Prediction of Prognosis in Glioblastoma Using Radiomics Features of Dynamic Contrast-Enhanced MRI
Elena Pak, Kyu Sung Choi, Seung Hong Choi, Chul‐Kee Park, Tae Min Kim, Sung‐Hye Park, Joo Ho Lee, Soon‐Tae Lee, Inpyeong Hwang, Roh‐Eul Yoo, Koung Mi Kang, Tae Jin Yun, Ji‐hoon Kim, Chul‐Ho Sohn
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: ) 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: = 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.