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Glioblastoma: does the pre-treatment geometry matter? A postcontrast T1 MRI-based study

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

Abstract

Background

The potential of a tumour’s volumetric measures obtained from pretreatment MRI sequences of glioblastoma (GBM) patients as predictors of clinical outcome has been controversial. Mathematical models of GBM growth have suggested a relation between a tumour’s geometry and its aggressiveness.

Methods

A multicenter retrospective clinical study was designed to study volumetric and geometrical measures on pretreatment postcontrast T1 MRIs of 117 GBM patients. Clinical variables were collected, tumours segmented, and measures computed including: contrast enhancing (CE), necrotic, and total volumes; maximal tumour diameter; equivalent spherical CE width and several geometric measures of the CE “rim”. The significance of the measures was studied using proportional hazards analysis and Kaplan-Meier curves.

Results

Kaplan-Meier and univariate Cox survival analysis showed that total volume [p = 0.034, Hazard ratio (HR) = 1.574], CE volume (p = 0.017, HR = 1.659), spherical rim width (p = 0.007, HR = 1.749), and geometric heterogeneity (p = 0.015, HR = 1.646) were significant parameters in terms of overall survival (OS). Multivariable Cox analysis for OS provided the later two parameters as age-adjusted predictors of OS (p = 0.043, HR = 1.536 and p = 0.032, HR = 1.570, respectively).

Conclusion

Patients with tumours having small geometric heterogeneity and/or spherical rim widths had significantly better prognosis. These novel imaging biomarkers have a strong individual and combined prognostic value for GBM patients.

Key Points

• Three-dimensional segmentation on magnetic resonance images allows the study of geometric measures.

• Patients with small width of contrast enhancing areas have better prognosis.

• The irregularity of contrast enhancing areas predicts survival in glioblastoma patients.

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Abbreviations

GBM:

Glioblastoma

PFS:

Progression-free Survival

OS:

Overall survival

KPS:

Karnofsky performance status

VAK:

Volume-Age-KPS

3D:

Three-dimensional

CE:

Contrast enhancing

DICOM:

Digital imaging and communication in medicine

VCE :

CE volume

VI :

Inner volume

V:

Total postcontrast T1 tumour volume

dmax 3D:

Maximum tumour diameter in 3D

δ s :

Average size of CE rim

GH :

Measure of geometric heterogeneity of the CE rim width

HR:

Hazard ratio

2D:

Bidimensional

Gd:

Gadolinium

p :

p-value

References

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

    Article  CAS  PubMed  Google Scholar 

  2. Ellingson BM (2015) Radiogenomics and imaging phenotypes in glioblastoma: novel observations and correlation with molecular characteristics. Curr Neurol Neurosci Rep 15:506

    Article  PubMed  Google Scholar 

  3. Zinn PO, Mahmood Z, Elbanan MG, Colen RR (2015) Imaging Genomics in Gliomas. Cancer J 21:225–234

    Article  CAS  PubMed  Google Scholar 

  4. Wen PY, Macdonald DR, Reardon DA, Cloughesy TF, Sorensen AG, Galanis E et al (2010) Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol 28:1963–1972

    Article  PubMed  Google Scholar 

  5. Iliadis G, Selviaridis P, Kalogera-Fountzila A, Fragkoulidi A, Baltas D, Tselis N et al (2009) The importance of tumor volume in the prognosis of patients with glioblastoma: comparison of computerized volumetry and geometric models. Strahlenther Onkol 185:743–750

    Article  PubMed  Google Scholar 

  6. Mazurowski MA, Zhang J, Peters KB, Hobbs H (2014) Computer-extracted MR imaging features are associated with survival in glioblastoma patients. J Neurooncol 120:483–488

    Article  PubMed  Google Scholar 

  7. Zinn PO, Sathyan P, Mahajan B, Bruyere J, Hegi M, Majumder S et al (2012) A novel volume-age-KPS (VAK) glioblastoma classification identifies a prognostic cognate microRNA-gene signature. PLoS One 7:e41522

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Mazurowski MA, Desjardins A, Malof JM (2013) Imaging descriptors improve the predictive power of survival models for glioblastoma patients. Neuro Oncol 15:1389–1394

    Article  PubMed  PubMed Central  Google Scholar 

  9. Zacharaki EI, Morita N, Bhatt P, O'Rourke DM, Melhem ER, Davatzikos C (2012) Survival analysis of patients with high-grade gliomas based on data mining of imaging variables. AJNR Am J Neuroradiol 33:1065–1071

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Pérez-García VM, Calvo GF, Belmonte-Beitia J, Diego D, Pérez-Romasanta LA (2011) Bright solitary waves in malignant gliomas. Phys Rev E 84:021921

    Article  Google Scholar 

  11. Ellingson BM, Bendszus M, Boxerman J et al (2015) Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials. Neuro Oncol 17:1188–1198

    Article  PubMed  PubMed Central  Google Scholar 

  12. Goel MK, Khanna P, Kishore J (2010) Understanding survival analysis: Kaplan-Meier estimate. Int J Ayurveda Res 1:274–278

    Article  PubMed  PubMed Central  Google Scholar 

  13. Zhang Z, Jiang H, Chen X, Bai J, Cui Y, Ren X et al (2014) Identifying the survival subtypes of glioblastoma by quantitative volumetric analysis of MRI. J Neurooncol 119:207–214

