Ma Haochuan, Xu Zhiyong, Zhou Rui, Liu Yihong, Zhu Yanjuan, Chang Xuesong, Chen Yadong, Zhang Haibo
The Second Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou, People's Republic of China.
Department of Oncology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Traditional Chinese Medicine, Guangzhou, People's Republic of China.
Int J Gen Med. 2021 Oct 28;14:7299-7310. doi: 10.2147/IJGM.S335040. eCollection 2021.
This study was designed to construct and validate a nomogram that was available for predicting cancer-specific survival (CSS) in patients with pulmonary large-cell neuroendocrine carcinoma (LCNEC).
Using the US Surveillance, Epidemiology, and End Results (SEER) database, we identified patients pathologically diagnosed as LCNEC from 1975 to 2016. Univariate and multivariate Cox regression was conducted to assess prognostic factors of CSS. A novel nomogram model was constructed and validated by the concordance index (C-index), calibration curves and decision curve analysis (DCA).
A total of 624 LCNEC patients were enrolled. Five prognostic factors for CSS were identified and merged to establish nomograms. In the training and validation cohorts, calibration curves displayed the nomogram predictions are in a good agreement with the actual survival. The C-Index of the training and validation cohorts were both higher than 0.8, and the DCA results showed that the nomogram has clinical validity and utility.
The proposed nomogram resulted in accurate CSS prognostic prediction for patients with LCNEC.
本研究旨在构建并验证一种可用于预测肺大细胞神经内分泌癌（LCNEC）患者癌症特异性生存（CSS）的列线图。
利用美国监测、流行病学和最终结果（SEER）数据库，我们识别出1975年至2016年间病理诊断为LCNEC的患者。进行单因素和多因素Cox回归以评估CSS的预后因素。通过一致性指数（C指数）、校准曲线和决策曲线分析（DCA）构建并验证了一种新型列线图模型。
共纳入624例LCNEC患者。确定了5个CSS的预后因素并合并以建立列线图。在训练和验证队列中，校准曲线显示列线图预测与实际生存情况高度一致。训练和验证队列的C指数均高于0.8，DCA结果表明列线图具有临床有效性和实用性。
所提出的列线图对LCNEC患者的CSS预后预测准确。
