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CT and CEA‐based machine learning model for predicting malignant pulmonary nodules
Computed tomography (CT), an efficient radiological technology, is used to detect lung cancer in the clinic. Carcinoembryonic antigen (CEA), a common tumor biomarker, is applied in the detection of various tumors. To highlight the advantages of two‐dimensional techniques and assist clinicians in opt...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9746043/ https://www.ncbi.nlm.nih.gov/pubmed/36056603 http://dx.doi.org/10.1111/cas.15561 |
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author | Liu, Man Zhou, Zhigang Liu, Fenghui Wang, Meng Wang, Yulin Gao, Mengyu Sun, Huifang Zhang, Xue Yang, Ting Ji, Longtao Li, Jiaqi Si, Qiufang Dai, Liping Ouyang, Songyun |
author_facet | Liu, Man Zhou, Zhigang Liu, Fenghui Wang, Meng Wang, Yulin Gao, Mengyu Sun, Huifang Zhang, Xue Yang, Ting Ji, Longtao Li, Jiaqi Si, Qiufang Dai, Liping Ouyang, Songyun |
author_sort | Liu, Man |
collection | PubMed |
description | Computed tomography (CT), an efficient radiological technology, is used to detect lung cancer in the clinic. Carcinoembryonic antigen (CEA), a common tumor biomarker, is applied in the detection of various tumors. To highlight the advantages of two‐dimensional techniques and assist clinicians in optimizing lung cancer diagnostic schemes, we established a favorable model combining CT and CEA. In the study, univariate analysis was performed to screen independent predictors in a training cohort of 271 patients with malignant pulmonary nodules (MPNs) and 92 with benign pulmonary nodules (BPNs). Six machine learning–based models involving five CT predictors (mediastinal lymph node enlargement, lobulation, vascular notch sign, spiculation, and nodule number) and lnCEA were constructed and validated in an independent cohort of 129 participants (92 MPNs and 37 BPNs) by SPSS Modeler. A nomogram and the Delong test were generated by R software. Finally, the model established by logistic regression had highest diagnostic efficiency (area under the curve [AUC] = 0.912). Moreover, the diagnostic ability of the logistic model in the validation cohort (AUC = 0.882, 80.4% sensitivity, 75.7% specificity) was higher than that of the Peking University model (AUC = 0.712, 68.5% sensitivity, 70.3% specificity) and the Mayo model (AUC = 0.745, 62.0% sensitivity, 75.7% specificity). Interestingly, for the participants with intermediate (10‐30 mm) and CEA‐negative nodule, the model reached an AUC of 0.835 (72.3% sensitivity, 83.3% specificity). The AUC for the early lung cancer was as high as 0.822 with 67.3% sensitivity and 78.9% specificity. As a conclusion, this promising model presents a new diagnostic strategy for the clinic to distinguish MPNs from BPNs. |
format | Online Article Text |
id | pubmed-9746043 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97460432022-12-14 CT and CEA‐based machine learning model for predicting malignant pulmonary nodules Liu, Man Zhou, Zhigang Liu, Fenghui Wang, Meng Wang, Yulin Gao, Mengyu Sun, Huifang Zhang, Xue Yang, Ting Ji, Longtao Li, Jiaqi Si, Qiufang Dai, Liping Ouyang, Songyun Cancer Sci ORIGINAL ARTICLES Computed tomography (CT), an efficient radiological technology, is used to detect lung cancer in the clinic. Carcinoembryonic antigen (CEA), a common tumor biomarker, is applied in the detection of various tumors. To highlight the advantages of two‐dimensional techniques and assist clinicians in optimizing lung cancer diagnostic schemes, we established a favorable model combining CT and CEA. In the study, univariate analysis was performed to screen independent predictors in a training cohort of 271 patients with malignant pulmonary nodules (MPNs) and 92 with benign pulmonary nodules (BPNs). Six machine learning–based models involving five CT predictors (mediastinal lymph node enlargement, lobulation, vascular notch sign, spiculation, and nodule number) and lnCEA were constructed and validated in an independent cohort of 129 participants (92 MPNs and 37 BPNs) by SPSS Modeler. A nomogram and the Delong test were generated by R software. Finally, the model established by logistic regression had highest diagnostic efficiency (area under the curve [AUC] = 0.912). Moreover, the diagnostic ability of the logistic model in the validation cohort (AUC = 0.882, 80.4% sensitivity, 75.7% specificity) was higher than that of the Peking University model (AUC = 0.712, 68.5% sensitivity, 70.3% specificity) and the Mayo model (AUC = 0.745, 62.0% sensitivity, 75.7% specificity). Interestingly, for the participants with intermediate (10‐30 mm) and CEA‐negative nodule, the model reached an AUC of 0.835 (72.3% sensitivity, 83.3% specificity). The AUC for the early lung cancer was as high as 0.822 with 67.3% sensitivity and 78.9% specificity. As a conclusion, this promising model presents a new diagnostic strategy for the clinic to distinguish MPNs from BPNs. John Wiley and Sons Inc. 2022-10-07 2022-12 /pmc/articles/PMC9746043/ /pubmed/36056603 http://dx.doi.org/10.1111/cas.15561 Text en © 2022 The Authors. Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. |
spellingShingle | ORIGINAL ARTICLES Liu, Man Zhou, Zhigang Liu, Fenghui Wang, Meng Wang, Yulin Gao, Mengyu Sun, Huifang Zhang, Xue Yang, Ting Ji, Longtao Li, Jiaqi Si, Qiufang Dai, Liping Ouyang, Songyun CT and CEA‐based machine learning model for predicting malignant pulmonary nodules |
title |
CT and CEA‐based machine learning model for predicting malignant pulmonary nodules |
title_full |
CT and CEA‐based machine learning model for predicting malignant pulmonary nodules |
title_fullStr |
CT and CEA‐based machine learning model for predicting malignant pulmonary nodules |
title_full_unstemmed |
CT and CEA‐based machine learning model for predicting malignant pulmonary nodules |
title_short |
CT and CEA‐based machine learning model for predicting malignant pulmonary nodules |
title_sort | ct and cea‐based machine learning model for predicting malignant pulmonary nodules |
topic | ORIGINAL ARTICLES |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9746043/ https://www.ncbi.nlm.nih.gov/pubmed/36056603 http://dx.doi.org/10.1111/cas.15561 |
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