Purpose To propose a new quality scoring tool, METhodological RadiomICs Score
(METRICS), to assess and improve research quality of radiomics studies. Methods We …

Artificial Intelligence (AI) is set to transform medical imaging by leveraging the vast data
contained in medical images. Deep learning and radiomics are the two main AI methods …

Hepatocellular carcinoma (HCC) remains not only a cause of a considerable part of
oncologic mortality, but also a diagnostic and therapeutic challenge for healthcare systems …

Objectives To investigate the intra-and inter-rater reliability of the total radiomics quality
score (RQS) and the reproducibility of individual RQS items' score in a large multireader …

Overall quality of radiomics research has been reported as low in literature, which
constitutes a major challenge to improve. Consistent, transparent, and accurate reporting is …

In the field of computer science, known as artificial intelligence, algorithms imitate reasoning
tasks that are typically performed by humans. The techniques that allow machines to learn …

Purpose To evaluate the diagnostic performance of radiomics in lymph node metastasis
(LNM) prediction in patients with papillary thyroid carcinoma (PTC) through a systematic …

Hepatic diffuse conditions and focal liver lesions represent two of the most common
scenarios to face in everyday radiological clinical practice. Thanks to the advances in …

Objective To systematically review radiomic feature reproducibility and model validation
strategies in recent studies dealing with CT and MRI radiomics of bone and soft-tissue …

Objectives To evaluate the methodological quality and diagnostic accuracy of MRI-based
radiomic studies predicting O6-methylguanine-DNA methyltransferase (MGMT) promoter …