Evidence map›Paper›PMID 41775532›Full record

ArticleJournal of applied clinical medical physics2026

Knowledge based automated planning for non-coplanar VMAT stereotactic radiosurgery in ocular malignancies.

Chase Cochran, Shane McCarthy, William St Clair, Damodar Pokhrel

Abstract read
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Article in Journal of applied clinical medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Chase CochranMedical Physics Graduate Program, Department of Radiation Medicine, University of Kentucky, Lexington, Kentucky, USA.
Shane McCarthyMedical Physics Graduate Program, Department of Radiation Medicine, University of Kentucky, Lexington, Kentucky, USA.ORCID https://orcid.org/0009-0001-9300-4857
William St ClairMedical Physics Graduate Program, Department of Radiation Medicine, University of Kentucky, Lexington, Kentucky, USA.
Damodar PokhrelMedical Physics Graduate Program, Department of Radiation Medicine, University of Kentucky, Lexington, Kentucky, USA.ORCID https://orcid.org/0000-0003-2694-1047

Funding

Varian Medical Systems
6 · The paper itself

Abstract

background and purposeStereotactic radiosurgery/radiotherapy (SRS/SRT) has emerged as a less invasive alternative to enucleation in the management of ocular malignancies. SRS/SRT planning is time-consuming, complex, and the plan quality depends on the experience of the treatment planner. We demonstrate the use of RapidPlan modeling in conjunction with HyperArc for the treatment of ocular diseases to improve planning efficiency, consistency, and quality. MATERIALS AND

methodsA HyperArc-based RapidPlan (HARP) model was iteratively trained on 80 patient datasets that simulated ocular malignancies. Twenty additional patient datasets were reserved for model testing and comparison with manual plans. Each testing dataset was both manually planned and replanned with the final RapidPlan model. Target volumes were defined in the HyperArc module as the GTV and PTV. The PTV was generated by a 2 mm static expansion of the GTV. 25 Gy in 1 fraction was prescribed, and all plans were normalized such that PTVD

resultsModel-based plans demonstrated similar degrees of conformality, gradient, and target coverage compared to manual planning. OAR sparing showed statistically significant improvements with model-based plans. Optic nerve Dmax (D0.03 cc) decreased to 4.56 Gy (manual: 7.18 Gy), and lens Dmax decreased to 7.64 Gy (manual: 9.36 Gy). Lacrimal gland mean dose decreased from 11.61 to 6.47 Gy. Modulation factor, monitor units, and beam-on times also all decreased. Patient-specific QA results showed improvements compared to manually generated plans. MC second check results also improved, increasing from an average of 97.81%-98.74%. Plan optimization times decreased significantly from approximately 120 min for manual planning to 15 min on average for model-based plans.

conclusionRapidPlan-generated ocular SRS plans showed either comparable performance or improvements in all plan metrics measured, compared to manual planning. Planning times were significantly reduced while maintaining or improving plan quality. Clinical implementation of this HARP model is ongoing at our clinic.

Indexed as

Eye NeoplasmsKnowledge BasesRadiosurgeryRadiotherapy, Intensity-ModulatedRadiotherapy Planning, Computer-AssistedAlgorithmsHumansOrgans at RiskQuality Assurance, Health CareRadiotherapy DosageHARP modelHyperArc SRSKBP modelocular tumorsRapidPlan

Identifiers

PMID41775532
PMCPMC12956477

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LicenceCC BY
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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.