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Use Case II: Imaging Biomarkers and New Trends for Integrated Glioblastoma Management

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Imaging Biomarkers

Abstract

Glioblastoma (GB) implies a devastating prognosis with an average survival of 14–16 months using the current standard of care treatment [1]. GB is the most frequent malignant tumour originating from the brain parenchyma, and it is characterised by a marked intratumoural heterogeneity, proneness to infiltrate throughout the brain parenchyma, robust angiogenesis and necrosis as well as intense resistance to apoptosis and genomic instability.

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Abbreviations

ADC:

Apparent diffusion coefficient

CHTH:

Chemotherapy

DCE:

Dynamic contrast-enhanced MRI

DSC:

Dynamic susceptibility contrast

DSS:

Decision support system

DWI:

Diffusion-weighted imaging

EHR:

Electronic health record

GB:

Glioblastoma

GUI:

Graphical user interface

Kep:

Contrast extraction coefficient

Ktrans:

Volume transfer coefficient

MR:

Magnetic resonance

MRI:

Magnetic resonance imaging

MRSI:

Magnetic resonance spectroscopy imaging

NGS:

Next-generation sequencing

PET:

Positron emission tomography

PWI:

Perfusion-weighted imaging

RCBV:

Relative cerebral blood volume

RT:

Radiotherapy

TMZ:

Temozolomide

UX:

User experience

WHO:

World Health Organization

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Acknowledgements

This work was partially supported by project TIN2013-43457-R: Caracterización de firmas biológicas de glioblastomas mediante modelos no-supervisados de predicción estructurada basados en biomarcadores de imagen, co-funded by the Ministerio de Economía y Competitividad of Spain; the project CON-2014-001 Unsupervised glioblastoma tumour component segmentation based on perfusion multi-parametric MRI and spatial/temporal constraints, co-funded by the Global Investigator Initiated Research Committee (GIIRC) research programme by BRACCO, the Flemish Government FWO project G.0869.12 N and the project CURIAM-FDFT: Solución computacional del modelo mul-tinivel in vivo de la dinámica de la angiogénesis para la detección temprana de respuesta a tratamiento en glioblastomas primarios, co-funded by the ITACA Institute, UPV. Additionally, E. Fuster-Garcia acknowledges the financial support from the programme PAID- 10–14: Ayudas para la Contratación de Doctores para el Acceso al SECTI founded by the Universitat Politècnica de València.

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Authors and Affiliations

  1. Instituto Universitario de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas, Universitat Politècnica de València, Camí de Vera s/n., 46022, València, España

    Elies Fuster-Garcia & Juan Miguel García-Gómez

  2. Dipartimento di Matematica, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, 10129, Italy

    Elena De Angelis

  3. Xcommerce BVBA, Namsesteenweg 248, Heverlee, 3001, Belgium

    Arthur Sraum

  4. INFO WORLD SRL, Strada Intrarea Glucozei 37-39, Bucuresti Sector 2, 023828, Romania

    Arthur Molnar

  5. Department of Electrical Engineering (ESAT), KU Leuven, Kasteelpark Arenberg 10, Box 2446, 3001, Heverlee, Belgium

    Sabine Van Huffel

  6. In Silico Oncology and In Silico Medicine Group, Laboratory of Microwaves and Fiber Optics, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, Iroon Polytechniou, Zografos, GR15780, Athens, Greece

    Georgios Stamatakos

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  1. Elies Fuster-Garcia
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  2. Juan Miguel García-Gómez
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  3. Elena De Angelis
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  7. Georgios Stamatakos
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Correspondence to Elies Fuster-Garcia .

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  1. La Fe University and Polithecnic Hospital, University of Valencia, Valencia, Spain

    Luis Martí-Bonmatí

  2. La Fe University and Polithecnic Hospital, University of Valencia, Valencia, Spain

    Angel Alberich-Bayarri

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Fuster-Garcia, E. et al. (2017). Use Case II: Imaging Biomarkers and New Trends for Integrated Glioblastoma Management. In: Martí-Bonmatí, L., Alberich-Bayarri, A. (eds) Imaging Biomarkers. Springer, Cham. https://doi.org/10.1007/978-3-319-43504-6_16

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  • DOI: https://doi.org/10.1007/978-3-319-43504-6_16

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