ArticleThorax2023
Differentiation of malignant from benign pleural effusions based on artificial intelligence.
Article in Thorax, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 16 citations in OpenAlex.
- Predicting PD-L1 + CD8 - status in non-small cell lung cancer tissue from clinical indicators using machine learning.Chinese medical journal · 2026Article
- Recent advances in artificial intelligence across interventional pulmonology: a narrative review.Journal of thoracic disease · 2026Review
- Unraveling the multidimensional pleural ecosystem of lung cancer-associated malignant pleural effusion.Cancer cell international · 2026Review
- Diagnosis of Lung Diseases in the Artificial Intelligence Era.International journal of molecular sciences · 2026Article
- A deep learning model based on ultrasound imaging to differentiate malignant from benign pleural effusion: a multicenter cohort study.Respiratory research · 2026Article
- Artificial Intelligence in Pleural Diseases: Current Applications and Next Steps.Thoracic research and practice · 2026Article
- Progress and challenges of artificial intelligence in lung cancer clinical translation.NPJ precision oncology · 2025Review
- Harnessing AI for Improved Detection and Classification of Pleural Effusion: Insights and Innovations.Canadian respiratory journal · 2025Review
- Risk factor identification and prediction of pleural effusion following coronary artery bypass grafting.American journal of translational research · 2025Article
- IR808@MnO nano-near infrared fluorescent dye's diagnostic value for malignant pleural effusion.Respiratory research · 2024Observational
- Automated detection and segmentation of pleural effusion on ultrasound images using an Attention U-net.Journal of applied clinical medical physics · 2024Article
- Development of a diagnostic algorithm to ascertain malignant pleural effusion utilizing clinical indicators and serum metal concentrations.Frontiers in oncology · 2024Article
- Development and validation of a machine learning model for differential diagnosis of malignant pleural effusion using routine laboratory data.Therapeutic advances in respiratory diseaseObservational
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Authors and funding
20 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThis study aimed to construct artificial intelligence models based on thoracic CT images to perform segmentation and classification of benign pleural effusion (BPE) and malignant pleural effusion (MPE).
methodsA total of 918 patients with pleural effusion were initially included, with 607 randomly selected cases used as the training cohort and the other 311 as the internal testing cohort; another independent external testing cohort with 362 cases was used. We developed a pleural effusion segmentation model (M1) by combining 3D spatially weighted U-Net with 2D classical U-Net. Then, a classification model (M2) was built to identify BPE and MPE using a CT volume and its 3D pleural effusion mask as inputs.
resultsThe average Dice similarity coefficient, Jaccard coefficient, precision, sensitivity, Hausdorff distance 95% (HD95) and average surface distance indicators in M1 were 87.6±5.0%, 82.2±6.2%, 99.0±1.0%, 83.0±6.6%, 6.9±3.8 and 1.6±1.1, respectively, which were better than those of the 3D U-Net and 3D spatially weighted U-Net. Regarding M2, the area under the receiver operating characteristic curve, sensitivity and specificity obtained with volume concat masks as input were 0.842 (95% CI 0.801 to 0.878), 89.4% (95% CI 84.4% to 93.2%) and 65.1% (95% CI 57.3% to 72.3%) in the external testing cohort. These performance metrics were significantly improved compared with those for the other input patterns.
conclusionsWe applied a deep learning model to the segmentation of pleural effusions, and the model showed encouraging performance in the differential diagnosis of BPE and MPE.
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