Korean Journal of Radiology

The Korean Society of Radiology (대한영상의학회)
권호 표지
DOI QR코드

DOI QR Code

Imaging-Based Versus Pathologic Survival Stratifications of Diffuse Glioma According to the 2021 WHO Classification System

  • So Jeong Lee (Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Ji Eun Park (Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Seo Young Park (Deparment of Statistics and Data Science, Korea National Open University) ;
  • Young-Hoon Kim (Department of Neurosurgery, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Chang Ki Hong (Department of Neurosurgery, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Jeong Hoon Kim (Department of Neurosurgery, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Ho Sung Kim (Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine)
  • Received : 2022.11.21
  • Accepted : 2023.05.20
  • Published : 2023.08.01
Download PDF
⟨ Previous Next ⟩

Abstract

Objective: Imaging-based survival stratification of patients with gliomas is important for their management, and the 2021 WHO classification system must be clinically tested. The aim of this study was to compare integrative imaging- and pathology-based methods for survival stratification of patients with diffuse glioma. Materials and Methods: This study included diffuse glioma cases from The Cancer Genome Atlas (training set: 141 patients) and Asan Medical Center (validation set: 131 patients). Two neuroradiologists analyzed presurgical CT and MRI to assign gliomas to five imaging-based risk subgroups (1 to 5) according to well-known imaging phenotypes (e.g., T2/FLAIR mismatch) and recategorized them into three imaging-based risk groups, according to the 2021 WHO classification: group 1 (corresponding to risk subgroup 1, indicating oligodendroglioma, isocitrate dehydrogenase [IDH]-mutant, and 1p19q-codeleted), group 2 (risk subgroups 2 and 3, indicating astrocytoma, IDH-mutant), and group 3 (risk subgroups 4 and 5, indicating glioblastoma, IDHwt). The progression-free survival (PFS) and overall survival (OS) were estimated for each imaging risk group, subgroup, and pathological diagnosis. Time-dependent area-under-the receiver operating characteristic analysis (AUC) was used to compare the performance between imaging-based and pathology-based survival model. Results: Both OS and PFS were stratified according to the five imaging-based risk subgroups (P < 0.001) and three imaging-based risk groups (P < 0.001). The three imaging-based groups showed high performance in predicting PFS at one-year (AUC, 0.787) and five-years (AUC, 0.823), which was similar to that of the pathology-based prediction of PFS (AUC of 0.785 and 0.837). Combined with clinical predictors, the performance of the imaging-based survival model for 1- and 3-year PFS (AUC 0.813 and 0.921) was similar to that of the pathology-based survival model (AUC 0.839 and 0.889). Conclusion: Imaging-based survival stratification according to the 2021 WHO classification demonstrated a performance similar to that of pathology-based survival stratification, especially in predicting PFS.

Keywords

Acknowledgement

We thank to Yeo Kyung Nam, MD (Shinchon Yonsei Hospital) and Minjae Kim, MD, PhD (Asan Medical Center) for participating as a reader of this study.

