RADIOMICS AND ITS APPLICATION IN ONCOLOGICAL STUDY: A BRIEF REVIEW

Abstract

Radiomics has experienced rapid growth in research since its inception in 2012. This development has transformed medical imaging from a qualitative approach to a quantitative analysis, extracting mineable data from images. Unlike traditional methods where radiologists primarily relied on pattern recognition for diagnosis and qualitative data collection was secondary, radiomics allows for the identification of subtle abnormalities that might be missed otherwise. Through extensive cohort studies across various imaging modalities like CT, MRI, USG, PET, etc., combined with handcrafted or deep learning software, radiomics has enabled quantitative analysis and classification of lesions or malignant tissues based on predefined parameters in radiomic features. This advancement allows for assessing tissue aggressiveness and planning treatment procedures accurately, especially beneficial in oncology for non-invasive tissue characterization through shape, texture, and statistical analysis from radiological images.