Radiomics is a field of medical study that aims to extract large amount of quantitative features from medical images using data-characterisation algorithms.These features, termed radiomic features, have the potential to uncover disease characteristics that fail to be appreciated by the naked eye. The hypothesis of radiomics is that the distinctive imaging features between disease forms may be useful for predicting prognosis and therapeutic response for various conditions, thus providing valuable information for personalised therapy. Radiomics emerged from the medical field of oncology and is the most advanced in applications within that field. However, the technique can be applied to any medical study where a disease or a condition can be tomographically imaged.
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Features
Radiomic features can be divided into five groups: size and shape based–features, descriptors of the image intensity histogram, descriptors of the relationships between image voxels (e.g. gray-level co-occurrence matrix (GLCM), run length matrix (RLM), size zone matrix (SZM), and neighborhood gray tone difference matrix (NGTDM) derived textures, textures extracted from filtered images, and fractal features. The mathematical definitions of these features are independent of imaging modality and can be found in the literature.
Prediction of clinical outcomes
Aerts et al. (2014) performed the first large-scale radiomic study that included three lung and two head-and-neck cancer cohorts, consisting of over 1000 patients. They assessed the prognostic values of over 400 textural and shape- and intensity-based features extracted from the computed tomography (CT) images acquired before any treatment. Tumor volumes were defined either by expert radiation oncologists or using semiautomatic segmentation methods. Their results identified a subset of radiomic features that may be useful for predicting patient survival and describing intratumoural heterogeneity. They also confirmed that the prognostic ability of these radiomics features may be transferred from lung to head-and-neck cancer. However, Parmar et al. (2015) demonstrated that prognostic value of some radiomic features may be cancer type dependent. Particularly, they observed that not every radiomic feature that significantly predicted the survival of lung cancer patients could also predict the survival of head-and-neck cancer patients and vice versa.
Several studies have also shown radiomic features are better at predicting treatment response than conventional measures, such as tumor volume and diameter, and the maximum radiotracer uptake on positron emission tomography (PET) imaging.
Prediction risk of distant metastasis
Metastatic potential of tumors may also be predicted by radiomic features. For example, thirty-five CT-based radiomic features were identified to be predictive of distant metastasis of lung cancer in a study by Coroller et al. in 2015. They thus concluded that radiomic features can be useful to identify patients with high risk of developing distant metastasis, guiding physicians to select the effective treatment for individual patients.
Assessment of cancer genetics
Lung tumor biological mechanisms were found to demonstrate distinct and complex imaging patterns. In particular, Aerts et al. (2014) showed that radiomic features were associated with biological gene sets, such as cell cycle phase, DNA recombination, regulation of immune system process, etc. Moreover, various mutations of glioblastoma (GBM), such as TP53, EGFR, and NF1, have been shown to be significantly predicted by magnetic resonance imaging (MRI) volumetric measures, including tumor volume, necrosis volume, and contrast enhancing volume.
Relationship to computational anatomy
Computational anatomy is the study of shape and form at the visible anatomical scale, or morphological scale. Computational anatomy characterizes shape and form using diffeomorphisms, which are smooth structures supporting differentiability in space. Studying shape and form with diffeomorphisms admits a metric structure in which anatomical configurations are embedded, called diffeomorphometry in which anatomical phenotypes can be compared with a metric.
Algorithms for computing metric distances are based on generating diffeomorphic mappings between different anatomaical coordinate systems. These algorithms are called LDDMM, standing for large deformation, diffeomorphic metric mapping. Codes exist for computing metric mappings between submanifolds of landmarks, curves, surfaces, and subvolumes.