ArticleJapanese journal of radiology2024
A deep learning model for translating CT to ventilation imaging: analysis of accuracy and impact on functional avoidance radiotherapy planning.
Article in Japanese journal of radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled 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.
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.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- Pooled it
- Integrated machine learning risk model for predicting radiation pneumonitis in lung cancer patients with interstitial lung disease.Annals of medicine · 2026Article
- Pulmonary ventilation images from planning CT images using deep learning on locally-advanced NSCLC patients.Journal of applied clinical medical physics · 2026Article
- Definitive radiotherapy for non-small-cell lung cancer: current practice and future directions.Japanese journal of radiology · 2026Review
- [Research progress on predicting radiation pneumonia based on four-dimensional computed tomography ventilation imaging in lung cancer radiotherapy].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Review
- Comprehensive clinical evaluation of novel 4DCT-based lung function imaging methods.Journal of applied clinical medical physics · 2025Article
- Clinical usefulness of four-dimensional dynamic ventilation CT for borderline resectable locally advanced esophageal cancer.Japanese journal of radiology · 2025Article
- Generative AI and large language models in nuclear medicine: current status and future prospects.Annals of nuclear medicine · 2024Review
Corrections and comments
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Authors and funding
11 authors at 4 institutions in 3 countries.
Funding
Abstract
purposeRadiotherapy planning incorporating functional lung images has the potential to reduce pulmonary toxicity. Free-breathing 4DCT-derived ventilation image (CTVI) may help quantify lung function. This study introduces a novel deep-learning model directly translating planning CT images into CTVI. We investigated the accuracy of generated images and the impact on functional avoidance planning. MATERIALS AND
methodsPaired planning CT and 4DCT scans from 48 patients with NSCLC were randomized to training (n = 41) and testing (n = 7) data sets. The ventilation maps were generated from 4DCT using a Jacobian-based algorithm to provide ground truth labels (CTVI
resultsCTVI
conclusionUsing deep-learning techniques, CTVI
Indexed as
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What OpenQuestion holds
Registered trials
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.