Korean Journal of Radiology

  • 제22권9호
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  • Pages.1514-1524
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  • 2021
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  • 1229-6929(pISSN)
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  • 2005-8330(eISSN)
대한영상의학회 (The Korean Society of Radiology)
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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)
  • 투고 : 2020.11.17
  • 심사 : 2021.04.07
  • 발행 : 2021.09.01
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초록

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.

키워드

과제정보

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

참고문헌

  1. Stupp R, Mason WP, van den Bent MJ, Weller M, Fisher B, Taphoorn MJ, et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 2005;352:987-996  https://doi.org/10.1056/NEJMoa043330
  2. Treps L, Edmond S, Harford-Wright E, Galan-Moya EM, Schmitt A, Azzi S, et al. Extracellular vesicle-transported Semaphorin3A promotes vascular permeability in glioblastoma. Oncogene 2016;35:2615-2623  https://doi.org/10.1038/onc.2015.317
  3. Dubois LG, Campanati L, Righy C, D'Andrea-Meira I, Spohr TC, Porto-Carreiro I, et al. Gliomas and the vascular fragility of the blood brain barrier. Front Cell Neurosci 2014;8:418 
  4. Park JE, Kim HS, Jo Y, Yoo RE, Choi SH, Nam SJ, et al. Radiomics prognostication model in glioblastoma using diffusion- and perfusion-weighted MRI. Sci Rep 2020;10:4250 
  5. Yankeelov TE, Gore JC. Dynamic contrast enhanced magnetic resonance imaging in oncology: theory, data acquisition, analysis, and examples. Curr Med Imaging Rev 2007;3:91-107  https://doi.org/10.2174/157340507780619179
  6. Thomas AA, Arevalo-Perez J, Kaley T, Lyo J, Peck KK, Shi W, et al. Dynamic contrast enhanced T1 MRI perfusion differentiates pseudoprogression from recurrent glioblastoma. J Neurooncol 2015;125:183-190  https://doi.org/10.1007/s11060-015-1893-z
  7. Hwang I, Choi SH, Park CK, Kim TM, Park SH, Won JK, et al. Dynamic contrast-enhanced MR imaging of nonenhancing T2 high-signal-intensity lesions in baseline and posttreatment glioblastoma: temporal change and prognostic value. AJNR Am J Neuroradiol 2020;41:49-56  https://doi.org/10.3174/ajnr.A6323
  8. van Dijken BRJ, van Laar PJ, Smits M, Dankbaar JW, Enting RH, van der Hoorn A. Perfusion MRI in treatment evaluation of glioblastomas: clinical relevance of current and future techniques. J Magn Reson Imaging 2019;49:11-22  https://doi.org/10.1002/jmri.26306
  9. Yoo RE, Choi SH, Kim TM, Park CK, Park SH, Won JK, et al. Dynamic contrast-enhanced MR imaging in predicting progression of enhancing lesions persisting after standard treatment in glioblastoma patients: a prospective study. Eur Radiol 2017;27:3156-3166  https://doi.org/10.1007/s00330-016-4692-9
  10. Kickingereder P, Burth S, Wick A, Gotz M, Eidel O, Schlemmer HP, et al. Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models. Radiology 2016;280:880-889  https://doi.org/10.1148/radiol.2016160845
  11. Zhou H, Vallieres M, Bai HX, Su C, Tang H, Oldridge D, et al. MRI features predict survival and molecular markers in diffuse lower-grade gliomas. Neuro Oncol 2017;19:862-870  https://doi.org/10.1093/neuonc/now256
  12. Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014;5:4006 
  13. Kim R, Choi SH, Yun TJ, Lee ST, Park CK, Kim TM, et al. Prognosis prediction of non-enhancing T2 high signal intensity lesions in glioblastoma patients after standard treatment: application of dynamic contrast-enhanced MR imaging. Eur Radiol 2017;27:1176-1185  https://doi.org/10.1007/s00330-016-4464-6
  14. Kim JY, Yoon MJ, Park JE, Choi EJ, Lee J, Kim HS. Radiomics in peritumoral non-enhancing regions: fractional anisotropy and cerebral blood volume improve prediction of local progression and overall survival in patients with glioblastoma. Neuroradiology 2019;61:1261-1272  https://doi.org/10.1007/s00234-019-02255-4
  15. Leys C, Ley C, Klein O, Bernard P, Licata L. Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median. J Exp Soc Psychol 2013;49:764-766  https://doi.org/10.1016/j.jesp.2013.03.013
  16. Chollet F. Deep learning with python. New York: Manning, 2018 
  17. Kim S, Shin J, Kim DY, Choi GH, Kim MJ, Choi JY. Radiomics on gadoxetic acid-enhanced magnetic resonance imaging for prediction of postoperative early and late recurrence of single hepatocellular carcinoma. Clin Cancer Res 2019;25:3847-3855  https://doi.org/10.1158/1078-0432.CCR-18-2861
