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 …

Background Radiomics extracts features from medical images more precisely and more
accurately than visual assessment. However, radiomics features are affected by CT scanner …

Objective: To define which are and how the radiomics features of jawbone pathologies are
extracted for diagnosis, predicting prognosis and therapeutic response. Methods: A …

Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue
characteristics and follow a well-understood path of technical, biological and clinical …

We aimed to evaluate radiomic features' stability across different region of interest (ROI)
sizes in CT and MR images. We chose a phantom with a homogenous internal structure so …

Purpose Development of a supervised machine-learning model capable of predicting
clinically relevant molecular subtypes of pancreatic ductal adenocarcinoma (PDAC) from …

Background Patients with small hepatocellular carcinoma (HCC)(≤ 3 cm) still have a poor
prognosis. The purpose of this study was to develop a radiomics nomogram to …

Intensity harmonization techniques (IHT) are mandatory to homogenize multicentric MRIs
before any quantitative analysis because signal intensities (SI) do not have standardized …

Radiomics features extracted from medical images have been widely reported to be useful
in the patient specific outcome modeling for variety of assessment and prediction purposes …

The differentiation of autoimmune pancreatitis (AIP) and pancreatic ductal adenocarcinoma
(PDAC) poses a relevant diagnostic challenge and can lead to misdiagnosis and …