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Novel 3D magnetic resonance fingerprinting radiomics in adult brain tumors: a feasibility study

  • Magnetic Resonance
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To test the feasibility of using 3D MRF maps with radiomics analysis and machine learning in the characterization of adult brain intra-axial neoplasms.

Methods

3D MRF acquisition was performed on 78 patients with newly diagnosed brain tumors including 33 glioblastomas (grade IV), 6 grade III gliomas, 12 grade II gliomas, and 27 patients with brain metastases. Regions of enhancing tumor, non-enhancing tumor, and peritumoral edema were segmented and radiomics analysis with gray-level co-occurrence matrices and gray-level run-length matrices was performed. Statistical analysis was performed to identify features capable of differentiating tumors based on type, grade, and isocitrate dehydrogenase (IDH1) status. Receiver operating curve analysis was performed and the area under the curve (AUC) was calculated for tumor classification and grading. For gliomas, Kaplan-Meier analysis for overall survival was performed using MRF T1 features from enhancing tumor region.

Results

Multiple MRF T1 and T2 features from enhancing tumor region were capable of differentiating glioblastomas from brain metastases. Although no differences were identified between grade 2 and grade 3 gliomas, differentiation between grade 2 and grade 4 gliomas as well as between grade 3 and grade 4 gliomas was achieved. MRF radiomics features were also able to differentiate IDH1 mutant from the wild-type gliomas. Radiomics T1 features for enhancing tumor region in gliomas correlated to overall survival (p < 0.05).

Conclusion

Radiomics analysis of 3D MRF maps allows differentiating glioblastomas from metastases and is capable of differentiating glioblastomas from metastases and characterizing gliomas based on grade, IDH1 status, and survival.

Key Points

• 3D MRF data analysis using radiomics offers novel tissue characterization of brain tumors.

• 3D MRF with radiomics offers glioma characterization based on grade, IDH1 status, and overall patient survival.

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Abbreviations

ATRX:

Alpha thalassemia mental retardation syndrome X-linked

AUC:

Area under the curve

CE:

Contrast enhanced

ED:

Peritumoral edema

ET:

Enhancing tumor

FLAIR:

Fluid-attenuated inversion recovery

FLIRT:

FMRIB Software Library’s Linear Image Registration Tool

GBs:

Glioblastomas

GLCM:

Gray-level co-occurrence matrix

GLRLM:

Gray-level run-length matrix

ICC:

Interobserver concordance

IDH1:

Isocitrate dehydrogenase 1

IMC1:

Information measure of correlation

IMC2:

Information measure of correlation 2

METs:

Metastases

MRF:

Magnetic resonance fingerprinting

MRI:

Magnetic resonance imaging

NET:

Nonenhancing tumor or necrosis

NSCLC:

Non-small cell lung cancer

RLNN:

Run-length nonuniformity normalized

ROI:

Region of interest

RSF:

Random survival forest

WHO:

World Health Organization

References

  1. Wang S, Meng M, Zhang X et al (2018) Texture analysis of diffusion weighted imaging for the evaluation of glioma heterogeneity based on different regions of interest. Oncol Lett 15:7297–7304

    Google Scholar 

  2. Ryu YJ, Choi SH, Park SJ et al (2014) Glioma: application of whole-tumor texture analysis of diffusion-weighted imaging for the evaluation of tumor heterogeneity. PLoS One 9:e108335. https://doi.org/10.1371/journal.pone.0108335

    Article  CAS  Google Scholar 

  3. O’Connor JPB, Aboagye EO, Adams JE et al (2017) Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol 14:169–186

    Article  Google Scholar 

  4. Haubold J, Demircioglu A, Gratz M et al (2020) Non-invasive tumor decoding and phenotyping of cerebral gliomas utilizing multiparametric 18F-FET PET-MRI and MR fingerprinting. Eur J Nucl Med Mol Imaging 47:1435–1445. https://doi.org/10.1007/s00259-019-04602-2

    Article  CAS  Google Scholar 

  5. Ma D, Gulani V, Seiberlich N et al (2013) Magnetic resonance fingerprinting. Nature 495:187–192

    Article  CAS  Google Scholar 

  6. Liu Y, Hamilton J, Rajagopalan S, Seiberlich N (2018) Cardiac magnetic resonance fingerprinting: technical overview and initial results. JACC Cardiovascular Imaging 11:1837–1853. https://doi.org/10.1016/j.jcmg.2018.08.028

  7. Wang CY, Coppo S, Mehta BB et al (2019) Magnetic resonance fingerprinting with quadratic RF phase for measurement of T2 * simultaneously with δf , T1 , and T2. Magn Reson Med 81:1849–1862. https://doi.org/10.1002/mrm.27543

    Article  Google Scholar 

  8. Badve C, Yu A, Dastmalchian S et al (2017) MR fingerprinting of adult brain tumors: initial experience. AJNR Am J Neuroradiol 38:492–499

    Article  CAS  Google Scholar 

  9. Dastmalchian S, Kilinc O, Onyewadume L et al (2021) Radiomic analysis of magnetic resonance fingerprinting in adult brain tumors. Eur J Nucl Med Mol Imaging 48:683–693. https://doi.org/10.1007/s00259-020-05037-w

    Article  CAS  Google Scholar 

  10. Onyewadume L, Kilinc O, Ghodsara S, et al (ISMRM 2018) Radiomic analysis of 3D MR fingerprinting in adult brain tumors. https://archive.ismrm.org/2018/0957.html. Accessed 10 May 2022.

