Identification of non-small cell lung cancer with chronic obstructive pulmonary disease using clinical symptoms and routine examination: a retrospective study
BackgroundPatients with non-small cell lung cancer (NSCLC) and patients with NSCLC combined with chronic obstructive pulmonary disease (COPD) have similar physiological conditions in early stages, and the latter have shorter survival times and higher mortality rates. The purpose of this study was to...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-07-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.1158948/full |
| _version_ | 1827891162593099776 |
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| author | Bing Zhuan Bing Zhuan Hong-Hong Ma Hong-Hong Ma Bo-Chao Zhang Bo-Chao Zhang Ping Li Ping Li Xi Wang Qun Yuan Zhao Yang Jun Xie |
| author_facet | Bing Zhuan Bing Zhuan Hong-Hong Ma Hong-Hong Ma Bo-Chao Zhang Bo-Chao Zhang Ping Li Ping Li Xi Wang Qun Yuan Zhao Yang Jun Xie |
| author_sort | Bing Zhuan |
| collection | DOAJ |
| description | BackgroundPatients with non-small cell lung cancer (NSCLC) and patients with NSCLC combined with chronic obstructive pulmonary disease (COPD) have similar physiological conditions in early stages, and the latter have shorter survival times and higher mortality rates. The purpose of this study was to develop and compare machine learning models to identify future diagnoses of COPD combined with NSCLC patients based on the patient’s disease and routine clinical data.MethodsData were obtained from 237 patients with COPD combined with NSCLC as well as NSCLC admitted to Ningxia Hui Autonomous Region People’s Hospital from October 2013 to July 2022. Six machine learning algorithms (K-nearest neighbor, logistic regression, eXtreme gradient boosting, support vector machine, naïve Bayes, and artificial neural network) were used to develop prediction models for NSCLC combined with COPD. Sensitivity, specificity, positive predictive value, negative predictive value, accuracy, F1 score, Mathews correlation coefficient (MCC), Kappa, area under the receiver operating characteristic curve (AUROC)and area under the precision-recall curve (AUPRC) were used as performance indicators to evaluate the performance of the models.Results135 patients with NSCLC combined with COPD, 102 patients with NSCLC were included in the study. The results showed that pulmonary function and emphysema were important risk factors and that the support vector machine-based identification model showed optimal performance with accuracy:0.946, recall:0.940, specificity:0.955, precision:0.972, npv:0.920, F1 score:0.954, MCC:0.893, Kappa:0.888, AUROC:0.975, AUPRC:0.987.ConclusionThe use of machine learning tools combining clinical symptoms and routine examination data features is suitable for identifying the risk of concurrent NSCLC in COPD patients. |
| first_indexed | 2024-03-12T21:21:51Z |
| format | Article |
| id | doaj.art-76edb272366a4ccea689853cb6f297cd |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-03-12T21:21:51Z |
| publishDate | 2023-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-76edb272366a4ccea689853cb6f297cd2023-07-28T17:05:03ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-07-011310.3389/fonc.2023.11589481158948Identification of non-small cell lung cancer with chronic obstructive pulmonary disease using clinical symptoms and routine examination: a retrospective studyBing Zhuan0Bing Zhuan1Hong-Hong Ma2Hong-Hong Ma3Bo-Chao Zhang4Bo-Chao Zhang5Ping Li6Ping Li7Xi Wang8Qun Yuan9Zhao Yang10Jun Xie11Department of Respiratory Medicine, Ningxia Hui Autonomous Region People’s Hospital, Yinchuan, Ningxia, ChinaDepartment of Respiratory Medicine, Ningxia Hui Autonomous Region People’s Hospital Affiliated to Ningxia Medical University, Yinchuan, Ningxia, ChinaDepartment of Respiratory Medicine, Ningxia Hui Autonomous Region People’s Hospital, Yinchuan, Ningxia, ChinaDepartment of Respiratory Medicine, Ningxia Hui Autonomous Region People’s Hospital Affiliated to Ningxia Medical University, Yinchuan, Ningxia, ChinaSchool of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Suzhou, Jiangsu, ChinaSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, ChinaDepartment of Respiratory Medicine, Ningxia Hui Autonomous Region People’s Hospital, Yinchuan, Ningxia, ChinaDepartment of