Korean Journal of Radiology

The Korean Society of Radiology (대한영상의학회)
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Prediction of Prognosis in Glioblastoma Using Radiomics Features of Dynamic Contrast-Enhanced MRI

  • Elena Pak (Department of Radiology, Seoul National University Hospital) ;
  • Kyu Sung Choi (Department of Radiology, Seoul National University Hospital) ;
  • Seung Hong Choi (Department of Radiology, Seoul National University Hospital) ;
  • Chul-Kee Park (Department of Neurosurgery and Biomedical Research Institute, Seoul National University Hospital) ;
  • Tae Min Kim (Department of Internal Medicine, Cancer Research Institute, Seoul National University Hospital) ;
  • Sung-Hye Park (Department of Pathology, Seoul National University Hospital) ;
  • Joo Ho Lee (Department of Radiation Oncology, Cancer Research Institute, Seoul National University Hospital) ;
  • Soon-Tae Lee (Department of Neurology, Seoul National University Hospital) ;
  • Inpyeong Hwang (Department of Radiology, Seoul National University Hospital) ;
  • Roh-Eul Yoo (Department of Radiology, Seoul National University Hospital) ;
  • Koung Mi Kang (Department of Radiology, Seoul National University Hospital) ;
  • Tae Jin Yun (Department of Radiology, Seoul National University Hospital) ;
  • Ji-Hoon Kim (Department of Radiology, Seoul National University Hospital) ;
  • Chul-Ho Sohn (Department of Radiology, Seoul National University Hospital)
  • Received : 2020.11.17
  • Accepted : 2021.04.07
  • Published : 2021.09.01
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Abstract

Objective: To develop a radiomics risk score based on dynamic contrast-enhanced (DCE) 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 (Ktrans), fractional volume of vascular plasma space (Vp), and fractional volume of extravascular extracellular space (Ve) maps of DCE MRI, wherein the regions of interest were based on both T1-weighted 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 (IDH) 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 DCE 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.

Keywords

Acknowledgement

We thank Yurim Kang, BS, and Seong Yeong Lee, BS, for their invaluable assistance with data collection and analysis.

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