Evidence map›Paper›PMID 42490839›Full record

ArticleFrontiers in oncology2026

Clinical implementation of an automated VMAT treatment planning script for head and neck cancer patients: three-year experience.

Nataliya Kovalchuk, Peng Dong, Caressa Hui, Ignacio Romero, Ziyi Wang, Lina Shah, Raveena Pandya, Michael Xiang, Everett J Moding, Michael F Gensheimer and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

14 authors.

Nataliya Kovalchuk *Department of Radiation Oncology, Stanford University, Stanford, CA, United States.
Peng Dong *Department of Radiation Oncology, Stanford University, Stanford, CA, United States.
Caressa HuiDepartment of Radiation Oncology, Stanford University, Stanford, CA, United States.
Ignacio RomeroDepartment of Radiation Oncology, Stanford University, Stanford, CA, United States.
Ziyi WangDepartment of Radiation Oncology, Stanford Health Care, Stanford, CA, United States.
Lina ShahDepartment of Radiation Oncology, Stanford Health Care, Stanford, CA, United States.
Raveena PandyaDepartment of Radiation Oncology, Stanford University, Stanford, CA, United States.
Michael XiangDepartment of Radiation Oncology, Stanford University, Stanford, CA, United States.
Everett J ModingDepartment of Radiation Oncology, Stanford University, Stanford, CA, United States.
Michael F GensheimerDepartment of Radiation Oncology, Stanford University, Stanford, CA, United States.
Beth M BeadleDepartment of Radiation Oncology, Stanford University, Stanford, CA, United States.
Quynh-Thu LeDepartment of Radiation Oncology, Stanford University, Stanford, CA, United States.
Lei XingDepartment of Radiation Oncology, Stanford University, Stanford, CA, United States.
Yong YangDepartment of Radiation Oncology, Stanford University, Stanford, CA, United States.

Funding

Leveraging deep learning for markerless motion management in radiation therapyR01CA256890 · NCI · STANFORD UNIVERSITY · PI XING, LEI · 2021 to 2025
$2.1M
Development of AI-Augmented quality assurance tools for radiation therapyR01CA275772 · NCI · STANFORD UNIVERSITY · PI Lei Xing · 2023 to 2026
$2.1M
NCI NIH HHS R01 CA256890NCI NIH HHS R01 CA275772
6 · The paper itself

Abstract

Purpose: To assess the impact of implementing an in-house automated volumetric modulated arc therapy (VMAT) planning script for patients with head and neck (HN) cancer. Methods: The automated planning script was implemented at our institution in April 2020. During validation, 10 auto-plans were compared with 10 corresponding manual plans for dosimetric indices, Five radiation oncologists blindly reviewed these plans for clinical acceptability and treatment preference. For clinical evaluation, dosimetric indices from 1000 HN patients consecutively treated between 2017 and 2023 (500 manual pre-implementation, 500 automated post-implementation) were compared using t-tests (p<0.05). Results: In validation testing, 10 auto-plans maintained PTV D Conclusions: Automated planning improved organ-at-risk sparing without compromising target coverage or dose homogeneity, with high clinical acceptability.

Indexed as

automationautoplanninghead and neck cancerradiation therapytreatment planning

Identifiers

PMID42490839
PMCPMC13375578

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.