Wang Keyi, Zhang Tao, Ni Jinliang, Chen Jianghong, Zhang Houliang, Wang Guangchun, Gu Yongzhe, Peng Bo, Mao Weipu, Wu Jianping
Department of Urology, Putuo People's Hospital, Tongji University School of Medicine, Shanghai, China.
Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Front Oncol. 2022 Oct 17;12:930473. doi: 10.3389/fonc.2022.930473. eCollection 2022.
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.
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.
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.
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.
本研究旨在确定恶性肾上腺肿瘤患者总生存期（OS）和癌症特异性生存期（CSS）的预后因素，并建立患者生存的预测列线图。
从监测、流行病学和最终结果（SEER）数据库中检索1988年至2015年期间诊断为恶性肾上腺肿瘤患者的临床特征。作为外部验证集，我们纳入了来自我们医疗中心的110例真实世界患者。采用单因素和多因素Cox回归分析来确定患者的预后因素。将Cox回归分析结果应用于建立列线图。
本研究共纳入2206例符合条件的患者。患者被随机分为训练集（1544例；70%）和验证集（662例；（30%）。确定性别、年龄、婚姻状况、组织学类型、肿瘤大小、SEER分期、手术和化疗是影响患者生存的预后因素。OS预测列线图包含所有危险因素，而CSS预测列线图排除了性别因素。受试者工作特征（ROC）曲线和决策曲线分析（DCA）表明，列线图的预测性能优于SEER分期。此外，临床影响曲线（CIC）表明，列线图在临床应用中是有效的预测模型。训练集中OS和CSS预测列线图的C指数分别为0.773（95%置信区间[CI]：0.761-0.785）和0.689（95%CI：0.675-0.703）。校准曲线显示列线图与实际观察结果之间具有显著一致性。此外，外部验证集的结果也表明，建立的列线图在预测恶性肾上腺肿瘤患者的生存方面表现良好。
确定以下临床变量为预后因素：年龄、婚姻状况、组织学类型、肿瘤大小、SEER分期、手术和化疗。恶性肾上腺肿瘤患者的列线图对OS和CSS具有准确的预测性能，有助于优化个体化临床治疗。
