Skip to main content

Advertisement

Log in

Radiomics risk score may be a potential imaging biomarker for predicting survival in isocitrate dehydrogenase wild-type lower-grade gliomas

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

Abstract

Objectives

Isocitrate dehydrogenase wild-type (IDHwt) lower-grade gliomas of histologic grades II and III follow heterogeneous clinical outcomes, which necessitates risk stratification. We aimed to evaluate whether radiomics from MRI would allow prediction of overall survival in patients with IDHwt lower-grade gliomas and to investigate the added prognostic value of radiomics over clinical features.

Methods

Preoperative MRIs of 117 patients with IDHwt lower-grade gliomas from January 2007 to February 2018 were retrospectively analyzed. The external validation cohort consisted of 33 patients from The Cancer Genome Atlas. A total of 182 radiomic features were extracted. Radiomics risk scores (RRSs) for overall survival were derived from the least absolute shrinkage and selection operator (LASSO) and elastic net. Multivariable Cox regression analyses, including clinical features and RRSs, were performed. The integrated areas under the receiver operating characteristic curves (iAUCs) from models with and without RRSs were calculated for comparisons. The prognostic value of RRS was assessed in the validation cohort.

Results

The RRS derived from LASSO and elastic net independently predicted survival with hazard ratios of 9.479 (95% confidence interval [CI], 3.220–27.847) and 6.148 (95% CI, 3.009–12.563), respectively. Those RRSs enhanced model performance for predicting overall survival (iAUC increased to 0.780–0.797 from 0.726), which was externally validated. The RRSs stratified IDHwt lower-grade gliomas in the validation cohort with significantly different survival.

Conclusion

Radiomics has the potential for noninvasive risk stratification and can improve prediction of overall survival in patients with IDHwt lower-grade gliomas when integrated with clinical features.

Key Points

Isocitrate dehydrogenase wild-type lower-grade gliomas with histologic grades II and III follow heterogeneous clinical outcomes, which necessitates further risk stratification.

Radiomics risk scores derived from MRI independently predict survival even after incorporating strong clinical prognostic features (hazard ratios 6.148–9.479).

Radiomics risk scores derived from MRI have the potential to improve survival prediction when added to clinical features (integrated areas under the receiver operating characteristic curves increased from 0.726 to 0.780–0.797).

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

iAUC:

Integrated area under the receiver operating characteristic curve

IDH:

Isocitrate dehydrogenase

IDHwt:

Isocitrate dehydrogenase wild-type

KPS:

Karnofsky Performance Status

OS:

Overall survival

ROC:

Receiver operating characteristic

RRS:

Radiomics risk score

TCGA:

The Cancer Genome Atlas

WHO:

World Health Organization

References

  1. 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  Google Scholar 

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

    Article  CAS  Google Scholar 

  3. 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  Google Scholar 

  4. 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  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  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  Google Scholar 

  7. Reuss DE, Kratz A, Sahm F et al (2015) Adult IDH wild type astrocytomas biologically and clinically resolve into other tumor entities. Acta Neuropathol 130:407–417

    Article  CAS  Google Scholar 

  8. 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  Google Scholar 

  9. 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  Google Scholar 

  10. 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  Google Scholar 

  11. 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  Google Scholar 

  12. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577

    Article  Google Scholar 

  13. Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006

    Article  CAS  Google Scholar 

  14. Kickingereder P, Gotz M, Muschelli J et al (2016) Large-scale radiomic profiling of recurrent glioblastoma identifies an imaging predictor for stratifying anti-angiogenic treatment response. Clin Cancer Res 22:5765–5771

    Article  CAS  Google Scholar 

  15. Bae S, Choi YS, Ahn SS et al (2018) Radiomic MRI phenotyping of glioblastoma: improving survival prediction. Radiology 289:797–806

    Article  Google Scholar 

  16. Hu LS, Ning S, Eschbacher JM et al (2017) Radiogenomics to characterize regional genetic heterogeneity in glioblastoma. Neuro Oncol 19:128–137

    Article  CAS  Google Scholar 

  17. Kickingereder P, Burth S, Wick A et al (2016) Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models. Radiology 280:880–889

    Article  Google Scholar 

  18. Zhou H, Vallieres 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  Google Scholar 

  19. Liu X, Li Y, Qian Z et al (2018) A radiomic signature as a non-invasive predictor of progression-free survival in patients with lower-grade gliomas. Neuroimage Clin 20:1070–1077

    Article  Google Scholar 

  20. Zhang X, Tian Q, Wang L et al (2018) Radiomics strategy for molecular subtype stratification of lower-grade glioma: detecting IDH and TP53 mutations based on multimodal MRI. J Magn Reson Imaging 48:916–926