  11. de Blank P, Badve C, Gold DR et al (2019) Magnetic resonance fingerprinting to characterize childhood and young adult brain tumors. Pediatr Neurosurg 54:310–318. https://doi.org/10.1159/000501696

    Article  Google Scholar 

  12. Badve C, Yu A, Rogers M et al (2015) Simultaneous T1 and T2 brain relaxometry in asymptomatic volunteers using magnetic resonance fingerprinting. Tomography:136–144

  13. Chen Y, Chen M-H, Baluyot KR et al (2019) MR fingerprinting enables quantitative measures of brain tissue relaxation times and myelin water fraction in the first five years of life. Neuroimage 186:782–793

    Article  Google Scholar 

  14. Liao C, Wang K, Cao X et al (2018) Detection of lesions in mesial temporal lobe epilepsy by using MR fingerprinting. Radiology 288:804–812

    Article  Google Scholar 

  15. Coristine A, Hamilton J, van Heeswijk RB, et al (2018). Cardiac magnetic resonance fingerprinting in heart transplant recipients. http://archive.ismrm.org/2018/0675.html. Accessed 21 Oct 2021.

  16. Panda A, Obmann VC, Lo W-C et al (2019) MR fingerprinting and ADC mapping for characterization of lesions in the transition zone of the prostate gland. Radiology 292:685–694. https://doi.org/10.1148/radiol.2019181705

    Article  Google Scholar 

  17. Yu AC, Badve C, Ponsky LE et al (2017) Development of a combined MR fingerprinting and diffusion examination for prostate cancer. Radiology 283:729–738. https://doi.org/10.1148/radiol.2017161599

    Article  Google Scholar 

  18. Shiradkar R, Panda A, Leo P et al (2020) T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning-derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology. Eur Radiol 31:1336–1346

    Article  Google Scholar 

  19. Chen Y, Jiang Y, Pahwa S et al (2016) MR fingerprinting for rapid quantitative abdominal imaging. Radiology 279:278–286

    Article  Google Scholar 

  20. Jaubert O, Cruz G, Bustin A et al (2020) Free-running cardiac magnetic resonance fingerprinting: joint T1/T2 map and cine imaging. Magn Reson Imaging 68:173–182. https://doi.org/10.1016/j.mri.2020.02.005

  21. Cloos MA, Assländer J, Abbas B et al (2019) Rapid radial T1 and T2 mapping of the hip articular cartilage with magnetic resonance fingerprinting. J Magn Reson Imaging 50:810–815. https://doi.org/10.1002/jmri.26615

    Article  Google Scholar 

  22. Ma D, Jones SE, Deshmane A et al (2019) Development of high-resolution 3D MR fingerprinting for detection and characterization of epileptic lesions. J Magn Reson Imaging 49:1333–1346

    Article  Google Scholar 

  23. Hamilton JI, Jiang Y, Chen Y et al (2017) MR fingerprinting for rapid quantification of myocardial T1 , T2 , and proton spin density. Magn Reson Med 77:1446–1458. https://doi.org/10.1002/mrm.26216

    Article  Google Scholar 

  24. 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 

  25. Jenkinson M, Bannister P, Brady M, Smith S (2002) Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17:825–841. https://doi.org/10.1016/s1053-8119(02)91132-8

    Article  Google Scholar 

  26. Haralick RM (1979) Statistical and structural approaches to texture. Proceedings of the IEEE 67:786–804

    Article  Google Scholar 

  27. Lupo JM, Cha S, Chang SM, Nelson SJ (2005) Dynamic susceptibility-weighted perfusion imaging of high-grade gliomas: characterization of spatial heterogeneity. AJNR Am J Neuroradiol 26:1446–1454

    Google Scholar 

  28. Hambardzumyan D, Bergers G (2015) Glioblastoma: defining tumor niches. Trends Cancer 1:252–265

    Article  Google Scholar 

  29. Wagner M, Nafe R, Jurcoane A et al (2011) Heterogeneity in malignant gliomas: a magnetic resonance analysis of spatial distribution of metabolite changes and regional blood volume. J Neurooncol 103:663–672. https://doi.org/10.1007/s11060-010-0443-y

    Article  Google Scholar 

  30. Barajas RF, Phillips JJ, Parvataneni R et al (2012) Regional variation in histopathologic features of tumor specimens from treatment-naive glioblastoma correlates with anatomic and physiologic MR Imaging. Neuro Oncol 14:942–954

  31. Ji B, Wang S, Liu Z et al (2019) Revealing hemodynamic heterogeneity of gliomas based on signal profile features of dynamic susceptibility contrast-enhanced MRI. Neuroimage Clin 23:101864. https://doi.org/10.1016/j.nicl.2019.101864

