A novel image signature-based radiomics method to achieve precise diagnosis and prognostic stratification of gliomas

Abstract

Radiomics has potential advantages in the noninvasive histopathological and molecular diagnosis of gliomas. We aimed to develop a novel image signature (IS)-based radiomics model to achieve multilayered preoperative diagnosis and prognostic stratification of gliomas. Herein, we established three separate case cohorts, consisting of 655 glioma patients, and carried out a retrospective study. Image and clinical data of three cohorts were used for training (N = 188), cross-validation (N = 411), and independent testing (N = 56) of the IS model. All tumors were segmented from magnetic resonance (MR) images by the 3D U-net, followed by extraction of high-throughput network features, which were referred to as IS. IS was then used to perform noninvasive histopathological diagnosis and molecular subtyping. Moreover, a new IS-based clustering method was applied for prognostic stratification in IDH-wild-type lower-grade glioma (IDHwt LGG) and triple-negative glioblastoma (1p19q retain/IDH wild-type/TERTp-wild-type GBM). The average accuracies of histological diagnosis and molecular subtyping were 89.8 and 86.1% in the cross-validation cohort, while these numbers reached 83.9 and 80.4% in the independent testing cohort. IS-based clustering method was demonstrated to successfully divide IDHwt LGG into two subgroups with distinct median overall survival time (48.63 vs 38.27 months respectively, P = 0.023), and two subgroups in triple-negative GBM with different median OS time (36.8 vs 18.2 months respectively, P = 0.013). Our findings demonstrate that our novel IS-based radiomics model is an effective tool to achieve noninvasive histo-molecular pathological diagnosis and prognostic stratification of gliomas. This IS model shows potential for future routine use in clinical practice.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: The overview of the method.
Fig. 2: Illustration of binary classifiers.
Fig. 3: Illustration of prognostic stratification of IDH1/2 wild-type LGG.
Fig. 4: Illustration of prognostic stratification of triple-negative GBM.
Fig. 5: Survival analysis of Group A-1 and Group A-2.

References

  1. 1.

    Reifenberger G, Wirsching HG, Knobbe-Thomsen CB, Weller M. Advances in the molecular genetics of gliomas—implications for classification and therapy. Nat Rev Clin Oncol. 2017;14:434–52.

    CAS  Article  Google Scholar 

  2. 2.

    Weller M, van den Bent M, Tonn JC, Stupp R, Preusser M, Cohen-Jonathan-Moyal E, et al. European Association for Neuro-Oncology (EANO) Task Force on Gliomas. European Association for Neuro-Oncology (EANO) guideline on the diagnosis and treatment of adult astrocytic and oligodendroglial gliomas. Lancet Oncol. 2017;18:315–29.

    Article  Google Scholar 

  3. 3.

    Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Cavenee WK, Burger PC, et al. The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol. 2007;114:97–109.

    Article  Google Scholar 

  4. 4.

    Quick-Weller J, Tichy J, Harter PN, Tritt S, Baumgarten P, Bähr O, et al. “Two is not enough”—impact of the number of tissue samples obtained from stereotactic brain biopsies in suspected glioblastoma. J Clin Neurosci. 2018;47:311–4.

    Article  Google Scholar 

  5. 5.

    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;131:803–20.

    Article  Google Scholar 

  6. 6.

    Shirahata M, Ono T, Stichel D, Schrimpf D, Reuss DE, Sahm F, et al. Novel, improved grading system(s) for IDH-mutant astrocytic gliomas. Acta Neuropathol. 2018;136:153–66.

    CAS  Article  Google Scholar 

  7. 7.

    Diamandis P, Aldape KD. Insights from molecular profiling of adult glioma. J Clin Oncol. 2017;35:2386–93.

    CAS  Article  Google Scholar 

  8. 8.

    GLASS Consortium. Glioma through the looking GLASS: molecular evolution of diffuse gliomas and the Glioma Longitudinal Analysis Consortium. Neuro Oncol. 2018;20:873–84.

    Article  Google Scholar 

  9. 9.

    Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, et al. Radiomics: the process and the challenges. Magn Reson Imaging. 2012;30:1234–48.

    Article  Google Scholar 

  10. 10.

    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.

    CAS  Article  Google Scholar 

  11. 11.

    Yu J, Shi Z, Lian Y, Li Z, Liu T, Gao Y, et al. Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma. Eur Radiol. 2017;27:3509–22.

    Article  Google Scholar 

  12. 12.

    Jiang C, Kong Z, Liu S, Feng S, Zhang Y, Zhu R, et al. Fusion radiomics features from conventional MRI predict MGMT promoter methylation status in lower grade gliomas. Eur J Radiol. 2019;121:108714.

    Article  Google Scholar 

  13. 13.

    Ji CH, Yu JH, Wang YY, Chen L, Shi ZF, Mao Y. Brain tumor segmentation in MR slices using improved GrowCut algorithm. In: Seventh International Conference on Graphic and Image Processing (ICGIP 2015) International Society for Optics and Photonics. 2015. https://doi.org/10.1117/12.2228230.

  14. 14.

    Perronnin F, Mensink T. Improving the fisher kernel for large-scale image classification. In: Proceedings of the 11th European Conference on Computer Vision. 2010. https://doi.org/10.1007/978-3-642-15561-1_11.

  15. 15.

    Zhang X, Xiong H, Zhou W, Lin W, Tian Q, Picking deep filter responses for fine-grained image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. https://doi.org/10.1109/CVPR.2016.128.

  16. 16.

    Wu G, Chen Y, Wang Y, Yu J, Lv X, Ju X, et al. Sparse representation-based radiomics for the diagnosis of brain tumors. IEEE Trans Med Imaging. 2016;37:893–905.

