Abstract Machine learning technology has a growing impact on radiation oncology with an
increasing presence in research and industry. The prevalence of diverse data including 3D …

Background Thorough data stewardship is a key enabler of comprehensive health research.
Processes such as data collection, storage, access, sharing, and analytics require …

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 …

Background and purpose Access to healthcare data is indispensable for scientific progress
and innovation. Sharing healthcare data is time-consuming and notoriously difficult due to …

Natural language processing (NLP), which aims to convert human language into
expressions that can be analyzed by computers, is one of the most rapidly developing and …

Introduction Emerging evidence suggests that data-driven support tools have found their
way into clinical decision-making in a number of areas, including cancer care. Improving …

Brain informatics is a novel interdisciplinary area that focuses on scientifically studying the
mechanisms of human brain information processing by integrating experimental cognitive …

Prediction modelling with radiomics is a rapidly developing research topic that requires
access to vast amounts of imaging data. Methods that work on decentralized data are …

Purpose Radiomics is the process to automate tumor feature extraction from medical
images. This has shown potential for quantifying the tumor phenotype and predicting …

For nearly 2 decades, adaptive radiation therapy (ART) has been proposed as a method to
account for changes in head and neck tumor and normal tissue to enhance therapeutic …