Evidence map›Paper›PMID 42100835›Full record

ArticleTechnology in cancer research & treatment

Individualized Prediction of Radiation Pneumonitis Using RP-GAN: Leveraging Global Lung Features and Explainable Artificial Intelligence.

Yang-Wei Hsieh, Pei-Ju Chao, Yi-Lun Liao, Wen-Ping Yun, Ling-Chuan Chang-Chien, Cheng-Shie Wuu, Yu-Wei Lin, Tsair-Fwu Lee

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Article in Technology in cancer research & treatment. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

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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

8 authors.

Yang-Wei HsiehMedical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan.
Pei-Ju ChaoMedical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan.
Yi-Lun LiaoMedical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan.
Wen-Ping YunMedical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan.
Ling-Chuan Chang-ChienMedical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan.
Cheng-Shie WuuDepartment of Radiation Oncology, Columbia University, New York, NY, USA.
Yu-Wei LinDepartment of Radiation Oncology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.
Tsair-Fwu LeeMedical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan.ORCID 0000-0002-3112-3594

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

IntroductionThis study aims to develop an individualized risk prediction model for radiation pneumonitis (RP) based on unsupervised image feature learning. A deep convolutional generative adversarial network (DCGAN) was utilized to automatically extract features from computed tomography (CT) images.MethodsA retrospective analysis was conducted on 180 lung cancer patients treated with volumetric modulated arc therapy (VMAT) at Kaohsiung Veterans General Hospital between 2015 and 2022. To mitigate clinical sample size limitations, rotation-based augmentation was employed to expand the training dataset. The pretreatment CT images were processed into three input configurations: whole-lung, V

Indexed as

Artificial IntelligenceLungLung NeoplasmsRadiation PneumonitisGenerative Adversarial NetworksGenerative Artificial IntelligenceHumansPrediction AlgorithmsRadiotherapy, Intensity-ModulatedRetrospective StudiesTomography, X-Ray Computeddeep convolutional generative adversarial network (DCGAN)explainable artificial intelligence (XAI)radiation pneumonitis (RP)risk prediction modelstacking ensemble learning

Identifiers

PMID42100835
PMCPMC13167338

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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.