    Article  Google Scholar 

  17. 17.

    Wu G, Wang Y, Yu J. Overall survival time prediction for high grade gliomas based on sparse representation framework. In: International MICCAI brainlesion workshop. Cham: Springer; 2017. pp. 77–87.

  18. 18.

    Zhu X, Suk H, Lee S, Shen D. Subspace regularized sparse multi-task learning for multi-class neurodegenerative disease identification. IEEE Trans Biomed Eng. 2016;63:607–18.

    Article  Google Scholar 

  19. 19.

    Aibaidula A, Chan AK, Shi Z, Li Y, Zhang R, Yang R, et al. Adult IDH wild-type lower-grade gliomas should be further stratified. Neuro Oncol. 2017;19:1327–37.

    CAS  Article  Google Scholar 

  20. 20.

    Diplas BH, He X, Brosnan-Cashman JA, Liu H, Chen LH, Wang Z, et al. The genomic landscape of TERT promoter wildtype-IDH wildtype glioblastoma. Nat Commun. 2018;9:2087.

    Article  Google Scholar 

  21. 21.

    Williams EA, Miller JJ, Tummala SS, Penson T, Iafrate AJ, Juratli TA, et al. TERT promoter wild-type glioblastomas show distinct clinical features and frequent PI3K pathway mutations. Acta Neuropathol Commun. 2018;6:106.

    CAS  Article  Google Scholar 

  22. 22.

    Kuwahara K, Ohba S, Nakae S, Hattori N, Pareira ES, Yamada S, et al. Clinical, histopathological, and molecular analyses of IDH-wild-type WHO grade II-III gliomas to establish genetic predictors of poor prognosis. Brain Tumor Pathol. 2019;36:135–43.

    Article  Google Scholar 

  23. 23.

    Labussière M, Boisselier B, Mokhtari K, Di Stefano AL, Rahimian A, Rossetto M, et al. Combined analysis of TERT, EGFR, and IDH status defines distinct prognostic glioblastoma classes. Neurology. 2014;83:1200–6.

    Article  Google Scholar 

  24. 24.

    Brat DJ, Aldape K, Colman H, Holland EC, Louis DN, Jenkins RB, et al. cIMPACT-NOW update 3: recommended diagnostic criteria for “Diffuse astrocytic glioma, IDH-wildtype, with molecular features of glioblastoma, WHO grade IV”. Acta Neuropathol. 2018;136:805–10.

    CAS  Article  Google Scholar 

  25. 25.

    Li ZJ, Wang YY, Yu JH, Guo Y, Cao W. Deep Learning based Radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma. Sci Rep. 2017;7:5467.

    Article  Google Scholar 

  26. 26.

    Johnson BE, Mazor T, Hong C, Barnes M, Aihara K, McLean C, et al. Mutational analysis reveals the origin and therapy-driven evolution of recurrent glioma. Science. 2014;343:189–93.

    CAS  Article  Google Scholar 

  27. 27.

    Tesileanu CMS, Dirven L, Wijnenga MMJ, Koekkoek JAF, Vincent AJPE, Dubbink HJ, et al. Survival of diffuse astrocytic glioma, IDH1/2-wildtype, with molecular features of glioblastoma, WHO grade IV: a confirmation of the cIMPACT-NOW criteria. Neuro Oncol. 2020;22:515–23.

    Article  Google Scholar 

  28. 28.

    Tang Q, Lian Y, Yu J, Wang Y, Shi Z, Chen L. Anatomic mapping of molecular subtypes in diffuse glioma. BMC Neurol. 2017;17:183.

    Article  Google Scholar 

  29. 29.

    Verhaak RG, Hoadley KA, Purdom E, Wang V, Qi Y, Wilkerson M, et al. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell. 2010;17:98–110.

    CAS  Article  Google Scholar 

  30. 30.

    Chan AK, Mao Y, Ng HK. TP53 and histone H3.3 mutations in triple-negative lower-grade gliomas. N Engl J Med. 2016;375:2206–8.

    Article  Google Scholar 

  31. 31.

    Arita H, Yamasaki K, Matsushita Y, Nakamura T, Shimokawa A, Takami H, et al. A combination of TERT promoter mutation and MGMT methylation status predicts clinically relevant subgroups of newly diagnosed glioblastomas. Acta Neuropathol Commun. 2016;4:79.

    Article  Google Scholar 

  32. 32.

    Hartmann C, Hentschel B, Wick W, Capper D, Felsberg J, Simon M, et al. Patients with IDH1 wild type anaplastic astrocytomas exhibit worse prognosis than IDH1-mutated glioblastomas, and IDH1 mutation status accounts for the unfavorable prognostic effect of higher age: implications for classification of gliomas. Acta Neuropathol. 2010;120:707–18.

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Xinhua Ding from the Department of Neurosurgery, Shanghai International Medical Center and Haixia Chen from the Department of Pathology, Shanghai International Medical Center for providing information on several glioma cases, their corresponding MR images and molecular data in this research.

Funding

The study was supported by the National Natural Science Foundation of China (91959127 and 81702471), National Natural Science Foundation of China (2015CB755500), Shanghai Municipal Science and Technology Major Project (No. 2018SHZDZX01), and ZJLab, Shanghai Municipal Health Commission Project (2018ZHYL0107).

Author information

Affiliations

Authors

Corresponding authors

Correspondence to Jinhua Yu or Zhifeng Shi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

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

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Luo, H., Zhuang, Q., Wang, Y. et al. A novel image signature-based radiomics method to achieve precise diagnosis and prognostic stratification of gliomas. Lab Invest (2020). https://doi.org/10.1038/s41374-020-0472-x

Download citation

Search

Quick links