HER2 classification in breast cancer cells: A new explainable machine learning application for immunohistochemistry

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dc.contributor.authorCordova, Claudio
dc.contributor.authorMuñoz, Roberto
dc.contributor.authorOlivares, Rodrigo
dc.contributor.authorMinonzio, Jean-Gabriel
dc.contributor.authorLozano, Carlo
dc.contributor.authorGonzalez, Paulina
dc.contributor.authorMarchant, Ivanny
dc.contributor.authorGonzález-Arriagada, Wilfredo
dc.contributor.authorOlivero, Pablo
dc.date.accessioned2024-09-05T15:54:47Z
dc.date.available2024-09-05T15:54:47Z
dc.date.issued2023-02
dc.descriptionIndexación: Scopus
dc.description.abstractThe immunohistochemical (IHC) evaluation of epidermal growth factor 2 (HER2) for the diagnosis of breast cancer is still qualitative with a high degree of inter-observer variability, and thus requires the incorporation of complementary techniques such as fluorescent in situ hybridization (FISH) to resolve the diagnosis. Implementing automatic algorithms to classify IHC biomarkers is crucial for typifying the tumor and deciding on therapy for each patient with better performance. The present study aims to demonstrate that, using an explainable Machine Learning (ML) model for the classification of HER2 photomicrographs, it is possible to determine criteria to improve the value of IHC analysis. We trained a logistic regression-based supervised ML model with 393 IHC microscopy images from 131 patients, to discriminate between upregulated and normal expression of the HER2 protein. Pathologists' diagnoses (IHC only) vs. the final diagnosis complemented with FISH (IHC + FISH) were used as training outputs. Basic performance metrics and receiver operating characteristic curve analysis were used together with an explainability algorithm based on Shapley Additive exPlanations (SHAP) values to understand training differences. The model could discriminate amplified IHC from normal expression with better performance when the training output was the IHC + FISH final diagnosis (IHC vs. IHC + FISH: area under the curve, 0.94 vs. 0.81). This may be explained by the increased analytical impact of the membrane distribution criteria over the global intensity of the signal, according to SHAP value interpretation. The classification model improved its performance when the training input was the final diagnosis, downplaying the weighting of the intensity of the IHC signal, suggesting that to improve pathological diagnosis before FISH consultation, it is necessary to emphasize subcellular patterns of staining. © 2023 Spandidos Publications. All rights reserved.
dc.description.urihttps://www.spandidos-publications.com/10.3892/ol.2022.13630
dc.identifier.citationOncology Letters. Volume 25, Issue 2. February 2023. Article number 13630
dc.identifier.doi10.3892/ol.2022.13630
dc.identifier.issn1792-1074
dc.identifier.urihttps://repositorio.unab.cl/handle/ria/59864
dc.language.isoen
dc.publisherSpandidos Publications
dc.rights.licenseCC BY-NC-ND 4.0 Attribution-NonCommercial-NoDerivatives 4.0 International Deed
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectBreast Cancer
dc.subjectHER2
dc.subjectIHC
dc.subjectML
dc.subjectSHAP
dc.titleHER2 classification in breast cancer cells: A new explainable machine learning application for immunohistochemistry
dc.typeArtículo
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