Evidence map›Paper›PMID 27370141›Full record

ArticleMedical physics2016

A comprehensive formulation for volumetric modulated arc therapy planning.

Dan Nguyen, Qihui Lyu, Dan Ruan, Daniel O'Connor, Daniel A Low, Ke Sheng

Abstract readEvaluation Study
In one paragraph

Article in Medical physics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

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  10. Many-isocenter optimization for robotic radiotherapy.Physics in medicine and biology · 2020
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Dan NguyenDepartment of Radiation Oncology, University of California Los Angeles, Los Angeles, California 90024.
Qihui LyuDepartment of Radiation Oncology, University of California Los Angeles, Los Angeles, California 90024.
Dan RuanDepartment of Radiation Oncology, University of California Los Angeles, Los Angeles, California 90024.
Daniel O'ConnorDepartment of Radiation Oncology, University of California Los Angeles, Los Angeles, California 90024.
Daniel A LowDepartment of Radiation Oncology, University of California Los Angeles, Los Angeles, California 90024.
Ke ShengDepartment of Radiation Oncology, University of California Los Angeles, Los Angeles, California 90024.

Funding

Motion Management of Pancreatic Cancer in MRI-Guided RadiotherapyR01CA188300 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SHENG, KE · 2015 to 2019
$1.7M
(NCI) Developing an Intermediate Energy Linac for Robotic RadiotherapyR43CA183390 · NCI · RADIABEAM TECHNOLOGIES, LLC · PI BOUCHER, SALIME · 2015 to 2016
$265k
NCI NIH HHS R01 CA188300NCI NIH HHS R43 CA183390
6 · The paper itself

Abstract

purposeVolumetric modulated arc therapy (VMAT) is a widely employed radiation therapy technique, showing comparable dosimetry to static beam intensity modulated radiation therapy (IMRT) with reduced monitor units and treatment time. However, the current VMAT optimization has various greedy heuristics employed for an empirical solution, which jeopardizes plan consistency and quality. The authors introduce a novel direct aperture optimization method for VMAT to overcome these limitations.

methodsThe comprehensive VMAT (comVMAT) planning was formulated as an optimization problem with an L2-norm fidelity term to penalize the difference between the optimized dose and the prescribed dose, as well as an anisotropic total variation term to promote piecewise continuity in the fluence maps, preparing it for direct aperture optimization. A level set function was used to describe the aperture shapes and the difference between aperture shapes at adjacent angles was penalized to control MLC motion range. A proximal-class optimization solver was adopted to solve the large scale optimization problem, and an alternating optimization strategy was implemented to solve the fluence intensity and aperture shapes simultaneously. Single arc comVMAT plans, utilizing 180 beams with 2° angular resolution, were generated for a glioblastoma multiforme case, a lung (LNG) case, and two head and neck cases-one with three PTVs (H&N3PTV) and one with foue PTVs (H&N4PTV)-to test the efficacy. The plans were optimized using an alternating optimization strategy. The plans were compared against the clinical VMAT (clnVMAT) plans utilizing two overlapping coplanar arcs for treatment.

resultsThe optimization of the comVMAT plans had converged within 600 iterations of the block minimization algorithm. comVMAT plans were able to consistently reduce the dose to all organs-at-risk (OARs) as compared to the clnVMAT plans. On average, comVMAT plans reduced the max and mean OAR dose by 6.59% and 7.45%, respectively, of the prescription dose. Reductions in max dose and mean dose were as high as 14.5 Gy in the LNG case and 15.3 Gy in the H&N3PTV case. PTV coverages measured by D95, D98, and D99 were within 0.25% of the prescription dose. By comprehensively optimizing all beams, the comVMAT optimizer gained the freedom to allow some selected beams to deliver higher intensities, yielding a dose distribution that resembles a static beam IMRT plan with beam orientation optimization.

conclusionsThe novel nongreedy VMAT approach simultaneously optimizes all beams in an arc and then directly generates deliverable apertures. The single arc VMAT approach thus fully utilizes the digital Linac's capability in dose rate and gantry rotation speed modulation. In practice, the new single VMAT algorithm generates plans superior to existing VMAT algorithms utilizing two arcs.

Indexed as

AlgorithmsBrain NeoplasmsGlioblastomaHead and Neck NeoplasmsHumansLung NeoplasmsRadiotherapy DosageRadiotherapy, Intensity-ModulatedRadiotherapy Planning, Computer-Assisted

Identifiers

PMID27370141
PMCPMC4912562

What OpenQuestion holds

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Registered trials

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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.