First experience of autonomous, un-supervised treatment planning integrated in adaptive MR-guided radiotherapy and delivered to a patient with prostate cancer.
Artificial intelligence
Automatic segmentation
Autonomous radiotherapy planning
MR-Linac
MR-guided radiotherapy
Particle swarm optimization
Journal
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
ISSN: 1879-0887
Titre abrégé: Radiother Oncol
Pays: Ireland
ID NLM: 8407192
Informations de publication
Date de publication:
06 2021
06 2021
Historique:
received:
17
02
2021
revised:
23
03
2021
accepted:
24
03
2021
pubmed:
5
4
2021
medline:
29
6
2021
entrez:
4
4
2021
Statut:
ppublish
Résumé
Currently clinical radiotherapy (RT) planning consists of a multi-step routine procedure requiring human interaction which often results in a time-consuming and fragmented process with limited robustness. Here we present an autonomous un-supervised treatment planning approach, integrated as basis for online adaptive magnetic resonance guided RT (MRgRT), which was delivered to a prostate cancer patient as a first-in-human experience. For an intermediate risk prostate cancer patient OARs and targets were automatically segmented using a deep learning-based software and logical volume operators. A baseline plan for the 1.5 T MR-Linac (20x3 Gy) was automatically generated using particle swarm optimization (PSO) without any human interaction. Plan quality was evaluated by predefined dosimetric criteria including appropriate tolerances. Online plan adaptation during clinical MRgRT was defined as first checkpoint for human interaction. OARs and targets were successfully segmented (3 min) and used for automatic plan optimization (300 min). The autonomous generated plan satisfied 12/16 dosimetric criteria, however all remained within tolerance. Without prior human validation, this baseline plan was successfully used during online MRgRT plan adaptation, where 14/16 criteria were fulfilled. As postulated, human interaction was necessary only during plan adaptation. Autonomous, un-supervised data preparation and treatment planning was first-in-human shown to be feasible for adaptive MRgRT and successfully applied. The checkpoint for first human intervention was at the time of online MRgRT plan adaptation. Autonomous planning reduced the time delay between simulation and start of RT and may thus allow for real-time MRgRT applications in the future.
Sections du résumé
BACKGROUND AND PURPOSE
Currently clinical radiotherapy (RT) planning consists of a multi-step routine procedure requiring human interaction which often results in a time-consuming and fragmented process with limited robustness. Here we present an autonomous un-supervised treatment planning approach, integrated as basis for online adaptive magnetic resonance guided RT (MRgRT), which was delivered to a prostate cancer patient as a first-in-human experience.
MATERIALS AND METHODS
For an intermediate risk prostate cancer patient OARs and targets were automatically segmented using a deep learning-based software and logical volume operators. A baseline plan for the 1.5 T MR-Linac (20x3 Gy) was automatically generated using particle swarm optimization (PSO) without any human interaction. Plan quality was evaluated by predefined dosimetric criteria including appropriate tolerances. Online plan adaptation during clinical MRgRT was defined as first checkpoint for human interaction.
RESULTS
OARs and targets were successfully segmented (3 min) and used for automatic plan optimization (300 min). The autonomous generated plan satisfied 12/16 dosimetric criteria, however all remained within tolerance. Without prior human validation, this baseline plan was successfully used during online MRgRT plan adaptation, where 14/16 criteria were fulfilled. As postulated, human interaction was necessary only during plan adaptation.
CONCLUSION
Autonomous, un-supervised data preparation and treatment planning was first-in-human shown to be feasible for adaptive MRgRT and successfully applied. The checkpoint for first human intervention was at the time of online MRgRT plan adaptation. Autonomous planning reduced the time delay between simulation and start of RT and may thus allow for real-time MRgRT applications in the future.
Identifiants
pubmed: 33812912
pii: S0167-8140(21)06172-7
doi: 10.1016/j.radonc.2021.03.032
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
197-201Informations de copyright
Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.