加利福尼亚州肺神经内分泌肿瘤的生存结果因社会人口因素而异。

Endocrine-related cancer Pub Date : 2023-12-08 Print Date: 2024-01-01 DOI:10.1530/ERC-23-0068
Claire K Mulvey, Alan Paciorek, Farhana Moon, Paige Steiding, Brandon Shih, Matthew A Gubens, Li Zhang, Emily K Bergsland, Iona Cheng
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引用次数: 0

摘要

肺神经内分泌肿瘤(NETs)几乎没有已知的生存预测因素。我们调查了1992-2019年加州癌症登记处(CCR)发生的肺NET病例(典型或非典型组织学)的社会人口学、临床病理和治疗因素与总生存率(OS)和肺癌特异性生存率(LCSS)的关系。OS采用Kaplan-Meier方法进行估计,并通过社会人口统计学和疾病因素单因素与对数秩检验进行比较。我们使用序列Cox比例风险回归进行多变量OS分析。LCSS使用Fine Gray竞争风险回归进行估计。共有6038例肺NET诊断(5569例为典型,469例为非典型类癌);大多数是女性(70%)和非西班牙裔白人(73%)。在我们的多变量模型中,社会人口统计学因素与OS独立相关,女性生存率较高(危险比[HR]0.62,95%置信区间[CI]0.57-0.68,p
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Survival outcomes for lung neuroendocrine tumors in California differ by sociodemographic factors.

Lung neuroendocrine tumors (NETs) have few known predictors of survival. We investigated associations of sociodemographic, clinicopathologic, and treatment factors with overall survival (OS) and lung cancer-specific survival (LCSS) for incident lung NET cases (typical or atypical histology) in the California Cancer Registry (CCR) from 1992 to 2019. OS was estimated with the Kaplan-Meier method and compared by sociodemographic and disease factors univariately with the log-rank test. We used sequential Cox proportional hazards regression for multivariable OS analysis. LCSS was estimated using Fine-Gray competing risks regression. There were 6038 lung NET diagnoses (5569 typical, 469 atypical carcinoid); most were women (70%) and non-Hispanic White (73%). In our multivariable model, sociodemographic factors were independently associated with OS, with better survival for women (hazard ratio (HR) 0.62, 95% confidence interval (CI) 0.57-0.68, P < 0.001), married (HR 0.76, 95% CI 0.70-0.84, P < 0.001), and residents of high socioeconomic status (SES) neighborhoods (HRQ5vsQ1 0.73, 95% CI 0.62-0.85, P < 0.001). Compared to cases with private insurance, OS was worse for cases with Medicare (HR 1.24, 95% CI 1.10-1.40, P < 0.001) or Medicaid/other public insurance (HR 1.45, 95% CI 1.24-1.68, P < 0.001). In our univariate model, non-Hispanic Black Californians had worse OS than other racial/ethnic groups, but differences attenuated after adjusting for stage at diagnosis. In our LCSS models, we found similar associations between sex and marital status on survival, but no differences in outcomes by SES or insurance. By race/ethnicity, American Indian cases had worse LCSS. In summary, beyond disease-related and treatment variables, sociodemographic factors were independently associated with survival in lung NETs.

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