ArticleDiagnostics (Basel, Switzerland)2024
Deep Learning-Based Slice Thickness Reduction for Computer-Aided Detection of Lung Nodules in Thick-Slice CT.
Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Impact of CT slice thickness reduction algorithm on AI-based lung nodule detection in chest CTs of colorectal cancer patients.Quantitative imaging in medicine and surgery · 2026Article
- Deep learning-based CT slice synthesis improves radiomic feature reproducibility and discriminative performance in lung nodule assessment.Insights into imaging · 2026Article
- Performance of a deep-learning-based lung nodule detection system using 0.25-mm thick ultra-high-resolution CT images.Japanese journal of radiology · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
BACKGROUND/
objectivesComputer-aided detection (CAD) systems for lung nodule detection often face challenges with 5 mm computed tomography (CT) scans, leading to missed nodules. This study assessed the efficacy of a deep learning-based slice thickness reduction technique from 5 mm to 1 mm to enhance CAD performance.
methodsIn this retrospective study, 687 chest CT scans were analyzed, including 355 with nodules and 332 without nodules. CAD performance was evaluated on nodules, to which all three radiologists agreed.
resultsThe slice thickness reduction technique significantly improved the area under the receiver operating characteristic curve (AUC) for scan-level analysis from 0.867 to 0.902, with a
conclusionsApplying a deep learning-based slice thickness reduction technique significantly enhances CAD performance in lung nodule detection on chest CT scans, supporting the clinical adoption of refined 1 mm CT scans for more accurate diagnoses.
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