A Nomogram for Predicting Cancer-Specific Survival in Young Patients With Advanced Lung Cancer Based on Competing Risk Model

IF 1.9 4区医学 Q3 RESPIRATORY SYSTEM

Clinical Respiratory Journal Pub Date : 2024-08-07 DOI:10.1111/crj.13800

Jiaxin Li, Bolin Pan, Qiying Huang, Chulan Zhan, Tong Lin, Yangzhi Qiu, Honglang Zhang, Xiaohong Xie, Xinqin Lin, Ming Liu, Liqiang Wang, Chengzhi Zhou

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引用次数: 0

Abstract

Background

Young lung cancer is a rare subgroup accounting for 5% of lung cancer. The aim of this study was to compare the causes of death (COD) among lung cancer patients of different age groups and construct a nomogram to predict cancer-specific survival (CSS) in young patients with advanced stage.

Methods

Lung cancer patients diagnosed between 2004 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and stratified into the young (18–45 years) and old (> 45 years) groups to compare their COD. Young patients diagnosed with advanced stage (IVa and IVb) from 2010 to 2015 were reselected and divided into training and validation cohorts (7:3). Independent prognostic factors were identified through the Fine-Gray's test and further integrated to the competing risk model. The area under the receiver operating characteristic curve (AUC), consistency index (C-index), and calibration curve were applied for validation.

Results

The proportion of cancer-specific death (CSD) in young patients was higher than that in old patients with early-stage lung cancer (p < 0.001), while there was no difference in the advanced stage (p = 0.999). Through univariate and multivariate analysis, 10 variables were identified as independent prognostic factors for CSS. The AUC of the 1-, 3-, and 5-year prediction of CSS was 0.688, 0.706, and 0.791 in the training cohort and 0.747, 0.752, and 0.719 in the validation cohort. The calibration curves demonstrated great accuracy. The C-index of the competing risk model was 0.692 (95% CI: 0.636–0.747) in the young patient cohort.

Conclusion

Young lung cancer is a distinct entity with a different spectrum of competing risk events. The construction of our nomogram can provide new insights into the management of young patients with lung cancer.

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基于竞争风险模型预测年轻晚期肺癌患者癌症特异性生存期的提名图

背景：年轻肺癌是一个罕见的亚组，占肺癌的5%。本研究旨在比较不同年龄组肺癌患者的死亡原因（COD），并构建一个提名图来预测晚期年轻患者的癌症特异性生存率（CSS）：从监测、流行病学和最终结果（SEER）数据库中提取了2004年至2015年间确诊的肺癌患者，并将其分为年轻组（18-45岁）和年长组（大于45岁），以比较他们的死因。重新选择了2010年至2015年诊断为晚期（IVa和IVb）的年轻患者，并将其分为训练组和验证组（7:3）。通过Fine-Gray检验确定了独立的预后因素，并进一步整合到竞争风险模型中。应用接收者操作特征曲线下面积（AUC）、一致性指数（C-index）和校准曲线进行验证：结果：年轻肺癌患者的癌症特异性死亡（CSD）比例高于老年早期肺癌患者（P 结论：年轻肺癌是一个独特的实体，其癌症特异性死亡比例高于老年早期肺癌患者：年轻肺癌是一个独特的实体，具有不同的竞争风险事件谱。我们构建的提名图可以为年轻肺癌患者的治疗提供新的见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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)

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