Deep learning applied to dose prediction in external radiation therapy: A narrative review.
Fiche publication
Date publication
août 2024
Journal
Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
Auteurs
Membres identifiés du Cancéropôle Est :
Pr GSCHWIND Régine
Tous les auteurs :
Lagedamon V, Leni PE, Gschwind R
Lien Pubmed
Résumé
Over the last decades, the use of artificial intelligence, machine learning and deep learning in medical fields has skyrocketed. Well known for their results in segmentation, motion management and posttreatment outcome tasks, investigations of machine learning and deep learning models as fast dose calculation or quality assurance tools have been present since 2000. The main motivation for this increasing research and interest in artificial intelligence, machine learning and deep learning is the enhancement of treatment workflows, specifically dosimetry and quality assurance accuracy and time points, which remain important time-consuming aspects of clinical patient management. Since 2014, the evolution of models and architectures for dose calculation has been related to innovations and interest in the theory of information research with pronounced improvements in architecture design. The use of knowledge-based approaches to patient-specific methods has also considerably improved the accuracy of dose predictions. This paper covers the state of all known deep learning architectures and models applied to external radiotherapy with a description of each architecture, followed by a discussion on the performance and future of deep learning predictive models in external radiotherapy.
Mots clés
Apprentissage profond, Assurance qualité, Deep learning, Dosimetry, Dosimétrie, External radiotherapy, Planification, Quality assurance, Radiothérapie externe, Traitement, Treatment planning
Référence
Cancer Radiother. 2024 08 12;: