Novel knowledge-based treatment planning model for hypofractionated radiotherapy of prostate cancer patients.
Automatic planning
Knowledge-based
Machine learning
Prostate radiotherapy
Journal
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
ISSN: 1724-191X
Titre abrégé: Phys Med
Pays: Italy
ID NLM: 9302888
Informations de publication
Date de publication:
Jan 2020
Jan 2020
Historique:
received:
29
07
2019
revised:
29
10
2019
accepted:
25
11
2019
pubmed:
10
12
2019
medline:
27
10
2020
entrez:
10
12
2019
Statut:
ppublish
Résumé
To demonstrate the strength of an innovative knowledge-based model-building method for radiotherapy planning using hypofractionated, multi-target prostate patients. An initial RapidPlan model was trained using 48 patients who received 60 Gy to prostate (PTV60) and 44 Gy to pelvic nodes (PTV44) in 20 fractions. To improve the model's goodness-of-fit, an intermediate model was generated using the dose-volume histograms of best-spared organs-at-risk (OARs) of the initial model. Using the intermediate model and manual tweaking, all 48 cases were re-planned. The final model, trained using these re-plans, was validated on 50 additional patients. The validated final model was used to determine any planning advantage of using three arcs instead of two on 16 VMAT cases and tested on 25 additional cases to determine efficacy for single-PTV (PTV60-only) treatment planning. For model validation, PTV V Our knowledge-based model delivers efficient, consistent plans with excellent PTV coverage and improved OAR sparing compared to clinical plans.
Identifiants
pubmed: 31816503
pii: S1120-1797(19)30514-9
doi: 10.1016/j.ejmp.2019.11.023
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
36-43Informations de copyright
Copyright © 2019 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.