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

Purpose The aim of this systematic review was to analyse literature on artificial intelligence
(AI) and radiomics, including all medical imaging modalities, for oncological and non …

Purpose: Isocitrate dehydrogenase (IDH) mutations in glioma patients confer longer survival
and may guide treatment decision making. We aimed to predict the IDH status of gliomas …

Objectives To evaluate radiomics studies according to radiomics quality score (RQS) and
Transparent Reporting of a multivariable prediction model for Individual Prognosis Or …

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

Background We attempted to train and validate a model of deep learning for the
preoperative prediction of the response of patients with intermediate-stage hepatocellular …

Purpose To determine the characteristics of and trends in research in the emerging field of
radiomics through bibliometric and hotspot analyses of relevant original articles published …

Gliomas can be classified into five molecular groups based on the status of IDH mutation,
1p/19q codeletion, and TERT promoter mutation, whereas they need to be obtained by …

Introduction: In this review we describe recent developments in the field of radiomics along
with current relevant literature linking it to tumor biology. We furthermore explore the …

We aimed to identify optimal machine-learning methods for radiomics-based prediction of
local failure and distant failure in advanced nasopharyngeal carcinoma (NPC). We enrolled …