Department of Medical Oncology, 162798The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China.
Department of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, 540681Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China.
Cancer Control. 2021 Jan-Dec;28:10732748211051228. doi: 10.1177/10732748211051228.
Combined small cell lung cancer (C-SCLC) represents a rare subtype of all small cell lung cancer cases, with limited studies investigated its prognostic factors. The aim of this study was to construct a novel nomogram to predict the overall survival (OS) of patients with C-SCLC.
In this retrospective study, a total of 588 C-SCLC patients were selected from the Surveillance, Epidemiology, and End Results database. The univariate and multivariate Cox analyses were performed to identify optimal prognostic variables and construct the nomogram, with concordance index (C-index), receiver operating characteristic curves, and calibration curves being used to evaluate its discrimination and calibration abilities. Furthermore, decision curve analysis (DCA), integrated discrimination improvement (IDI), and net reclassification index (NRI) were also adopted to assess its clinical utility and predictive ability compared with the classic TNM staging system.
Seven independent predictive factors were identified to construct the nomogram, including T stage, N stage, M stage, brain metastasis, liver metastasis, surgery, and chemotherapy. We observed a higher C-index in both the training (.751) and validation cohorts (.736). The nomogram has higher area under the curve in predicting 6-, 12-, 18-, 24-, and 36-month survival probability of patients with C-SCLC. Meanwhile, the calibration curves also revealed high consistencies between the actual and predicted OS. DCA revealed that the nomogram could provide greater clinical net benefits to these patients. We found that the NRI for 6- and 12-month OS were .196 and .225, and the IDI for 6- and 12-month OS were .217 and .156 in the training group, suggesting that the nomogram can predict a more accurate survival probability. Similar results were also observed in the validation cohort.
We developed and verified a novel nomogram that can help clinicians recognize high-risk patients with C-SCLC and predict their OS.
合并小细胞肺癌（C-SCLC）是所有小细胞肺癌病例中罕见的亚型，目前研究其预后因素的研究较少。本研究旨在构建一种新的列线图来预测 C-SCLC 患者的总生存期（OS）。
在这项回顾性研究中，我们从监测、流行病学和最终结果数据库中选择了 588 例 C-SCLC 患者。我们进行单因素和多因素 Cox 分析，以确定最佳的预后变量，并构建列线图，使用一致性指数（C 指数）、接受者操作特征曲线和校准曲线来评估其区分和校准能力。此外，还采用决策曲线分析（DCA）、综合判别改善（IDI）和净重新分类指数（NRI）来评估与经典 TNM 分期系统相比，该列线图的临床实用性和预测能力。
确定了 7 个独立的预测因素来构建列线图，包括 T 分期、N 分期、M 分期、脑转移、肝转移、手术和化疗。我们观察到训练集（0.751）和验证集（0.736）的 C 指数都更高。列线图在预测 C-SCLC 患者 6、12、18、24 和 36 个月生存率方面具有更高的曲线下面积。同时，校准曲线也显示出实际 OS 与预测 OS 之间的高度一致性。DCA 显示，该列线图可以为这些患者提供更大的临床净收益。我们发现，训练组中 6 个月和 12 个月 OS 的 NRI 分别为 0.196 和 0.225，6 个月和 12 个月 OS 的 IDI 分别为 0.217 和 0.156，这表明该列线图可以预测更准确的生存概率。在验证组中也观察到了类似的结果。
我们开发并验证了一种新的列线图，可帮助临床医生识别 C-SCLC 的高危患者并预测其 OS。
