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Exploratory Analysis of Radiomics Features on a Head and Neck Cancer Public Dataset

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Hybrid Artificial Intelligent Systems (HAIS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12344))

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Abstract

The prediction of the stage and evolution of cancer is a matter of strong social interest. The head and neck cancer is not one of the most prevalent and deathly cancers, but it is quite challenging due to its morphological variability and the risk of proliferation. In this paper we report results on a radiomics features based machine learning approaches trying to predict (a) the stage of the cancer, (b) the need for surgery, and (c) the survival after 2000 days. Results are encouraging, but a lot of work need to be done in order to attain an accuracy leading to clinical use.

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Notes

  1. 1.

    https://www.cancerimagingarchive.net.

  2. 2.

    https://wiki.cancerimagingarchive.net/display/Public/Head-Neck-Radiomics-HN1.

  3. 3.

    https://www.radiomics.io/pyradiomics.html.

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Author information

Authors and Affiliations

  1. Grupo de Inteligencia Computacional (GIC), Universidad del País Vasco (UPV/EHU), San Sebastián, Spain

    Oier Echaniz & Manuel Graña

  2. Asociación de Ciencias de la programación Python San Sebastian (ACPYSS), San Sebastián, Spain

    Oier Echaniz & Manuel Graña

  3. Otorhinolaryngology-Head and Neck Surgery Department, Hospital Universitario Donostia, San Sebastián, Spain

    Carlos M. Chiesa-Estomba

Authors
  1. Oier Echaniz
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  2. Carlos M. Chiesa-Estomba
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  3. Manuel Graña
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Corresponding author

Correspondence to Manuel Graña .

Editor information

Editors and Affiliations

  1. University of Oviedo, Oviedo, Spain

    Enrique Antonio de la Cal

  2. University of Oviedo, Oviedo, Spain

    José Ramón Villar Flecha

  3. University of A Coruña, Ferrol, Spain

    Héctor Quintián

  4. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Echaniz, O., Chiesa-Estomba, C.M., Graña, M. (2020). Exploratory Analysis of Radiomics Features on a Head and Neck Cancer Public Dataset. In: de la Cal, E.A., Villar Flecha, J.R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2020. Lecture Notes in Computer Science(), vol 12344. Springer, Cham. https://doi.org/10.1007/978-3-030-61705-9_60

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  • DOI: https://doi.org/10.1007/978-3-030-61705-9_60

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61704-2

  • Online ISBN: 978-3-030-61705-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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