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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons Australia, Ltd 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8201526/
https://www.ncbi.nlm.nih.gov/pubmed/33973725
http://dx.doi.org/10.1111/1759-7714.13991
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author 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
author_facet 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
author_sort Liu, Si‐Qi
collection PubMed
description 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.
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spelling pubmed-82015262021-06-16 Using a risk model for probability of cancer in pulmonary nodules 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 Thorac Cancer Original Articles 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. John Wiley & Sons Australia, Ltd 2021-05-11 2021-06 /pmc/articles/PMC8201526/ /pubmed/33973725 http://dx.doi.org/10.1111/1759-7714.13991 Text en © 2021 The Authors. Thoracic Cancer published by China Lung Oncology Group and John Wiley & Sons Australia, Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Articles
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
Using a risk model for probability of cancer in pulmonary nodules
title Using a risk model for probability of cancer in pulmonary nodules
title_full Using a risk model for probability of cancer in pulmonary nodules
title_fullStr Using a risk model for probability of cancer in pulmonary nodules
title_full_unstemmed Using a risk model for probability of cancer in pulmonary nodules
title_short Using a risk model for probability of cancer in pulmonary nodules
title_sort using a risk model for probability of cancer in pulmonary nodules
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8201526/
https://www.ncbi.nlm.nih.gov/pubmed/33973725
http://dx.doi.org/10.1111/1759-7714.13991
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