Objective The main aim of the present systematic review was a comprehensive overview of
the Radiomics Quality Score (RQS)–based systematic reviews to highlight common issues …

Abstract Machine learning (ML) is a branch of artificial intelligence centered on algorithms
which do not need explicit prior programming to function but automatically learn from …

With this review, we aimed to provide a synopsis of recently proposed applications of machine
learning (ML) in radiology focusing on prostate magnetic resonance imaging (MRI). After …

Even though radiomics can hold great potential for supporting clinical decision-making, its
current use is mostly limited to academic research, without applications in routine clinical …

Background Radiomics have the potential to further increase the value of MRI in prostate
cancer management. However, implementation in clinical practice is still far and concerns have …

Photon-counting computed tomography (CT) is a technology that has attracted increasing
interest in recent years since, thanks to new-generation detectors, it holds the promise to …

Background Prostate volume, as determined by magnetic resonance imaging (MRI), is a
useful biomarker both for distinguishing between benign and malignant pathology and can be …

Objectives The aim of this study was to systematically review the literature and perform a
meta-analysis of machine learning (ML) diagnostic accuracy studies focused on clinically …

Rationale and Objectives To evaluate an MRI radiomics-powered machine learning (ML)
model's performance for the identification of deep myometrial invasion (DMI) in endometrial …

Purpose Prostate multiparametric MRI (mpMRI) is the imaging modality of choice for detecting
clinically significant prostate cancer (csPCa). Among various parameters, lesion maximum …