hosp, Biomed Res Inst, Seoul, South Korea
[8] Seoul Natl Univ Hosp, Canc Res Inst, Dept Internal Med, Seoul, South Korea
[9] Seoul Natl Univ Hosp, Canc Res Inst, Dept Radiat Oncol, Seoul, South Korea
基金
新加坡国家研究基金会;
关键词
Glioblastoma; Progression; Dynamic contrast enhanced MRI; K-trans; V-e; V-p; Radiomics; SIGNAL-INTENSITY LESIONS; STANDARD TREATMENT; SURVIVAL; DIMENSIONALITY; TEMOZOLOMIDE; DIAGNOSIS;
D O I
10.3348/kjr.2020.1433
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Objective: To develop a radiomics risk score based on dynamic contrast-enhanced (OCE) MRI for prognosis prediction in patients with glioblastoma. Materials and Methods: One hundred and fifty patients (92 male [61.3%]; mean age +/- standard deviation, 60.5 +/- 13.5 years) with glioblastoma who underwent preoperative MRI were enrolled in the study. Six hundred and forty-two radiomic features were extracted from volume transfer constant (K-trans), fractional volume of vascular plasma space (V-p), and fractional volume of extravascular extracellular space (V-s) maps of OCE MRI, wherein the regions of interest were based on both T1weighted contrast-enhancing areas and non-enhancing T2 hyperintense areas. Using feature selection algorithms, salient radiomic features were selected from the 642 features. Next, a radiomics risk score was developed using a weighted combination of the selected features in the discovery set (n = 105); the risk score was validated in the validation set (n = 45) by investigating the difference in prognosis between the "radiomics risk score" groups. Finally, multivariable Cox regression analysis for progression-free survival was performed using the radiomics risk score and clinical variables as covariates. Results: 16 radiomic features obtained from non-enhancing T2 hyperintense areas were selected among the 642 features identified. The radiomics risk score was used to stratify high- and low-risk groups in both the discovery and validation sets (both p < 0.001 by the log-rank test). The radiomics risk score and presence of isocitrate dehydrogenase (ION) mutation showed independent associations with progression-free survival in opposite directions (hazard ratio, 3.56; p = 0.004 and hazard ratio, 0.34; p = 0.022, respectively). Conclusion: We developed and validated the "radiomics risk score" from the features of OCE MRI based on non-enhancing T2 hyperintense areas for risk stratification of patients with glioblastoma. It was associated with progression-free survival independently of IDH mutation status.
引用
收藏
页码:1514 / 1524
页数:11
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