ArticleScientific reports2024
Comparison of AI software tools for automated detection, quantification and categorization of pulmonary nodules in the HANSE LCS trial.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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.
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Who cites it
6 citing papers in PubMed.
- Lung nodule detection and potential impact on guideline-based management: a retrospective post-market evaluation of three commercial software systems.European radiology · 2026Article
- 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
- Retrospective Evaluation of an AI-Based Computer-Aided Detection Algorithm for Lung Cancer Detection on Cardiac CT: A Multicenter Study.Journal of Korean medical science · 2026Article
- Scientific evidence of commercial artificial intelligence products for pulmonary nodule assessment on CT scans: a systematic review.European radiology · 2026Article
- [Influence of artificial intelligence on decisions in radiology using the example of lung cancer screening].Radiologie (Heidelberg, Germany) · 2025Article
- Artificial intelligence in automated detection of lung nodules: a narrative review.International journal of physiology, pathophysiology and pharmacology · 2025Review
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Authors and funding
10 authors.
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Abstract
Participant management in a lung cancer screening (LCS) depends on the assigned Lung Imaging Reporting and Data System (Lung-RADS) category, which is based on reliable detection and measurement of pulmonary nodules. The aim of this study was to compare the agreement of two AI-based software tools for detection, quantification and categorization of pulmonary nodules in an LCS program in Northern Germany (HANSE-trial). 946 low-dose baseline CT-examinations were analyzed by two AI software tools regarding lung nodule detection, quantification and categorization and compared to the final radiologist read. The relationship between detected nodule volumes by both software tools was assessed by Pearson correlation (r) and tested for significance using Wilcoxon signed-rank test. The consistency of Lung-RADS classifications between Software tool 1 (S1, Aview v2.5, Coreline Soft, Seoul, Korea) and Software tool 2 (S2, Prototype ''ChestCTExplore'', software version ToDo, Siemens Healthineers, Forchheim, Germany) was evaluated by Cohen's kappa (κ) and percentual agreement (PA).The derived volumes of true positive nodules were strongly correlated (r > 0.95), the volume derived by S2 was significantly higher than by S1 (P < 0.0001, mean difference: 6mm
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