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Radiomics based on multicontrast MRI can precisely differentiate among glioma subtypes and predict tumour-proliferative behaviour

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Abstract

Purpose

To explore the feasibility and diagnostic performance of radiomics based on anatomical, diffusion and perfusion MRI in differentiating among glioma subtypes and predicting tumour proliferation.

Methods

220 pathology-confirmed gliomas and ten contrasts were included in the retrospective analysis. After being registered to T2FLAIR images and resampling to 1 mm3 isotropically, 431 radiomics features were extracted from each contrast map within a semi-automatic defined tumour volume. For single-contrast and the combination of all contrasts, correlations between the radiomics features and pathological biomarkers were revealed by partial correlation analysis, and multivariate models were built to identify the best predictive models with adjusted 0.632+ bootstrap AUC.

Results

In univariate analysis, both non-wavelet and wavelet radiomics features were correlated significantly with tumour grade and the Ki-67 labelling index. The max R was 0.557 (p = 2.04E-14) in T1C for tumour grade and 0.395 (p = 2.33E-07) in ADC for Ki-67. In the multivariate analysis, the combination of all-contrast radiomics features had the highest AUCs in both differentiating among glioma subtypes and predicting proliferation compared with those in single-contrast images. For low-/high-grade gliomas, the best AUC was 0.911. In differentiating among glioma subtypes, the best AUC was 0.896 for grades II–III, 0.997 for grades II–IV, and 0.881 for grades III–IV. In predicting proliferation levels, multicontrast features led to an AUC of 0.936.

Conclusion

Multicontrast radiomics supplies complementary information on both geometric characters and molecular biological traits, which correlated significantly with tumour grade and proliferation. Combining all-contrast radiomics models might precisely predict glioma biological behaviour, which may be attributed to presurgical personal diagnosis.

Key Points

• Multicontrast MRI radiomics features are significantly correlated with tumour grade and Ki-67 LI.

• Multimodality MRI provides independent but supplemental information in assessing glioma pathological behaviour.

• Combined multicontrast MRI radiomics can precisely predict glioma subtypes and proliferation levels.

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Abbreviations

ADC:

Apparent diffusion coefficient

ASL:

Arterial spin labelling imaging

CBF:

Cerebral blood flow

DWI:

Diffusion-weighted imaging

eADC:

Exponential apparent diffusion coefficient

FLAIR:

Fluid-attenuated inversion recovery

T1C:

Contrast-enhanced T1-weighted images

T2FSE:

T2-weighted fast-echo images

VOI:

Volume of interest

References

  1. van den Bent MJ (2014) Practice changing mature results of RTOG study 9802: another positive PCV trial makes adjuvant chemotherapy part of standard of care in low-grade glioma. Neuro Oncol 16:1570–1574

  2. van den Bent MJ, Wefel JS, Schiff D et al (2011) Response assessment in neuro-oncology (a report of the RANO group): assessment of outcome in trials of diffuse low-grade gliomas. Lancet Oncol 12:583–593

    Article  PubMed  Google Scholar 

  3. Cancer Genome Atlas Research Network, Brat DJ, Verhaak RG et al (2015) Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas. N Engl J Med 372:2481–2498

    Article  CAS  Google Scholar 

  4. Stupp R, Mason WP, van den Bent MJ et al (2005) Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352:987–996

    Article  PubMed  CAS  Google Scholar 

  5. Gerdes J, Lemke H, Baisch H, Wacker HH, Schwab U, Stein H (1984) Cell cycle analysis of a cell proliferation-associated human nuclear antigen defined by the monoclonal antibody Ki-67. J Immunol 133:1710–1715

  6. Chen WJ, He DS, Tang RX, Ren FH, Chen G (2015) Ki-67 is a valuable prognostic factor in gliomas: evidence from a systematic review and meta-analysis. Asian Pac J Cancer Prev 16:411–420

