ArticleEuropean radiology2026
Comparison of clinical-radiological and radiomics features for predicting pulmonary nodule malignancy in a multicenter study of mixed clinical and surveillance populations.
Article in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 2 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.
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.
Multi-centers Validation of a Circulating Tumor DNA Assay to Differentiate Benign and Malignant Pulmonary Nodules Via Targeted High-throughput DNA Methylation Sequencing
Diagnostic and Monitoring Significance of Circulating Tumor DNA (ctDNA) Methylation Analysis by Next-Generation Sequencing in Benign and Malignant Pulmonary Nodules
Who cites it
2 citing papers in PubMed.
- Recent advances in artificial intelligence across interventional pulmonology: a narrative review.Journal of thoracic disease · 2026Review
- A fusion model based on tumor and peritumoral CT radiomics for differentiating bronchiolar adenoma from lung adenocarcinoma.Frontiers in oncology · 2026Article
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Authors and funding
13 authors.
Funding
Abstract
objectivesTo develop and validate an automated radiomics-based model to objectively assess the malignancy risk of pulmonary nodules, overcoming the limitations of manual CT interpretation. MATERIALS AND
methodsThis prospective, multicenter diagnostic accuracy study enrolled 1895 patients with 1909 pulmonary nodules (1181 malignant; 728 benign) from 27 centers between 2017 and 2023. Clinical data and chest CT images were collected, and 25 radiological and 2153 radiomics features were extracted after 3D U-net-based segmentation. Three predictive models were developed: clinical-radiological ("Human Reading"), radiomics-only ("Radiomics"), and a combined model. Nodules were divided into training (n = 830), internal validation (n = 214), and external validation (n = 865) sets. The primary endpoint was diagnostic accuracy, assessed by AUC.
resultsParticipants included 888 men and 1007 women (mean age, 54.8 ± 11 years). In internal validation, the human reading and radiomics models achieved similar performance (AUC 0.88 [95% CI: 0.82-0.94] vs 0.88 [0.83-0.93]; p = 0.87). External validation confirmed comparable results (AUC 0.86 [0.83-0.88] vs 0.85 [0.82-0.87]; p = 0.56). The combined model outperformed both (AUC gain + 2.4% [vs radiomics], p < 0.001; + 1.7% [vs human reading], p = 0.0025).
conclusionIntegrating radiomics with clinical-radiological features enhances pulmonary nodule malignancy prediction, offering an effective and scalable tool for lung cancer risk assessment, particularly where radiological expertise is limited. CLINICAL
trial registrationNCT03181490, NCT03651986. KEY POINTS: Question Can an automated radiomics-based model accurately predict the malignancy risk of pulmonary nodules, reducing the subjectivity and workload of manual CT interpretation? Findings In a multicenter cohort of 1895 patients, a combined radiomics-clinical model achieved the highest diagnostic accuracy (AUC 0.87-0.90), outperforming human reading alone. Clinical relevance Integrating radiomics with clinical-radiological features enables objective and scalable lung cancer risk assessment, potentially improving early detection and diagnostic consistency across diverse clinical settings.
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Registered trials
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