Evidence map›Paper›PMID 41699829›Full record

ArticleJournal of applied clinical medical physics2026

Dynamic range optimization for treatment time reduction in respiratory-gated proton therapy using RayStation v2025.

Sungkoo Cho, Jung Il Yu, Hee Chul Park, Kwanghyun Jo

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. 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

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Sungkoo ChoDepartment of Radiation Oncology, Samsung Medical Center, Seoul, Republic of Korea.
Jung Il YuDepartment of Radiation Oncology, Samsung Medical Center, Seoul, Republic of Korea.
Hee Chul ParkDepartment of Radiation Oncology, Samsung Medical Center, Seoul, Republic of Korea.
Kwanghyun JoDepartment of Radiation Oncology, Samsung Medical Center, Seoul, Republic of Korea.

Funding

National Research Foundation of Korea NRF-2022R1F1A1064286
6 · The paper itself

Abstract

purposeThis study introduces dynamic range optimization, which constrains the lower limit of the minimum MU per spot, as a planning-based strategy to enhance efficiency in respiratory-gated proton therapy and evaluates the resultant dosimetric trade-offs.

methodsWe analyzed 101 hepatocellular carcinoma patients who received line-scanning proton therapy. We developed a computational framework to predict the total treatment time, integrating dynamic range-mediated changes in beam-on time (BoT) with respiratory gating dynamics. The model was validated against fully dynamic range-optimized plans generated by RayStation v2025 across five dynamic range values (no constraint, 200, 100, 50, and 10). These values represent the ratio of maximum to minimum MU allowed within an energy layer; specifically, a lower dynamic range value imposes a stricter constraint by elevating the minimum MU floor, thereby reducing intensity modulation flexibility while increasing delivery speed. Dosimetric trade-offs were quantified using fully re-optimized plans for a representative case under dynamic range = 100 and 10 constraints. The study used a layer switching time (T

resultsDecreasing the dynamic range systematically reduced beam-on time in a sigmoid-shape pattern. The computational model's predictions for beam-on time showed a maximum error of 10.4% compared to RayStation v2025 calculations, confirming its accuracy. Total treatment time reduction reached a plateau at certain dynamic range thresholds, particularly when layer delivery plus switching was completed within a single gating-on period, indicating no further efficiency gain from additional dynamic range reduction. While target coverage remained equivalent across dynamic range values, lower dynamic range values systematically increased normal tissue doses due to reduced intensity modulation capability, with organs proximal to the target experiencing the largest relative increases (e.g., gallbladder mean dose increased 16.4% at DR = 10).

conclusionDynamic range optimization is an effective, planning-based method to reduce treatment time in respiratory-gated proton therapy, operating independently of patient cooperation or system modifications. The developed RayStation-integrated tool enables clinicians to identify the optimal patient-specific dynamic range, balancing efficiency gains against acceptable dosimetric quality, particularly for organs-at-risk adjacent to targets. This approach provides a technical foundation for individualized dynamic range selection during treatment planning.

Indexed as

Carcinoma, HepatocellularLiver NeoplasmsProton TherapyRadiotherapy Planning, Computer-AssistedRespiratory-Gated Imaging TechniquesAlgorithmsHumansOrgans at RiskRadiotherapy DosageRadiotherapy, Intensity-ModulatedTime Factorsbeam‐on time efficiencydynamic rangeintensity‐modulated proton therapyliver cancerrespiratory gating

Identifiers

PMID41699829
PMCPMC12909597

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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.