ArticleJournal of clinical laboratory analysis2021
Development of risk prediction models for lung cancer based on tumor markers and radiological signs.
Article in Journal of clinical laboratory analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- Biomarkers Suitable for Early Detection of Intrathoracic Cancers in Primary Care: A Systematic Review.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2025Pooled it
- Head-to-head comparison of the diagnostic accuracy of pleural fluid, serum carcinoembryonic antigen, and their ratio for malignant pleural effusion: a systematic review and meta-analysis.Journal of thoracic disease · 2025Article
- Development and validation of CT radiomics diagnostic models: differentiating benign from malignant pulmonary nodules and evaluating malignancy degree.Journal of thoracic disease · 2025Article
- Determination of Tumor Marker Screening for Lung Cancer Using ROC Curves.Disease markers · 2024Article
- Lung cancer risk discrimination of prediagnostic proteomics measurements compared with existing prediction tools.Journal of the National Cancer Institute · 2023Article
- Development and validation of a nomogram model for lung cancer based on radiomics artificial intelligence score and clinical blood test data.Frontiers in oncology · 2023Article
- Diagnostic value of periostin in lung cancer-related malignant pleural effusion.Journal of clinical laboratory analysis · 2022Article
- A Convolutional Neural Network-Based Intelligent Medical System with Sensors for Assistive Diagnosis and Decision-Making in Non-Small Cell Lung Cancer.Sensors (Basel, Switzerland) · 2021Article
- Development of risk prediction models for lung cancer based on tumor markers and radiological signs.Journal of clinical laboratory analysis · 2021Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurate prediction of malignancy risk for pulmonary lesions with pleural effusion improves early diagnosis of lung cancer. This study aimed to develop and validate a model to predict lung cancer.
methodsClinical data of 536 patients with pulmonary diseases were collected. The risk factors were identified by regression analysis. Three prediction models were developed. The predictive performances of the models were measured by the area under the curves (AUCs) and calibrated with 1000 bootstrap samples to minimize the over-fitting bias. The net benefits of the models were evaluated by decision curve analysis. Finally, a separate cohort of 134 patients was used to validate the models externally.
resultsSeven independent risk factors were identified from 18 clinical variables, which included the pleural fluid carcinoembryonic antigen (CEA), serum cytokeratin-19 fragment (CYFRA 21-1), the ratio of CEA in the pleural fluid to serum, extrathoracic cancer history (>5 years), tumor size, vessel convergence, and lobulation. The AUCs of the three models were 0.976, 0.927, and 0.944 in the training set and 0.930, 0.845, and 0.944 in the external set, respectively. The accuracies of the three models were 89.6%, 81.4%, and 88.8%. Model 1 showed the best iteration fit (R
conclusionThe advantageous model could assess the risk of lung cancer in patients with pleural effusion and act as a useful tool for early identification of lung cancer.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.