Artificial Intelligence (AI) has emerged as a useful aid in numerous clinical applications for
diagnosis and treatment decisions. Deep neural networks have shown the same or better …

Artificial intelligence (AI) has the potential to fundamentally alter the way medicine is
practised. AI platforms excel in recognizing complex patterns in medical data and provide a …

Radiomics, the high-throughput mining of quantitative image features from standard-of-care
medical imaging that enables data to be extracted and applied within clinical-decision …

Quantitative extraction of high-dimensional mineable data from medical images is a process
known as radiomics. Radiomics is foreseen as an essential prognostic tool for cancer risk …

Radiomics extracts and mines large number of medical imaging features quantifying tumor
phenotypic characteristics. Highly accurate and reliable machine-learning approaches can …

Human cancers exhibit strong phenotypic differences that can be visualized noninvasively
by medical imaging. Radiomics refers to the comprehensive quantification of tumour …

This study aims at developing a joint FDG-PET and MRI texture-based model for the early
evaluation of lung metastasis risk in soft-tissue sarcomas (STSs). We investigate if the …

Background and purpose Radiomics provides opportunities to quantify the tumor phenotype
non-invasively by applying a large number of quantitative imaging features. This study …

Background and purpose Contouring of organs at risk (OARs) is an important but time
consuming part of radiotherapy treatment planning. The aim of this study was to investigate …

Purpose Machine learning classification algorithms (classifiers) for prediction of treatment
response are becoming more popular in radiotherapy literature. General Machine learning …