Quantitative image analysis, also known as radiomics, channeling its "omics" counter parts from molecular biology (genomics, proteomics, metabolomics, etc.) aims to interrogate acquired image scans during diagnostic or therapeutic procedures for reusable datapoints.1 The large-scale extraction of these datapoints from known regions of interest (ROI) can be achieved using hand-crafted or machine-engineered feature extraction.2 The hand-crafted feature extraction is based on intensity, shape, size (volume), and texture describing the geometric properties and the distribution of intensities of the ROI in relation to their spatial distribution. Common examples of these features include first-order voxel intensity metrics (eg, mean, minimum, skewness, etc.) or second-order features focusing on the statistical interrelationships between neighboring voxels (texture patterns) within the ROIs (eg, gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), grey level size-zone matrix (GLSZM), and neighborhood gray tone difference matrix (NGTDM), etc.). The adopted definition of these features and their nomenclature follows the Image Biomarker Standardization Initiative (IBSI).3 The machine engineered features are typically extracted using deep learning techniques using convolutional neural networks (CNNs) and their variants, where such features are automatically derived as part of the overall machine learning task.

The radiation oncology field is an image-rich treatment modality, which makes a fertile environment for application of radiomics as discussed later in this article. Computed tomography (CT) is the most commonly used modality for treatment planning, dose calculation, image guidance as well as onboard setup correction. These CT images could be fan beam (used for simulation) or cone-beam onboard imaging (used for verification). Other modalities are also incorporated into the radiotherapy workflow such magnetic resonance (MRI) or positron emission tomography (PET) initially for improved target definition during treatment planning and more recently for image-guidance during delivery (MR- or PET-Linac). Moreover, 4D-imaging with amplitude or phase binning are also being used in radiotherapy to account for organ movements, especially due to respiratory motion. Applications of radiomics in radiotherapy have varied from treatment planning to response prediction, with more focus on the latter as discussed here.