Abstract “Radiomics,” as it refers to the extraction and analysis of a large number of
advanced quantitative radiological features from medical images using high-throughput …

Quantitative image analysis, also known as radiomics, aims to analyze large-scale
quantitative features extracted from acquired medical images using hand-crafted or machine …

Objectives: To identify a computed tomography (CT)-based radiomic signature for predicting
progression-free survival (PFS) in stage IV anaplastic lymphoma kinase (ALK)-positive non …

The recent integration of open-source data with machine learning models, especially in the
medical field, has opened new doors to studying disease progression and/or regression …

Purpose A phase I clinical trial was designed to test the feasibility and toxicity of
administering high-dose spatially fractionated radiation therapy to magnetic resonance …

Multi-parametric MRI (mpMRI) has proven itself a clinically useful tool to assess prostate
cancer (PCa). Our objective was to generate PCa risk maps to quantify the volume and …

Simple Summary In this study, we built clinical-and radiomics-based models to predict
lesions/patients at low risk based on a combined clinical-genomic classification system …

Glioblastoma is a high-grade aggressive neoplasm characterised by significant intra-tumoral
spatial heterogeneity. Personalising therapy for this tumour requires non-invasive tools to …

Magnetic resonance imaging (MRI) plays an important role in the modern radiation therapy
(RT) workflow. In comparison with computed tomography (CT) imaging, which is the …

Prostate cancer identification and assessment of clinical significance continues to be a
challenge. Routine multiparametric magnetic resonance imaging has shown to be useful in …