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Identification of prognostic factors for predicting survival of patients with malignant adrenal tumors: A population-based study

BACKGROUND: This study aimed to identify the prognostic factors for overall survival (OS) and cancer-specific survival (CSS) in patients with malignant adrenal tumors and establish a predictive nomogram for patient survival. METHODS: The clinical characteristics of patients diagnosed with malignant...

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Autores principales: Wang, Keyi, Zhang, Tao, Ni, Jinliang, Chen, Jianghong, Zhang, Houliang, Wang, Guangchun, Gu, Yongzhe, Peng, Bo, Mao, Weipu, Wu, Jianping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9619049/
https://www.ncbi.nlm.nih.gov/pubmed/36324596
http://dx.doi.org/10.3389/fonc.2022.930473
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author Wang, Keyi
Zhang, Tao
Ni, Jinliang
Chen, Jianghong
Zhang, Houliang
Wang, Guangchun
Gu, Yongzhe
Peng, Bo
Mao, Weipu
Wu, Jianping
author_facet Wang, Keyi
Zhang, Tao
Ni, Jinliang
Chen, Jianghong
Zhang, Houliang
Wang, Guangchun
Gu, Yongzhe
Peng, Bo
Mao, Weipu
Wu, Jianping
author_sort Wang, Keyi
collection PubMed
description BACKGROUND: This study aimed to identify the prognostic factors for overall survival (OS) and cancer-specific survival (CSS) in patients with malignant adrenal tumors and establish a predictive nomogram for patient survival. METHODS: The clinical characteristics of patients diagnosed with malignant adrenal tumors between 1988 and 2015 were retrieved from the Surveillance, Epidemiology and End Results (SEER) database. As the external validation set, we included 110 real-world patients from our medical centers. Univariate and multivariate Cox regressions were implemented to determine the prognostic factors of patients. The results from Cox regression were applied to establish the nomogram. RESULTS: A total of 2,206 eligible patients were included in our study. Patients were randomly assigned to the training set (1,544; 70%) and the validation set (662; 30%). It was determined that gender, age, marital status, histological type, tumor size, SEER stage, surgery, and chemotherapy were prognostic factors that affected patient survival. The OS prediction nomogram contained all the risk factors, while gender was excluded in the CSS prediction nomogram. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) indicated that the nomogram had a better predictive performance than SEER stage. Moreover, the clinical impact curve (CIC) showed that the nomograms functioned as effective predictive models in clinical application. The C-index of nomogram for OS and CSS prediction was 0.773 (95% confidence interval [CI]: 0.761–0.785) and 0.689 (95% CI: 0.675–0.703) in the training set. The calibration curves exhibited significant agreement between the nomogram and actual observation. Additionally, the results from the external validation set also presented that established nomograms functioned well in predicting the survival of patients with malignant adrenal tumors. CONCLUSIONS: The following clinical variables were identified as prognostic factors: age, marital status, histological type, tumor size, SEER stage, surgery, and chemotherapy. The nomogram for patients with malignant adrenal tumors contained the accurate predictive performance of OS and CSS, contributing to optimizing individualized clinical treatments.
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spelling pubmed-96190492022-11-01 Identification of prognostic factors for predicting survival of patients with malignant adrenal tumors: A population-based study Wang, Keyi Zhang, Tao Ni, Jinliang Chen, Jianghong Zhang, Houliang Wang, Guangchun Gu, Yongzhe Peng, Bo Mao, Weipu Wu, Jianping Front Oncol Oncology BACKGROUND: This study aimed to identify the prognostic factors for overall survival (OS) and cancer-specific survival (CSS) in patients with malignant adrenal tumors and establish a predictive nomogram for patient survival. METHODS: The clinical characteristics of patients diagnosed with malignant adrenal tumors between 1988 and 2015 were retrieved from the Surveillance, Epidemiology and End Results (SEER) database. As the external validation set, we included 110 real-world patients from our medical centers. Univariate and multivariate Cox regressions were implemented to determine the prognostic factors of patients. The results from Cox regression were applied to establish the nomogram. RESULTS: A total of 2,206 eligible patients were included in our study. Patients were randomly assigned to the training set (1,544; 70%) and the validation set (662; 30%). It was determined that gender, age, marital status, histological type, tumor size, SEER stage, surgery, and chemotherapy were prognostic factors that affected patient survival. The OS prediction nomogram contained all the risk factors, while gender was excluded in the CSS prediction nomogram. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) indicated that the nomogram had a better predictive performance than SEER stage. Moreover, the clinical impact curve (CIC) showed that the nomograms functioned as effective predictive models in clinical application. The C-index of nomogram for OS and CSS prediction was 0.773 (95% confidence interval [CI]: 0.761–0.785) and 0.689 (95% CI: 0.675–0.703) in the training set. The calibration curves exhibited significant agreement between the nomogram and actual observation. Additionally, the results from the external validation set also presented that established nomograms functioned well in predicting the survival of patients with malignant adrenal tumors. CONCLUSIONS: The following clinical variables were identified as prognostic factors: age, marital status, histological type, tumor size, SEER stage, surgery, and chemotherapy. The nomogram for patients with malignant adrenal tumors contained the accurate predictive performance of OS and CSS, contributing to optimizing individualized clinical treatments. Frontiers Media S.A. 2022-10-17 /pmc/articles/PMC9619049/ /pubmed/36324596 http://dx.doi.org/10.3389/fonc.2022.930473 Text en Copyright © 2022 Wang, Zhang, Ni, Chen, Zhang, Wang, Gu, Peng, Mao and Wu 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). 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 Oncology
Wang, Keyi
Zhang, Tao
Ni, Jinliang
Chen, Jianghong
Zhang, Houliang
Wang, Guangchun
Gu, Yongzhe
Peng, Bo
Mao, Weipu
Wu, Jianping
Identification of prognostic factors for predicting survival of patients with malignant adrenal tumors: A population-based study
title Identification of prognostic factors for predicting survival of patients with malignant adrenal tumors: A population-based study
title_full Identification of prognostic factors for predicting survival of patients with malignant adrenal tumors: A population-based study
title_fullStr Identification of prognostic factors for predicting survival of patients with malignant adrenal tumors: A population-based study
title_full_unstemmed Identification of prognostic factors for predicting survival of patients with malignant adrenal tumors: A population-based study
title_short Identification of prognostic factors for predicting survival of patients with malignant adrenal tumors: A population-based study
title_sort identification of prognostic factors for predicting survival of patients with malignant adrenal tumors: a population-based study
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9619049/
https://www.ncbi.nlm.nih.gov/pubmed/36324596
http://dx.doi.org/10.3389/fonc.2022.930473
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