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European Radiology

, Volume 29, Issue 4, pp 1968–1977 | Cite as

Morphological MRI-based features provide pretreatment survival prediction in glioblastoma

  • Julián Pérez-Beteta
  • David Molina-GarcíaEmail author
  • Alicia Martínez-González
  • Araceli Henares-Molina
  • Mariano Amo-Salas
  • Belén Luque
  • Elena Arregui
  • Manuel Calvo
  • José M. Borrás
  • Juan Martino
  • Carlos Velásquez
  • Bárbara Meléndez-Asensio
  • Ángel Rodríguez de Lope
  • Raquel Moreno
  • Juan A. Barcia
  • Beatriz Asenjo
  • Manuel Benavides
  • Ismael Herruzo
  • Pedro C. Lara
  • Raquel Cabrera
  • David Albillo
  • Miguel Navarro
  • Luis A. Pérez-Romasanta
  • Antonio Revert
  • Estanislao Arana
  • Víctor M. Pérez-García
Head and Neck

Abstract

Objectives

We wished to determine whether tumor morphology descriptors obtained from pretreatment magnetic resonance images and clinical variables could predict survival for glioblastoma patients.

Methods

A cohort of 404 glioblastoma patients (311 discoveries and 93 validations) was used in the study. Pretreatment volumetric postcontrast T1-weighted magnetic resonance images were segmented to obtain the relevant morphological measures. Kaplan-Meier, Cox proportional hazards, correlations, and Harrell’s concordance indexes (c-indexes) were used for the statistical analysis.

Results

A linear prognostic model based on the outstanding variables (age, contrast-enhanced (CE) rim width, and surface regularity) identified a group of patients with significantly better survival (p < 0.001, HR = 2.57) with high accuracy (discovery c-index = 0.74; validation c-index = 0.77). A similar model applied to totally resected patients was also able to predict survival (p < 0.001, HR = 3.43) with high predictive value (discovery c-index = 0.81; validation c-index = 0.92). Biopsied patients with better survival were well identified (p < 0.001, HR = 7.25) by a model including age and CE volume (c-index = 0.87).

Conclusions

Simple linear models based on small sets of meaningful MRI-based pretreatment morphological features and age predicted survival of glioblastoma patients to a high degree of accuracy. The partition of the population using the extent of resection improved the prognostic value of those measures.

Key Points

• A combination of two MRI-based morphological features (CE rim width and surface regularity) and patients’ age outperformed previous prognosis scores for glioblastoma.

• Prognosis models for homogeneous surgical procedure groups led to even more accurate survival prediction based on Kaplan-Meier analysis and concordance indexes.

Keywords

Glioblastoma Prognosis Biomarkers Survival analysis Multivariate analysis 

Abbreviations

3D

Three-dimensional

c-index

Concordance index

CE

Contrast-enhanced

CoI

Confidence interval

DICOM

Digital imaging and communication in medicine

GBM

Glioblastoma

HR

Hazard ratio

MA

Morphology- and age-based

MAB

Morphology- and age-based prognosis score for biopsied patients

MASR

Morphology- and age-based prognosis score for subtotally resected patients

MATR

Morphology- and age-based prognosis score for totally resected patients

MRI

Magnetic resonance images

OS

Overall survival

P

p value

TCIA

The Cancer Image Archive

WHO

World Health Organization

Notes

Acknowledgements

We would like to thank C. López (Radiology Department, Hospital General de Ciudad Real), M. Claramonte (Neurosurgery Department, Hospital General de Ciudad Real), L. Iglesias (Neurosurgery Department, Hospital Clínico San Carlos), J. Avecillas (Radiology Department, Hospital Clínico San Carlos), J. M. Villanueva (Medical Oncology Department, Hospital Universitario de Salamanca), and J. C. Paniagua (Medical Oncology Department, Hospital Universitario de Salamanca) for their help in the data collection. We would also like to thank J. A. Ortiz Alhambra (Mathematical Oncology Laboratory) and A. Fernández-Romero (Mathematical Oncology Laboratory) for their help in the tumor segmentation tasks.