    Article  Google Scholar 

  32. Deoni SCL (2010) Quantitative relaxometry of the brain. Top Magn Reson Imaging 21:101–113

    Article  Google Scholar 

  33. Deoni SCL, Dean DC, O’Muircheartaigh J et al (2012) Investigating white matter development in infancy and early childhood using myelin water faction and relaxation time mapping. Neuroimage 63:1038–1053. https://doi.org/10.1016/j.neuroimage.2012.07.037

    Article  Google Scholar 

  34. Louis DN, Perry A, Wesseling P et al (2021) The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro Oncol 23:1231–1251. https://doi.org/10.1093/neuonc/noab106

  35. Lee EJ, terBrugge K, Mikulis D et al (2011) Diagnostic value of peritumoral minimum apparent diffusion coefficient for differentiation of glioblastoma multiforme from solitary metastatic lesions. AJR Am J Roentgenol 196:71–76. https://doi.org/10.2214/AJR.10.4752

    Article  Google Scholar 

  36. Soliman RK, Essa AA, Elhakeem AAS et al (2021) Texture analysis of apparent diffusion coefficient (ADC) map for glioma grading: analysis of whole tumoral and peri-tumoral tissue. Diagn Interv Imaging 102:287–295

    Article  Google Scholar 

  37. Duron L, Balvay D, Vande Perre S et al (2019) Gray-level discretization impacts reproducible MRI radiomics texture features. PLoS One 14:e0213459. https://doi.org/10.1371/journal.pone.0213459

    Article  CAS  Google Scholar 

Download references

Funding

This study has received funding by National Institutes of Health 1R01BB017219 award (Principal Investigator: Dr. Mark Griswold) and 1R01EB016728 award (Principal Investigators: Drs. Mark Griswold and Vikas Gulani). This project was also supported by the Clinical and Translational Science Collaborative (CTSC) of Cleveland which is funded by the National Institutes of Health (NIH), National Center for Advancing Translational Science (NCATS), Clinical and Translational Science Award (CTSA) grant, UL1TR002548 (Principal Investigator: Dr. Chaitra Badve). AES is supported by NIH CA217956, the Peter D Cristal Chair, the Center of Excellence for Translational Neuro Oncol, the Gerald R. Kaufman Fund for Glioma Research at University Hospitals of Cleveland, the Kimble Family Foundation, and the Ferry Family Foundation at University Hospitals of Cleveland. The content is solely the responsibility of the authors and do not necessarily represent the official views of the NIH.

Author information

Authors and Affiliations

  1. Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, Seidman Cancer Center and Case Comprehensive Cancer Center, 11100 Euclid Ave, Cleveland, OH, 44106, USA

    Charit Tippareddy, Rasim Boyacıoğlu, Jeffrey Sunshine, Mark Griswold, Dan Ma & Chaitra Badve

  2. Department of Neurosurgery, West Virginia University Health Sciences Center, Morgantown, WV, USA

    Louisa Onyewadume

  3. Departments of Neurosurgery and Pathology, Seidman Cancer Center and Case Comprehensive Cancer Center, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, OH, USA

    Andrew E. Sloan

  4. Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Research and Education Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA

    Gi-Ming Wang

  5. Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA

    Nirav T. Patil, Siyuan Hu & Mark Griswold

  6. Center for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda, MD, USA

    Jill S. Barnholtz-Sloan

  7. Trans-Divisional Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA

    Jill S. Barnholtz-Sloan

  8. Department of Radiology, Michigan Institute of Imaging Technology and Translation, Michigan Medicine, Ann Arbor, MI, USA

    Vikas Gulani

Authors
  1. Charit Tippareddy
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  2. Louisa Onyewadume
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  6. Siyuan Hu
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  8. Rasim Boyacıoğlu
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  10. Jeffrey Sunshine
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  11. Mark Griswold
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  12. Dan Ma
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Corresponding author

Correspondence to Chaitra Badve.

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Guarantor

The scientific guarantor of this publication is Dr. Chaitra Badve.

Conflict of interest

The authors of this manuscript declare relationships with the following companies: Case Western Reserve University and University Hospitals receive research support from Siemens. Chaitra Badve, Dan Ma, Mark Griswold, and Jeffrey Sunshine have patent applications on MRF and its applications. Charit Tippareddy, Louisa Onyewadume, Andrew E. Sloan, Gi-Ming Wang, Nirav T. Patil, Jill S. Barnholtz-Sloan, and Rasim Boyacıoğlu have nothing to disclose.

Statistics and biometry

Three authors have significant statistical expertise: Dr. Jill Barnholtz-Sloan, Gi-Ming Wang, and Nirav Patil.

Informed consent

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

Ethical approval

Institutional Review Board approval was obtained.

Methodology

  • prospective

  • diagnostic or prognostic study

  • performed at one institution

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Tippareddy, C., Onyewadume, L., Sloan, A.E. et al. Novel 3D magnetic resonance fingerprinting radiomics in adult brain tumors: a feasibility study. Eur Radiol 33, 836–844 (2023). https://doi.org/10.1007/s00330-022-09067-w

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