Literature DB >> 34175222

Automation in radiotherapy treatment planning: Examples of use in clinical practice and future trends for a complete automated workflow.

P Meyer1, M-C Biston2, C Khamphan3, T Marghani4, J Mazurier5, V Bodez3, L Fezzani4, P A Rigaud4, G Sidorski5, L Simon6, C Robert7.   

Abstract

Modern radiotherapy treatment planning is a complex and time-consuming process that requires the skills of experienced users to obtain quality plans. Since the early 2000s, the automation of this planning process has become an important research topic in radiotherapy. Today, the first commercial automated treatment planning solutions are available and implemented in a growing number of clinical radiotherapy departments. It should be noted that these various commercial solutions are based on very different methods, implying a daily practice that varies from one center to another. It is likely that this change in planning practices is still in its infancy. Indeed, the rise of artificial intelligence methods, based in particular on deep learning, has recently revived research interest in this subject. The numerous articles currently being published announce a lasting and profound transformation of radiotherapy planning practices in the years to come. From this perspective, an evolution of initial training for clinical teams and the drafting of new quality assurance recommendations is desirable.
Copyright © 2021 Société française de radiothérapie oncologique (SFRO). Published by Elsevier Masson SAS. All rights reserved.

Keywords:  Automated radiotherapy treatment planning; Dose mimicking; Dose prediction; Imitation de dose; Planification de traitement automatisée; Prédiction de dose

Year:  2021        PMID: 34175222     DOI: 10.1016/j.canrad.2021.06.006

Source DB:  PubMed          Journal:  Cancer Radiother        ISSN: 1278-3218            Impact factor:   1.018


  1 in total

1.  Clinical evaluation of two AI models for automated breast cancer plan generation.

Authors:  Esther Kneepkens; Nienke Bakx; Maurice van der Sangen; Jacqueline Theuws; Peter-Paul van der Toorn; Dorien Rijkaart; Jorien van der Leer; Thérèse van Nunen; Els Hagelaar; Hanneke Bluemink; Coen Hurkmans
Journal:  Radiat Oncol       Date:  2022-02-05       Impact factor: 3.481

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.