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Clinical diagnosis model of spinal meningiomas based on the surveillance, epidemiology, and end results database
Most spinal meningiomas (SM) are benign lesions of the thoracic spine and are usually treated surgically. This study aimed to explore treatment strategies and construct a nomogram for SM. Data on patients with SM from 2000 to 2019 were extracted from the Surveillance, Epidemiology, and End Results d...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9971498/ https://www.ncbi.nlm.nih.gov/pubmed/36865629 http://dx.doi.org/10.3389/fsurg.2023.1008605 |
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author | Jiang, Yong’An Chen, Peng Liang, JiaWei Long, XiaoYan Cai, JiaHong Zhang, Yi Cheng, ShiQi Zhang, Yan |
author_facet | Jiang, Yong’An Chen, Peng Liang, JiaWei Long, XiaoYan Cai, JiaHong Zhang, Yi Cheng, ShiQi Zhang, Yan |
author_sort | Jiang, Yong’An |
collection | PubMed |
description | Most spinal meningiomas (SM) are benign lesions of the thoracic spine and are usually treated surgically. This study aimed to explore treatment strategies and construct a nomogram for SM. Data on patients with SM from 2000 to 2019 were extracted from the Surveillance, Epidemiology, and End Results database. First, the distributional properties and characteristics of the patients were descriptively evaluated, and the patients were randomly divided into training and testing groups in a 6:4 ratio. Least absolute shrinkage and selection operator (LASSO) regression was used to screen the survival predictors. Kaplan–Meier curves explained survival probability by different variables. The nomogram was constructed based on the results of LASSO regression. The predictive power of the nomogram was identified using the concordance index, time-receiver operating characteristics, decision curve analysis, and calibration curves. We recruited 1,148 patients with SM. LASSO results for the training group showed that sex (coefficient, 0.004), age (coefficient, 0.034), surgery (coefficient, −0.474), tumor size (coefficient, 0.008), and marital status (coefficient, 0.335) were prognostic factors. The nomogram prognostic model showed good diagnostic ability in both the training and testing groups, with a C-index of 0.726, 95% (0.679, 0.773); 0.827, 95% (0.777, 0.877). The calibration and decision curves suggested that the prognostic model had better diagnostic performance and good clinical benefit. In the training and testing groups, the time-receiver operating characteristic curve showed that SM had moderate diagnostic ability at different times, and the survival rate of the high-risk group was significantly lower than that of the low-risk group (training group: p = 0.0071; testing group: p = 0.00013). Our nomogram prognostic model may have a crucial role in predicting the six-month, one-year, and two-year survival outcomes of patients with SM and may be useful for surgical clinicians to formulate treatment plans. |
format | Online Article Text |
id | pubmed-9971498 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-99714982023-03-01 Clinical diagnosis model of spinal meningiomas based on the surveillance, epidemiology, and end results database Jiang, Yong’An Chen, Peng Liang, JiaWei Long, XiaoYan Cai, JiaHong Zhang, Yi Cheng, ShiQi Zhang, Yan Front Surg Surgery Most spinal meningiomas (SM) are benign lesions of the thoracic spine and are usually treated surgically. This study aimed to explore treatment strategies and construct a nomogram for SM. Data on patients with SM from 2000 to 2019 were extracted from the Surveillance, Epidemiology, and End Results database. First, the distributional properties and characteristics of the patients were descriptively evaluated, and the patients were randomly divided into training and testing groups in a 6:4 ratio. Least absolute shrinkage and selection operator (LASSO) regression was used to screen the survival predictors. Kaplan–Meier curves explained survival probability by different variables. The nomogram was constructed based on the results of LASSO regression. The predictive power of the nomogram was identified using the concordance index, time-receiver operating characteristics, decision curve analysis, and calibration curves. We recruited 1,148 patients with SM. LASSO results for the training group showed that sex (coefficient, 0.004), age (coefficient, 0.034), surgery (coefficient, −0.474), tumor size (coefficient, 0.008), and marital status (coefficient, 0.335) were prognostic factors. The nomogram prognostic model showed good diagnostic ability in both the training and testing groups, with a C-index of 0.726, 95% (0.679, 0.773); 0.827, 95% (0.777, 0.877). The calibration and decision curves suggested that the prognostic model had better diagnostic performance and good clinical benefit. In the training and testing groups, the time-receiver operating characteristic curve showed that SM had moderate diagnostic ability at different times, and the survival rate of the high-risk group was significantly lower than that of the low-risk group (training group: p = 0.0071; testing group: p = 0.00013). Our nomogram prognostic model may have a crucial role in predicting the six-month, one-year, and two-year survival outcomes of patients with SM and may be useful for surgical clinicians to formulate treatment plans. Frontiers Media S.A. 2023-02-14 /pmc/articles/PMC9971498/ /pubmed/36865629 http://dx.doi.org/10.3389/fsurg.2023.1008605 Text en © 2023 Jiang, Chen, Liang, Cai, Zhang, Cheng and Zhang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Surgery Jiang, Yong’An Chen, Peng Liang, JiaWei Long, XiaoYan Cai, JiaHong Zhang, Yi Cheng, ShiQi Zhang, Yan Clinical diagnosis model of spinal meningiomas based on the surveillance, epidemiology, and end results database |
title | Clinical diagnosis model of spinal meningiomas based on the surveillance, epidemiology, and end results database |
title_full | Clinical diagnosis model of spinal meningiomas based on the surveillance, epidemiology, and end results database |
title_fullStr | Clinical diagnosis model of spinal meningiomas based on the surveillance, epidemiology, and end results database |
title_full_unstemmed | Clinical diagnosis model of spinal meningiomas based on the surveillance, epidemiology, and end results database |
title_short | Clinical diagnosis model of spinal meningiomas based on the surveillance, epidemiology, and end results database |
title_sort | clinical diagnosis model of spinal meningiomas based on the surveillance, epidemiology, and end results database |
topic | Surgery |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9971498/ https://www.ncbi.nlm.nih.gov/pubmed/36865629 http://dx.doi.org/10.3389/fsurg.2023.1008605 |
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