Glioma Grade Classification via Omics Imaging

Lucia Maddalena, Ilaria Granata, Ichcha Manipur, Mario Manzo, Mario Guarracino

Abstract

Omics imaging is an emerging interdisciplinary field concerned with the integration of data collected from biomedical images and omics experiments. Bringing together information coming from different sources, it permits to reveal hidden genotype-phenotype relationships, with the aim of better understanding the onset and progression of many diseases, and identifying new diagnostic and prognostic biomarkers. In this work, we present an omics imaging approach to the classification of different grades of gliomas, which are primary brain tumors arising from glial cells, as this is of critical clinical importance for making decisions regarding initial and subsequent treatment strategies. Imaging data come from analyses available in The Cancer Imaging Archive, while omics attributes are extracted by integrating metabolic models with transcriptomic data available from the Genomic Data Commons portal. We investigate the results of feature selection for the two types of data separately, as well as for the integrated data, providing hints on the most distinctive ones that can be exploited as biomarkers for glioma grading. Moreover, we show how the integrated data can provide additional clinical information as compared to the two types of data separately, leading to higher performance. We believe our results can be valuable to clinical tests in practice.

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Paper Citation


in Harvard Style

Maddalena L., Granata I., Manipur I., Manzo M. and Guarracino M. (2020). Glioma Grade Classification via Omics Imaging.In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, ISBN 978-989-758-398-8, pages 82-92. DOI: 10.5220/0009167700820092


in Bibtex Style

@conference{bioimaging20,
author={Lucia Maddalena and Ilaria Granata and Ichcha Manipur and Mario Manzo and Mario Guarracino},
title={Glioma Grade Classification via Omics Imaging},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING,},
year={2020},
pages={82-92},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009167700820092},
isbn={978-989-758-398-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING,
TI - Glioma Grade Classification via Omics Imaging
SN - 978-989-758-398-8
AU - Maddalena L.
AU - Granata I.
AU - Manipur I.
AU - Manzo M.
AU - Guarracino M.
PY - 2020
SP - 82
EP - 92
DO - 10.5220/0009167700820092