    Article  Google Scholar 

  21. Ren Y, Zhang X, Rui W et al (2019) Noninvasive prediction of IDH1 mutation and ATRX expression loss in low-grade gliomas using multiparametric MR radiomic features. J Magn Reson Imaging 49:808–817

    Article  Google Scholar 

  22. Li Y, Qian Z, Xu K et al (2018) MRI features predict p53 status in lower-grade gliomas via a machine-learning approach. Neuroimage Clin 17:306–311

    Article  Google Scholar 

  23. Liu X, Li Y, Li S et al (2019) IDH mutation-specific radiomic signature in lower-grade gliomas. Aging (Albany NY) 11:673–696

    Article  CAS  Google Scholar 

  24. Pedano N, Flanders A, Scarpace L et al (2016) Radiology data from the cancer genome atlas low grade glioma [TCGA-LGG] collection. Cancer Imaging Archive

  25. Tustison NJ, Avants BB, Cook PA et al (2010) N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29:1310–1320

    Article  Google Scholar 

  26. Sled JG, Zijdenbos AP, Evans AC (1998) A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 17:87–97

    Article  CAS  Google Scholar 

  27. Shinohara RT, Sweeney EM, Goldsmith J et al (2014) Statistical normalization techniques for magnetic resonance imaging. Neuroimage Clin 6:9–19

    Article  Google Scholar 

  28. Fedorov A, Beichel R, Kalpathy-Cramer J et al (2012) 3D slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging 30:1323–1341

    Article  Google Scholar 

  29. Taha AA, Hanbury A (2015) Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging 15:29

    Article  Google Scholar 

  30. Lee M, Woo B, Kuo MD, Jamshidi N, Kim JH (2017) Quality of radiomic features in glioblastoma multiforme: impact of semi-automated tumor segmentation software. Korean J Radiol 18:498–509

    Article  Google Scholar 

  31. van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107

    Article  Google Scholar 

  32. 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  Google Scholar 

  33. 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  Google Scholar 

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

    Article  Google Scholar 

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

  36. 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  Google Scholar 

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

    Article  Google Scholar 

  38. Akaike H (1974) A new look at the statistical model identification. IEEE Transactions on Automatic Control 19:716–723

  39. 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 

  40. Kickingereder P, Neuberger U, Bonekamp D et al (2018) Radiomic subtyping improves disease stratification beyond key molecular, clinical, and standard imaging characteristics in patients with glioblastoma. Neuro Oncol 20:848–857

    Article  CAS  Google Scholar 

  41. Ingrisch M, Schneider MJ, Norenberg D et al (2017) Radiomic analysis reveals prognostic information in T1-weighted baseline magnetic resonance imaging in patients with glioblastoma. Invest Radiol 52:360–366

    Article  Google Scholar 

  42. Soni N, Priya S, Bathla G (2019) Texture Analysis in Cerebral Gliomas: A Review of the Literature. AJNR Am J Neuroradiol 40:928–934

  43. Suárez-García JG, Hernández-López JM, Moreno-Barbosa E, de Celis-Alonso B (2020) A simple model for glioma grading based on texture analysis applied to conventional brain MRI. bioRxiv. https://doi.org/10.1101/2020.01.29.924712

  44. Meyer M, Reimand J, Lan X et al (2015) Single cell-derived clonal analysis of human glioblastoma links functional and genomic heterogeneity. Proc Natl Acad Sci U S A 112:851–856

    Article  CAS  Google Scholar 

  45. Liu Y, Zhang X, Feng N et al (2018) The effect of glioblastoma heterogeneity on survival stratification: a multimodal MR imaging texture analysis. Acta Radiol 59:1239–1246

    Article  Google Scholar 

  46. Yang D, Rao G, Martinez J, Veeraraghavan A, Rao A (2015) Evaluation of tumor-derived MRI-texture features for discrimination of molecular subtypes and prediction of 12-month survival status in glioblastoma. Med Phys 42:6725–6735

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The authors acknowledge M.S. Haesol Shin from the Department of Biostatistics and Computing, Yonsei University College of Graduate, for her support in statistical analysis. We also acknowledge Sang Wook Kim from the Department of Biomedical Engineering, Korea University, and Dongmin Choi from the Department of Computer Science, Yonsei University, for their support in radiomic feature extraction.

Funding

This research 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 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 280 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., Kim, H. et al. Radiomics risk score may be a potential imaging biomarker for predicting survival in isocitrate dehydrogenase wild-type lower-grade gliomas. Eur Radiol 30, 6464–6474 (2020). https://doi.org/10.1007/s00330-020-07089-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-020-07089-w

Keywords

Navigation