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2025-06
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info:eu-repo/semantics/openAccess
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Universidad Nacional de Educación a Distancia (UNED). E.T.S. de Ingeniería Informática
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Resumen
The increasing demand for biomarker testing in oncology highlights the need for scalable and cost-effective alternatives to molecular profiling. This master's thesis investigates whether deep learning (DL) models can predict clinically relevant genomic biomarkers directly from routine histopathology whole-slide images, focusing on PIK3CA gene mutation in breast cancer. The study applies a radiogenomics pipeline that includes tile extraction, stain normalization, and the use of multiple DL architectures. A subset of the TCGA-BRCA dataset was used, selecting diagnostic slides labelled for PIK3CA mutation status. Tiles were extracted and normalized using the Macenko method, then fed into models including VGG-19, ResNet-18, ResNet-50, and Swin Transformer. Two configurations were explored: cross-validation (C-V) ensemble models and single-model training with C-V tuned hyperparameters. Aggregation strategies and thresholding techniques were systematically evaluated to assess their impact on model performance.
Performance metrics were computed on an unseen test set composed of subcohorts from medical centers not included in the training phase. A C-V ensemble model using a ResNet-18 pretrained on ImageNet and Macenko stain normalization achieved the best AUROC (0.733), with a sensitivity of 0.817, specificity of 0.451, and an F1 score of 0.757, raising the question of whether predicting PIK3CA mutation status from histopathology alone may ultimately be feasible.
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Categorías UNESCO
Palabras clave
breast cancer, deep learning, histopathology, radiogenomics, whole-slide images
Citación
Arregui Hernández, Ernesto Santiago. Trabajo fin de Máster: "Prediction of genomic biomarkers in breast cancer from images of routine histopathology tissue slides of solid tumors using deep learning models". Universidad Nacional de Educación a Distancia (UNED), 2025
Centro
E.T.S. de Ingeniería Informática
Departamento
Inteligencia Artificial