Assessment of immunological features in muscle-invasive bladder cancer prognosis using ensemble learning
MetadataShow full item record
Altmetrics Handle Statistics
Altmetrics DOI Statistics
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.
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
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/).
DescriptionFunding: 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].
Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.