Evidence map›Paper›PMID 39704674›Full record

ArticleJournal of applied clinical medical physics2025

Optimization of target grouping in distributive stereotactic radiosurgery using the excel evolutionary solver.

Chester Ramsey, Samuel Gallemore, Joseph Bowling

Abstract read
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Article in Journal of applied clinical medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Chester RamseyThompson Cancer Survival Center, Knoxville, Tennessee, USA.ORCID https://orcid.org/0009-0009-1102-3519
Samuel GallemoreDepartment of Nuclear Engineering Complex, University of Tennessee, Knoxville, Tennessee, USA.
Joseph BowlingFort Sanders Regional Gamma Knife Center, Knoxville, Tennessee, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeDistributive stereotactic radiosurgery (dSRS) is a form of fractionation where groups of metastases are treated with a full single-fraction dose on different days. The challenge with dSRS is determining optimal target groupings to maximize the distance between targets treated in the same fraction. This study aimed to develop and validate an accessible optimization technique for distributing brain metastases into optimal treatment fractions using a genetic algorithm.

methodsThe Evolutionary Solver in Excel was used to optimize the grouping of target volumes for distributive SRS fractionation. The algorithm's performance was tested using three geometric test cases with known optimal solutions, 400 simulations with randomly distributed target volumes, and clinical data from five GammaKnife patients. The objective function was defined as the sum of average distances between target volumes within each fraction, with constraints ensuring 2-5 targets per fraction, each target being assigned to only one fraction, and a constraint on the minimum distance between any two targets in the same fraction.

resultsThe Evolutionary Solver successfully identified optimal target groupings in all geometric test cases. Compared to random groupings, the mean distance between target volumes increased by 9%, from 68.1 ± 0.8  to 74.2 ± 1.1 mm post-optimization, while the minimum distance between targets increased by 57%, from 24.9 ± 5.9  to 39.1 ± 7.5 mm. In clinical test cases, the mean distances improved from 81.6 ± 11.9 mm for manual target grouping to 85.6 ± 14.5 mm for optimized target grouping. The minimum separation improved from 35.2 ± 14.5 mm with manual grouping to 51.6 ± 14.7 mm with optimized grouping, corresponding to a mean improvement of 16.4 ± 6.1 mm.

conclusionThe Evolutionary Solver in Excel provides a systematic and reproducible method for optimizing distributive target groupings in SRS and enhances spatial separation.

Indexed as

AlgorithmsBrain NeoplasmsRadiosurgeryRadiotherapy Planning, Computer-AssistedHumansRadiotherapy DosageRadiotherapy, Intensity-Modulateddistributive fractionationEvolutionary SolverGammaKnifegenetic algorithmSRS

Identifiers

PMID39704674
PMCPMC11969113

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.