分期双侧手术治疗同步多发性原发性肺癌预后因素的鉴定

IF 1.9 4区 医学 Q3 RESPIRATORY SYSTEM
Hui Zhang, Qiang Liu, Lian Chen, Liwei Song, Feng Mao, Wenyong Zhou, Jiantao Li, Zuodong Song, Wang Miao, Yang Shentu
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引用次数: 0

摘要

导言:分期双侧手术被广泛用于治疗同步多发性原发性肺癌(SMPLC);然而,生存结果的预后因素仍不明确。本研究旨在确定接受分期双侧手术的 SMPLC 患者的预后因素,并构建总生存期(OS)和无复发生存期(RFS)的预测模型。 方法 研究纳入了2010年1月至2017年7月期间在我院确诊并接受分期双侧手术治疗的256例SMPLC患者。采用多变量 Cox 比例危险回归确定 OS 和 RFS 的预后因素。此外,还利用时间依赖性接收者操作特征曲线构建了预测模型。 结果 在256名患者中,10人(3.95%)死于疾病,24人(9.41%)复发。吸烟(危险比 [HR]:5.128;95% 置信区间 [CI]:吸烟(危险比 [HR]:5.128;95% 置信区间 [CI]:1.442-18.233;P = 0.012)和最晚期病理 TNM (pTNM) 分期(II + III)(HR:12.938;95% CI:2.650-63.176;P = 0.002)被认为是不良 OS 的重要预测因素。建立了一个预测 OS 的预后模型,其 5 年曲线下面积 (AUC) 为 0.854。此外,最晚期 pTNM 分期(II + III)与 RFS 差相关(HR:5.964;95% CI:2.669-13.327;p <;0.001),预测模型的 5 年 RFS AUC 为 0.718。 结论 本研究显示,吸烟和最晚期 pTNM 分期是与双侧 SMPLC 患者不良 OS 相关的独立预后因素。此外,pTNM 最晚期也与 RFS 差有关。所建立的预测模型对OS和RFS的预后表现适中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of the Prognostic Factors for Synchronous Multiple Primary Lung Cancer Treated With Staged Bilateral Surgery

Introduction

Staged bilateral surgery is widely used to treat synchronous multiple primary lung cancer (SMPLC); however, the prognostic factors for survival outcomes remain unclear. This study aimed to identify prognostic factors and construct a predictive model for overall survival (OS) and recurrence-free survival (RFS) in patients with SMPLC who underwent staged bilateral surgery.

Methods

The study included 256 patients diagnosed with SMPLC and treated with staged bilateral surgery at our hospital between January 2010 and July 2017. Multivariate Cox proportional-hazard regression was used to identify prognostic factors for OS and RFS. Additionally, a predictive model was constructed using time-dependent receiver operating characteristic curves.

Results

Among the 256 patients, 10 (3.95%) succumbed to the disease and 24 (9.41%) experienced recurrence. Smoking (hazard ratio [HR]: 5.128; 95% confidence interval [CI]: 1.442–18.233; p = 0.012) and most advanced pathological TNM (pTNM) stage (II + III) (HR: 12.938; 95% CI: 2.650–63.176; p = 0.002) were identified as significant predictors of poor OS. A prognostic model was developed for predicting OS, with a 5-year area under the curve (AUC) of 0.854. Furthermore, most advanced pTNM stage (II + III) was associated with poor RFS (HR: 5.964; 95% CI: 2.669–13.327; p < 0.001), and the predictive model exhibited a 5-year AUC of 0.718 for RFS.

Conclusion

This study revealed that smoking and most advanced pTNM stage were independent prognostic factors associated with poor OS in patients with bilateral SMPLC. Moreover, most advanced pTNM stage was also linked to unfavorable RFS. The developed predictive model demonstrated moderate prognostic performance for both OS and RFS.

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来源期刊
Clinical Respiratory Journal
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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