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xDEEP-MSI: Explainable bias-rejecting microsatellite instability deep learning system in colorectal cancer

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dc.contributor.author Bustos, Aurelia
dc.contributor.author Payá, Artemio
dc.contributor.author Torrubia, Andrés
dc.contributor.author Jover, Rodrigo
dc.contributor.author Llor, Xavier
dc.contributor.author Bessa Caserras, Xavier
dc.contributor.author Castells, Antoni
dc.contributor.author Carracedo, Ángel
dc.contributor.author Alenda, Cristina
dc.date.accessioned 2022-07-28T15:24:35Z
dc.date.available 2022-07-28T15:24:35Z
dc.date.issued 2021
dc.identifier.citation Bustos A, Payá A, Torrubia A, Jover R, Llor X, Bessa X, Castells A, Carracedo Á, Alenda C. xDEEP-MSI: Explainable bias-Rejecting microsatellite instability deep learning system in colorectal cancer. Biomolecules. 2021 Nov 29;11(12):1786. DOI: 10.3390/biom11121786
dc.identifier.issn 2218-273X
dc.identifier.uri http://hdl.handle.net/10230/53879
dc.description.abstract The prediction of microsatellite instability (MSI) using deep learning (DL) techniques could have significant benefits, including reducing cost and increasing MSI testing of colorectal cancer (CRC) patients. Nonetheless, batch effects or systematic biases are not well characterized in digital histology models and lead to overoptimistic estimates of model performance. Methods to not only palliate but to directly abrogate biases are needed. We present a multiple bias rejecting DL system based on adversarial networks for the prediction of MSI in CRC from tissue microarrays (TMAs), trained and validated in 1788 patients from EPICOLON and HGUA. The system consists of an end-to-end image preprocessing module that tile samples at multiple magnifications and a tissue classification module linked to the bias-rejecting MSI predictor. We detected three biases associated with the learned representations of a baseline model: the project of origin of samples, the patient's spot and the TMA glass where each spot was placed. The system was trained to directly avoid learning the batch effects of those variables. The learned features from the bias-ablated model achieved maximum discriminative power with respect to the task and minimal statistical mean dependence with the biases. The impact of different magnifications, types of tissues and the model performance at tile vs patient level is analyzed. The AUC at tile level, and including all three selected tissues (tumor epithelium, mucin and lymphocytic regions) and 4 magnifications, was 0.87 ± 0.03 and increased to 0.9 ± 0.03 at patient level. To the best of our knowledge, this is the first work that incorporates a multiple bias ablation technique at the DL architecture in digital pathology, and the first using TMAs for the MSI prediction task.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher MDPI
dc.relation.ispartof Biomolecules. 2021 Nov 29;11(12):1786
dc.rights © 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/).
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.title xDEEP-MSI: Explainable bias-rejecting microsatellite instability deep learning system in colorectal cancer
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.3390/biom11121786
dc.subject.keyword Adversarial networks
dc.subject.keyword Bias ablation
dc.subject.keyword Colorectal carcinoma
dc.subject.keyword Deep neural networks
dc.subject.keyword Digital pathology
dc.subject.keyword Microsatellite instability
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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