Optimal Equations for Describing the Relationship Between Prostate Volume, Number of Sources, and Total Activity in Permanent Prostate Brachytherapy

Aronowitz, Jesse N.; Michalski, Jeff M.; Merrick, Gregory S.; Sylvester, John; Crook, Juanita; Butler, Wayne M.; Mawson, Christie; Pratt, David; Naidoo, Devi; Karolczuk, Kathryn

doi:10.1097/coc.0b013e31819d3684

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…Nomograms can be based on either linear or power equations. Historically, power equations have been most often used, but it had previously been demonstrated that, for the range of volumes typically implanted, a power curve is almost ‘linear’ [ 23 ]; our current experience confirms this ( Fig. 1 ).…”

Section: Discussionsupporting

confidence: 80%

A simple technique for the generation of institution-specific nomograms for permanent prostate cancer brachytherapy

Lafata¹,

Bushe²,

Aronowitz³

2014

jcb

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PurposeNomograms once had a vital role in prostate brachytherapy practice. Although some of their functions have been assumed by computerized dosimetry, many programs still find them useful to determine the number and strength of seeds to be ordered in advance of the implant. As it has been demonstrated that brachytherapists differ in their implant practices and preferences (in regard to seed distribution and total implanted activity), we propose a simple technique for generating institution-specific nomograms.Material and methodsUsing the data generated by previous implants, we demonstrate a simple technique, utilizing ubiquitous software, for generating nomograms to predict seed number, strength, and total implant activity.ResultsDespite wide variations in the degree of post-implant swelling (+12% to +56%), nine of the first ten implants using the generated nomogram have met all target dose parameters.ConclusionsIt is appropriate for each institution to develop nomograms specific to their prostate brachytherapy technique and constraints. Nomograms can estimate the number and strength of seeds to be ordered, and may partially compensate for the effects of implant-induced swelling.

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Section: Discussionsupporting

confidence: 80%

A simple technique for the generation of institution-specific nomograms for permanent prostate cancer brachytherapy

Lafata¹,

Bushe²,

Aronowitz³

2014

jcb

Self Cite

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“…As stated above, general recommendations are sometimes provided by manufacturers; however, it quickly becomes clear to develop prediction methods, specific for each institutions’ prostate LDR-BT technique and its constraints [ 6 ]. In the past, studies have been conducted in an attempt to build practical tools, from curve fitting [ 7 , 8 ] to nomograms [ 9 ]. However, these interesting methods are somewhat out-of-date [ 10 , 11 ], and may no longer correspond to today’s medical devices or practical habits.…”

Section: Purposementioning

confidence: 99%

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

Boussion

Schick

Dissaux

et al. 2021

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Purpose: 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 R 2 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.

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American Brachytherapy Society consensus guidelines for transrectal ultrasound-guided permanent prostate brachytherapy

et al. 2012

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Optimal Equations for Describing the Relationship Between Prostate Volume, Number of Sources, and Total Activity in Permanent Prostate Brachytherapy

Abstract: Linear and power equations are equally suitable for generating institution-specific nomograms.

Cited by 6 publications

References 14 publications

A simple technique for the generation of institution-specific nomograms for permanent prostate cancer brachytherapy

A simple technique for the generation of institution-specific nomograms for permanent prostate cancer brachytherapy

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

American Brachytherapy Society consensus guidelines for transrectal ultrasound-guided permanent prostate brachytherapy

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