ArticleHeliyon2023
Novel clinical radiomic nomogram method for differentiating malignant from non-malignant pleural effusions.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Unraveling the multidimensional pleural ecosystem of lung cancer-associated malignant pleural effusion.Cancer cell international · 2026Review
- Incremental predictive value of a CT-based deep learning radiomics model for differentiating benign and malignant pleural effusions.Frontiers in medicine · 2026Article
- Differential diagnosis model for tuberculous and malignant pleural effusion combining U-Net automatic segmentation and deep learning.Frontiers in medicine · 2026Article
- Advanced imaging techniques and artificial intelligence in pleural diseases: a narrative review.European respiratory review : an official journal of the European Respiratory Society · 2025Review
- The value of CT-based radiomics for differentiation of pleural effusions in bacterial pneumonia andTranslational pediatrics · 2025Article
- A CT-based radiomics predictive nomogram to identify pulmonary tuberculosis from community-acquired pneumonia: a multicenter cohort study.Frontiers in cellular and infection microbiology · 2024Article
- Development and validation of a radiomics nomogram for diagnosis of malignant pleural effusion.Discover oncology · 2023Article
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Authors and funding
8 authors.
Funding
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Abstract
Objectives: To establish a clinical radiomics nomogram that differentiates malignant and non-malignant pleural effusions. Methods: A total of 146 patients with malignant pleural effusion (MPE) and 93 patients with non-MPE (NMPE) were included. The ROI image features of chest lesions were extracted using CT. Univariate analysis was performed, and least absolute shrinkage selection operator and multivariate logistic analysis were used to screen radiomics features and calculate the radiomics score. A nomogram was constructed by combining clinical factors with radiomics scores. ROC curve and decision curve analysis (DCA) were used to evaluate the prediction effect. Results: After screening, 19 radiomics features and 2 clinical factors were selected as optimal predictors to establish a combined model and construct a nomogram. The AUC of the combined model was 0.968 (95% confidence interval [CI] = 0.944-0.986) in the training cohort and 0.873 (95% CI = 0.796-0.940) in the validation cohort. The AUC value of the combined model was significantly higher than those of the clinical and radiomics models (0.968 vs. 0.874 vs. 0.878, respectively). This was similar in the validation cohort (0.873, 0.764, and 0.808, respectively). DCA confirmed the clinical utility of the radiomics nomogram. Conclusion: CT-based radiomics showed better diagnostic accuracy and model fit than clinical and radiological features in distinguishing MPE from NMPE. The combination of both achieved better diagnostic performance. These findings support the clinical application of the nomogram in diagnosing MPE using chest CT.
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