Medical imaging plays an indispensable role in evaluating, predicting, and monitoring a
range of medical conditions. Radiomics, a specialized branch of medical imaging, utilizes …

Background Application of radiomics proceeds by extracting and analysing imaging features
based on generic morphological, textural, and statistical features defined by formulas …

Objectives Disseminated bone marrow (BM) involvement is frequent in multiple myeloma
(MM). Whole-body magnetic resonance imaging (wb-MRI) enables to evaluate the whole …

Background Radiomics is a noninvasive method using machine learning to support
personalised medicine. Preprocessing filters such as wavelet and Laplacian-of-Gaussian …

Background The aim of this work is to evaluate the performance of radiomics predictions for
a range of molecular, genomic and clinical targets in patients with clear cell renal cell …

Objectives In radiomics, different feature normalization methods, such as z-Score or Min–
Max, are currently utilized, but their specific impact on the model is unclear. We aimed to …

We developed and validated a multimodal radiomic machine learning approach to
noninvasively predict the expression of lymphocyte cell-specific protein-tyrosine kinase …

Radiomic datasets can be class-imbalanced, for instance, when the prevalence of diseases
varies notably, meaning that the number of positive samples is much smaller than that of …

Purpose To explore the clinical value of contrast-enhanced computed tomography (CECT)
radiomics in predicting primary site response to neoadjuvant chemotherapy in high-risk …

Background Radiomics models trained on data from one center typically show a decline of
performance when applied to data from external centers, hindering their introduction into …