ArticleThoracic cancer2021
Using a risk model for probability of cancer in pulmonary nodules.
Article in Thoracic cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Exosomal 3'tiRNA-PheGAA and interleukin-6 as novel biomarkers for non-invasive detection of NSCLC.Discover oncology · 2026Article
- Risk factors for lung parenchyma hemorrhage and hemoptysis during computed tomography-guided microwave ablation in patients with stage I non-small cell lung cancer: A bicentric retrospective study.Thoracic cancer · 2024Article
- Establishing and validation of the VBV score for assessing Lung ground-glass nodules based on high-resolution computed tomography.Journal of cardiothoracic surgery · 2024Article
- Rethinking the prognosis model of differentiated thyroid carcinoma.Frontiers in endocrinology · 2024Article
- Clinical and Computed Tomography Characteristics of Solitary Pulmonary Nodules Caused by Fungi: A Comparative Study.Infection and drug resistance · 2022Article
- Using a risk model for probability of cancer in pulmonary nodules.Thoracic cancer · 2021Article
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Authors and funding
14 authors.
Funding
Abstract
backgroundConsidering the high morbidity and mortality of lung cancer and the high incidence of pulmonary nodules, clearly distinguishing benign from malignant lung nodules at an early stage is of great significance. However, determining the kind of lung nodule which is more prone to lung cancer remains a problem worldwide.
methodsA total of 480 patients with pulmonary nodule data were collected from Shandong, China. We assessed the clinical characteristics and computed tomography (CT) imaging features among pulmonary nodules in patients who had undergone video-assisted thoracoscopic surgery (VATS) lobectomy from 2013 to 2018. Preliminary selection of features was based on a statistical analysis using SPSS. We used WEKA to assess the machine learning models using its multiple algorithms and selected the best decision tree model using its optimization algorithm.
resultsThe combination of decision tree and logistics regression optimized the decision tree without affecting its AUC. The decision tree structure showed that lobulation was the most important feature, followed by spiculation, vessel convergence sign, nodule type, satellite nodule, nodule size and age of patient.
conclusionsOur study shows that decision tree analyses can be applied to screen individuals for early lung cancer with CT. Our decision tree provides a new way to help clinicians establish a logical diagnosis by a stepwise progression method, but still needs to be validated for prospective trials in a larger patient population.
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