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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
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.
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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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