PDF(Pubmed)
Abstract：
UNASSIGNED: The prognostic value of an effective biomarker, pan-immune-inflammation value (PIV), for head and neck squamous cell carcinoma (HNSCC) patients after radical surgery or chemoradiotherapy has not been well explored. This study aimed to construct and validate nomograms based on PIV to predict survival outcomes of HNSCC patients.
UNASSIGNED: A total of 161 HNSCC patients who underwent radical surgery were enrolled retrospectively for development cohort. The cutoff of PIV was determined using the maximally selected rank statistics method. Multivariable Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to develop two nomograms (Model A and Model B) that predict disease-free survival (DFS). The concordance index, receiver operating characteristic curves, calibration curves, and decision curve analysis were used to evaluate the nomograms. A cohort composed of 50 patients who received radiotherapy or chemoradiotherapy (RT/CRT) alone was applied for generality testing of PIV and nomograms.
UNASSIGNED: Patients with higher PIV (≥123.3) experienced a worse DFS (HR, 5.01; 95% CI, 3.25-7.72; p<0.0001) and overall survival (OS) (HR, 5.23; 95% CI, 3.34-8.18; p<0.0001) compared to patients with lower PIV (<123.3) in the development cohort. Predictors of Model A included age, TNM stage, neutrophil-to-lymphocyte ratio (NLR), and PIV, and that of Model B included TNM stage, lymphocyte-to-monocyte ratio (LMR), and PIV. In comparison with TNM stage alone, the two nomograms demonstrated good calibration and discrimination and showed satisfactory clinical utility in internal validation. The generality testing results showed that higher PIV was also associated with worse survival outcomes in the RT/CRT cohort and the possibility that the two nomograms may have a universal applicability for patients with different treatments.
UNASSIGNED: The nomograms based on PIV, a simple but useful indicator, can provide prognosis prediction of individual HNSCC patients after radical surgery and may be broadly applicated for patients after RT/CRT alone.
UNASSIGNED: A total of 161 HNSCC patients who underwent radical surgery were enrolled retrospectively for development cohort. The cutoff of PIV was determined using the maximally selected rank statistics method. Multivariable Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to develop two nomograms (Model A and Model B) that predict disease-free survival (DFS). The concordance index, receiver operating characteristic curves, calibration curves, and decision curve analysis were used to evaluate the nomograms. A cohort composed of 50 patients who received radiotherapy or chemoradiotherapy (RT/CRT) alone was applied for generality testing of PIV and nomograms.
UNASSIGNED: Patients with higher PIV (≥123.3) experienced a worse DFS (HR, 5.01; 95% CI, 3.25-7.72; p<0.0001) and overall survival (OS) (HR, 5.23; 95% CI, 3.34-8.18; p<0.0001) compared to patients with lower PIV (<123.3) in the development cohort. Predictors of Model A included age, TNM stage, neutrophil-to-lymphocyte ratio (NLR), and PIV, and that of Model B included TNM stage, lymphocyte-to-monocyte ratio (LMR), and PIV. In comparison with TNM stage alone, the two nomograms demonstrated good calibration and discrimination and showed satisfactory clinical utility in internal validation. The generality testing results showed that higher PIV was also associated with worse survival outcomes in the RT/CRT cohort and the possibility that the two nomograms may have a universal applicability for patients with different treatments.
UNASSIGNED: The nomograms based on PIV, a simple but useful indicator, can provide prognosis prediction of individual HNSCC patients after radical surgery and may be broadly applicated for patients after RT/CRT alone.
摘要：
■有效生物标志物的预后价值，泛免疫炎症值(PIV)，对于头颈部鳞状细胞癌（HNSCC）患者，在根治性手术或放化疗后尚未得到很好的探索。本研究旨在构建和验证基于PIV的列线图，以预测HNSCC患者的生存结果。
■共有161例接受根治性手术的HNSCC患者被纳入回顾性研究队列。使用最大选择的秩统计方法确定PIV的截止值。进行了多变量Cox回归和最小绝对收缩和选择算子（LASSO）回归分析，以开发两个预测无病生存（DFS）的列线图（模型A和模型B）。一致性指数，接收机工作特性曲线，校正曲线,和决策曲线分析用于评估列线图。由50例仅接受放疗或放化疗（RT/CRT）的患者组成的队列用于PIV和列线图的一般性测试。
■PIV较高（≥123.3）的患者DFS较差（HR，5.01；95%CI，3.25-7.72；p<0.0001）和总生存期（OS）（HR，与发展队列中PIV较低（<123.3）的患者相比，5.23；95％CI，3.34-8.18；p<0.0001）。模型A的预测因素包括年龄，TNM阶段,中性粒细胞与淋巴细胞比率（NLR），还有PIV,模型B包括TNM阶段，淋巴细胞与单核细胞比率（LMR），和PIV。与单独的TNM阶段相比，这两个列线图显示出良好的校准和鉴别,并在内部验证中显示出令人满意的临床效用.一般性测试结果表明，在RT/CRT队列中，较高的PIV也与较差的生存结果相关，并且两个列线图可能对接受不同治疗的患者具有普遍适用性。
■基于PIV的列线图，一个简单但有用的指标，可以为根治性手术后的单个HNSCC患者提供预后预测，可广泛应用于单纯RT/CRT术后的患者。
■共有161例接受根治性手术的HNSCC患者被纳入回顾性研究队列。使用最大选择的秩统计方法确定PIV的截止值。进行了多变量Cox回归和最小绝对收缩和选择算子（LASSO）回归分析，以开发两个预测无病生存（DFS）的列线图（模型A和模型B）。一致性指数，接收机工作特性曲线，校正曲线,和决策曲线分析用于评估列线图。由50例仅接受放疗或放化疗（RT/CRT）的患者组成的队列用于PIV和列线图的一般性测试。
■PIV较高（≥123.3）的患者DFS较差（HR，5.01；95%CI，3.25-7.72；p<0.0001）和总生存期（OS）（HR，与发展队列中PIV较低（<123.3）的患者相比，5.23；95％CI，3.34-8.18；p<0.0001）。模型A的预测因素包括年龄，TNM阶段,中性粒细胞与淋巴细胞比率（NLR），还有PIV,模型B包括TNM阶段，淋巴细胞与单核细胞比率（LMR），和PIV。与单独的TNM阶段相比，这两个列线图显示出良好的校准和鉴别,并在内部验证中显示出令人满意的临床效用.一般性测试结果表明，在RT/CRT队列中，较高的PIV也与较差的生存结果相关，并且两个列线图可能对接受不同治疗的患者具有普遍适用性。
■基于PIV的列线图，一个简单但有用的指标，可以为根治性手术后的单个HNSCC患者提供预后预测，可广泛应用于单纯RT/CRT术后的患者。
