Glioma classification using multimodal radiology and histology data

Hamidinekoo, Azam, Pieciak, Tomasz, Afzali, Maryam, Akanyeti, Otar and Yuan, Yinyin 2021. Glioma classification using multimodal radiology and histology data. Presented at: 6th International Brain Lesion Workshop (BrainLes 2020), Lima, Peru, 04 October 2020. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Lecture Notes in Computer Science. Lecture Notes in Computer Science , vol.12659 Springer Verlag, pp. 508-518. 10.1007/978-3-030-72087-2_45

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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.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Psychology Cardiff University Brain Research Imaging Centre (CUBRIC)
Publisher:	Springer Verlag
ISBN:	9783030720865
ISSN:	0302-9743
Date of First Compliant Deposit:	14 June 2021
Date of Acceptance:	15 September 2020
Last Modified:	15 Jun 2021 13:46
URI:	https://orca.cardiff.ac.uk/id/eprint/141896

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