Examinando por Autor "Olivares, Rodrigo"

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    HER2 classification in breast cancer cells: A new explainable machine learning application for immunohistochemistry
    (Spandidos Publications, 2023-02) Cordova, Claudio; Muñoz, Roberto; Olivares, Rodrigo; Minonzio, Jean-Gabriel; Lozano, Carlo; Gonzalez, Paulina; Marchant, Ivanny; González-Arriagada, Wilfredo; Olivero, Pablo
    The 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.
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    Use of self-organizing maps for the classification of cardiometabolic risk and physical fitness in adolescents
    (Routledge, 2024) Yáñez-Sepúlveda, Rodrigo; Olivares, Rodrigo; Ravelo, Camilo; Cortés-Roco, Guillermo; Zavala-Crichton, Juan Pablo; Hinojosa-Torres, Claudio; de Souza-Lima, Josivaldo; Monsalves-Álvarez, Matías; Reyes-Amigo, Tomás; Hurtado-Almonacid, Juan; Páez-Herrera, Jacqueline; Mahecha-Matsudo, Sandra; Olivares-Arancibia, Jorge; Clemente-Suárez, Vicente Javier
    This study aimed to automatically classify physical fitness and cardiometabolic risk in a Chilean adolescent using self-organizing maps. This cross-sectional study analysed a nationally representative database from the Physical Education Quality Measurement System (n = 7197). Physical fitness and cardiometabolic risk variables were derived from anthropometric indicators. Self-Organizing maps (SOM) were employed to identify participant profiles based on an unsupervised predictive model. After implementing and training the SOM, a detailed analysis of the generated maps was conducted to interpret the revealed relationships and clusters. The analysis resulted in three classification groups, categorizing the sample into low, moderate, and high-risk levels. Students with better physical fitness exhibited lower cardiometabolic risk levels and a lower body mass index. SOM, through an unsupervised model, is a reliable tool for classifying cardiometabolic risk and physical fitness in adolescents. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.