Skip to main content

Advertisement

Log in

MR image phenotypes may add prognostic value to clinical features in IDH wild-type lower-grade gliomas

  • Neuro
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Purpose

To identify significant prognostic magnetic resonance imaging (MRI) features and their prognostic value when added to clinical features in patients with isocitrate dehydrogenase wild-type (IDHwt) lower-grade gliomas.

Materials and methods

Preoperative MR images of 158 patients (discovery set = 112, external validation set = 46) with IDHwt lower-grade gliomas (WHO grade II or III) were retrospectively analyzed using the Visually Accessible Rembrandt Images feature set. Radiologic risk scores (RRSs) for overall survival were derived from the least absolute shrinkage and selection operator and elastic net. Multivariable Cox regression analysis, including age, Karnofsky Performance score, extent of resection, WHO grade, and RRS, was performed. The added prognostic value of RRS was calculated by comparing the integrated area under the receiver operating characteristic curve (iAUC) between models with and without RRS.

Results

The presence of cysts, pial invasion, and cortical involvement were favorable prognostic factors, while ependymal extension, multifocal or multicentric distribution, nonlobar location, proportion of necrosis > 33%, satellites, and eloquent cortex involvement were significantly associated with worse prognosis. RRS independently predicted survival and significantly enhanced model performance for survival prediction when integrated to clinical features (iAUC increased to 0.773–0.777 from 0.737), which was successfully validated on the validation set (iAUC increased to 0.805–0.830 from 0.735).

Conclusion

MRI features associated with prognosis in patients with IDHwt lower-grade gliomas were identified. RRSs derived from MRI features independently predicted survival and significantly improved performance of survival prediction models when integrated into clinical features.

Key Points

• Comprehensive analysis of MRI features conveys prognostic information in patients with isocitrate dehydrogenase wild-type lower-grade gliomas.

• Presence of cysts, pial invasion, and cortical involvement of the tumor were favorable prognostic factors.

• Radiological phenotypes derived from MRI independently predict survival and have the potential to improve survival prediction when added to clinical features.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Abbreviations

AIC:

Akaike information criterion

CET:

Enhancing tumor

CI:

Confidence interval

FLAIR:

Fluid-attenuated inversion recovery

FOV:

Field of view

iAUC:

Integrated area under the receiver operating characteristic curve

IDH:

Isocitrate dehydrogenase

IDHwt:

IDH wild-type

KPS:

Karnofsky Performance Status

LASSO:

Least absolute shrinkage and selection operator

LL:

Log likelihood

nCET:

Non-enhancing tumor

OS:

Overall survival

RRS:

Radiologic risk score

T1C:

Contrast-enhanced T1-weighted imaging

TCGA:

The Cancer Genome Atlas

TCIA:

The Cancer Imaging Archive

TE:

Echo time

TR:

Repetition time

VASARI:

Visually Accessible Rembrandt Images

WHO:

World Health Organization

References

  1. Louis DN, Perry A, Reifenberger G et al (2016) The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 131:803–820

    Article  PubMed  Google Scholar 

  2. Jiao Y, Killela PJ, Reitman ZJ et al (2012) Frequent ATRX, CIC, FUBP1 and IDH1 mutations refine the classification of malignant gliomas. Oncotarget 3:709–722

    Article  PubMed  PubMed Central  Google Scholar 

  3. Brat DJ, Verhaak RG, Aldape KD et al (2015) Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas. N Engl J Med 372:2481–2498

    Article  CAS  PubMed  Google Scholar 

  4. Yan H, Parsons DW, Jin G et al (2009) IDH1 and IDH2 mutations in gliomas. N Engl J Med 360:765–773

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Metellus P, Coulibaly B, Colin C et al (2010) Absence of IDH mutation identifies a novel radiologic and molecular subtype of WHO grade II gliomas with dismal prognosis. Acta Neuropathol 120:719–729

    Article  PubMed  Google Scholar 

  6. Eckel-Passow JE, Lachance DH, Molinaro AM et al (2015) Glioma groups based on 1p/19q, IDH, and TERT promoter mutations in tumors. N Engl J Med 372:2499–2508

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Aibaidula A, Chan AK, Shi Z et al (2017) Adult IDH wild-type lower-grade gliomas should be further stratified. Neuro Oncol 19:1327–1337

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Chan AK, Yao Y, Zhang Z et al (2015) TERT promoter mutations contribute to subset prognostication of lower-grade gliomas. Mod Pathol 28:177–186

    Article  CAS  PubMed  Google Scholar 

  9. Chan AK, Yao Y, Zhang Z et al (2015) Combination genetic signature stratifies lower-grade gliomas better than histological grade. Oncotarget 6:20885–20901

    Article  PubMed  PubMed Central  Google Scholar 

  10. Brat DJ, Aldape K, Colman H et al (2018) cIMPACT-NOW update 3: recommended diagnostic criteria for “diffuse astrocytic glioma, IDH-wildtype, with molecular features of glioblastoma, WHO grade IV”. Acta Neuropathol 136:805–810

