Evidence map›Paper›PMID 36324596›Full record

ArticleFrontiers in oncology2022

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

Keyi Wang, Tao Zhang, Jinliang Ni, Jianghong Chen, Houliang Zhang, Guangchun Wang, Yongzhe Gu, Bo Peng, Weipu Mao, Jianping Wu

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Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Keyi WangDepartment of Urology, Putuo People's Hospital, Tongji University School of Medicine, Shanghai, China.
Tao ZhangDepartment of Urology, Putuo People's Hospital, Tongji University School of Medicine, Shanghai, China.
Jinliang NiShanghai Clinical College, Anhui Medical University, Hefei, China.
Jianghong ChenDepartment of Surgery, Traditional Chinese Medicine Hospital of Jiulongpo District, Chongqing, China.
Houliang ZhangDepartment of Urology, Putuo People's Hospital, Tongji University School of Medicine, Shanghai, China.
Guangchun WangDepartment of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Yongzhe GuDepartment of Neurology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Bo PengDepartment of Urology, Putuo People's Hospital, Tongji University School of Medicine, Shanghai, China.
Weipu MaoDepartment of Urology, Putuo People's Hospital, Tongji University School of Medicine, Shanghai, China.
Jianping WuDepartment of Urology, Affiliated Zhongda Hospital of Southeast University, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

malignant adrenal tumorsnomogramsprognostic factorsSEER programsurvival

Identifiers

PMID36324596
PMCPMC9619049

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