ArticleTranslational oncology2021
Driverless artificial intelligence framework for the identification of malignant pleural effusion.
Article in Translational oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Biochemistry-based machine learning algorithms in differentiating pleural effusion: current status and perspective.ERJ open research · 2026Review
- Automated Detection and Classification of Pleural Effusion on Computed Tomography Using Deep Learning.Journal of imaging informatics in medicine · 2026Article
- Artificial Intelligence in Pleural Diseases: Current Applications and Next Steps.Thoracic research and practice · 2026Article
- Development and external validation of machine-learning models for diagnosing malignant pleural effusion.Frontiers in oncology · 2026Article
- Machine learning-based Diagnostic model for determining the etiology of pleural effusion using Age, ADA and LDH.Respiratory research · 2025Article
- An interpretable framework for gastric cancer classification using multi-channel attention mechanisms and transfer learning approach on histopathology images.Scientific reports · 2025Article
- Exploring blood-brain barrier passage using atomic weighted vector and machine learning.Journal of molecular modeling · 2024Article
- Prediction of HER2 status via random forest in 3257 Chinese patients with gastric cancer.Clinical and experimental medicine · 2023Article
- COSMOS: a platform for real-time morphology-based, label-free cell sorting using deep learning.Communications biology · 2023Article
- Deep learning for diagnosis of malign pleural effusion on computed tomography images.Clinics (Sao Paulo, Brazil) · 2023Article
- Predicting EGFR mutation, ALK rearrangement, and uncommon EGFR mutation in NSCLC patients by driverless artificial intelligence: a cohort study.Respiratory research · 2022Article
- Accurate diagnosis and prognosis prediction of gastric cancer using deep learning on digital pathological images: A retrospective multicentre study.EBioMedicine · 2021Article
- Development and Validation of a Scoring System for Early Diagnosis of Malignant Pleural Effusion Based on a Nomogram.Frontiers in oncology · 2021Article
- Biological and cytological-morphological assessment of tuberculous pleural effusions.Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologieArticle
- 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
4 authors.
Funding
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Abstract
Our study aimed to explore the applicability of deep learning and machine learning techniques to distinguish MPE from BPE. We initially used a retrospective cohort with 726 PE patients to train and test the predictive performances of the driverless artificial intelligence (AI), and then stacked with a deep learning and five machine learning models, namely gradient boosting machine (GBM), extreme gradient boosting (XGBoost), extremely randomized trees (XRT), distributed random forest (DRF), and generalized linear models (GLM). Furthermore, a prospective cohort with 172 PE patients was applied to detect the external validity of the predictive models. The area under the curve (AUC) in the training, test and validation set were deep learning (0.995, 0.848, 0.917), GBM (0.981, 0.910, 0.951), XGBoost (0.933, 0.916, 0.935), XRT (0.927, 0.909, 0.963), DRF (0.906, 0.809, 0.969), and GLM (0.898, 0.866, 0.892), respectively. Although the Deep Learning model had the highest AUC in the training set (AUC = 0.995), GBM demonstrated stable and high predictive efficiency in three data sets. The final AI model by stacked ensemble yielded optimal diagnostic performance with AUC of 0.991, 0.912 and 0.953 in the training, test and validation sets, respectively. Using the driverless AI framework based on the routinely collected clinical data could significantly improve diagnostic performance in distinguishing MPE from BPE.
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