The potential of e-learning interventions for AI-assisted contouring skills in radiotherapy (E33046)
Source: www.iaea.org Author: Kamal Akbarov The International Atomic Energy Agency (IAEA) is launching a new Coordinated Research Project (CRP) aimed at investigating the potential of artificial intelligence (AI) to enhance contouring skills in radiotherapy, especially focusing on increasing accuracy of delineation of organs at risk in head and neck cancers. Radiation oncology has evolved rapidly in recent decades in terms of innovations in treatment equipment, volumetric imaging, information technology and increased knowledge in cancer biology. New delivery technologies and associated imaging modalities have enabled highly optimized precision radiation therapy and contributed to improvements in tumor control and cancer patient cure. The selection and contouring of target volumes and organs at risk (OARs) has become a key step in modern radiation oncology. Concepts and terms for definition of gross tumor volume, clinical target volume and OARs have been continuously evolving and have become widely disseminated and accepted by the international radiation oncology community. However, clinical research from single institutions and multicentre experiences has provided evidence for major variations in contouring for both target volumes and OARs. In recent years, AI-based methods, such as deep learning, have improved auto-segmentation drastically. It is generally believed that the use of such tools will lead to lower the inter-observer variation and time savings for clinical staff. A wide palette of commercial deep learning-based auto-segmentation solutions are emerging with the promise of leveraging the aforementioned benefits. While the objective performances for deep learning-based auto-segmentation in retrospective studies are very promising, the actual clinical benefit is largely [...]