  18. Simon N, Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw 2011;39:1-13  https://doi.org/10.18637/jss.v039.i05
  19. Liang C, Huang Y, He L, Chen X, Ma Z, Dong D, et al. The development and validation of a CT-based radiomics signature for the preoperative discrimination of stage I-II and stage III-IV colorectal cancer. Oncotarget 2016;7:31401-31412  https://doi.org/10.18632/oncotarget.8919
  20. Beig N, Bera K, Prasanna P, Antunes J, Correa R, Singh S, et al. Radiogenomic-based survival risk stratification of tumor habitat on Gd-T1w MRI is associated with biological processes in glioblastoma. Clin Cancer Res 2020;26:1866-1876  https://doi.org/10.1158/1078-0432.CCR-19-2556
  21. Rizzo S, Botta F, Raimondi S, Origgi D, Fanciullo C, Morganti AG, et al. Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp 2018;2:1-8  https://doi.org/10.1186/s41747-017-0034-1
  22. Rathore S, Akbari H, Doshi J, Shukla G, Rozycki M, Bilello M, et al. Radiomic signature of infiltration in peritumoral edema predicts subsequent recurrence in glioblastoma: implications for personalized radiotherapy planning. J Med Imaging (Bellingham) 2018;5:021219 
  23. Schoenegger K, Oberndorfer S, Wuschitz B, Struhal W, Hainfellner J, Prayer D, et al. Peritumoral edema on MRI at initial diagnosis: an independent prognostic factor for glioblastoma? Eur J Neurol 2009;16:874-878  https://doi.org/10.1111/j.1468-1331.2009.02613.x
  24. Choi KS, Choi SH, Jeong B. Prediction of IDH genotype in gliomas with dynamic susceptibility contrast perfusion MR imaging using an explainable recurrent neural network. Neuro Oncol 2019;21:1197-1209  https://doi.org/10.1093/neuonc/noz095
  25. Jung SC, Yeom JA, Kim JH, Ryoo I, Kim SC, Shin H, et al. Glioma: application of histogram analysis of pharmacokinetic parameters from T1-weighted dynamic contrast-enhanced MR imaging to tumor grading. AJNR Am J Neuroradiol 2014;35:1103-1110  https://doi.org/10.3174/ajnr.A3825
  26. Ulyte A, Katsaros VK, Liouta E, Stranjalis G, Boskos C, Papanikolaou N, et al. Prognostic value of preoperative dynamic contrast-enhanced MRI perfusion parameters for high-grade glioma patients. Neuroradiology 2016;58:1197-1208  https://doi.org/10.1007/s00234-016-1741-7
  27. Trunk GV. A problem of dimensionality: a simple example. IEEE Trans Pattern Anal Mach Intell 1979;1:306-307  https://doi.org/10.1109/TPAMI.1979.4766926
  28. Verleysen M, Francois D. The curse of dimensionality in data mining and time series prediction. In: Cabestany J, Prieto A, Sandoval F, eds. Computational intelligence and bioinspired systems. Berlin, Heidelberg: Springer, 2005:758-770 
  29. Stupp R, Brada M, van den Bent MJ, Tonn JC, Pentheroudakis G; ESMO Guidelines Working Group. High-grade glioma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 2014;25:iii93-iii101  https://doi.org/10.1093/annonc/mdu050
  30. SongTao Q, Lei Y, Si G, YanQing D, HuiXia H, XueLin Z, et al. IDH mutations predict longer survival and response to temozolomide in secondary glioblastoma. Cancer Sci 2012;103:269-273  https://doi.org/10.1111/j.1349-7006.2011.02134.x
  31. Beiko J, Suki D, Hess KR, Fox BD, Cheung V, Cabral M, et al. IDH1 mutant malignant astrocytomas are more amenable to surgical resection and have a survival benefit associated with maximal surgical resection. Neuro Oncol 2014;16:81-91  https://doi.org/10.1093/neuonc/not159
  32. Cohen AL, Holmen SL, Colman H. IDH1 and IDH2 mutations in gliomas. Curr Neurol Neurosci Rep 2013;13:345 
  33. Huang J, Yu J, Tu L, Huang N, Li H, Luo Y. Isocitrate dehydrogenase mutations in glioma: from basic discovery to therapeutics development. Front Oncol 2019;9:506 
  34. Li ZC, Bai H, Sun Q, Zhao Y, Lv Y, Zhou J, et al. Multiregional radiomics profiling from multiparametric MRI: identifying an imaging predictor of IDH1 mutation status in glioblastoma. Cancer Med 2018;7:5999-6009  https://doi.org/10.1002/cam4.1863
  35. Hsieh KL, Chen CY, Lo CM. Radiomic model for predicting mutations in the isocitrate dehydrogenase gene in glioblastomas. Oncotarget 2017;8:45888-45897  https://doi.org/10.18632/oncotarget.17585
  36. Tan Y, Mu W, Wang XC, Yang GQ, Gillies RJ, Zhang H. Improving survival prediction of high-grade glioma via machine learning techniques based on MRI radiomic, genetic and clinical risk factors. Eur J Radiol 2019;120:108609