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目的:基于更加全面广泛变量构建一个新的预测小细胞肺癌（SCLC）预后的列线图模型。方法:回顾性收集2015年1月至2018年12月山西医科大学附属肿瘤医院经病理证实为SCLC的722例患者资料，其中男592例，女130例，年龄23~82（61±9）岁。使用随机种子数133将患者分为训练集（422例）和验证集（300例）。使用Kaplan-Meier法进行生存分析，单因素使用Log-rank检验分析临床变量对SCLC预后的影响，将单因素分析中n P<0.05的变量纳入多因素Cox回归模型，基于多因素分析中n P<0.05的变量绘制列线图，使用受试者工作特征（ROC）曲线、整合布莱尔评分（IBS）以及决策曲线（DCA）评价模型的区分能力、预测误差值和临床净收益，并与美国癌症联合委员会提出的第8版TNM分期进行比较。n 结果:男性、单核粒细胞计数（MON）异常、神经元特异性烯醇化酶（NSE）异常、细胞角蛋白19片段（Cyfra211）异常、M1a期、M1b期、M1c期、放疗、化疗≥4周期和预防性脑照射（PCI）是SCLC预后的影响因素［n HR值（n 95%CI）分别为1.39（1.00~1.92）、1.29（1.02~1.63）、1.41（1.11~1.80）、2.02（1.48~2.76）、1.09（0.77~1.55）、1.44（0.94~2.22）、2.01（1.49~2.71）、0.75（0.57~0.98）、0.40（0.31~0.51）和0.42（0.26~0.68）；均n P<0.05）］。基于以上变量建立列线图模型，曲线下面积（AUC）在训练集和验证集中分别为0.814（95%n CI：0.765~0.862）和0.787（95%n CI：0.725~0.849），高于第8版TNM分期［0.616（95%n CI：0.558~0.674）和0.648（95%n CI：0.581~0.715）］；校准曲线表明其预测SCLC的2年生存率与实际具有较好的一致性；IBS表明其较TNM分期具有更小的预测误差值（训练集：0.132比0.169；验证集：0.138比0.169）；DCA表明其较TNM分期有更宽的阈值范围（训练集：0.01~0.96比0.01~0.85，验证集：0.01~0.94比0.01~0.86）和更高的临床净获益（训练集：列线图在0.19~0.96阈值范围内获益高于TNM分期；验证集：列线图在0.19~0.94阈值范围内获益高于TNM分期）。n 结论:本研究建立的基于性别、MON、NSE、Cyfra211、M分期、放疗、化疗周期和PCI共8个变量的SCLC 2年生存率列线图模型，能够为SCLC更准确的预后评估和治疗方案的选择提供参考。“,”Objective:To construct a novel prognostic nomogram model based on more comprehensive variables for patients with small-cell lung cancer (SCLC).Methods:The data of 722 patients with SCLC confirmed by pathology in Affiliated Cancer Hospital of Shanxi Medical University from January 2015 to December 2018 were retrospectively analyzed [including 592 males and 130 females, aged from 23 to 82(61±9) years]. A random seed count of 133 was used to divide those patients into training set (n n=422) and validation set (n n=300). Kaplan-Meier was used for survival curves analysis and univariate Log-rank test was used for evaluating the influence of clinical variables on the prognosis of sclc, variables with n P<0.05 in univariate analysis were included in a multivariate Cox regression model. The nomogram was constructed based on the variables whichn P<0.05 in multivariate analysis. Receiver operating characteristic (ROC) curve, calibration by Integrated Brier score (IBS) and clinical net benefit by decision curve analysis (DCA) were used to evaluate model discriminative power, prediction error value, and clinical net benefit, and compared with the American Joint Committee on Cancer 8n th TNM.n Results:Male, abnormal monocyte (MON) counts, abnormal neuron specific enolase (NSE), abnormal cytokeratin 19 fragment (Cyfra211), M1a stage, M1b stage, M1c stage, radiotherapy (RT), chemotherapy ≥4 cycles and prophylactic cranial irradiation (PCI) were prognostic factors for SCLC[n HR(95%n CI)=1.39(1.00-1.92), 1.29(1.02-1.63), 1.41(1.11-1.80), 2.02(1.48-2.76), 1.09(0.77-1.55), 1.44(0.94-2.22), 2.01(1.49-2.71), 0.75(0.57-0.98), 0.40(0.31-0.51)and 0.42(0.26-0.68), respectively, all n P<0.05]. The area under ROC curve (AUC) of the nomogram in training set and validation set were 0.814(95%n CI: 0.765-0.862)and 0.787 (95%n CI: 0.725-0.849), which were higher than TNM [0.616(95%n CI: 0.558-0.674) and 0.648(95%n CI: 0.581-0.715)].The calibration curve showed a good correlation between the nomogram prediction and actual observation for the 2-year overall survival (OS). IBS indicted a lower prediction error rate (training set: 0.132 vs 0.169; validation set: 0.138 vs 0.169). DCA showed a wider threshold range than TNM (training set: 0.01-0.96 vs 0.01-0.85, validation set: 0.01-0.94 vs 0.01-0.86) and a greater improvement of the clinical net benefit (in training set the nomogram had a greater clinical benefit than TNM in the range of 0.19-0.96, and remained in validation set in the range of 0.19-0.94).n Conclusion:The established nomogram model for predicting 2-year OS in patients with SCLC based on 8 variables, including gender, MON, NSE, Cyfra211, M stage, RT, CT cycles and PCI can be used for an more accurately prognosis prediction and reference for therapeutic regimen selection.