Simple Summary Head and neck tumors (HNTs) are associated with a high mortality due to
their commonly insidious and asymptomatic development. Regarding risk stratification and …

The application of imaging biomarkers in oncology is still in its infancy, but with the
expansion of radiomics and radiogenomics a revolution is expected in this field. This may be …

Recent advancements in computational power, machine learning, and artificial intelligence
technology have enabled automated evaluation of medical images to generate quantitative …

Head and neck cancer has great regional anatomical complexity, as it can develop in
different structures, exhibiting diverse tumour manifestations and high intratumoural …

Background Radiomics is a rapidly growing field used to leverage medical radiological
images by extracting quantitative features. These are supposed to characterize a patient's …

Tumor heterogeneity is a well-known prognostic factor in head and neck squamous cell
carcinoma (HNSCC). A major limitation of tissue-and blood-derived tumor markers is the …

Introduction “Radiomics” extracts and mines a large number of medical imaging features in a
non-invasive and cost-effective way. The underlying assumption of radiomics is that these …

Artificial intelligence (AI)-based models have become a growing area of interest in predictive
medicine and have the potential to aid physician decision-making to improve patient …

With the continuous development of medical image informatics technology, more and more
high-throughput quantitative data could be extracted from digital medical images, which has …

Introduction: An increasing number of parameters can be considered when making
decisions in oncology. Tumor characteristics can also be extracted from imaging through the …