Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered
data with nuanced decision making. Cancer offers a unique context for medical decisions …

Radiomics converts medical images into mineable data via a high-throughput extraction of
quantitative features used for clinical decision support. However, these radiomic features are …

The domain of investigation of radiomics consists of large-scale radiological image analysis
and association with biological or clinical endpoints. The purpose of the present study is to …

Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male
population. The diagnosis, the identification of aggressive disease, and the post-treatment …

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 …

In this study we assessed the repeatability of radiomics features on small prostate tumors
using test-retest Multiparametric Magnetic Resonance Imaging (mpMRI). The premise of …

Radiomics characterizes tumor phenotypes by extracting large numbers of quantitative
features from radiological images. Radiomic features have been shown to provide …

Prostate cancer is one of the most common forms of cancer and the third leading cause of
cancer death in North America. As an integrated part of computer-aided detection (CAD) …

Purpose To compare biparametric contrast-free radiomic machine learning (RML), mean
apparent diffusion coefficient (ADC), and radiologist assessment for characterization of …

Multi-parametric MRI (mp-MRI) is considered the best non-invasive imaging modality for
diagnosing prostate cancer (PCa). However, mp-MRI for PCa diagnosis is currently limited …