Prognostic Nomogram for Predicting Survival in Asian Patients With Small-Cell Lung Cancer: A Comprehensive Population-Based Study and External Verification

IF 1.9 4区医学 Q3 RESPIRATORY SYSTEM Clinical Respiratory Journal Pub Date : 2024-11-09 DOI:10.1111/crj.70021

Yuanli Xia, Jingjing Qu, Yufang Wang, Yanping Zhu, Jianying Zhou, Jianya Zhou

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Abstract

Background

The incidence of small cell lung cancer (SCLC) among Asian patients is on the rise. Nevertheless, there remains a deficiency in precise prognostic models tailored to the specific needs of this patient population. It is imperative to develop a novel nomogram aimed at forecasting the prognosis of Asian SCLC patients.

Methods

The SEER database supplied data on 661 Asian SCLC patients, who were then divided into training and internal validation sets through a random selection process. In addition, we identified 212 patients from a Chinese medical institution for the purpose of creating an external validation cohort. To forecast survival, we employed both univariate and multivariate analyses. The performance of our nomogram was assessed through calibration plots, the concordance index (C-index), and decision curve analysis (DCA).

Results

Five independent prognostic factors were determined and integrated into the nomogram. C-index values for the training and internal validation cohorts were 0.774 (95% confidence interval [CI] = 0.751–0.797) and 0.731 (95%CI = 0.690–0.772), respectively. In the external validation cohort, the C-index is 0.712 (95% CI = 0.655–0.7692). Calibration curves demonstrated highly accurate predictions. When compared to the AJCC staging system, our model exhibited improved net benefits in DCA. Furthermore, the risk stratification system effectively differentiated patients with varying survival risks.

Conclusion

We have created a novel nomogram for predicting the survival of Asian patients with SCLC. This nomogram has been subjected to external validation and has shown its superiority over the conventional TNM staging system. It offers a more precise and reliable means of forecasting the prognosis of Asian SCLC patients.

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预测亚洲小细胞肺癌患者生存期的预后提名图：基于人群的综合研究和外部验证。

背景：小细胞肺癌（SCLC）在亚洲患者中的发病率呈上升趋势。然而，针对这一患者群体特殊需求的精确预后模型仍然不足。当务之急是开发一种新的提名图，用于预测亚洲 SCLC 患者的预后：方法：SEER数据库提供了661名亚裔SCLC患者的数据，然后通过随机选择的方法将这些患者分为训练集和内部验证集。此外，我们还从一家中国医疗机构找到了 212 名患者，以建立外部验证队列。为了预测生存率，我们采用了单变量和多变量分析。我们通过校准图、一致性指数（C-index）和决策曲线分析（DCA）对提名图的性能进行了评估：结果：确定了五个独立的预后因素，并将其整合到提名图中。训练队列和内部验证队列的 C-index 值分别为 0.774（95% 置信区间 [CI] = 0.751-0.797）和 0.731（95%CI = 0.690-0.772）。在外部验证队列中，C 指数为 0.712（95% CI = 0.655-0.7692）。校准曲线显示了高度准确的预测。与 AJCC 分期系统相比，我们的模型在 DCA 中显示出更好的净效益。此外，风险分层系统还能有效区分不同生存风险的患者：我们创建了一个新颖的提名图，用于预测亚洲 SCLC 患者的生存率。这一提名图已经过外部验证，并显示其优于传统的 TNM 分期系统。它为预测亚洲 SCLC 患者的预后提供了一种更精确、更可靠的方法。

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来源期刊

Clinical Respiratory Journal 医学-呼吸系统

CiteScore

3.70

自引率

0.00%

发文量

104

审稿时长

>12 weeks

期刊介绍： Overview Effective with the 2016 volume, this journal will be published in an online-only format. Aims and Scope The Clinical Respiratory Journal (CRJ) provides a forum for clinical research in all areas of respiratory medicine from clinical lung disease to basic research relevant to the clinic. We publish original research, review articles, case studies, editorials and book reviews in all areas of clinical lung disease including: Asthma Allergy COPD Non-invasive ventilation Sleep related breathing disorders Interstitial lung diseases Lung cancer Clinical genetics Rhinitis Airway and lung infection Epidemiology Pediatrics CRJ provides a fast-track service for selected Phase II and Phase III trial studies. Keywords Clinical Respiratory Journal, respiratory, pulmonary, medicine, clinical, lung disease, Abstracting and Indexing Information Academic Search (EBSCO Publishing) Academic Search Alumni Edition (EBSCO Publishing) Embase (Elsevier) Health & Medical Collection (ProQuest) Health Research Premium Collection (ProQuest) HEED: Health Economic Evaluations Database (Wiley-Blackwell) Hospital Premium Collection (ProQuest) Journal Citation Reports/Science Edition (Clarivate Analytics) MEDLINE/PubMed (NLM) ProQuest Central (ProQuest) Science Citation Index Expanded (Clarivate Analytics) SCOPUS (Elsevier)