Improved Algorithm for Estimation of Linear Energy Transfer in Carbon Ion Radiotherapy Plans.
Carbon ion radiotherapy
estimation
linear energy transfer
relative biological effectiveness
treatment planning
Journal
Anticancer research
ISSN: 1791-7530
Titre abrégé: Anticancer Res
Pays: Greece
ID NLM: 8102988
Informations de publication
Date de publication:
Jul 2023
Jul 2023
Historique:
received:
18
03
2023
revised:
06
05
2023
accepted:
08
05
2023
medline:
26
6
2023
pubmed:
23
6
2023
entrez:
23
6
2023
Statut:
ppublish
Résumé
This study aimed to develop an improved algorithm for linear energy transfer (LET) estimation in carbon ion radiotherapy (CIRT) using relative biological effectiveness (RBE) and to establish a clinical pipeline for LET assessment. New approximation functions for LET versus RBE were developed for the overkill region. LET estimation performance was examined at two facilities (A and B) using archival- and Monte Carlo simulation-derived LET data, respectively, as a reference. A clinical pipeline for LET assessment was developed using Python and treatment planning systems (TPS). In dataset A, LET estimation accuracy in the overkill region was improved by 80.0%. In dataset B, estimation accuracy was 2.3%±0.67% across 5 data points examined. LET distribution and LET-volume histograms were visualized for multiple CIRT plans. The new algorithm showed a greater LET estimation performance at multiple facilities using the same TPS. A clinical pipeline for LET assessment was established.
Sections du résumé
BACKGROUND/AIM
OBJECTIVE
This study aimed to develop an improved algorithm for linear energy transfer (LET) estimation in carbon ion radiotherapy (CIRT) using relative biological effectiveness (RBE) and to establish a clinical pipeline for LET assessment.
MATERIALS AND METHODS
METHODS
New approximation functions for LET versus RBE were developed for the overkill region. LET estimation performance was examined at two facilities (A and B) using archival- and Monte Carlo simulation-derived LET data, respectively, as a reference. A clinical pipeline for LET assessment was developed using Python and treatment planning systems (TPS).
RESULTS
RESULTS
In dataset A, LET estimation accuracy in the overkill region was improved by 80.0%. In dataset B, estimation accuracy was 2.3%±0.67% across 5 data points examined. LET distribution and LET-volume histograms were visualized for multiple CIRT plans.
CONCLUSION
CONCLUSIONS
The new algorithm showed a greater LET estimation performance at multiple facilities using the same TPS. A clinical pipeline for LET assessment was established.
Identifiants
pubmed: 37351961
pii: 43/7/2975
doi: 10.21873/anticanres.16468
doi:
Substances chimiques
Carbon
7440-44-0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2975-2984Informations de copyright
Copyright © 2023 International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.