| dc.description.abstract | Breast cancer is a highly heterogeneous disease, exhibiting substantial spatiotemporal variations in gene expression, biochemistry, histopathology, and macroscopic structure. The complexities in the evaluation of malignant tumor heterogeneity, are pushing current clinical examinations to their limits and accounting for most of the failures of targeted therapies, and uncertainties in the clinical outcome among breast cancer patients. The incorporation of state-of-the-art image analysis methods, and emerging machine learning techniques, is providing the application of detailed data-driven quantitative studies of imaging phenotypes, termed Radiomics. Specifically, an immense number of quantifiable imaging features, are extracted with high-throughput computing from medical images, and correlated with other clinical data, for unraveling the complex underlying pathophysiological mechanisms which are potentially reflected in tissue macroscopic phenotype. Therefore, radiomic analysis is holding a key role in the future of clinical decision support, as it provides powerful tools for assessing the spatiotemporal heterogeneity of breast cancer, aiming at increasing the diagnostic accuracy and predicting patient’s outcome toward individualized therapy planning. © 2020 Elsevier Inc. All rights reserved. | en |
