Radiotherapy has long been the mainstay of treatment for patients with head and neck
cancer and has traditionally involved a stage-dependent strategy whereby all patients with …

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

Purpose: HPV-associated head and neck squamous cell carcinoma (HPV+ HNSCC) is the
most common HPV-associated malignancy in the United States and continues to increase in …

Purpose This study aimed to predict local tumor control (LC) after radiochemotherapy of
head and neck squamous cell carcinoma (HNSCC) and human papillomavirus (HPV) status …

Purpose To evaluate the utility of texture analysis for the differentiation of renal tumors,
including the various renal cell carcinoma subtypes and oncocytoma. Materials and …

Background/Aim: To investigate whether a radiomic machine learning (ML) approach
employing texture-analysis (TA) features extracted from primary tumor lesions (PTLs) is able …

Objectives This study was conducted in order to evaluate a novel risk stratification model
using dual-energy CT (DECT) texture analysis of head and neck squamous cell carcinoma …

Objectives: Human papillomavirus (HPV) positive oropharyngeal cancer (oropharyngeal
squamous cell carcinoma, OPSCC) is biologically and clinically different from HPV negative …

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 …

In the context of clinical oncology, a fundamental goal of radiomics is the extraction of large
amounts of quantitative features whose subsequent analysis can be used for decision …