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

The neuroimaging signs of the clot in acute ischemic stroke are relevant for clot biology and
its response to treatment. The diagnostic and predictive value of clot imaging is confirmed by …

Objectives Optimizing a machine learning (ML) pipeline for radiomics analysis involves
numerous choices in data set composition, preprocessing, and model selection. Objective …

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 …

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 …

Objective. The development of radiation-induced fibrosis after stereotactic ablative
radiotherapy (SABR) can obscure follow-up images and delay detection of a local …

Brain tumor mortality is high, and accurate classification before treatment can improve
patient prognosis. Radiomics, which extracts numerous features from medical images, has …

Purpose To systematically assess the quality of radiomics research in giant cell tumor of
bone (GCTB) and to test the feasibility of analysis at the level of radiomics feature. Methods …

Background and purpose Radiomics models trained with limited single institution data are
often not reproducible and generalisable. We developed radiomics models that predict loco …

Manual delineation of volumes of interest (VOIs) by experts is considered the gold-standard
method in radiomics analysis. However, it suffers from inter-and intra-operator variability. A …