References

  1. Hammoud MA, Sawaya R, Shi W, Thall PF, Leeds NE. Prognostic significance of preoperative MRI scans in glioblastoma multiforme. J Neurooncol 1996;27:65-73
  2. Lacroix M, Abi-Said D, Fourney DR, Gokaslan ZL, Shi W, DeMonte F, et al. A multivariate analysis of 416 patients with glioblastoma multiforme: prognosis, extent of resection, and survival. J Neurosurg 2001;95:190-198
  3. Diehn M, Nardini C, Wang DS, McGovern S, Jayaraman M, Liang Y, et al. Identification of noninvasive imaging surrogates for brain tumor gene-expression modules. Proc Natl Acad Sci U S A 2008;105:5213-5218
  4. Patel SH, Poisson LM, Brat DJ, Zhou Y, Cooper L, Snuderl M, et al. T2-FLAIR mismatch, an imaging biomarker for IDH and 1p/19q status in lower-grade gliomas: a TCGA/TCIA project. Clin Cancer Res 2017;23:6078-6085
  5. Smits M, van den Bent MJ. Imaging correlates of adult glioma genotypes. Radiology 2017;284:316-331
  6. Carrillo JA, Lai A, Nghiemphu PL, Kim HJ, Phillips HS, Kharbanda S, et al. Relationship between tumor enhancement, edema, IDH1 mutational status, MGMT promoter methylation, and survival in glioblastoma. AJNR Am J Neuroradiol 2012;33:1349-1355
  7. Nam YK, Park JE, Park SY, Lee M, Kim M, Nam SJ, et al. Reproducible imaging-based prediction of molecular subtype and risk stratification of gliomas across different experience levels using a structured reporting system. Eur Radiol 2021;31:7374-7385
  8. Louis DN, Wesseling P, Paulus W, Giannini C, Batchelor TT, Cairncross JG, et al. cIMPACT-NOW update 1: Not Otherwise Specified (NOS) and Not Elsewhere Classified (NEC). Acta Neuropathol 2018;135:481-484
  9. Juratli TA, Tummala SS, Riedl A, Daubner D, Hennig S, Penson T, et al. Radiographic assessment of contrast enhancement and T2/FLAIR mismatch sign in lower grade gliomas: correlation with molecular groups. J Neurooncol 2019;141:327-335
  10. Lee MK, Park JE, Jo Y, Park SY, Kim SJ, Kim HS. Advanced imaging parameters improve the prediction of diffuse lower-grade gliomas subtype, IDH mutant with no 1p19q codeletion: added value to the T2/FLAIR mismatch sign. Eur Radiol 2020;30:844-854
  11. Johnson DR, Kaufmann TJ, Patel SH, Chi AS, Snuderl M, Jain R. There is an exception to every rule-T2-FLAIR mismatch sign in gliomas. Neuroradiology 2019;61:225-227
  12. Onishi S, Yamasaki F, Takano M, Yonezawa U, Taguchi A, Sugiyama K, et al. NIMG-01. T2wi-Flair mismatch sign in lower grade glioma and dysembryoplastic neuroepithelial tumor. Neuro Oncol 2019;21(Suppl 6):vi161
  13. Gritsch S, Batchelor TT, Gonzalez Castro LN. Diagnostic, therapeutic, and prognostic implications of the 2021 World Health Organization classification of tumors of the central nervous system. Cancer 2022;128:47-58
  14. Arevalo O, Valenzuela R, Esquenazi Y, Rao M, Tran B, Zhu J, et al. The 2016 World Health Organization classification of tumors of the central nervous system: a practical approach for gliomas, part 2. Isocitrate dehydrogenase status-imaging correlation. Neurographics 2017;7:344-349
  15. Kanazawa T, Fujiwara H, Takahashi H, Nishiyama Y, Hirose Y, Tanaka S, et al. Imaging scoring systems for preoperative molecular diagnoses of lower-grade gliomas. Neurosurg Rev 2019;42:433-441
  16. Maynard J, Okuchi S, Wastling S, Busaidi AA, Almossawi O, Mbatha W, et al. World Health Organization Grade II/III glioma molecular status: prediction by MRI morphologic features and apparent diffusion coefficient. Radiology 2020;296:111-121
  17. 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
  18. Kim YJ, Chang KH, Song IC, Kim HD, Seong SO, Kim YH, et al. Brain abscess and necrotic or cystic brain tumor: discrimination with signal intensity on diffusion-weighted MR imaging. AJR Am J Roentgenol 1998;171:1487-1490
  19. Cancer Genome Atlas Research Network; Brat DJ, Verhaak RG, Aldape KD, Yung WK, Salama SR, et al. Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas. N Engl J Med 2015;372:2481-2498
  20. Buckner JC, Shaw EG, Pugh SL, Chakravarti A, Gilbert MR, Barger GR, et al. Radiation plus procarbazine, CCNU, and vincristine in low-grade glioma. N Engl J Med 2016;374:1344-1355
  21. Bell EH, Pugh SL, McElroy JP, Gilbert MR, Mehta M, Klimowicz AC, et al. Molecular-based recursive partitioning analysis model for glioblastoma in the temozolomide era: a correlative analysis based on NRG oncology RTOG 0525. JAMA Oncology 2017;3:784-792
  22. Leao DJ, Craig PG, Godoy LF, Leite CC, Policeni B. Response assessment in neuro-oncology criteria for gliomas: practical approach using conventional and advanced techniques. Am J Neuroradiol 2020;41:10-20
  23. Wen PY, Macdonald DR, Reardon DA, Cloughesy TF, Sorensen AG, Galanis E, et al. Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol 2010;28:1963-1972
  24. Wesseling P, Capper D. WHO 2016 classification of gliomas. Neuropathol Appl Neurobiol 2018;44:139-150
  25. Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, et al. The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 2016;131:803-820
  26. Jang EB, Kim HS, Park JE, Park SY, Nam YK, Nam SJ, et al. Diffuse glioma, not otherwise specified: imaging-based risk stratification achieves histomolecular-level prognostication. Eur Radiol 2022;32:7780-7788
  27. Gutman DA, Cooper LA, Hwang SN, Holder CA, Gao J, Aurora TD, et al. MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set. Radiology 2013;267:560-569
  28. Nicolasjilwan M, Hu Y, Yan C, Meerzaman D, Holder CA, Gutman D, et al. Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients. J Neuroradiol 2015;42:212-221
  29. Chaichana K, Parker S, Olivi A, Quinones-Hinojosa A. A proposed classification system that projects outcomes based on preoperative variables for adult patients with glioblastoma multiforme. J Neurosurg 2010;112:997-1004
  30. Zhao K, Liu R, Li Z, Liu M, Zhao Y, Xue Z, et al. The imaging features and prognosis of gliomas involving the subventricular zone: an MRI study. Clin Neurol Neurosurg 2022;222:107465
  31. Pope WB, Sayre J, Perlina A, Villablanca JP, Mischel PS, Cloughesy TF. MR imaging correlates of survival in patients with high-grade gliomas. AJNR Am J Neuroradiol 2005;26:2466-2474
  32. Katki HA. Quantifying risk stratification provided by diagnostic tests and risk predictions: comparison to AUC and decision curve analysis. Stat Med 2019;38:2943-2955
  33. Han KL, Ren M, Wick W, Abrey L, Das A, Jin J, et al. Progression-free survival as a surrogate endpoint for overall survival in glioblastoma: a literature-based meta-analysis from 91 trials. Neuro Oncol 2014;16:696-706
  34. Lamborn KR, Yung WK, Chang SM, Wen PY, Cloughesy TF, DeAngelis LM, et al. Progression-free survival: an important end point in evaluating therapy for recurrent high-grade gliomas. Neuro Oncol 2008;10:162-170
  35. Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, et al. An image is worth 16x16 words: transformers for image recognition at scale. arXiv:2010.11929 [Preprint]. [posted October 22, 2020; revised June 3, 2021; cited October 6, 2021]. https://arxiv.org/abs/2010.11929