Respiratory Medicine, Ningxia Hui Autonomous Region People’s Hospital Affiliated to Ningxia Medical University, Yinchuan, Ningxia, ChinaDepartment of Respiratory Medicine, Affiliated Suzhou Science and Technology Town Hospital of Nanjing Medical University, Suzhou, Jiangsu, ChinaDepartment of Respiratory Medicine, Affiliated Suzhou Science and Technology Town Hospital of Nanjing Medical University, Suzhou, Jiangsu, ChinaDepartment of Respiratory Medicine, Affiliated Suzhou Science and Technology Town Hospital of Nanjing Medical University, Suzhou, Jiangsu, ChinaDepartment of Thoracic Surgery, Affiliated Suzhou Science and Technology Town Hospital of Nanjing Medical University, Suzhou, Jiangsu, ChinaBackgroundPatients with non-small cell lung cancer (NSCLC) and patients with NSCLC combined with chronic obstructive pulmonary disease (COPD) have similar physiological conditions in early stages, and the latter have shorter survival times and higher mortality rates. The purpose of this study was to develop and compare machine learning models to identify future diagnoses of COPD combined with NSCLC patients based on the patient’s disease and routine clinical data.MethodsData were obtained from 237 patients with COPD combined with NSCLC as well as NSCLC admitted to Ningxia Hui Autonomous Region People’s Hospital from October 2013 to July 2022. Six machine learning algorithms (K-nearest neighbor, logistic regression, eXtreme gradient boosting, support vector machine, naïve Bayes, and artificial neural network) were used to develop prediction models for NSCLC combined with COPD. Sensitivity, specificity, positive predictive value, negative predictive value, accuracy, F1 score, Mathews correlation coefficient (MCC), Kappa, area under the receiver operating characteristic curve (AUROC)and area under the precision-recall curve (AUPRC) were used as performance indicators to evaluate the performance of the models.Results135 patients with NSCLC combined with COPD, 102 patients with NSCLC were included in the study. The results showed that pulmonary function and emphysema were important risk factors and that the support vector machine-based identification model showed optimal performance with accuracy:0.946, recall:0.940, specificity:0.955, precision:0.972, npv:0.920, F1 score:0.954, MCC:0.893, Kappa:0.888, AUROC:0.975, AUPRC:0.987.ConclusionThe use of machine learning tools combining clinical symptoms and routine examination data features is suitable for identifying the risk of concurrent NSCLC in COPD patients.https://www.frontiersin.org/articles/10.3389/fonc.2023.1158948/fullNSCLCCOPDmachine learningidentificationdetectionpulmonary function |
| spellingShingle | Bing Zhuan Bing Zhuan Hong-Hong Ma Hong-Hong Ma Bo-Chao Zhang Bo-Chao Zhang Ping Li Ping Li Xi Wang Qun Yuan Zhao Yang Jun Xie Identification of non-small cell lung cancer with chronic obstructive pulmonary disease using clinical symptoms and routine examination: a retrospective study Frontiers in Oncology NSCLC COPD machine learning identification detection pulmonary function |
| title | Identification of non-small cell lung cancer with chronic obstructive pulmonary disease using clinical symptoms and routine examination: a retrospective study |
| title_full | Identification of non-small cell lung cancer with chronic obstructive pulmonary disease using clinical symptoms and routine examination: a retrospective study |
| title_fullStr | Identification of non-small cell lung cancer with chronic obstructive pulmonary disease using clinical symptoms and routine examination: a retrospective study |
| title_full_unstemmed | Identification of non-small cell lung cancer with chronic obstructive pulmonary disease using clinical symptoms and routine examination: a retrospective study |
| title_short | Identification of non-small cell lung cancer with chronic obstructive pulmonary disease using clinical symptoms and routine examination: a retrospective study |
| title_sort | identification of non small cell lung cancer with chronic obstructive pulmonary disease using clinical symptoms and routine examination a retrospective study |
| topic | NSCLC COPD machine learning identification detection pulmonary function |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2023.1158948/full |
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