Radiomics, which involves the use of high-dimensional quantitative imaging features for
predictive purposes, is a powerful tool for develo** and testing medical hypotheses …

Importance Advances in genomics have led to the recognition that tumors are populated by
distinct genotypic subgroups that drive tumor development and progression. The spatial and …

Gliomas belong to a group of central nervous system tumors, and consist of various sub-
regions. Gold standard labeling of these sub-regions in radiographic imaging is essential for …

In the past decade, the field of medical image analysis has grown exponentially, with an
increased number of pattern recognition tools and an increase in data set sizes. These …

Radiomics extracts and mines large number of medical imaging features quantifying tumor
phenotypic characteristics. Highly accurate and reliable machine-learning approaches can …

Glioblastoma is the most common aggressive adult brain tumor. Numerous studies have
reported results from either private institutional data or publicly available datasets. However …

After seminal papers over the period 2009–2011, the use of texture analysis of PET/CT
images for quantification of intratumour uptake heterogeneity has received increasing …

From diagnostics to prognosis to response prediction, new applications for radiomics are
rapidly being developed. One of the fastest evolving branches involves linking imaging …

Deep learning-based radiomics (DLR) was developed to extract deep information from
multiple modalities of magnetic resonance (MR) images. The performance of DLR for …

Radiomics describes a broad set of computational methods that extract quantitative features
from radiographic images. The resulting features can be used to inform imaging diagnosis …