Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong Province, China.
School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi Province, China.
Cancer Med. 2021 Jun;10(11):3756-3769. doi: 10.1002/cam4.3919. Epub 2021 May 7.
Parotid-gland carcinoma (PGC) is a relatively rare tumor that comprises a group of heterogeneous histologic subtypes. We used the Surveillance, Epidemiology, and End Results (SEER) program database to apply a competing-risks analysis to PGC patients, and then established and validated predictive nomograms for PGC.
Specific screening criteria were applied to identify PGC patients and extract their clinical and other characteristics from the SEER database. We used the cumulative incidence function to estimate the cumulative incidence rates of PGC-specific death (GCD) and other cause-specific death (OCD), and tested for differences between groups using Gray's test. We then identified independent prognostic factors by applying the Fine-Gray proportional subdistribution hazard approach, and constructed predictive nomograms based on the results. Calibration curves and the concordance index (C-index) were employed to validate the nomograms.
We finally identified 4,075 eligible PGC patients who had been added to the SEER database from 2004 to 2015. Their 1-, 3-, and 5-year cumulative incidence rates of GCD were 10.1%, 21.6%, and 25.7%, respectively, while those of OCD were 2.9%, 6.6%, and 9.0%. Age, race, World Health Organization histologic risk classification, differentiation grade, American Joint Committee on Cancer (AJCC) T stage, AJCC N stage, AJCC M stage, and RS (radiotherapy and surgery status) were independent predictors of GCD, while those of OCD were age, sex, marital status, AJCC T stage, AJCC M stage, and RS. These factors were integrated for constructing predictive nomograms. The results for calibration curves and the C-index suggested that the nomograms were well calibrated and had good discrimination ability.
We have used the SEER database to establish-to the best of our knowledge-the first competing-risks nomograms for predicting the 1-, 3-, and 5-year cause-specific mortality in PGC. The nomograms showed relatively good performance and can be used in clinical practice to assist clinicians in individualized treatment decision-making.
腮腺癌（PGC）是一种相对罕见的肿瘤，由一组异质性组织学亚型组成。我们使用监测、流行病学和最终结果（SEER）计划数据库对 PGC 患者进行竞争风险分析，然后建立和验证了用于预测 PGC 的预测列线图。
应用特定的筛选标准从 SEER 数据库中识别 PGC 患者并提取其临床和其他特征。我们使用累积发生率函数估计 PGC 特异性死亡（GCD）和其他原因特异性死亡（OCD）的累积发生率，并使用 Gray 检验检验组间差异。然后，我们通过应用 Fine-Gray 比例亚分布风险方法确定独立的预后因素，并根据结果构建预测列线图。校准曲线和一致性指数（C 指数）用于验证列线图。
我们最终确定了从 2004 年到 2015 年添加到 SEER 数据库中的 4075 名符合条件的 PGC 患者。他们的 1、3 和 5 年 GCD 的累积发生率分别为 10.1%、21.6%和 25.7%，而 OCD 的累积发生率分别为 2.9%、6.6%和 9.0%。年龄、种族、世界卫生组织组织学风险分类、分化程度、美国癌症联合委员会（AJCC）T 分期、AJCC N 分期、AJCC M 分期和 RS（放疗和手术状态）是 GCD 的独立预测因素，而 OCD 的独立预测因素是年龄、性别、婚姻状况、AJCC T 分期、AJCC M 分期和 RS。这些因素被整合到构建预测列线图中。校准曲线和 C 指数的结果表明，该列线图具有良好的校准度和区分能力。
我们使用 SEER 数据库建立了（据我们所知）用于预测 PGC 1、3 和 5 年原因特异性死亡率的首个竞争风险列线图。该列线图表现出较好的性能，可在临床实践中使用，以帮助临床医生做出个体化治疗决策。
