A machine-learning approach based on 409 treatments to predict optimal number of iodine-125 seeds in low-dose-rate prostate brachytherapy

Nicolas Boussion; Ulrike Schick; Gurvan Dissaux; Luc Ollivier; Gaëlle Goasduff; Olivier Pradier; Antoine Valeri; Dimitris Visvikis

doi:10.5114/jcb.2021.109789

eISSN: 2081-2841
ISSN: 1689-832X

Journal of Contemporary Brachytherapy

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5/2021
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abstract:

Original paper

A machine-learning approach based on 409 treatments to predict optimal number of iodine-125 seeds in low-dose-rate prostate brachytherapy

Nicolas Boussion

^{1, 2}

,

Ulrike Schick

^{1, 2}

,

Gurvan Dissaux

^{1, 2}

,

Luc Ollivier

²

,

Gaëlle Goasduff

²

,

Olivier Pradier

^{1, 2}

,

Antoine Valeri

^{1, 3, 4}

,

Dimitris Visvikis

¹

LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
Radiation Oncology Department, CHU, Brest, France
Urology Department, CHU, Brest, France
CeRePP, Paris, France

J Contemp Brachytherapy 2021; 13, 5: 541–548

DOI: https://doi.org/10.5114/jcb.2021.109789

Online publish date: 2021/10/07

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AMA

Boussion N, Schick U, Dissaux G, et al. A machine-learning approach based on 409 treatments to predict optimal number of iodine-125 seeds in low-dose-rate prostate brachytherapy. Journal of Contemporary Brachytherapy. 2021;13(5):541-548. doi:10.5114/jcb.2021.109789.

APA

Boussion, N., Schick, U., Dissaux, G., Ollivier, L., Goasduff, G.,  & Pradier, O. et al. (2021). A machine-learning approach based on 409 treatments to predict optimal number of iodine-125 seeds in low-dose-rate prostate brachytherapy. Journal of Contemporary Brachytherapy, 13(5), 541-548. https://doi.org/10.5114/jcb.2021.109789

Chicago

Boussion, Nicolas, Ulrike Schick, Gurvan Dissaux, Luc Ollivier, Gaëlle Goasduff, Olivier Pradier,  and Antoine Valeri et al. 2021. "A machine-learning approach based on 409 treatments to predict optimal number of iodine-125 seeds in low-dose-rate prostate brachytherapy". Journal of Contemporary Brachytherapy 13 (5): 541-548. doi:10.5114/jcb.2021.109789.

Harvard

Boussion, N., Schick, U., Dissaux, G., Ollivier, L., Goasduff, G., Pradier, O., Valeri, A.,  and Visvikis, D. (2021). A machine-learning approach based on 409 treatments to predict optimal number of iodine-125 seeds in low-dose-rate prostate brachytherapy. Journal of Contemporary Brachytherapy, 13(5), pp.541-548. https://doi.org/10.5114/jcb.2021.109789

MLA

Boussion, Nicolas et al. "A machine-learning approach based on 409 treatments to predict optimal number of iodine-125 seeds in low-dose-rate prostate brachytherapy." Journal of Contemporary Brachytherapy, vol. 13, no. 5, 2021, pp. 541-548. doi:10.5114/jcb.2021.109789.

Vancouver

Boussion N, Schick U, Dissaux G, Ollivier L, Goasduff G, Pradier O et al. A machine-learning approach based on 409 treatments to predict optimal number of iodine-125 seeds in low-dose-rate prostate brachytherapy. Journal of Contemporary Brachytherapy. 2021;13(5):541-548. doi:10.5114/jcb.2021.109789.

Introduction
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.

Material and methods
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.

Results
The best coefficients of determination R2 were given by support vector regression, with values attaining 0.928, 0.948, and 0.968 for training set, test set, and whole set, respectively. In terms of predicted seeds in test group, mean square error, median absolute error, mean absolute error, and maximum error were 2.55, 0.92, 1.21, and 7.29, respectively. The use of obtained abacus in 38 additional patients resulted in saving of 493 seeds (393 vs. 886 remaining seeds).

Conclusions
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.

keywords:

A machine-learning approach based on 409 treatments to predict optimal number of iodine-125 seeds in low-dose-rate prostate brachytherapy

Nicolas Boussion 1, 2 , Ulrike Schick 1, 2 , Gurvan Dissaux 1, 2 , Luc Ollivier 2 , Gaëlle Goasduff 2 , Olivier Pradier 1, 2 , Antoine Valeri 1, 3, 4 , Dimitris Visvikis 1

low-dose-rate brachytherapy, prostate cancer, radioactive seeds, machine-learning

Nicolas Boussion

^{1, 2}

,

Ulrike Schick

^{1, 2}

,

Gurvan Dissaux

^{1, 2}

,

Luc Ollivier

²

,

Gaëlle Goasduff

²

,

Olivier Pradier

^{1, 2}

,

Antoine Valeri

^{1, 3, 4}

,

Dimitris Visvikis

¹