  7. Wakimoto H, Aoyagi M, Nakayama T et al (1996) Prognostic significance of Ki-67 labeling indices obtained using MIB-1 monoclonal antibody in patients with supratentorial astrocytomas. Cancer 77:373–380

    Article  PubMed  CAS  Google Scholar 

  8. Law M, Young R, Babb J et al (2006) Comparing perfusion metrics obtained from a single compartment versus pharmacokinetic modeling methods using dynamic susceptibility contrast-enhanced perfusion MR imaging with glioma grade. AJNR Am J Neuroradiol 27:1975–1982

    PubMed  CAS  Google Scholar 

  9. Caulo M, Panara V, Tortora D et al (2014) Data-driven grading of brain gliomas: a multiparametric MR imaging study. Radiology 272:494–503

    Article  PubMed  Google Scholar 

  10. Inano R, Oishi N, Kunieda T et al (2014) Voxel-based clustered imaging by multiparameter diffusion tensor images for glioma grading. Neuroimage Clin 5:396–407

    Article  PubMed  PubMed Central  Google Scholar 

  11. Krabbe K, Gideon P, Wagn P, Hansen U, Thomsen C, Madsen F (1997) MR diffusion imaging of human intracranial tumours. Neuroradiology 39:483–489

  12. Garrett ME, Nauhaus I, Marshel JH, Callaway EM (2014) Topography and areal organization of mouse visual cortex. J Neurosci 34:12587–12600

  13. Sui Y, Wang H, Liu G et al (2015) Differentiation of Low- and High-Grade Pediatric Brain Tumors with High b-Value Diffusion-weighted MR Imaging and a Fractional Order Calculus Model. Radiology 277:489–496

    Article  PubMed  PubMed Central  Google Scholar 

  14. Kumar V, Gu Y, Basu S et al (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30:1234–1248

    Article  PubMed  PubMed Central  Google Scholar 

  15. Yip SS, Aerts HJ (2016) Applications and limitations of radiomics. Phys Med Biol 61:R150–R166

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Avanzo M, Stancanello J, El Naqa I (2017) Beyond imaging: The promise of radiomics. Phys Med 38:122–139

    Article  PubMed  Google Scholar 

  17. Li Y, Liu X, Xu K et al (2018) MRI features can predict EGFR expression in lower grade gliomas: A voxel-based radiomic analysis. Eur Radiol 28:356–362

  18. Kermany DS, Goldbaum M, Cai W et al (2018) Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell Cell 172:1122–1131

  19. Huang YQ, Liang CH, He L et al (2016) Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer. J Clin Oncol 34:2157–2164

    Article  PubMed  Google Scholar 

  20. Vallières M, Freeman CR, Skamene SR, El Naqa I (2015) A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol 60:5471–5496

  21. 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  PubMed  PubMed Central  CAS  Google Scholar 

  22. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: Images Are More than Pictures, They Are Data. Radiology 278:563–577

    Article  PubMed  Google Scholar 

  23. Louis DN, Ohgaki H, Wiestler OD et al (2007) The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol 114:97–109

    Article  PubMed  PubMed Central  Google Scholar 

  24. Beesley MF, McLaren KM (2002) Cytokeratin 19 and galectin-3 immunohistochemistry in the differential diagnosis of solitary thyroid nodules. Histopathology 41:236–243

    Article  PubMed  CAS  Google Scholar 

  25. Jiang R, Jiang J, Zhao L et al (2015) Diffusion kurtosis imaging can efficiently assess the glioma grade and cellular proliferation. Oncotarget 6:42380–42393

    PubMed  PubMed Central  Google Scholar 

  26. Sahiner B, Chan HP, Hadjiiski L (2008) Classifier performance estimation under the constraint of a finite sample size: resampling schemes applied to neural network classifiers. Neural Netw 21:476–483

    Article  PubMed  Google Scholar 

  27. 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  PubMed  PubMed Central  CAS  Google Scholar 

