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A CT-based nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodule according to the 2021 WHO classification
PURPOSE: To establish a nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodules (SSNs) according to the 2021 WHO classification. METHODS: A total of 656 patients who underwent SSNs resection were retrospectively enrolled. Among them, 407 patients were assigned to the de...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9446567/ https://www.ncbi.nlm.nih.gov/pubmed/36064495 http://dx.doi.org/10.1186/s40644-022-00483-1 |
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author | Song, Qilong Song, Biao Li, Xiaohu Wang, Bin Li, Yuan Chen, Wu Wang, Zhaohua Wang, Xu Yu, Yongqiang Min, Xuhong Ma, Dongchun |
author_facet | Song, Qilong Song, Biao Li, Xiaohu Wang, Bin Li, Yuan Chen, Wu Wang, Zhaohua Wang, Xu Yu, Yongqiang Min, Xuhong Ma, Dongchun |
author_sort | Song, Qilong |
collection | PubMed |
description | PURPOSE: To establish a nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodules (SSNs) according to the 2021 WHO classification. METHODS: A total of 656 patients who underwent SSNs resection were retrospectively enrolled. Among them, 407 patients were assigned to the derivation cohort and 249 patients were assigned to the validation cohort. Univariate and multi-variate logistic regression algorithms were utilized to identity independent risk factors of adenocarcinomas. A nomogram based on the risk factors was generated to predict the risk of adenocarcinomas. The discrimination ability of the nomogram was evaluated using the concordance index (C-index), its performance was calibrated using a calibration curve, and its clinical significance was evaluated using decision curves and clinical impact curves. RESULTS: Lesion size, mean CT value, vascular change and lobulation were identified as independent risk factors for adenocarcinomas. The C-index of the nomogram was 0.867 (95% CI, 0.833-0.901) in derivation cohort and 0.877 (95% CI, 0.836-0.917) in validation cohort. The calibration curve showed good agreement between the predicted and actual risks. Analysis of the decision curves and clinical impact curves revealed that the nomogram had a high standardized net benefit. CONCLUSIONS: A nomogram for predicting the risk of adenocarcinomas in patients with SSNs was established in light of the 2021 WHO classification. The developed model can be adopted as a pre-operation tool to improve the surgical management of patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40644-022-00483-1. |
format | Online Article Text |
id | pubmed-9446567 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-94465672022-09-07 A CT-based nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodule according to the 2021 WHO classification Song, Qilong Song, Biao Li, Xiaohu Wang, Bin Li, Yuan Chen, Wu Wang, Zhaohua Wang, Xu Yu, Yongqiang Min, Xuhong Ma, Dongchun Cancer Imaging Research Article PURPOSE: To establish a nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodules (SSNs) according to the 2021 WHO classification. METHODS: A total of 656 patients who underwent SSNs resection were retrospectively enrolled. Among them, 407 patients were assigned to the derivation cohort and 249 patients were assigned to the validation cohort. Univariate and multi-variate logistic regression algorithms were utilized to identity independent risk factors of adenocarcinomas. A nomogram based on the risk factors was generated to predict the risk of adenocarcinomas. The discrimination ability of the nomogram was evaluated using the concordance index (C-index), its performance was calibrated using a calibration curve, and its clinical significance was evaluated using decision curves and clinical impact curves. RESULTS: Lesion size, mean CT value, vascular change and lobulation were identified as independent risk factors for adenocarcinomas. The C-index of the nomogram was 0.867 (95% CI, 0.833-0.901) in derivation cohort and 0.877 (95% CI, 0.836-0.917) in validation cohort. The calibration curve showed good agreement between the predicted and actual risks. Analysis of the decision curves and clinical impact curves revealed that the nomogram had a high standardized net benefit. CONCLUSIONS: A nomogram for predicting the risk of adenocarcinomas in patients with SSNs was established in light of the 2021 WHO classification. The developed model can be adopted as a pre-operation tool to improve the surgical management of patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40644-022-00483-1. BioMed Central 2022-09-05 /pmc/articles/PMC9446567/ /pubmed/36064495 http://dx.doi.org/10.1186/s40644-022-00483-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Song, Qilong Song, Biao Li, Xiaohu Wang, Bin Li, Yuan Chen, Wu Wang, Zhaohua Wang, Xu Yu, Yongqiang Min, Xuhong Ma, Dongchun A CT-based nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodule according to the 2021 WHO classification |
title | A CT-based nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodule according to the 2021 WHO classification |
title_full | A CT-based nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodule according to the 2021 WHO classification |
title_fullStr | A CT-based nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodule according to the 2021 WHO classification |
title_full_unstemmed | A CT-based nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodule according to the 2021 WHO classification |
title_short | A CT-based nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodule according to the 2021 WHO classification |
title_sort | ct-based nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodule according to the 2021 who classification |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9446567/ https://www.ncbi.nlm.nih.gov/pubmed/36064495 http://dx.doi.org/10.1186/s40644-022-00483-1 |
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