Evidence map›Paper›PMID 41408441›Full record

ArticleScientific reports2025

Personalized intensity modulation radiation therapy auto-planning for esophageal cancer.

Chih-Yuan Lin, An-Cheng Shiau, Yao-Ching Wang, Yu-Cheng Kuo, Ti-Hao Wang, Shih-Ming Hsu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Chih-Yuan LinDepartment of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan.
An-Cheng ShiauDepartment of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Yao-Ching WangDepartment of Radiation Oncology, China Medical University Hospital, Taichung, Taiwan.
Yu-Cheng KuoCollege of Medicine, China Medical University, Taichung, Taiwan.
Ti-Hao WangDepartment of Radiation Oncology, China Medical University Hospital, Taichung, Taiwan. thothwang@gmail.com.
Shih-Ming HsuDepartment of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan. smhsu@nycu.edu.tw.

Funding

China Medical University Hospital (CMUH), Taichung, Taiwan DMR-113-049National Science and Technology Council, Taiwan NSTC 113-2221-E-039-018
6 · The paper itself

Abstract

Esophageal cancer (EC) is among the most common malignancies worldwide, with radiotherapy as a key treatment. Intensity-modulated radiation therapy (IMRT) offers highly conformal dose distributions while sparing critical organs; however, planning is complex due to the esophagus' central location and proximity to vital structures. This study aimed to develop a personalized, automated IMRT planning system for EC. A retrospective analysis of 301 patients treated with 45 Gy in 25 fractions was conducted. Patient data, including CT images, structure sets, and clinical plan (CP) dose distributions, were collected to develop and internally validate the model. Geometric predictors of lung and heart doses were identified based on planning target volume (PTV) shape, size, and proximity to organs. External validation was subsequently performed on an independent cohort of 20 patients. Predictive models were built using univariate linear regression. The auto-planning (AP) system, developed via Python interfaced with the Eclipse Scripting Application Programming Interface (PyESAPI). Among all geometric predictors, LungCropPTV

Indexed as

Esophageal NeoplasmsPrecision MedicineRadiotherapy, Intensity-ModulatedRadiotherapy Planning, Computer-AssistedAgedFemaleHeartHumansLungMaleMiddle AgedRadiotherapy DosageRetrospective StudiesTomography, X-Ray ComputedAutomatic treatment planningEsophageal cancerPlan optimizationRadiotherapy

Identifiers

PMID41408441
PMCPMC12823690

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.