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A Framework Based on Metabolic Networks and Biomedical Images Data to Discriminate Glioma Grades

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Book cover Biomedical Engineering Systems and Technologies (BIOSTEC 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1400))

Abstract

Collecting and integrating information from different data sources is a successful approach to investigate complex biological phenomena and to address tasks such as disease subtyping, biomarker prediction, target, and mechanisms identification. Here, we describe an integrative framework, based on the combination of transcriptomics data, metabolic networks, and magnetic resonance images, to classify different grades of glioma, one of the most common types of primary brain tumors arising from glial cells. The framework is composed of three main blocks for feature sorting, choosing the best number of sorted features, and classification model building. We investigate different methods for each of the blocks, highlighting those that lead to the best results. Our approach demonstrates how the integration of molecular and imaging data achieves better classification performance than using the individual data-sets, also comparing results with state-of-the-art competitors. The proposed framework can be considered as a starting point for a clinically relevant grading system, and the related software made available lays the foundations for future comparisons.

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Notes

  1. 1.

    https://www.med.upenn.edu/sbia/brats2018.html.

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Acknowledgments

The work was carried out also within the activities of all the authors as members of the ICAR-CNR INdAM Research Unit. M. Manzo acknowledges the guidance and supervision of Prof. Alfredo Petrosino during the years spent working together. The authors would like to thank G. Trerotola for technical support.

Author information

Authors and Affiliations

  1. ICAR, National Research Council, Via P. Castellino 111, 80131, Naples, Italy

    Lucia Maddalena, Ilaria Granata & Ichcha Manipur

  2. ITS, University of Naples “L’Orientale”, Via Nuova Marina 59, 80132, Naples, Italy

    Mario Manzo

  3. University of Cassino and Southern Lazio, Cassino, Italy

    Mario R. Guarracino

Authors
  1. Lucia Maddalena
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  2. Ilaria Granata
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  3. Ichcha Manipur
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  4. Mario Manzo
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  5. Mario R. Guarracino
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Corresponding author

Correspondence to Lucia Maddalena .

Editor information

Editors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Xuesong Ye

  2. Fraunhofer Portugal Research Center for Assistive Information and Communication Solutions, Porto, Portugal

    Filipe Soares

  3. Nice Sophia Antipolis University, Nice Cedex 2, France

    Elisabetta De Maria

  4. Universidad Politécnica de Madrid, Madrid, Spain

    Pedro Gómez Vilda

  5. University of Milano-Bicocca, Milan, Italy

    Federico Cabitza

  6. Instituto de Telecomunicações, University of Lisbon, Lisbon, Portugal

    Ana Fred

  7. Universidade Nova de Lisboa, Caparica, Portugal

    Hugo Gamboa

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Maddalena, L., Granata, I., Manipur, I., Manzo, M., Guarracino, M.R. (2021). A Framework Based on Metabolic Networks and Biomedical Images Data to Discriminate Glioma Grades. In: Ye, X., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2020. Communications in Computer and Information Science, vol 1400. Springer, Cham. https://doi.org/10.1007/978-3-030-72379-8_9

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  • DOI: https://doi.org/10.1007/978-3-030-72379-8_9

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