    Article  PubMed  Google Scholar 

  14. Ramakrishna R, Barber J, Kennedy G, Rizvi Win RH, Ojemann GA, Berger MS et al (2010) Imaging features of invasion and preoperative and postoperative tumor burden in previously untreated glioblastomas: correlation with survival. Surg Neurol Int 1:40

    Article  PubMed  PubMed Central  Google Scholar 

  15. 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 

  16. Wangaryattawanich P, Hatami R, 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 

  17. Deisboeck DS, Guiot C, Delsanto PP, Pugno N (2006) Does cancer growth depend on surface extension? Med Hypotheses 67:1338–1341

    Article  CAS  PubMed  Google Scholar 

  18. Macyszyn L, Akbari H, Pisapia JM, Da X, Attiah M, Pigrish V, et al (2015) Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques. Neuro Oncol 18:417–425

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

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

We would like to acknowledge Juan Belmonte (Universidad de Castilla-La Mancha) for discussions. The scientific guarantor of this publication is Víctor M. Manuel Pérez-García (Victor.PerezGarcia@uclm.es), full professor and head of Department of Mathematics at Universidad de Castilla-La Mancha (Spain). 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. This work has been supported by Ministerio de Economía y Competitividad/FEDER, Spain [grant numbers: MTM2012-31073 and MTM2015-71200-R], Consejería de Educación Cultura y Deporte from Junta de Comunidades de Castilla-La Mancha (Spain) [grant number PEII-2014-031-P] and James S. Mc. Donnell Foundation (USA) 21st Century Science Initiative in Mathematical and Complex Systems Approaches for Brain Cancer (Special Initiative Collaborative – Planning Grant 220020420 and Collaborative award 220020450). Complex statistical methods were necessary for this paper. Víctor M. Pérez-García, Alicia Martínez-González and David Molina (Matematicians) have significant statistical expertise. Institutional Review Board approval was obtained. Written informed consent was obtained from all subjects (patients) in this study. Methodology: retrospective, observational, multicenter study.

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Authors and Affiliations

  1. Laboratory of Mathematical Oncology, Edificio Politécnico, Instituto de Matemática Aplicada a la Ciencia y la Ingeniería, Universidad de Castilla-La Mancha, Avenida de Camilo José Cela, 3, 13071, Ciudad Real, Spain

    Julián Pérez-Beteta, Alicia Martínez-González, David Molina, Mariano Amo-Salas, Belén Luque & Víctor M. Pérez-García

  2. Hospital General de Ciudad Real, c/ Obispo Rafael Torija, Ciudad Real, Spain

    Elena Arregui, Manuel Calvo, José M. Borrás, Carlos López & Marta Claramonte

  3. Hospital Clínico San Carlos, Madrid, Spain

    Juan A. Barcia, Lidia Iglesias & Josué Avecillas

  4. Hospital Universitario de Salamanca, Salamanca, Spain

    David Albillo, Miguel Navarro, José M. Villanueva, Juan C. Paniagua & Luis Pérez-Romasanta

  5. Hospital Marqués de Valdecilla, Santander, Spain

    Juan Martino & Carlos Velásquez

  6. Hospital Carlos Haya, Málaga, Spain

    Beatriz Asenjo, Manuel Benavides & Ismael Herruzo

  7. Facultad de Matemáticas, Universidad de Sevilla, Sevilla, Spain

    María del Carmen Delgado & Ana del Valle

  8. Auckland Radiation Oncology, Auckland, New Zealand

    Anthony Falkov

  9. Neurosurgery Department, Bern Inselspital, Bern, Switzerland

    Philippe Schucht

  10. Instituto Valenciano de Oncología, Valencia, Spain

    Estanislao Arana

Authors
  1. Julián Pérez-Beteta
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  2. Alicia Martínez-González
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  3. David Molina
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  4. Mariano Amo-Salas
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  5. Belén Luque
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  6. Elena Arregui
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  7. Manuel Calvo
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  8. José M. Borrás
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  9. Carlos López
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  10. Marta Claramonte
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  11. Juan A. Barcia
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  12. Lidia Iglesias
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  13. Josué Avecillas
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  14. David Albillo
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  15. Miguel Navarro
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  16. José M. Villanueva
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  17. Juan C. Paniagua
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  18. Juan Martino
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  19. Carlos Velásquez
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  20. Beatriz Asenjo
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  21. Manuel Benavides
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  22. Ismael Herruzo
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  23. María del Carmen Delgado
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  24. Ana del Valle
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  25. Anthony Falkov
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  26. Philippe Schucht
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  27. Estanislao Arana
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  28. Luis Pérez-Romasanta
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  29. Víctor M. Pérez-García
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Corresponding author

Correspondence to Julián Pérez-Beteta.

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Pérez-Beteta, J., Martínez-González, A., Molina, D. et al. Glioblastoma: does the pre-treatment geometry matter? A postcontrast T1 MRI-based study. Eur Radiol 27, 1096–1104 (2017). https://doi.org/10.1007/s00330-016-4453-9

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  • DOI: https://doi.org/10.1007/s00330-016-4453-9

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