Li Yan, Bian Xiaohui, Ouyang Junyu, Wei Shuyi, He Meizhi, Luo Zelong
Department of Oncology, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong, China,
The Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, China.
Cancer Manag Res. 2018 Dec 13;10:6949-6959. doi: 10.2147/CMAR.S187169. eCollection 2018.
To develop nomogram models to predict individualized estimates of overall survival (OS) and cancer-specific survival (CSS) in patients with adrenocortical carcinoma (ACC).
A total of 751 patients with ACC were identified within the Surveillance Epidemiology, and End Results (SEER) database between 1973 and 2015. The predictors comprised marital status, sex, age at diagnosis, year of diagnosis, laterality, histologic grade, ethnicity, historic stage, radiation therapy, chemotherapy, and surgery of primary site. Based on the results of the multivariate logistic regression analyses, the nomogram models were used for predicting OS and CSS in patients with ACC. The nomograms were tested using concordance index (C-index) and calibration curves.
In univariate and multivariate analyses of OS, OS was significantly associated with age at diagnosis, year of diagnosis, histologic grade, historic stage, and chemotherapy. In univariate and multivariate analyses of CSS, age at diagnosis, year of diagnosis, historic stage, and chemotherapy were the independent risk factors with CSS. These characteristics were included in the nomograms predicting OS and CSS. The nomograms demonstrated good accuracy in predicting OS and CSS, with the C-index of 0.677 and 0.672.
These clinically useful tools predicted OS and CSS in patients with ACC using readily available clinicopathologic factors and could aid individualized clinical decision making.
建立列线图模型以预测肾上腺皮质癌(ACC)患者的总生存期(OS)和癌症特异性生存期(CSS)的个体估计值。
在监测、流行病学和最终结果(SEER)数据库中,识别出1973年至2015年间的751例ACC患者。预测因素包括婚姻状况、性别、诊断时年龄、诊断年份、肿瘤位置、组织学分级、种族、历史分期、放射治疗、化疗以及原发部位手术情况。基于多因素逻辑回归分析结果,列线图模型用于预测ACC患者的OS和CSS。使用一致性指数(C指数)和校准曲线对列线图进行检验。
在OS的单因素和多因素分析中,OS与诊断时年龄、诊断年份、组织学分级、历史分期和化疗显著相关。在CSS的单因素和多因素分析中,诊断时年龄、诊断年份、历史分期和化疗是CSS的独立危险因素。这些特征被纳入预测OS和CSS的列线图中。列线图在预测OS和CSS方面显示出良好的准确性,C指数分别为0.677和0.672。
这些临床实用工具利用易于获得的临床病理因素预测ACC患者的OS和CSS,并有助于个体化临床决策。