Development and validation of nomogram models for predicting postoperative prognosis of early-stage laryngeal squamous cell carcinoma

IF 2.5 4区 医学 Q3 ONCOLOGY
Xu Juan , Huang Jiali , Liu Ziqi , Zhang Liqing , Zhou Han
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引用次数: 0

Abstract

Background

We aimed to investigate the postoperative prognosis in patients with early-stage laryngeal squamous cell carcinoma (LSCC) in association with the preoperative blood markers and clinicopathological characteristics and to develop nomograms for individual risk prediction.

Methods

The clinical data of 353 patients with confirmed early-stage LSCC between 2009 and 2018 were retrospectively retrieved from the First Affiliated Hospital with Nanjing Medical University. All patients were randomly divided into the training and testing groups in a 7:3 ratio. Univariate and multivariate analyses were performed, followed by the construction of nomograms to predict recurrence-free survival (RFS) and overall survival (OS). Finally, the nomograms were verified internally, and the predictive capability of the nomograms was evaluated and compared with that of tumour T staging.

Results

Univariate and multivariate analyses identified platelet counts (PLT), fibrinogen (FIB), and platelet to lymphocyte ratio (PLR) were independent factors for RFS, and FIB, systemic immune-inflammation index (SII), and haemoglobin (HGB) were independent prognostic factors for OS. The nomograms showed higher predictive C-indexes than T staging. Furthermore, decision curve analysis (DCA) revealed that the net benefit of the nomograms’ calculation model was superior to that of T staging.

Conclusions

We established and validated nomograms to predict postoperative 1-, 3- and 5-year RFS and OS in patients with early-stage LSCC based on significant blood markers and clinicopathological characteristics. These models might help clinicians make personalized treatment decisions.

用于预测早期喉鳞状细胞癌术后预后的提名图模型的开发与验证
背景我们旨在研究早期喉鳞状细胞癌(LSCC)患者术后预后与术前血液标志物和临床病理特征的相关性,并建立用于个体风险预测的提名图。方法回顾性检索南京医科大学第一附属医院2009年至2018年间353例确诊早期LSCC患者的临床资料。所有患者按7:3的比例随机分为训练组和测试组。进行单变量和多变量分析,然后构建预测无复发生存率(RFS)和总生存率(OS)的提名图。结果经单变量和多变量分析发现,血小板计数(PLT)、纤维蛋白原(FIB)和血小板与淋巴细胞比值(PLR)是预测无复发生存期(RFS)的独立因素,而纤维蛋白原、全身免疫炎症指数(SII)和血红蛋白(HGB)是预测无复发生存期(OS)的独立预后因素。与T分期相比,提名图显示了更高的预测C指数。此外,决策曲线分析(DCA)显示,提名图计算模型的净效益优于T分期。结论我们根据重要的血液标志物和临床病理特征,建立并验证了预测早期LSCC患者术后1年、3年和5年RFS和OS的提名图。这些模型可帮助临床医生做出个性化的治疗决策。
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来源期刊
Current Problems in Cancer
Current Problems in Cancer 医学-肿瘤学
CiteScore
5.10
自引率
0.00%
发文量
71
审稿时长
15 days
期刊介绍: Current Problems in Cancer seeks to promote and disseminate innovative, transformative, and impactful data on patient-oriented cancer research and clinical care. Specifically, the journal''s scope is focused on reporting the results of well-designed cancer studies that influence/alter practice or identify new directions in clinical cancer research. These studies can include novel therapeutic approaches, new strategies for early diagnosis, cancer clinical trials, and supportive care, among others. Papers that focus solely on laboratory-based or basic science research are discouraged. The journal''s format also allows, on occasion, for a multi-faceted overview of a single topic via a curated selection of review articles, while also offering articles that present dynamic material that influences the oncology field.
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