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Glioma Classification Using Multimodal Radiology and Histology Data

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Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12659))

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Abstract

Gliomas are brain tumours with a high mortality rate. There are various grades and sub-types of this tumour, and the treatment procedure varies accordingly. Clinicians and oncologists diagnose and categorise these tumours based on visual inspection of radiology and histology data. However, this process can be time-consuming and subjective. The computer-assisted methods can help clinicians to make better and faster decisions. In this paper, we propose a pipeline for automatic classification of gliomas into three sub-types: oligodendroglioma, astrocytoma, and glioblastoma, using both radiology and histopathology images. The proposed approach implements distinct classification models for radiographic and histologic modalities and combines them through an ensemble method. The classification algorithm initially carries out tile-level (for histology) and slice-level (for radiology) classification via a deep learning method, then tile/slice-level latent features are combined for a whole-slide and whole-volume sub-type prediction. The classification algorithm was evaluated using the data set provided in the CPM-RadPath 2020 challenge. The proposed pipeline achieved the F1-Score of 0.886, Cohen’s Kappa score of 0.811 and Balance accuracy of 0.860. The ability of the proposed model for end-to-end learning of diverse features enables it to give a comparable prediction of glioma tumour sub-types.

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Acknowledgements

Azam Hamidinekoo acknowledges the support by Children’s Cancer and Leukaemia Group (CCLGA201906). Tomasz Pieciak acknowledges the Polish National Agency for Academic Exchange for project PN/BEK/2019/1/00421 under the Bekker programme. Maryam Afzali was supported by a Wellcome Trust Investigator Award (096646/Z/11/Z). Otar Akanyeti acknowledges Sér Cymru Cofund II Research Fellowship grant. Yinyin Yuan acknowledges funding from Cancer Research UK Career Establishment Award (CRUK C45982/A21808), CRUK Early Detection Program Award (C9203/A28770), CRUK Sarcoma Accelerator (C56167/A29363), CRUK Brain Tumour Award (C25858/A28592), Rosetrees Trust (A2714), Children’s Cancer and Leukaemia Group (CCLGA201906), NIH U54 CA217376, NIH R01 CA185138, CDMRP BreastCancer Research Program Award BC132057, European Commission ITN (H2020-MSCA-ITN-2019), and The Royal Marsden/ICR National Institute of Health Research Biomedical Research Centre. This research was also supported in part by PLGrid Infrastructure. Tomasz Pieciak acknowledges Mr. Maciej Czuchry from CYFRONET Academic Computer Centre (Kraków, Poland) for outstanding support.

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Authors and Affiliations

  1. Division of Molecular Pathology, Institute of Cancer Research (ICR), London, UK

    Azam Hamidinekoo & Yinyin Yuan

  2. Centre for Evolution and Cancer, Institute of Cancer Research (ICR), London, UK

    Azam Hamidinekoo & Yinyin Yuan

  3. LPI, ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain

    Tomasz Pieciak

  4. AGH University of Science and Technology, Kraków, Poland

    Tomasz Pieciak

  5. CUBRIC, School of Psychology, Cardiff University, Cardiff, UK

    Maryam Afzali

  6. Department of Computer Science, Aberystwyth University, Ceredigion, UK

    Otar Akanyeti

Authors
  1. Azam Hamidinekoo
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  2. Tomasz Pieciak
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  3. Maryam Afzali
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  4. Otar Akanyeti
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  5. Yinyin Yuan
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Corresponding author

Correspondence to Azam Hamidinekoo .

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Editors and Affiliations

  1. University of Zurich, Zurich, Switzerland

    Alessandro Crimi

  2. University of Pennsylvania, Philadelphia, PA, USA

    Spyridon Bakas

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Hamidinekoo, A., Pieciak, T., Afzali, M., Akanyeti, O., Yuan, Y. (2021). Glioma Classification Using Multimodal Radiology and Histology Data. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. Lecture Notes in Computer Science(), vol 12659. Springer, Cham. https://doi.org/10.1007/978-3-030-72087-2_45

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  • DOI: https://doi.org/10.1007/978-3-030-72087-2_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72086-5

  • Online ISBN: 978-3-030-72087-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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