Background This paper reviews recent literature employing Artificial Intelligence/Machine
Learning (AI/ML) methods for diagnostic evaluation of head and neck cancers (HNC) using …

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

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 …

There is an expanding body of literature that describes the application of deep learning and
other machine learning and artificial intelligence methods with potential relevance to …

The present era has seen a surge in artificial intelligence-related research in oncology,
mainly using deep learning, because of powerful computer hardware, improved algorithms …

Opinion statement Head and neck cancers can be used as a paradigm for exploring “big
data” applications in oncology. Computational strategies derived from big data science hold …

Background Infection with human papilloma virus (HPV) is one of the most relevant
prognostic factors in advanced oropharyngeal cancer (OPC) treatment. In this study we …

Objective To compare the CT texture feature reproducibility of 2D and 3D segmentations
and their machine learning (ML)–based classifications for predicting human papilloma virus …

Accurate early detection of the human papillomavirus (HPV) status in head and neck cancer
(HNC) is crucial to identify at‐risk populations, stratify patients, personalized treatment …

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 …