Evidence map›Paper›PMID 31426721›Full record

ArticleTechnology in cancer research & treatment2019

A Collimator Setting Optimization Algorithm for Dual-Arc Volumetric Modulated Arc Therapy in Pancreas Stereotactic Body Radiation Therapy.

Xinyi Li, Jackie Wu, Manisha Palta, You Zhang, Yang Sheng, Jiahan Zhang, Chunhao Wang

Abstract read
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Article in Technology in cancer research & treatment, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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0cells of the map it votes in
4citing papers 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

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Xinyi Li1 Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA.ORCID 0000-0001-9995-8101
Jackie Wu1 Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA.ORCID 0000-0001-8235-2322
Manisha Palta1 Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA.
You Zhang3 Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Yang Sheng1 Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA.
Jiahan Zhang1 Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA.ORCID 0000-0002-4288-6503
Chunhao Wang1 Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA.ORCID 0000-0002-6945-7119

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo optimize collimator setting to improve dosimetric quality of pancreas volumetric modulated arc therapy plan for stereotactic body radiation therapy. MATERIALS AND

methodsFifty-five volumetric modulated arc therapy cases in stereotactic body radiation therapy of pancreas were retrospectively included in this study with internal review board approval. Different from the routine practice of initializing collimator settings with a template, the proposed algorithm simultaneously optimizes the collimator angles and jaw positions that are customized to the patient geometry. Specifically, this algorithm includes 2 key steps: (1) an iterative optimization algorithm via simulated annealing that generates a set of potential collimator settings from 39 cases with pancreas stereotactic body radiation therapy, and (2) a multi-leaf collimator modulation scoring system that makes the final decision of the optimal collimator settings (collimator angles and jaw positions) based on organs at risk sparing criteria. For validation, the other 16 cases with pancreas stereotactic body radiation therapy were analyzed. Two plans were generated for each validation case, with one plan optimized using the proposed algorithm (

resultsBoth plan groups had comparable target dose coverage and mean doses of all organs at risk. However, organs at risk (stomach, duodenum, large/small bowel) maximum dose sparing (

conclusionsThe proposed collimator settings optimization algorithm successfully improved dosimetric performance for dual-arc pancreas volumetric modulated arc therapy plans in stereotactic body radiation therapy of pancreas. This algorithm has the capability of immediate clinical application.

Indexed as

RadiosurgeryRadiotherapy, Intensity-ModulatedRadiotherapy Planning, Computer-AssistedAlgorithmsHumansOrgans at RiskPancreatic NeoplasmsRadiotherapy DosageRetrospective Studiescollimator settingsIMRToptimizationpancreas cancerSBRTsimulated annealingtreatment planningVMAT

Identifiers

PMID31426721
PMCPMC6702773

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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.