Insights into radiomics: impact of feature selection and classification

Radiomics is an innovative discipline in medical imaging that uses advanced quantitative feature extraction from radiological images to provide a non-invasive method of interpreting the intricate biological panorama of diseases. This discipline takes advantage of the unique characteristics of medical imaging, where radiation or ultrasound combines with biological tissues, to reveal disease features and important biomarkers that are invisible to the human eye. Radiomics plays a crucial role in healthcare, spanning disease diagnosis, prognosis, recurrences, treatment response assessment, and personalized medicine. Radiomics uses a systematic approach that includes image preprocessing, segmentation, feature extraction, feature selection, classification, and evaluation. This survey attempts to shed light on the crucial roles that feature selection and classification play in discovering important biomarkers and forecasting disease directions despite the challenges posed by high dimensionality (i.e., when the data contains a huge number of features). By analyzing 47 relevant research articles, this study has provided several insights into the key techniques used across different stages of the radiology workflow. The findings indicate that 27 articles utilized the SVM classifier, while 23 of the surveyed studies used the LASSO feature selection approach. This demonstrates how these particular methodologies have been widely used in Radiomics research. The assessment did, however, also point out areas that require more research, such as evaluating the stability of feature selection and classification algorithms and adopting novel approaches like ensemble and hybrid selection methods. Additionally, we examine some of the challenges and emerging subfields within the field of radiomics.