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Gutman DA, Cooper LA, Hwang SN et al (2013) MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set. Radiology 267:560–569

    Article  PubMed  PubMed Central  Google Scholar 

  12. Gutman DA, Dunn WD, Grossmann P et al (2015) Somatic mutations associated with MRI-derived volumetric features in glioblastoma. Neuroradiology 57:1227–1237

    Article  PubMed  PubMed Central  Google Scholar 

  13. Wangaryattawanich P, Hatami M, Wang J et al (2015) Multicenter imaging outcomes study of The Cancer Genome Atlas glioblastoma patient cohort: imaging predictors of overall and progression-free survival. Neuro Oncol 17:1525–1537

    Article  PubMed  PubMed Central  Google Scholar 

  14. Takano S, Tian W, Matsuda M et al (2011) Detection of IDH1 mutation in human gliomas: comparison of immunohistochemistry and sequencing. Brain Tumor Pathol 28:115–123

    Article  CAS  PubMed  Google Scholar 

  15. Choi J, Lee EY, Shin KJ, Minn YK, Kim J, Kim SH (2013) IDH1 mutation analysis in low cellularity specimen: a limitation of diagnostic accuracy and a proposal for the diagnostic procedure. Pathol Res Pract 209:284–290

    Article  CAS  PubMed  Google Scholar 

  16. Riemenschneider MJ, Jeuken JW, Wesseling P, Reifenberger G (2010) Molecular diagnostics of gliomas: state of the art. Acta Neuropathol 120:567–584

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33:159–174

    Article  CAS  PubMed  Google Scholar 

  18. Friedman J, Hastie T, Tibshirani R (2010) Regularization paths for generalized linear models via coordinate descent. J Stat Softw 33:1–22

    Article  PubMed  PubMed Central  Google Scholar 

  19. Microsoft, Ooi H (2017) glmnetUtils: utilities for ‘Glmnet’. R package version 1.1. Available via https://CRAN.R-project.org/package=glmnetUtils

  20. Simon N, Friedman J, Hastie T, Tibshirani R (2011) Regularization paths for Cox’s proportional hazards model via coordinate descent. J Stat Softw 39:1–13

    Article  PubMed  PubMed Central  Google Scholar 

  21. Contal C, O’Quigley J (1999) An application of changepoint methods in studying the effect of age on survival in breast cancer. Comput Stat Data Anal 30:253–270

    Article  Google Scholar 

  22. Akaike H (1975) A new look at the statistical model identification. IEEE Trans Autom Control 19:716–723

    Article  Google Scholar 

  23. Heagerty PJ, Zheng Y (2005) Survival model predictive accuracy and ROC curves. Biometrics 61:92–105

    Article  PubMed  Google Scholar 

  24. Audigier V, White IR, Jolani S et al (2018) Multiple imputation for multilevel data with continuous and binary variables. Stat Sci 33:160–183

    Article  Google Scholar 

  25. Marshall A, Altman DG, Holder RL, Royston P (2009) Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines. BMC Med Res Methodol 9:57

    Article  PubMed  PubMed Central  Google Scholar 

  26. Houillier C, Wang X, Kaloshi G et al (2010) IDH1 or IDH2 mutations predict longer survival and response to temozolomide in low-grade gliomas. Neurology 75:1560–1566

    Article  CAS  PubMed  Google Scholar 

  27. Leu S, von Felten S, Frank S et al (2013) IDH/MGMT-driven molecular classification of low-grade glioma is a strong predictor for long-term survival. Neuro Oncol 15:469–479

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Park YW, Han K, Ahn SS et al (2018) Whole-tumor histogram and texture analyses of DTI for evaluation of IDH1-mutation and 1p/19q-codeletion status in World Health Organization grade II gliomas. AJNR Am J Neuroradiol 39:693–698

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Park YW, Han K, Ahn SS et al (2018) Prediction of IDH1-mutation and 1p/19q-codeletion status using preoperative MR imaging phenotypes in lower grade gliomas. AJNR Am J Neuroradiol 39:37–42

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Jakola AS, Zhang Y-H, Skjulsvik AJ et al (2018) Quantitative texture analysis in the prediction of IDH status in low-grade gliomas. Clin Neurol Neurosurg 164:114–120

    Article  PubMed  Google Scholar 

  31. Eichinger P, Alberts E, Delbridge C et al (2017) Diffusion tensor image features predict IDH genotype in newly diagnosed WHO grade II/III gliomas. Sci Rep 7:13396

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Patil CG, Yi A, Elramsisy A et al (2012) Prognosis of patients with multifocal glioblastoma: a case-control study. J Neurosurg 117:705–711

    Article  PubMed  Google Scholar 

  33. Hassaneen W, Levine NB, Suki D et al (2011) Multiple craniotomies in the management of multifocal and multicentric glioblastoma. Clinical article. J Neurosurg 114:576–584

    Article  PubMed  Google Scholar 

  34. Zhou H, Vallières M, Bai HX et al (2017) MRI features predict survival and molecular markers in diffuse lower-grade gliomas. Neuro Oncol 19:862–870

