Radiomics Image Processing and Machine Learning: A Comprehensive Review
Keywords:
Radiomics, Machine Learning, Deep Learning, Image Processing, Feature Extraction, Predictive Modeling, Quantitative Imaging, Medical Imaging Analytics.Abstract
Radiomics, which is the high-throughput extraction of quantitative features of medical images, is a paradigm shift in diagnostic and prognostic medicine. Radiomics can be used to find new biomarkers that could be used to define disease phenotypes in a way that human eyes cannot process to locate them by transforming the standard-of-care imaging into data which can be mined. This is a cumulative review that attempts to compile existing literature to analyze the role of integrating sophisticated image processing pipeline and machine learning (ML) or deep learning (DL) algorithms in radiomics. We investigate the general workflow, including image acquisition and segmentation, feature extraction, feature selection, and model development, and both the possibilities provided by it and the technical difficulties. The review uses various applications such as the assessment of pulmonary diseases, oncology, and cardiac risk prediction as an illustration of the state-of-the-art. Threatening issues like feature reproducibility, model interpretability, heterogeneity of data and the need to have a robust validation are addressed in detail. And, last but not the least, we present future directions, which include the necessity of universal protocols, explainable AI (XAI), multimodal data fusion and ethical application of these potent technologies in clinical practices.
