Browsing by Author "Smith, Alexandra Nicole"
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- ItemThe use of deep learning to predict HER2 status in breast cancer directly from histopathology slides(Stellenbosch : Stellenbosch University, 2024-03) Smith, Alexandra Nicole; Coetzer, Johannes; Stellenbosch University. Faculty of Science. Dept. of Applied Mathematics.ENGLISH ABSTRACT: The treatment of breast cancer is significantly influenced by the identification of various molecular biomarkers, including Human Epidermal Growth Factor Receptor 2 (HER2). Current techniques for determining HER2 status involve immunohistochemistry (IHC) and in-situ hybridisation (ISH) methods. HER2 testing, which is routinely applied in cases of invasive breast cancer, serves as the primary biomarker guiding HER2-targeted therapies. HE-stained whole slide images, which are more cost-effective, time-efficient, and routinely produced during pathological examinations, present an opportunity for leveraging deep learning to enhance the accuracy, speed, and affordability of HER2 status determination. This thesis introduces a deep learning framework for predicting HER2 status directly from the morphological features observed in histopathological slides. The proposed system has two stages: initially, a deep learning model is employed to differentiate between benign and malignant tissues in whole slide images, using annotated regions of invasive tumours. Following this, the effectiveness of Inception-v4 and Inception-ResNet-v2 architectures in biomarker status prediction is explored, comparing their performance against previous model architectures utilised for this task, namely Inception-v3 and ResNet34. The study utilises a dataset comprising whole slide images from 147 patients, sourced from the publicly available Cancer Genome Atlas (TCGA). Models are trained using 256 ◊ 256 patches extracted from these slides. The best-performing model, Inception-v4, achieved an area under the receiver operating characteristic curve (AUC) of 0.849 (95% confidence interval (CI): 0.845 ≠ 0.853) per-tile and 0.767 (CI:0.556 ≠ 0.955) per-slide in the test set. This research demonstrates the capability of deep learning models to accurately predict HER2 status directly from histopathological whole slide images, offering a more cost- and time-efficient method for identifying clinical biomarkers, with the potential to inform and accelerate the selection of breast cancer treatments.