Rihyeon Kim1, Seung Hong Choi2,3,4,5, Tae Jin Yun1, Soon-Tae Lee6, Chul-Kee Park7, Tae Min Kim8, Ji-Hoon Kim1, Sun-Won Park9, Chul-Ho Sohn1, Sung-Hye Park10, Il Han Kim11. 1. Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 110-744, Republic of Korea. 2. Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 110-744, Republic of Korea. verocay@snuh.org. 3. Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, 103 Daehak-ro, Jongno-gu, Seoul, 110-799, Republic of Korea. verocay@snuh.org. 4. Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul National University, Daehak-dong, Gwanak-gu, Seoul, 151-742, Republic of Korea. verocay@snuh.org. 5. School of Chemical and Biological Engineering, Seoul National University, Daehak-dong, Gwanak-gu, Seoul, 151-742, Republic of Korea. verocay@snuh.org. 6. Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea. 7. Department of Neurosurgery, Seoul National University Hospital, Seoul, Republic of Korea. 8. Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea. 9. Department of Radiology, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea. 10. Department of Pathology, Seoul National University Hospital, Seoul, Republic of Korea. 11. Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea.
Abstract
OBJECTIVES: To identify candidate imaging biomarkers for early disease progression in glioblastoma multiforme (GBM) patients by analysis of dynamic contrast-enhanced (DCE) MR parameters of non-enhancing T2 high signal intensity (SI) lesions. METHODS: Forty-nine GBM patients who had undergone preoperative DCE MR imaging and received standard treatment were retrospectively included. According to the Response Assessment in Neuro-Oncology criteria, patients were classified into progression (n = 21) or non-progression (n = 28) groups. We analysed the pharmacokinetic parameters of Ktrans, Ve and Vp within non-enhancing T2 high SI lesions of each tumour. The best percentiles of each parameter from cumulative histograms were identified by the area under the receiver operating characteristic curve (AUC) and were compared using multivariate stepwise logistic regression. RESULTS: For the differentiation of early disease progression, the highest AUC values were found in the 99th percentile of Ktrans (AUC 0.954), the 97th percentile of Ve (AUC 0.815) and the 94th percentile of Vp (AUC 0.786) (all p < 0.05). The 99th percentile of Ktrans was the only significant independent variable from the multivariate stepwise logistic regression (p = 0.002). CONCLUSIONS: We found that the Ktrans of non-enhancing T2 high SI lesions in GBM patients holds potential as a candidate prognostic marker in future prospective studies. KEY POINTS: • DCE MR imaging provides candidate prognostic marker of GBM after standard treatment. • Cumulative histogram was applied to include entire non-enhancing T2 high SI lesions. • The 99th percentile value of Ktrans was the most likely potential biomarker.
OBJECTIVES: To identify candidate imaging biomarkers for early disease progression in glioblastoma multiforme (GBM) patients by analysis of dynamic contrast-enhanced (DCE) MR parameters of non-enhancing T2 high signal intensity (SI) lesions. METHODS: Forty-nine GBM patients who had undergone preoperative DCE MR imaging and received standard treatment were retrospectively included. According to the Response Assessment in Neuro-Oncology criteria, patients were classified into progression (n = 21) or non-progression (n = 28) groups. We analysed the pharmacokinetic parameters of Ktrans, Ve and Vp within non-enhancing T2 high SI lesions of each tumour. The best percentiles of each parameter from cumulative histograms were identified by the area under the receiver operating characteristic curve (AUC) and were compared using multivariate stepwise logistic regression. RESULTS: For the differentiation of early disease progression, the highest AUC values were found in the 99th percentile of Ktrans (AUC 0.954), the 97th percentile of Ve (AUC 0.815) and the 94th percentile of Vp (AUC 0.786) (all p < 0.05). The 99th percentile of Ktrans was the only significant independent variable from the multivariate stepwise logistic regression (p = 0.002). CONCLUSIONS: We found that the Ktrans of non-enhancing T2 high SI lesions in GBM patients holds potential as a candidate prognostic marker in future prospective studies. KEY POINTS: • DCE MR imaging provides candidate prognostic marker of GBM after standard treatment. • Cumulative histogram was applied to include entire non-enhancing T2 high SI lesions. • The 99th percentile value of Ktrans was the most likely potential biomarker.
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