Using a risk model for probability of cancer in pulmonary nodules. Issue 12 (11th May 2021)

• Record Type: Journal Article
• Authors: Liu, Si‐Qi; Ma, Xiao‐Bin; Song, Wan‐Mei; Li, Yi‐Fan; Li, Ning; Wang, Li‐Na; Liu, Jin‐Yue; Tao, Ning‐Ning; Li, Shi‐Jin; Xu, Ting‐Ting; Zhang, Qian‐Yun; An, Qi‐Qi; Liang, Bin; Li, Huai‐Chen
• Is Part Of: Thoracic cancer. Volume 12:Issue 12(2021)
• Page Start: 1881
• Page End: 1889
• Publication Date: 2021-05-11
• Subjects: decision tree -- logistics regression -- lung cancer -- pulmonary nodules; Chest -- Cancer -- Periodicals; Chest -- Cancer -- Treatment -- Periodicals; Chest -- Surgery -- Periodicals; 616.99494005
• DOI: 10.1111/1759-7714.13991
• Languages: English
• ISSNs: 1759-7706

Abstract

Abstract: Background: Considering 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. Methods: A 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. Results: The 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. Conclusions: Our 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. Abstract : Considering the high morbidity and mortality of lung cancer and the high incidence of pulmonary nodules, it is of great significance to clearly distinguish between benign and malignant lung nodules at early stages. However, what kind of lung nodules are more prone to lung cancer is still a worldwide problem. In this study, 458 cases with pulmonary nodules data were collected from Shandong, China. We assessed the clinical characteristics and low‐dose computed tomography (LDCT) imaging features among pulmonary nodules in patients who had undergone video‐assisted thoracoscopic surgery (VATS) lobectomy from 2013 to 2018 to determine the machine learning models using multiple algorithms and select the best decision tree model. Our 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.