Evidence map›Paper›PMID 38660134›Full record

ArticleFrontiers in oncology2024

Automated contouring, treatment planning, and quality assurance for VMAT craniospinal irradiation (VMAT-CSI).

Eric Simiele, Ignacio O Romero, Jen-Yeu Wang, Yizheng Chen, Yuliia Lozko, Yuliia Severyn, Lawrie Skinner, Yong Yang, Lei Xing, Iris Gibbs and 2 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
2.6field-weighted citation impact, top 11% 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

5 citing papers in PubMed, 6 citations in OpenAlex.

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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

12 authors at 1 institution in 1 country.

Eric Simiele *Department of Radiation Oncology, Stanford University, Stanford, CA, United States.
Ignacio O Romero *Department of Radiation Oncology, Stanford University, Stanford, CA, United States.
Jen-Yeu WangDepartment of Radiation Oncology, Stanford University, Stanford, CA, United States.
Yizheng ChenDepartment of Radiation Oncology, Stanford University, Stanford, CA, United States.
Yuliia LozkoDepartment of Radiation Oncology, Stanford University, Stanford, CA, United States.
Yuliia SeverynDepartment of Radiation Oncology, Stanford University, Stanford, CA, United States.
Lawrie SkinnerDepartment of Radiation Oncology, Stanford University, Stanford, CA, United States.
Yong YangDepartment of Radiation Oncology, Stanford University, Stanford, CA, United States.
Lei XingDepartment of Radiation Oncology, Stanford University, Stanford, CA, United States.
Iris GibbsDepartment of Radiation Oncology, Stanford University, Stanford, CA, United States.
Susan M Hiniker *Department of Radiation Oncology, Stanford University, Stanford, CA, United States.
Nataliya Kovalchuk *Department of Radiation Oncology, Stanford University, Stanford, CA, United States.
Stanford University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Create a comprehensive automated solution for pediatric and adult VMAT-CSI including contouring, planning, and plan check to reduce planning time and improve plan quality. Methods: Seventy-seven previously treated CSI patients (age, 2-67 years) were used for creation of an auto-contouring model to segment 25 organs at risk (OARs). The auto-contoured OARs were evaluated using the Dice Similarity Coefficient (DSC), 95% Hausdorff Distance (HD95), and a qualitative ranking by one physician and one physicist (scale: 1-acceptable, 2-minor edits, 3-major edits). The auto-planning script was developed using the Varian Eclipse Scripting API and tested with 20 patients previously treated with either low-dose VMAT-CSI (12 Gy) or high-dose VMAT-CSI (36 Gy + 18 Gy boost). Clinically relevant metrics, planning time, and blinded physician review were used to evaluate significance of differences between the auto and manual plans. Finally, the plan preparation for treatment and plan check processes were automated to improve efficiency and safety of VMAT-CSI. Results: The auto-contours achieved an average DSC of 0.71 ± 0.15, HD95 of 4.81 ± 4.68, and reviewers' ranking of 1.22 ± 0.39, indicating close to "acceptable-as-is" contours. Compared to the manual CSI plans, the auto-plans for both dose regimens achieved statistically significant reductions in body V50% and D Conclusions: Reductions in contouring and planning time without sacrificing plan quality were obtained using the developed auto-planning process. The auto-planning scripts and documentation will be made freely available to other institutions and clinics.

Indexed as

auto-contouringauto-planningcraniospinal irradiation (CSI)deep learningEclipse Scripting API (ESAPI)VMAT-CSI

Identifiers

PMID38660134
PMCPMC11039907
OpenAlexW4394688785

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.