This review discusses potential oncologic and nononcologic applications of CT texture
analysis (CTTA CT texture analysis), an emerging area of “radiomics” that extracts, analyzes …

Objectives We aimed to systematically evaluate the prognostic prediction accuracy of
radiomics features extracted from pre-treatment imaging in patients with pancreatic ductal …

Objectives To investigate machine learning approaches for radiomics-based prediction of
prognostic biomarkers and molecular subtypes of breast cancer using quantification of tumor …

Objectives We investigated the value of CT texture analysis (CTTA) in predicting prognosis
of unresectable pancreatic cancer. Methods Sixty patients with unresectable pancreatic …

Purpose: Increasing evidence suggests radiomics features extracted from computed
tomography (CT) images may be useful in prognostic models for patients with nonsmall cell …

Objectives The aim of this study was to test whether texture analysis and machine learning
enable the detection of myocardial infarction (MI) on non–contrast-enhanced low radiation …

Purpose To identify the impact of reconstruction algorithms on CT radiomic features of
pulmonary tumors and to reveal and compare the intra-and inter-reader and inter …

Background To assess whether CT-derived texture features predict survival in patients
undergoing resection for pancreatic ductal adenocarcinoma (PDAC). Methods Thirty …

Background and purpose Radiomics allows extraction of quantifiable features from imaging.
This study performs a systematic review and meta-analysis of the performance of radiomics …

Objective: The purpose of the current study is to investigate whether texture analysis-based
machine learning algorithms could help devise a non-invasive imaging biomarker for …