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Jafri NF, Clarke JL, Weinberg V, Barani IJ, Cha S (2013) Relationship of glioblastoma multiforme to the subventricular zone is associated with survival. Neuro Oncol 15:91–96

    Article  CAS  PubMed  Google Scholar 

  36. Lim DA, Cha S, Mayo MC et al (2007) Relationship of glioblastoma multiforme to neural stem cell regions predicts invasive and multifocal tumor phenotype. Neuro Oncol 9:424–429

    Article  PubMed  PubMed Central  Google Scholar 

  37. Young GS, Macklin EA, Setayesh K et al (2011) Longitudinal MRI evidence for decreased survival among periventricular glioblastoma. J Neurooncol 104:261–269

    Article  PubMed  Google Scholar 

  38. Mistry AM, Hale AT, Chambless LB, Weaver KD, Thompson RC, Ihrie RA (2017) Influence of glioblastoma contact with the lateral ventricle on survival: a meta-analysis. J Neurooncol 131:125–133

    Article  CAS  PubMed  Google Scholar 

  39. Liu S, Wang Y, Fan X et al (2016) Anatomical involvement of the subventricular zone predicts poor survival outcome in low-grade astrocytomas. PLoS One 11:e0154539–e0154539

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Sanai N, Alvarez-Buylla A, Berger MS (2005) Neural stem cells and the origin of gliomas. N Engl J Med 353:811–822

    Article  CAS  PubMed  Google Scholar 

  41. Zhu Y, Guignard F, Zhao D et al (2005) Early inactivation of p53 tumor suppressor gene cooperating with NF1 loss induces malignant astrocytoma. Cancer Cell 8:119–130

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Maldaun MV, Suki D, Lang FF et al (2004) Cystic glioblastoma multiforme: survival outcomes in 22 cases. J Neurosurg 100:61–67

    Article  PubMed  Google Scholar 

  43. Zhou J, Reddy MV, Wilson BKJ et al (2018) MR imaging characteristics associate with tumor-associated macrophages in glioblastoma and provide an improved signature for survival prognostication. AJNR Am J Neuroradiol. https://doi.org/10.3174/ajnr.A5441

    Article  PubMed  Google Scholar 

  44. Utsuki S, Oka H, Suzuki S et al (2006) Pathological and clinical features of cystic and noncystic glioblastomas. Brain Tumor Pathol 23:29–34

    Article  PubMed  Google Scholar 

  45. Villanueva-Meyer JE, Wood MD, Choi BS et al (2018) MRI features and IDH mutational status of grade II diffuse gliomas: impact on diagnosis and prognosis. AJR Am J Roentgenol 210:621–628

    Article  PubMed  Google Scholar 

  46. Villanueva-Meyer JE, Wood MD, Choi BS et al (2017) MRI features and IDH mutational status of grade II diffuse gliomas: impact on diagnosis and prognosis. AJR Am J Roentgenol 210:621–628

    Article  PubMed  PubMed Central  Google Scholar 

  47. Patel SH, Poisson LM, Brat DJ et al (2017) T2-FLAIR mismatch, an imaging biomarker for IDH and 1p/19q status in lower grade gliomas: a TCGA/TCIA project. Clin Cancer Res. https://doi.org/10.1158/1078-0432.ccr-17-0560

    Article  CAS  PubMed  Google Scholar 

  48. Neill E, Luks T, Dayal M et al (2017) Quantitative multi-modal MR imaging as a non-invasive prognostic tool for patients with recurrent low-grade glioma. J Neurooncol 132:171–179

    Article  PubMed  PubMed Central  Google Scholar 

  49. Lee M, Han K, Ahn SS et al (2019) The added prognostic value of radiological phenotype combined with clinical features and molecular subtype in anaplastic gliomas. J Neurooncol 142:129–138

    Article  CAS  PubMed  Google Scholar 

  50. van der Heijden GJ, Donders AR, Stijnen T, Moons KG (2006) Imputation of missing values is superior to complete case analysis and the missing-indicator method in multivariable diagnostic research: a clinical example. J Clin Epidemiol 59:1102–1109

    Article  PubMed  Google Scholar 

  51. Moons KG, Altman DG, Reitsma JB et al (2015) Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med 162:W1–W73

    Article  PubMed  Google Scholar 

  52. Ten Haaf K, Jeon J, Tammemagi MC et al (2017) Risk prediction models for selection of lung cancer screening candidates: a retrospective validation study. PLoS Med 14:e1002277

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

This study has received funding from the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, Information and Communication Technologies & Future Planning (2017R1D1A1B03030440).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sung Soo Ahn.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Sung Soo Ahn.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise.

Kyunghwa Han, Ph.D., from the Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, has significant statistical expertise and is one of the authors.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

(DOCX 28 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Park, C.J., Han, K., Shin, H. et al. MR image phenotypes may add prognostic value to clinical features in IDH wild-type lower-grade gliomas. Eur Radiol 30, 3035–3045 (2020). https://doi.org/10.1007/s00330-020-06683-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-020-06683-2

Keywords

Navigation