The Promises of AI on Radiomics for Medical Research and Its Implementation Framework

Abstract

Medical research has recently been greatly benefited from the radiomics approach. Using radiomics allows for noninvasive estimation of the pathology of cancer metastases before the collection of data usually obtained after surgery, which provides an early prediction of the outcome. This study sheds light on the implementation of radiomics in medical research. This paper outlined the main components of the radiomic framework, which include image acquisition, data collection and loading, image segmentation, feature extraction, feature selection, and data analysis. Moreover, it described the implementation steps for applying machine learning and deep neural network algorithms to radiomics. As a result of using deep neural networks, promising results have been obtained. As a result of this work, researchers should be aware of all technical issues in the radiomics framework that may affect the extraction of radiomics.