Funding

This research has been supported by the Ministerio de Economía y Competitividad/FEDER, Spain (grant number MTM2015-71200-R), and James S. Mc. Donnell Foundation Twenty-First Century Science Initiative in Mathematical and Complex Systems Approaches for Brain Cancer (Collaborative award 220020450).

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Victor Manuel Pérez-García, full professor and head of Department of Mathematics at Universidad de Castilla-La Mancha (Spain).

Conflict of interest

The authors declare that they have no conflict of interest.

Statistics and biometry

Complex statistical methods were necessary for this paper. However, Victor M. Pérez-García, Alicia Martínez-González, and David Molina (mathematicians) have 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

• Multicenter study

Supplementary material

330_2018_5758_MOESM1_ESM.docx (143 kb)
ESM 1 (DOCX 142 kb)

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Copyright information

© European Society of Radiology 2018
corrected publication November 2018

Authors and Affiliations

  • Julián Pérez-Beteta
    • 1
  • David Molina-García
    • 1
    Email author
  • Alicia Martínez-González
    • 1
  • Araceli Henares-Molina
    • 1
  • Mariano Amo-Salas
    • 1
  • Belén Luque
    • 1
  • Elena Arregui
    • 2
  • Manuel Calvo
    • 2
  • José M. Borrás
    • 3
  • Juan Martino
    • 4
  • Carlos Velásquez
    • 4
  • Bárbara Meléndez-Asensio
    • 5
  • Ángel Rodríguez de Lope
    • 6
  • Raquel Moreno
    • 7
  • Juan A. Barcia
    • 8
  • Beatriz Asenjo
    • 9
  • Manuel Benavides
    • 10
  • Ismael Herruzo
    • 11
  • Pedro C. Lara
    • 12
  • Raquel Cabrera
    • 12
  • David Albillo
    • 13
  • Miguel Navarro
    • 14
  • Luis A. Pérez-Romasanta
    • 15
  • Antonio Revert
    • 16
  • Estanislao Arana
    • 17
  • Víctor M. Pérez-García
    • 1
  1. 1.Mathematical Oncology Laboratory (MôLAB), Department of MathematicsUniversidad de Castilla-La ManchaCiudad RealSpain
  2. 2.Department of RadiologyHospital General de Ciudad RealCiudad RealSpain
  3. 3.Department of NeurosurgeryHospital General de Ciudad RealCiudad RealSpain
  4. 4.Department of NeurosurgeryHospital Universitario Marqués de Valdecilla and Fundación Instituto de Investigación Marqués de ValdecillaSantanderSpain
  5. 5.Department of Molecular BiologyHospital Virgen de la SaludToledoSpain
  6. 6.Department of NeurosurgeryHospital Virgen de la SaludToledoSpain
  7. 7.Department of RadiologyHospital Virgen de la SaludToledoSpain
  8. 8.Department of NeurosurgeryHospital Clínico San CarlosMadridSpain
  9. 9.Department of RadiologyHospital Carlos HayaMálagaSpain
  10. 10.Department of Medical OncologyHospital Carlos HayaMálagaSpain
  11. 11.Department of Radiation OncologyHospital Carlos HayaMálagaSpain
  12. 12.Department of Radiation OncologyHospital Universitario Doctor NegrínGran CanariaSpain
  13. 13.Department of RadiologyHospital Universitario de SalamancaSalamancaSpain
  14. 14.Department of Medical OncologyHospital Universitario de SalamancaSalamancaSpain
  15. 15.Department of Radiation OncologyHospital Universitario de SalamancaSalamancaSpain
  16. 16.Department of RadiologyHospital de ManisesValenciaSpain
  17. 17.Department of RadiologyFundación Instituto Valenciano de OncologíaValenciaSpain

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