Evidence map›Paper›PMID 39966735›Full record

ArticleBMC cancer2025

Segmentation methods and dosimetric evaluation of 3D-printed immobilization devices in head and neck radiotherapy.

Yunpeng Yin, Weisha Zhang, Lian Zou, Xiangxiang Liu, Luxin Yu, Ming Wang

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Article in BMC cancer, 2025. 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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4citing papers in PubMed
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1 · What the graph read from it

What it found

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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

6 authors.

Yunpeng YinCollege of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu, 610059, China.
Weisha ZhangCancer Center, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China.
Lian ZouCancer Center, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China.
Xiangxiang LiuCancer Center, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China.
Luxin YuCancer Center, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China. yuluxin@med.uestc.edu.cn.
Ming WangCollege of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu, 610059, China. wangming1@cdut.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTreatment planning systems (TPS) often exclude immobilization devices from optimization and calculation, potentially leading to inaccurate dose estimates. This study employed deep learning methods to automatically segment 3D-printed head and neck immobilization devices and evaluate their dosimetric impact in head and neck VMAT.

methodsComputed tomography (CT) positioning images from 49 patients were used to train the Mask2Former model to segment 3D-printed headrests and MFIFs. Based on the results, four body structure sets were generated for each patient to evaluate the impact on dose distribution in volumetric modulated arc therapy (VMAT) plans: S (without immobilization devices), S

resultsThe Mask2Former model achieved a mean average precision (mAP) of 0.898 and 0.895, with a Dice index of 0.956 and 0.939 for the 3D-printed headrest on the validation and test sets, respectively. For the MFIF, the Dice index was 0.980 and 0.981 on the validation and test sets, respectively. Compared to P, P

conclusionsThis study highlights the potential of Mask2Former in 3D-printed headrest and MFIF segmentation automation, providing a novel approach to enhance personalized radiation therapy plan accuracy. The attenuation effects of 3D-printed headrests and MFIFs reduce V

Indexed as

Head and Neck NeoplasmsImmobilizationPrinting, Three-DimensionalRadiotherapy, Intensity-ModulatedRadiotherapy Planning, Computer-AssistedDeep LearningHumansRadiometryRadiotherapy DosageTomography, X-Ray Computed3D printingImage segmentationRadiation therapy

Identifiers

PMID39966735
PMCPMC11837297

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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.