Using a risk model for probability of cancer in pulmonary nodules.
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 r...
| Publicado en: | Thoracic Cancer Vol. 12; no. 12; pp. 1881 - 1890 |
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| Autores principales: | , , , , , , , , , , , , , |
| Formato: | algorithm research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Jun2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=150889103&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150889103 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17597706 BDCY jtl: Thoracic Cancer issn: 17597706 maglogo: Y pubinfo: dt: Jun2021 vid: 12 iid: 12 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 150889103 150224325 150889103 150889103 10.1111/1759-7714.13991 150889103 ppf: 1881 ppct: 9 formats: tig: atl: Using a risk model for probability of cancer in pulmonary nodules. aug: au: 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 affil: Department of Respiratory and Critical Care Medicine, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China sug: subj: Lung Pathology Lung Neoplasms Risk Factors Risk Assessment Methods Models, Theoretical Human China Radiography, Thoracic Tomography, X-Ray Computed Thoracic Surgery, Video-Assisted Pneumonectomy Methods Machine Learning Algorithms Decision Trees Early Detection of Cancer Cancer Screening ab: 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. pubtype: Academic Journal doctype: algorithm research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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