Multi-center analysis of machine-learning predicted dose parameters in brachytherapy for cervical cancer.
Cervical cancer
DVH prediction
Image guided brachytherapy
Overlap volume histogram
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:
05 2022
05 2022
Historique:
received:
01
12
2021
revised:
14
02
2022
accepted:
16
02
2022
pubmed:
28
2
2022
medline:
20
5
2022
entrez:
27
2
2022
Statut:
ppublish
Résumé
Image-guided adaptive brachytherapy (IGABT) is a key component in the treatment of cervical cancer, but the nature of the clinical workflow makes it vulnerable to suboptimal plans, as the theoretical optimal plan depends heavily on organ configuration. Patient anatomy-based quality-assurance (QA) with overlap volume histograms (OVHs) is a promising tool to detect such suboptimal plans, and in this analysis its suitability as a multi-institutional clinical QA tool is investigated. A total of 223 plans of 145 patients treated in accordance with the current state-of-the-art IGABT protocols from UMC Utrecht (UMCU) and Erasmus MC (EMC) were included. Machine-learning models were trained to predict dose D Intra-validation of UMCU data demonstrated OVH model robustness to needle use and applicator type. The model trained on UMCU data was found to be robust within the same protocol on EMC data, for all investigated OARs. Mean squared error between planned and predicted D OVH-based models can provide a solid basis for multi-institutional QA when trained on a sufficiently strict protocol. Further research will quantify the model's impact as a QA tool.
Sections du résumé
BACKGROUND AND PURPOSE
Image-guided adaptive brachytherapy (IGABT) is a key component in the treatment of cervical cancer, but the nature of the clinical workflow makes it vulnerable to suboptimal plans, as the theoretical optimal plan depends heavily on organ configuration. Patient anatomy-based quality-assurance (QA) with overlap volume histograms (OVHs) is a promising tool to detect such suboptimal plans, and in this analysis its suitability as a multi-institutional clinical QA tool is investigated.
MATERIALS AND METHODS
A total of 223 plans of 145 patients treated in accordance with the current state-of-the-art IGABT protocols from UMC Utrecht (UMCU) and Erasmus MC (EMC) were included. Machine-learning models were trained to predict dose D
RESULTS
Intra-validation of UMCU data demonstrated OVH model robustness to needle use and applicator type. The model trained on UMCU data was found to be robust within the same protocol on EMC data, for all investigated OARs. Mean squared error between planned and predicted D
CONCLUSION
OVH-based models can provide a solid basis for multi-institutional QA when trained on a sufficiently strict protocol. Further research will quantify the model's impact as a QA tool.
Identifiants
pubmed: 35219799
pii: S0167-8140(22)00107-4
doi: 10.1016/j.radonc.2022.02.022
pii:
doi:
Types de publication
Journal Article
Multicenter Study
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
169-175Informations de copyright
Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.