Machine learning-based prediction of molecular subtypes of breast cancer using DCE MRI

Aghadavood Marnani, Javad (2023) Machine learning-based prediction of molecular subtypes of breast cancer using DCE MRI. Masters thesis, Memorial University of Newfoundland.

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Breast cancer is a prevalent disease that can be classified into four molecular subtypes based on genetic and molecular markers. This study aimed to develop a machine learning-based approach to classify molecular subtypes of breast cancer using radiomics features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE MRI). The comprehensive dataset used in this study included 4428 radiomics features per patient, as well as clinical features, making it a valuable resource for future research. Our methodology involved several stages, including image preprocessing, feature extraction, initial and final feature selection, and data cleaning techniques, such as data imputation and Local Outlier Factor (LOF), to ensure the quality of the dataset. We conducted hyperparameter tuning and robustness analysis to optimize the performance of the machine learning algorithms. The results were evaluated in three scenarios: 4-label classification, binary, and 3-label classifications. Our approach achieved up to 85% F1 score in binary classifications and improved the overall accuracy of classifying the four molecular subtypes of breast cancer by 12%, which represents a significant improvement over the original study. These findings suggest that machine learning algorithms can be a powerful tool for improving the diagnosis and treatment of breast cancer, paving the way for personalized medicine approaches. Furthermore, the proposed approach can be applied to other datasets and may be useful in other areas of medical research that rely on radiomics features extracted from medical images.

Item Type: Thesis (Masters)
Item ID: 16063
Additional Information: Includes bibliographical references (pages 86-99)
Keywords: DCE MRI, breast Cancer, radiomics features, machine learning, molecular subtypes
Department(s): Science, Faculty of > Mathematics and Statistics
Date: May 2023
Date Type: Submission
Digital Object Identifier (DOI):
Library of Congress Subject Heading: Machine learning; Breast--Cancer;Magnetic resonance imaging

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