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) …

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 …

In the past decade, the field of medical image analysis has grown exponentially, with an
increased number of pattern recognition tools and an increase in data set sizes. These …

Deep learning-based radiomics (DLR) was developed to extract deep information from
multiple modalities of magnetic resonance (MR) images. The performance of DLR for …

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

Haralick texture features are common texture descriptors in image analysis. To compute the
Haralick features, the image gray-levels are reduced, a process called quantization. The …

Abstract Changes of radiomic features over time in longitudinal images, delta radiomics, can
potentially be used as a biomarker to predict treatment response. This study aims to develop …

Radiomics is an emerging field of image analysis with potential applications in patient risk
stratification. This study developed and evaluated machine learning models using …

Multiparametric MRI (mpMRI) is an imaging modality that combines anatomical MR imaging
with one or more functional MRI sequences. It has become a versatile tool for detecting and …

Purpose Prostate cancer classification has a significant impact on the prognosis and
treatment planning of patients. Currently, this classification is based on the Gleason score …