Evidence map›Paper›PMID 34604054›Full record

ArticleFrontiers in oncology2021

An Investigation of Non-Coplanar Volumetric Modulated Radiation Therapy for Locally Advanced Unresectable Pancreatic Cancer Using a Trajectory Optimization Method.

Gong Wang, Hao Wang, Hongqing Zhuang, Ruijie Yang

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 39% of its field
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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. Article
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

4 authors at 2 institutions in 1 country.

Gong WangDepartment of Radiation Oncology, Peking University Third Hospital, Beijing, China.
Hao WangDepartment of Radiation Oncology, Peking University Third Hospital, Beijing, China.
Hongqing ZhuangDepartment of Radiation Oncology, Peking University Third Hospital, Beijing, China.
Ruijie YangDepartment of Radiation Oncology, Peking University Third Hospital, Beijing, China.
Peking University Third Hospital · CNPeking University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis study was conducted in order to develop a trajectory optimization algorithm for non-coplanar volumetric modulated arc therapy (VMAT) and investigate the potential of organs at risk (OARs) sparing in locally advanced pancreatic cancer patients using non-coplanar VMAT. METHODS AND MATERIALS: Firstly, a cost map that represents the ray-OAR voxel intersections at each source position was generated using a ray-tracing algorithm. A graph search algorithm was then used to determine the least-cost path from the cost map. Lastly, full arcs or partial arcs were selected based on the least-cost path to generate the non-coplanar VMAT (ncVMAT) trajectories. Clinical coplanar VMAT (coVMAT) plans for 11 patients diagnosed with locally advanced unresectable pancreatic cancer (LAPC) receiving 45 to 70 Gy in 25 fractions were replanned using non-coplanar VMAT trajectories. Both coplanar and non-coplanar plans were normalized to cover 95% of the PTV

resultsWith ncVMAT, the mean coverage of PTV

conclusionA trajectory optimization algorithm was developed for non-coplanar VMAT. Compared with conventional coplanar VMAT, non-coplanar VMAT resulted in improved coverage and conformity to the targets. The sparing of OARs was significantly improved in non-coplanar VMAT compared with coVMAT plans for locally advanced pancreatic cancer.

Indexed as

non-coplanarpancreatic cancertrajectory optimizationtreatment planningVMAT

Identifiers

PMID34604054
PMCPMC8485751
OpenAlexW3200326385

What OpenQuestion holds

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LicenceCC BY
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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.