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. 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. 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. 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.

中文翻译：

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预测早期喉鳞状细胞癌术后预后的列线图模型的建立和验证

我们的目的是研究早期喉鳞状细胞癌（LSCC）患者的术后预后与术前血液标志物和临床病理特征的关系，并开发用于个体风险预测的列线图。回顾性检索南京医科大学第一附属医院2009年至2018年确诊的353例早期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，然后构建列线图来预测无复发生存期 (RFS) 和总生存期 (OS)。最后对列线图进行内部验证，评估列线图的预测能力并与肿瘤T分期进行比较。单变量和多变量分析确定血小板计数 (PLT)、纤维蛋白原 (FIB) 和血小板与淋巴细胞比 (PLR) 是 RFS 的独立因素，FIB、全身免疫炎症指数 (SII) 和血红蛋白 (HGB) 是独立的预后因素操作系统的因素。列线图显示出比 T 分期更高的预测 C 指数。此外，决策曲线分析（DCA）显示列线图计算模型的净效益优于 T 分期。我们建立并验证了列线图，根据显着的血液标志物和临床病理特征来预测早期 LSCC 患者术后 1 年、3 年和 5 年的 RFS 和 OS。这些模型可能有助于临床医生做出个性化的治疗决策。