A machine-learning approach based on 409 treatments to predict optimal number of iodine-125 seeds in low-dose-rate prostate brachytherapy.
low-dose-rate brachytherapy
machine-learning
prostate cancer
radioactive seeds
Journal
Journal of contemporary brachytherapy
ISSN: 1689-832X
Titre abrégé: J Contemp Brachytherapy
Pays: Poland
ID NLM: 101506276
Informations de publication
Date de publication:
Oct 2021
Oct 2021
Historique:
received:
06
05
2021
accepted:
11
07
2021
entrez:
11
11
2021
pubmed:
12
11
2021
medline:
12
11
2021
Statut:
ppublish
Résumé
Low-dose-rate brachytherapy is a key treatment for low-risk or favorable intermediate-risk prostate cancer. The number of radioactive seeds inserted during the procedure depends on prostate volume, and is not easy to predict without pre-planning. Consequently, a large number of unused seeds may be left after treatment. The objective of the present study was to predict the exact number of seeds for future patients using machine learning and a database of 409 treatments. Database consisted of 18 dosimetric and efficiency parameters for each of 409 cases. Nine predictive algorithms based on machine-learning were compared in this database, which was divided into training group (80%) and test group (20%). Ten-fold cross-validation was applied to obtain robust statistics. The best algorithm was then used to build an abacus able to predict number of implanted seeds from expected prostate volume only. As an evaluation, the abacus was also applied on an independent series of 38 consecutive patients. The best coefficients of determination Machine-learning-based abacus proposed in this study aims at estimating the necessary number of seeds for future patients according to past experience. This new abacus, based on 409 treatments and successfully tested in 38 new patients, is a good alternative to non-specific recommendations.
Identifiants
pubmed: 34759979
doi: 10.5114/jcb.2021.109789
pii: 45381
pmc: PMC8565637
doi:
Types de publication
Journal Article
Langues
eng
Pagination
541-548Informations de copyright
Copyright © 2021 Termedia.
Déclaration de conflit d'intérêts
The authors report no conflict of interest.
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