ArticleTomography (Ann Arbor, Mich.)2025
Impact of Deep Learning 3D CT Super-Resolution on AI-Based Pulmonary Nodule Characterization.
Article in Tomography (Ann Arbor, Mich.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- High-fidelity super-resolution CT radiomics for non-invasive EGFR mutation prediction in lung adenocarcinoma: a multi-center pooled analysis.La Radiologia medica · 2026Observational
- Photon-counting detector computed tomography in thoracic oncology: revolutionizing tumor imaging through precision and detail.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
BACKGROUND/
objectivesCorrect pulmonary nodule volumetry and categorization is paramount for accurate diagnosis in lung cancer screening programs. CT scanners with slice thicknesses of multiple millimetres are still common worldwide, and slice thickness has an adverse effect on the accuracy of the pulmonary nodule volumetry.
methodsWe propose a deep learning based super-resolution technique to generate thin-slice CT images from thick-slice CT images. Analysis of the lung nodule volumetry and categorization accuracy was performed using commercially available AI-based lung cancer screening software.
resultsThe accuracy of pulmonary nodule categorization increased from 72.7 percent to 94.5 percent when thick-slice CT images were converted to generated-thin-slice CT images.
conclusionsApplying the super-resolution-based slice generation on thick-slice CT images prior to automatic nodule evaluation significantly increases the accuracy of pulmonary nodule volumetry and corresponding pulmonary nodule category.
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Registered trials
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