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Morphological MRI-based features provide pretreatment survival prediction in glioblastoma

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A Correction to this article was published on 13 December 2018

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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.

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Change history

  • 13 December 2018

    The original version of this article, published on 15 October 2018, unfortunately contained a mistake. The following correction has therefore been made in the original: The name of Mariano Amo-Salas and the affiliation of Ismael Herruzo were presented incorrectly.

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

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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).

Author information

Author notes

  1. Estanislao Arana and Víctor M. Pérez-García are joint senior authors.

  2. Julián Pérez-Beteta and David Molina-García contributed equally to this work.

Authors and Affiliations

  1. Mathematical Oncology Laboratory (MôLAB), Department of Mathematics, Universidad de Castilla-La Mancha, 13071, Ciudad Real, Spain

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

  2. Department of Radiology, Hospital General de Ciudad Real, Ciudad Real, Spain

    Elena Arregui & Manuel Calvo

  3. Department of Neurosurgery, Hospital General de Ciudad Real, Ciudad Real, Spain

    José M. Borrás

  4. Department of Neurosurgery, Hospital Universitario Marqués de Valdecilla and Fundación Instituto de Investigación Marqués de Valdecilla, Santander, Spain

    Juan Martino & Carlos Velásquez

  5. Department of Molecular Biology, Hospital Virgen de la Salud, Toledo, Spain

    Bárbara Meléndez-Asensio

  6. Department of Neurosurgery, Hospital Virgen de la Salud, Toledo, Spain

    Ángel Rodríguez de Lope

  7. Department of Radiology, Hospital Virgen de la Salud, Toledo, Spain

    Raquel Moreno

  8. Department of Neurosurgery, Hospital Clínico San Carlos, Madrid, Spain

    Juan A. Barcia

  9. Department of Radiology, Hospital Carlos Haya, Málaga, Spain

    Beatriz Asenjo

  10. Department of Medical Oncology, Hospital Carlos Haya, Málaga, Spain

    Manuel Benavides

  11. Department of Radiation Oncology, Hospital Carlos Haya, Málaga, Spain

    Ismael Herruzo

  12. Department of Radiation Oncology, Hospital Universitario Doctor Negrín, Gran Canaria, Spain

    Pedro C. Lara & Raquel Cabrera

  13. Department of Radiology, Hospital Universitario de Salamanca, Salamanca, Spain

    David Albillo

  14. Department of Medical Oncology, Hospital Universitario de Salamanca, Salamanca, Spain

    Miguel Navarro

  15. Department of Radiation Oncology, Hospital Universitario de Salamanca, Salamanca, Spain

    Luis A. Pérez-Romasanta

  16. Department of Radiology, Hospital de Manises, Valencia, Spain

    Antonio Revert

  17. Department of Radiology, Fundación Instituto Valenciano de Oncología, Valencia, Spain

    Estanislao Arana

Authors
  1. Julián Pérez-Beteta
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  2. David Molina-García
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  3. Alicia Martínez-González
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  4. Araceli Henares-Molina
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  5. Mariano Amo-Salas
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  6. Belén Luque
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  7. Elena Arregui
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  8. Manuel Calvo
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  12. Bárbara Meléndez-Asensio
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  13. Ángel Rodríguez de Lope
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  14. Raquel Moreno
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  15. Juan A. Barcia
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  16. Beatriz Asenjo
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  17. Manuel Benavides
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  18. Ismael Herruzo
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  19. Pedro C. Lara
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  20. Raquel Cabrera
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  21. David Albillo
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  22. Miguel Navarro
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  23. Luis A. Pérez-Romasanta
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  24. Antonio Revert
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  25. Estanislao Arana
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  26. Víctor M. Pérez-García
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Corresponding author

Correspondence to David Molina-García.

Ethics declarations

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

Additional information

The original version of this article was revised: The name of Mariano Amo-Salas and the affiliation of Ismael Herruzo were presented incorrectly.

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Pérez-Beteta, J., Molina-García, D., Martínez-González, A. et al. Morphological MRI-based features provide pretreatment survival prediction in glioblastoma. Eur Radiol 29, 1968–1977 (2019). https://doi.org/10.1007/s00330-018-5758-7

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