Department of Radiotherapy, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital Affiliated to Shanxi Medical University, No.3, Zhigongxin Street, Taiyuan, Shanxi, 030010, China.
Department of Thoracic Surgery, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital Affiliated to Shanxi Medical University, No.3, Zhigongxin Street, Taiyuan, Shanxi, 030013, China.
BMC Cancer. 2024 Oct 10;24(1):1257. doi: 10.1186/s12885-024-12791-9.
Though several nomograms have been established to predict the survival probability of patients with small-cell lung cancer (SCLC), none involved enough variables. This study aimed to construct a novel prognostic nomogram and compare its performance with other models.
Seven hundred twenty-two patients were pathologically diagnosed with SCLC in Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital Affiliated to Shanxi Medical University from January 2016 to December 2018. We input Forty-one factors by reviewing the medical records. The nomogram was constructed based on the variables identified by univariate and multivariate analyses in the training set and validated in the validation set. Then we compared the performance of the models in terms of discrimination, calibration, and clinical net benefit.
There were eight variables involved in the nomogram: gender, monocyte (MON), neuron-specific enolase (NSE), cytokeratin 19 fragments (Cyfra211), M stage, radiotherapy (RT), chemotherapy cycles (CT cycles), and prophylactic cranial irradiation (PCI). The calibration curve showed a good correlation between the nomogram prediction and actual observation for overall survival (OS). The area under the curve (AUC) of the nomogram was higher, and the Integrated Brier score (IBS) was lower than other models, indicating a more accurate prediction. Decision curve analysis (DCA) showed a significant improvement in the clinical net benefit compared to the other models.
We constructed a novel nomogram to predict OS for patients with SCLC using more comprehensive and objective variables. It performed better than existing models and would assist clinicians in individually estimating risk and making a therapeutic regimen.
虽然已经建立了几种预测小细胞肺癌（SCLC）患者生存概率的列线图，但没有一个涉及足够的变量。本研究旨在构建一种新的预后列线图，并将其与其他模型进行比较。
从 2016 年 1 月至 2018 年 12 月，在山西省肿瘤医院、中国医学科学院肿瘤医院山西医院、山西医科大学附属肿瘤医院，对 722 例经病理诊断为 SCLC 的患者进行了回顾性分析。我们通过查阅病历输入了 41 个因素。基于训练集中单变量和多变量分析确定的变量构建列线图，并在验证集中验证。然后，我们从区分度、校准度和临床净获益方面比较了模型的性能。
该列线图涉及 8 个变量：性别、单核细胞（MON）、神经元特异性烯醇化酶（NSE）、细胞角蛋白 19 片段（Cyfra211）、M 期、放疗（RT）、化疗周期（CT 周期）和预防性颅脑照射（PCI）。校准曲线显示，总体生存（OS）的列线图预测与实际观察之间存在良好的相关性。列线图的曲线下面积（AUC）更高，综合 Brier 评分（IBS）更低，表明预测更准确。决策曲线分析（DCA）显示，与其他模型相比，临床净获益有显著改善。
我们构建了一种新的列线图，使用更全面和客观的变量来预测 SCLC 患者的 OS。它的性能优于现有的模型，可以帮助临床医生个体估计风险并制定治疗方案。
