ArticlePloS one2013
Novel and convenient method to evaluate the character of solitary pulmonary nodule-comparison of three mathematical prediction models and further stratification of risk factors.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed, 38 citations in OpenAlex.
- Establishment and validation of multiclassification prediction models for pulmonary nodules based on machine learning.The clinical respiratory journal · 2024Article
- Nomogram combining clinical and radiological characteristics for predicting the malignant probability of solitary pulmonary nodules measuring ≤ 2 cm.Frontiers in oncology · 2023Article
- A machine learning-based PET/CT model for automatic diagnosis of early-stage lung cancer.Frontiers in oncology · 2023Article
- Predictors of malignancy in patients with solitary pulmonary nodules undergoing pulmonary resection.The clinical respiratory journal · 2022Article
- Comprehensive Analysis of Clinical Logistic and Machine Learning-Based Models for the Evaluation of Pulmonary Nodules.JTO clinical and research reports · 2022Article
- Clinical-radiological predictive model in differential diagnosis of small (≤ 20 mm) solitary pulmonary nodules.BMC pulmonary medicine · 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
- Evaluation of Prediction Models for Identifying Malignancy in Pulmonary Nodules Detected via Low-Dose Computed Tomography.JAMA network open · 2020Article
- Comparison of Veterans Affairs, Mayo, Brock classification models and radiologist diagnosis for classifying the malignancy of pulmonary nodules in Chinese clinical population.Translational lung cancer research · 2019Article
- Implementation planning for lung cancer screening in China.Precision clinical medicine · 2019Review
- [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
- Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions.Current epidemiology reports · 2017Article
- Assessment of the cancer risk factors of solitary pulmonary nodules.Oncotarget · 2017Article
- Malignancy risk estimation of pulmonary nodules in screening CTs: Comparison between a computer model and human observers.PloS one · 2017Article
- LUNGx Challenge for computerized lung nodule classification.Journal of medical imaging (Bellingham, Wash.) · 2016Article
- Solid pulmonary nodule risk assessment and decision analysis: comparison of four prediction models in 285 cases.European radiology · 2016Observational
- Establishment of a mathematic model for predicting malignancy in solitary pulmonary nodules.Journal of thoracic disease · 2015Article
- Lung nodules: A comprehensive review on current approach and management.Annals of thoracic medicineReview
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7 authors at 1 institution in 1 country.
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Abstract
objectiveTo study risk factors that affect the evaluation of malignancy in patients with solitary pulmonary nodules (SPN) and verify different predictive models for malignant probability of SPN.
methodsRetrospectively analyzed 107 cases of SPN with definite post-operative histological diagnosis whom underwent surgical procedures in China-Japan Friendship Hospital from November of 2010 to February of 2013. Age, gender, smoking history, malignancy history of patients, imaging features of the nodule including maximum diameter, position, spiculation, lobulation, calcification and serum level of CEA and Cyfra21-1 were assessed as potential risk factors. Univariate analysis model was used to establish statistical correlation between risk factors and post-operative histological diagnosis. Receiver operating characteristic (ROC) curves were drawn using different predictive models for malignant probability of SPN to get areas under the curves (AUC values), sensitivity, specificity, positive predictive values, negative predictive values for each model, respectively. The predictive effectiveness of each model was statistically assessed subsequently.
resultsIn 107 patients, 78 cases were malignant (72.9%), 29 cases were benign (27.1%). Statistical significant difference was found between benign and malignant group in age, maximum diameter, serum level of Cyfra21-1, spiculation, lobulation and calcification of the nodules. The AUC values were 0.786±0.053 (Mayo model), 0.682±0.060 (VA model) and 0.810±0.051 (Peking University People's Hospital model), respectively.
conclusionsSerum level of Cyfra21-1, patient's age, maximum diameter of the nodule, spiculation, lobulation and calcification of the nodule are independent risk factors associated with the malignant probability of SPN. Peking University People's Hospital model is of high accuracy and clinical value for patients with SPN. Adding serum index (e.g. Cyfra21-1) into the prediction models as a new risk factor and adjusting the weight of age in the models might improve the accuracy of prediction for SPN.
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