Hewitt, Katherine J;
Löffler, Chiara ML;
Muti, Hannah Sophie;
Berghoff, Anna Sophie;
Eisenlöffel, Christian;
van Treeck, Marko;
Carrero, Zunamys I;
... Kather, Jakob Nikolas; + view all
(2023)
Direct image to subtype prediction for brain tumors using deep learning.
Neuro-Oncology Advances
, 5
(1)
, Article vdad139. 10.1093/noajnl/vdad139.
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Abstract
Background: Deep Learning (DL) can predict molecular alterations of solid tumors directly from routine histopathology slides. Since the 2021 update of the World Health Organization (WHO) diagnostic criteria, the classification of brain tumors integrates both histopathological and molecular information. We hypothesize that DL can predict molecular alterations as well as WHO subtyping of brain tumors from hematoxylin and eosin-stained histopathology slides. // Methods: We used weakly supervised DL and applied it to three large cohorts of brain tumor samples, comprising N = 2845 patients. // Results: We found that the key molecular alterations for subtyping, IDH and ATRX, as well as 1p19q codeletion, were predictable from histology with an area under the receiver operating characteristic curve (AUROC) of 0.95, 0.90, and 0.80 in the training cohort, respectively. These findings were upheld in external validation cohorts with AUROCs of 0.90, 0.79, and 0.87 for prediction of IDH, ATRX, and 1p19q codeletion, respectively. // Conclusions: In the future, such DL-based implementations could ease diagnostic workflows, particularly for situations in which advanced molecular testing is not readily available.
| Type: | Article |
|---|---|
| Title: | Direct image to subtype prediction for brain tumors using deep learning |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/noajnl/vdad139 |
| Publisher version: | http://dx.doi.org/10.1093/noajnl/vdad139 |
| Language: | English |
| Additional information: | Copyright © The Author(s) 2023. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | Adult-type diffuse gliomas, deep learning, IDH, molecular signatures, subtype |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Neurodegenerative Diseases |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10184828 |
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