ArticleEuropean radiology experimental2025
Artificial intelligence-based automated matching of pulmonary nodules on follow-up chest CT.
Article in European radiology experimental, 2025. 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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6 citing papers in PubMed.
- Automated artificial intelligence performance for longitudinal pulmonary nodule matching in lung cancer screening.European radiology · 2026Article
- Beyond the heart: a pictorial review of extracardiac findings on cardiac CT.The international journal of cardiovascular imaging · 2026Review
- Review
- Review
- Septic Shock in Hematological Malignancies: Role of Artificial Intelligence in Predicting Outcomes.Current oncology (Toronto, Ont.) · 2025Review
- Role of Quantitative CEUS in the Diagnosis of Peripheral Pulmonary Lesions: A Systematic Review.Cancers · 2025Review
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11 authors.
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Abstract
backgroundThe growing demand for follow-up imaging highlights the need for tools supporting the assessment of pulmonary nodules over time. We evaluated the performance of an artificial intelligence (AI)-based system for automated nodule matching.
methodsIn this single-center study, patients with nodules and ≤ 2 chest computed tomography (CT) examinations were retrospectively selected. An AI-based algorithm was used for automated nodule detection and matching. The matching rate and the causes for incorrect matching were evaluated for the ten largest lesions (5-30 mm in diameter) registered on baseline CT. The dependence of the matching rate on nodule number and localization was also analyzed.
resultsOne hundred patients (46 females), with a median age of 62 years (interquartile range 57-69), and 253 CTs were included. Focusing on the ten largest lesions, 1,141 lesions were identified, of which 36 (3.2%) were other structures incorrectly identified as nodules (false-positives). Of the 1,105 identified nodules, 964 (87.2%) were correctly detected and matched. The matching rate for nodules registered in both baseline and follow-up scans was 97.8%. The matching rate per case ranged 80.0-100.0% (median 90.0%). Correct matching rate decreased in follow-up examinations to over 50 nodules (p = 0.003), with an overrepresentation of missed matching. Matching rates were higher in parenchymal (91.8%), peripheral (84.4%), and juxtavascular (82.4%) nodules than in juxtaphrenic nodules (71.1%) (p < 0.001). Missed matching was overrepresented in juxtavascular, and incorrect assignment in juxtaphrenic nodules.
conclusionThe correct automated-matching rate of metastatic pulmonary nodules in follow-up examinations was high, but it depends on localization and a number of nodules. RELEVANCE STATEMENT: The algorithm enables precise follow-up matching of pulmonary nodules, potentially providing a solid basis for standardized and accurate evaluations. Understanding the algorithm's strengths and weaknesses based on nodule localization and number enhances the interpretation of AI-based results. KEY POINTS: The AI algorithm achieved a correct nodule matching rate of 87.2% and up to 97.8% when considering nodules detected in both baseline and follow-up scans. Matching accuracy depended on nodule number and localization. This algorithm has the potential to support response evaluation criteria in solid tumor-based evaluations in clinical practice.
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