The screening of optimal primary tumor resection candidates in patients with small cell lung cancer: a population-based predictive model.

IF 1.5 4区 医学 Q4 ONCOLOGY
Translational cancer research Pub Date : 2025-02-28 Epub Date: 2025-02-26 DOI:10.21037/tcr-24-1419
Zhidong Wang, Cheng Gong, Youpu Zhang, Yongxiang Qian, Yang Liu, Ce Chao
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引用次数: 0

Abstract

Background: Although a strong survival benefit has been observed among small cell lung cancer (SCLC) patients undergoing surgery, not all SCLC patients benefit from surgery. To help clinicians make choices and decisions regarding surgical intervention, we have developed an effective model to screen beneficial candidates based on population and tumor characteristics.

Methods: Patients with SCLC were acquired from the Surveillance, Epidemiology, and End Results database. Propensity score matching (PSM) was performed to balance covariates between the surgery and non-surgery groups. We assumed that patients undergoing surgery between 2014 and 2018 would benefit from the procedure if their median cancer-specific survival (CSS) time was longer than that of non-surgical patients. Univariate and multivariable logistic analyses were used to identify independent factors of surgical benefit in the surgery group. According to these preoperative factors, a nomogram was built and then internal and external validation were performed.

Results: In total, 35,214 patients with complete data were included for subsequent analysis, 1,364 of whom underwent surgery. Before and after PSM, surgery was an independent factor of long-term survival, with a median CSS time of 37.00 months for the surgery group compared to 16.00 months for the non-surgery group. A multivariable logistic model identified T stage, N stage, M stage, tumor site, and age as independent factors, which were used to establish a stable predictive model.

Conclusions: We have built a preoperative predictive model for SCLC patients to screen for optimal surgery candidates. This model has the potential to help clinicians determine whether it is beneficial to operate on patients with SCLC.

筛选小细胞肺癌患者最佳原发肿瘤切除候选者:基于人群的预测模型。
背景:虽然在接受手术的小细胞肺癌(SCLC)患者中观察到很强的生存获益,但并非所有SCLC患者都能从手术中获益。为了帮助临床医生做出手术干预的选择和决定,我们开发了一个有效的模型来筛选基于人群和肿瘤特征的有益候选人。方法:从监测、流行病学和最终结果数据库中获取SCLC患者。采用倾向评分匹配(PSM)来平衡手术组和非手术组之间的协变量。我们假设,如果2014年至2018年间接受手术的患者的中位癌症特异性生存(CSS)时间长于非手术患者,则该手术将受益。采用单变量和多变量logistic分析来确定手术组手术获益的独立因素。根据这些术前因素,建立nomogram,并进行内外验证。结果:共有35214例资料完整的患者纳入后续分析,其中1364例患者接受了手术。在PSM前后,手术是长期生存的独立因素,手术组的中位CSS时间为37.00个月,而非手术组的中位CSS时间为16.00个月。多变量logistic模型确定T期、N期、M期、肿瘤部位、年龄为独立因素,建立稳定的预测模型。结论:我们建立了SCLC患者的术前预测模型,以筛选最佳手术候选人。该模型有可能帮助临床医生确定对SCLC患者进行手术是否有益。
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来源期刊
CiteScore
2.10
自引率
0.00%
发文量
252
期刊介绍: Translational Cancer Research (Transl Cancer Res TCR; Print ISSN: 2218-676X; Online ISSN 2219-6803; http://tcr.amegroups.com/) is an Open Access, peer-reviewed journal, indexed in Science Citation Index Expanded (SCIE). TCR publishes laboratory studies of novel therapeutic interventions as well as clinical trials which evaluate new treatment paradigms for cancer; results of novel research investigations which bridge the laboratory and clinical settings including risk assessment, cellular and molecular characterization, prevention, detection, diagnosis and treatment of human cancers with the overall goal of improving the clinical care of cancer patients. The focus of TCR is original, peer-reviewed, science-based research that successfully advances clinical medicine toward the goal of improving patients'' quality of life. The editors and an international advisory group of scientists and clinician-scientists as well as other experts will hold TCR articles to the high-quality standards. We accept Original Articles as well as Review Articles, Editorials and Brief Articles.
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