Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study On Anticancer Molecular Targeted Drugs, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
BMC Cancer. 2023 Apr 5;23(1):312. doi: 10.1186/s12885-023-10797-3.
Pulmonary large cell neuroendocrine carcinoma (LCNEC) and small cell lung cancer (SCLC) are two types of high-grade neuroendocrine carcinomas of the lung with poor prognosis. LCNEC has not been thoroughly studied due to its rarity, data are also lacking regarding the survival comparison and prognosis analysis of patients with locally advanced or metastatic LCNEC and SCLC.
Data of patients with LCNEC, SCLC, and other NSCLC who were diagnosed from 1975 to 2019 were extracted from the Surveillance, Epidemiology and End Results (SEER) database to estimate incidence. Those in stage III-IV and being diagnosed from 2010 to 2015 were utilized further to investigate their clinical characteristics and prognosis. Propensity score matching (PSM) analyses at a ratio of 1:2 was used to compare their survival outcomes. Nomograms of LCNEC and SCLC were established with internal validation, and the nomogram of SCLC was externally validated by 349 patients diagnosed in Cancer hospital, Chinese Academy of Medical Sciences & Peking Union Medical College from January 1, 2012 to December 31, 2018.
The incidence of LCNEC has been increasing in recent decades, meanwhile that of SCLC and other types of NSCLC were decreasing. A total of 91,635 lung cancer patients, including 785 with LCNEC, 15,776 with SCLC, and 75,074 with other NSCLC were enrolled for further analysis. The survival of stage III-IV LCNEC resembles that of SCLC, and significantly worse than other types of NSCLC before and after PSM analysis. In pretreatment prognostic analysis, age, T stage, N stage, M stage, bone metastasis, liver metastasis, and brain metastasis were found to be associated with the survival of both LCNEC and SCLC, besides sex, bilaterality, and lung metastasis were additional prognostic factors for SCLC. Two nomograms and convenient online tools respectively for LCNEC and SCLC were established accordingly with favorable predicting accuracy of < 1-year, < 2-year, < 3-year survival probabilities. In external validation of the SCLC nomogram with a Chinese cohort, the AUCs of 1-year, 2-year and 3-year ROC were 0.652, 0.669, and 0.750, respectively. All the results of 1-, 2-, 3- year variable-dependent ROC curves verified the superior prognostic value of our nomograms for LCNEC and SCLC over the traditional T/N/M staging system.
Based on large sample-based cohort, we compared the epidemiological trends and survival outcomes between locally advanced or metastatic LCNEC, SCLC, and other NSCLC. Furthermore, two prognostic evaluation approaches respectively for LCNEC and SCLC might present as practical tools for clinicians to predict the survival outcome of these patients and facilitate risk stratification.
肺大细胞神经内分泌癌(LCNEC)和小细胞肺癌(SCLC)是两种预后较差的肺部高级别神经内分泌癌。由于 LCNEC 较为罕见,因此对其研究尚不透彻,此外,对于局部晚期或转移性 LCNEC 和 SCLC 患者的生存比较和预后分析也缺乏相关数据。
从 1975 年至 2019 年的监测、流行病学和最终结果(SEER)数据库中提取 LCNEC、SCLC 和其他非小细胞肺癌(NSCLC)患者的数据,以估计发病率。对 2010 年至 2015 年诊断为 III-IV 期的患者进行进一步研究,以调查其临床特征和预后。采用 1:2 的倾向评分匹配(PSM)分析比较其生存结局。使用内部验证为 LCNEC 和 SCLC 建立列线图,并使用中国医学科学院肿瘤医院和北京协和医学院从 2012 年 1 月 1 日至 2018 年 12 月 31 日诊断的 349 例患者对 SCLC 列线图进行外部验证。
LCNEC 的发病率在最近几十年呈上升趋势,而 SCLC 和其他类型的 NSCLC 的发病率则呈下降趋势。共纳入 91635 例肺癌患者,其中 785 例为 LCNEC,15776 例为 SCLC,75074 例为其他 NSCLC。III-IV 期 LCNEC 的生存情况与 SCLC 相似,在 PSM 分析前后均明显差于其他类型的 NSCLC。在预处理预后分析中,年龄、T 分期、N 分期、M 分期、骨转移、肝转移和脑转移与 LCNEC 和 SCLC 的生存相关,除性别、双侧性和肺转移外,这些因素也是 SCLC 的附加预后因素。分别为 LCNEC 和 SCLC 建立了两个列线图和方便的在线工具,具有较好的<1 年、<2 年和<3 年生存概率预测准确性。在对来自中国队列的 SCLC 列线图进行外部验证中,1 年、2 年和 3 年 ROC 的 AUC 分别为 0.652、0.669 和 0.750。所有 1 年、2 年和 3 年的变量依赖性 ROC 曲线的结果都验证了我们的列线图在预测 LCNEC 和 SCLC 患者的生存结局方面优于传统的 T/N/M 分期系统。
基于大样本队列,我们比较了局部晚期或转移性 LCNEC、SCLC 和其他 NSCLC 之间的流行病学趋势和生存结局。此外,LCNEC 和 SCLC 的两种预后评估方法可作为临床医生预测这些患者生存结局和进行风险分层的实用工具。