ArticleJournal of thoracic disease2015
Establishment of a mathematic model for predicting malignancy in solitary pulmonary nodules.
Article in Journal of thoracic disease, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 4 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 4 syntheses or guidelines pooled it, 33 citations in OpenAlex.
- Incidence and risk factors for malignancy in patients with incidental solitary pulmonary nodules: a systematic review and meta-analysis.Annals of medicine · 2026Pooled it
- Predictive model for the probability of malignancy in solitary pulmonary nodules: a meta-analysis.Journal of cardiothoracic surgery · 2022Pooled it
- Lung cancer risk prediction models based on pulmonary nodules: A systematic review.Thoracic cancer · 2022Pooled it
- Solitary pulmonary nodule malignancy predictive models applicable to routine clinical practice: a systematic review.Systematic reviews · 2021Pooled it
- Could the probability of surgical indication be determined after first episode of primary spontaneous pneumothorax?General thoracic and cardiovascular surgery · 2023Trial
- Establishment and validation of multiclassification prediction models for pulmonary nodules based on machine learning.The clinical respiratory journal · 2024Article
- LIONS PREY: A New Logistic Scoring System for the Prediction of Malignant Pulmonary Nodules.Cancers · 2024Article
- A strategy to reduce the false-positive rate after low-dose computed tomography in lung cancer screening: A multicenter prospective cohort study.Cancer medicine · 2023Article
- Nomogram combining clinical and radiological characteristics for predicting the malignant probability of solitary pulmonary nodules measuring ≤ 2 cm.Frontiers in oncology · 2023Article
- Comprehensive Analysis of Clinical Logistic and Machine Learning-Based Models for the Evaluation of Pulmonary Nodules.JTO clinical and research reports · 2022Article
- [Advances and Clinical Application of Malignant Probability Prediction Models for Solitary Pulmonary Nodule].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2021Review
- Integration of PET/CT Radiomics and Semantic Features for Differentiation between Active Pulmonary Tuberculosis and Lung Cancer.Molecular imaging and biology · 2021Article
- Development of risk prediction models for lung cancer based on tumor markers and radiological signs.Journal of clinical laboratory analysis · 2021Article
- Establishment and validation of a mathematical diagnosis model to distinguish benign pulmonary nodules from early non-small cell lung cancer in Chinese people.Translational lung cancer research · 2020Article
- Observational
- [Establishment and Verification of A Novel Predictive Model of Malignancy for Non-solid Pulmonary Nodules].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2019Article
- Comparison Between Radiological Semantic Features and Lung-RADS in Predicting Malignancy of Screen-Detected Lung Nodules in the National Lung Screening Trial.Clinical lung cancer · 2018Article
- Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions.Current epidemiology reports · 2017Article
- Differential diagnosis of solitary pulmonary nodules with dual-source spiral computed tomography.Experimental and therapeutic medicine · 2016Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe aim of this study was to establish a model for predicting the probability of malignancy in solitary pulmonary nodules (SPNs) and provide guidance for the diagnosis and follow-up intervention of SPNs.
methodsWe retrospectively analyzed the clinical data and computed tomography (CT) images of 294 patients with a clear pathological diagnosis of SPN. Multivariate logistic regression analysis was used to screen independent predictors of the probability of malignancy in the SPN and to establish a model for predicting malignancy in SPNs. Then, another 120 SPN patients who did not participate in the model establishment were chosen as group B and used to verify the accuracy of the prediction model.
resultsMultivariate logistic regression analysis showed that there were significant differences in age, smoking history, maximum diameter of nodules, spiculation, clear borders, and Cyfra21-1 levels between subgroups with benign and malignant SPNs (P<0.05). These factors were identified as independent predictors of malignancy in SPNs. The area under the curve (AUC) was 0.910 [95% confidence interval (CI), 0.857-0.963] in model with Cyfra21-1 significantly better than 0.812 (95% CI, 0.763-0.861) in model without Cyfra21-1 (P=0.008). The area under receiver operating characteristic (ROC) curve of our model is significantly higher than the Mayo model, VA model and Peking University People's (PKUPH) model. Our model (AUC =0.910) compared with Brock model (AUC =0.878, P=0.350), the difference was not statistically significant.
conclusionsThe model added Cyfra21-1 could improve prediction. The prediction model established in this study can be used to assess the probability of malignancy in SPNs, thereby providing help for the diagnosis of SPNs and the selection of follow-up interventions.
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