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Assessment of immunological features in muscle-invasive bladder cancer prognosis using ensemble learning

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Gavriel_2021_Cancers_Immunological_CC.pdf (9.435Mb)
Date
01/04/2021
Author
Gavriel, Christos
Dimitriou, Neofytos
Brieu, Nicolas
Nearchou, Ines P.
Arandelovic, Oggie
Schmidt, Günter
Harrison, David J.
Caie, Peter D.
Keywords
Immuno-oncology
Tumour microenvironment
Tumour budding
PD-L1
Macrophages
Lymphocytes
Prognosis
survival analysis
Machine learning
Digital pathology
QA75 Electronic computers. Computer science
QR180 Immunology
RB Pathology
RC0254 Neoplasms. Tumors. Oncology (including Cancer)
NDAS
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Abstract
The clinical staging and prognosis of muscle-invasive bladder cancer (MIBC) routinely includes the assessment of patient tissue samples by a pathologist. Recent studies corroborate the importance of image analysis in identifying and quantifying immunological markers from tissue samples that can provide further insight into patient prognosis. In this paper, we apply multiplex immunofluorescence to MIBC tissue sections to capture whole-slide images and quantify potential prognostic markers related to lymphocytes, macrophages, tumour buds, and PD-L1. We propose a machine-learning-based approach for the prediction of 5 year prognosis with different combinations of image, clinical, and spatial features. An ensemble model comprising several functionally different models successfully stratifies MIBC patients into two risk groups with high statistical significance (p value < 1×10−5). Critical to improving MIBC survival rates, our method correctly classifies 71.4% of the patients who succumb to MIBC, which is significantly more than the 28.6% of the current clinical gold standard, the TNM staging system.
Citation
Gavriel , C , Dimitriou , N , Brieu , N , Nearchou , I P , Arandelovic , O , Schmidt , G , Harrison , D J & Caie , P D 2021 , ' Assessment of immunological features in muscle-invasive bladder cancer prognosis using ensemble learning ' , Cancers , vol. 13 , no. 7 , 1624 . https://doi.org/10.3390/cancers13071624
Publication
Cancers
Status
Peer reviewed
DOI
https://doi.org/10.3390/cancers13071624
ISSN
2072-6694
Type
Journal article
Rights
Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/4.0/).
Description
Funding: This research received financial support from Definiens GmbH and the Industrial Centre for AI Research in digital Diagnostics (iCAIRD) which is funded by Innovate UK on behalf of UK Research and Innovation (UKRI) [project number: 104690].
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  • University of St Andrews Research
URL
https://www.mdpi.com/2072-6694/13/7/1624
URI
http://hdl.handle.net/10023/21752

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