  28. Tien RD, Felsberg GJ, Friedman H, Brown M, MacFall J (1994) MR imaging of high-grade cerebral gliomas: value of diffusion-weighted echoplanar pulse sequences. AJR Am J Roentgenol 162:671–677

  29. Lyng H, Haraldseth O, Rofstad EK (2000) Measurement of cell density and necrotic fraction in human melanoma xenografts by diffusion weighted magnetic resonance imaging. Magn Reson Med 43:828–836

    Article  PubMed  CAS  Google Scholar 

  30. Silva AC, Kim SG, Garwood M (2000) Imaging blood flow in brain tumors using arterial spin labeling. Magn Reson Med 44:169–173

    Article  PubMed  CAS  Google Scholar 

  31. Chawla S, Wang S, Wolf RL et al (2007) Arterial spin-labeling and MR spectroscopy in the differentiation of gliomas. AJNR Am J Neuroradiol 28:1683–1689

    Article  PubMed  CAS  Google Scholar 

  32. Kim HS, Kim SY (2007) A prospective study on the added value of pulsed arterial spin-labeling and apparent diffusion coefficients in the grading of gliomas. AJNR Am J Neuroradiol 28:1693–1699

    Article  PubMed  CAS  Google Scholar 

  33. Hwan-Ho C, Hyunjin P (2017) Classification of low-grade and high-grade glioma using multi-modal image radiomics features. 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Seogwipo, 2017, pp. 3081–3084

  34. Zhang X, Yan LF, Hu YC et al (2017) Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features. Oncotarget 8:47816–47830

    PubMed  PubMed Central  Google Scholar 

  35. Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446

    Article  PubMed  PubMed Central  Google Scholar 

  36. Thrall JH, Li X, Li Q et al (2018) Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success. J Am Coll Radiol 15:504–508

  37. Itakura H, Achrol AS, Mitchell LA et al (2015) Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities. Sci Transl Med 7:303ra138

  38. Gevaert O, Mitchell LA, Achrol AS et al (2015) Glioblastoma Multiforme: Exploratory Radiogenomic Analysis by Using Quantitative Image Features. Radiology 276:313

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We acknowledge Dr. M Vallières for his patient responses regarding the radiomics data post-processing. We also acknowledge Dr. Yang Fan from General Electric Company (China) for his generous contributions to manuscript revision. We also thank Dr. Xiaowei Chen (10 years’ experience) for his generous work in checking the VOIs.

Funding

This study has received funding by the National Program of the Ministry of Science and Technology of China during the “12th Five-Year Plan” (ID: 2011BAI08B10) and the National Natural Science Foundation of China (No. 81171308, No. 81570462, and No. 81730049)

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Authors and Affiliations

  1. Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095, JieFang Avenue, Wuhan, Hubei, China

    Changliang Su, Jingjing Jiang, Shun Zhang, Jingjing Shi, Nanxi Shen, Jiaxuan Zhang, Li Li, Lingyun Zhao, Ju Zhang, Yuanyuan Qin & Wenzhen Zhu

  2. Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Kaibin Xu & Yong Liu

  3. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China

    Kaibin Xu & Yong Liu

  4. Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Yong Liu

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  1. Changliang Su
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  2. Jingjing Jiang
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  8. Li Li
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  10. Ju Zhang
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Corresponding authors

Correspondence to Yong Liu or Wenzhen Zhu.

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Guarantor

The scientific guarantor of this publication is Wenzhen Zhu.

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.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Observational

• Performed at one institution

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Su, C., Jiang, J., Zhang, S. et al. Radiomics based on multicontrast MRI can precisely differentiate among glioma subtypes and predict tumour-proliferative behaviour. Eur Radiol 29, 1986–1996 (2019). https://doi.org/10.1007/s00330-018-5704-8

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  • DOI: https://doi.org/10.1007/s00330-018-5704-8

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