PDF(Pubmed)
Abstract：
UNASSIGNED: This study aimed to establish a comprehensive clinical prognostic risk model based on pulmonary function tests. This model was intended to guide the evaluation and predictive management of patients with resectable stage I-III non-small cell lung cancer (NSCLC) receiving neoadjuvant chemoimmunotherapy.
UNASSIGNED: Clinical pathological characteristics and prognostic survival data for 175 patients were collected. Univariate and multivariate Cox regression analyses, and least absolute shrinkage and selection operator (LASSO) regression analysis were employed to identify variables and construct corresponding models. These variables were integrated to develop a ridge regression model. The models\' discrimination and calibration were evaluated, and the optimal model was chosen following internal validation. Comparative analyses between the risk scores or groups of the optimal model and clinical factors were conducted to explore the potential clinical application value.
UNASSIGNED: Univariate regression analysis identified smoking, complete pathologic response (CPR), and major pathologic response (MPR) as protective factors. Conversely, T staging, D-dimer/white blood cell ratio (DWBCR), D-dimer/fibrinogen ratio (DFR), and D-dimer/minute ventilation volume actual ratio (DMVAR) emerged as risk factors. Evaluation of the models confirmed their capability to accurately predict patient prognosis, exhibiting ideal discrimination and calibration, with the ridge regression model being optimal. Survival analysis demonstrated that the disease-free survival (DFS) in the high-risk group (HRG) was significantly shorter than in the low-risk group (LRG) (P=2.57×10-13). The time-dependent receiver operating characteristic (ROC) curve indicated that the area under the curve (AUC) values at 1 year, 2 years, and 3 years were 0.74, 0.81, and 0.79, respectively. Clinical correlation analysis revealed that men with lung squamous cell carcinoma or comorbid chronic obstructive pulmonary disease (COPD) were predominantly in the LRG, suggesting a better prognosis and potentially identifying a beneficiary population for this treatment combination.
UNASSIGNED: The prognostic model developed in this study effectively predicts the prognosis of patients with NSCLC receiving neoadjuvant chemoimmunotherapy. It offers valuable predictive insights for clinicians, aiding in developing treatment plans and monitoring disease progression.
UNASSIGNED: Clinical pathological characteristics and prognostic survival data for 175 patients were collected. Univariate and multivariate Cox regression analyses, and least absolute shrinkage and selection operator (LASSO) regression analysis were employed to identify variables and construct corresponding models. These variables were integrated to develop a ridge regression model. The models\' discrimination and calibration were evaluated, and the optimal model was chosen following internal validation. Comparative analyses between the risk scores or groups of the optimal model and clinical factors were conducted to explore the potential clinical application value.
UNASSIGNED: Univariate regression analysis identified smoking, complete pathologic response (CPR), and major pathologic response (MPR) as protective factors. Conversely, T staging, D-dimer/white blood cell ratio (DWBCR), D-dimer/fibrinogen ratio (DFR), and D-dimer/minute ventilation volume actual ratio (DMVAR) emerged as risk factors. Evaluation of the models confirmed their capability to accurately predict patient prognosis, exhibiting ideal discrimination and calibration, with the ridge regression model being optimal. Survival analysis demonstrated that the disease-free survival (DFS) in the high-risk group (HRG) was significantly shorter than in the low-risk group (LRG) (P=2.57×10-13). The time-dependent receiver operating characteristic (ROC) curve indicated that the area under the curve (AUC) values at 1 year, 2 years, and 3 years were 0.74, 0.81, and 0.79, respectively. Clinical correlation analysis revealed that men with lung squamous cell carcinoma or comorbid chronic obstructive pulmonary disease (COPD) were predominantly in the LRG, suggesting a better prognosis and potentially identifying a beneficiary population for this treatment combination.
UNASSIGNED: The prognostic model developed in this study effectively predicts the prognosis of patients with NSCLC receiving neoadjuvant chemoimmunotherapy. It offers valuable predictive insights for clinicians, aiding in developing treatment plans and monitoring disease progression.
摘要：
■本研究旨在建立基于肺功能检查的综合临床预后风险模型。该模型旨在指导接受新辅助化学免疫疗法的可切除I-III期非小细胞肺癌（NSCLC）患者的评估和预测管理。
■收集175例患者的临床病理特征和预后生存数据。单变量和多变量Cox回归分析，采用最小绝对收缩和选择算子（LASSO）回归分析来识别变量并构建相应的模型。整合这些变量以建立岭回归模型。对模型的鉴别和校准进行了评估，并在内部验证后选择了最佳模型。将最优模型的风险评分或分组与临床因素进行比较分析，以探讨其潜在的临床应用价值。
■单变量回归分析确定吸烟，完全病理反应（CPR），和主要病理反应（MPR）作为保护因素。相反，T分期,D-二聚体/白细胞比值(DWBCR),D-二聚体/纤维蛋白原比值（DFR），D-二聚体/分钟通气量实际比值(DMVAR)为危险因素。对这些模型的评估证实了它们准确预测患者预后的能力，表现出理想的辨别和校准，岭回归模型是最优的。生存分析表明，高危组（HRG）的无病生存期（DFS）明显短于低危组（LRG）（P=2.57×10-13）。时间依赖性受试者工作特征（ROC）曲线表明，1年时的曲线下面积（AUC）值，2年,3年分别为0.74、0.81和0.79。临床相关分析显示,男性肺鳞状细胞癌或慢性阻塞性肺疾病(COPD)患者以LRG为主,提示更好的预后，并可能确定该治疗组合的受益人群。
■本研究开发的预后模型可有效预测接受新辅助化学免疫治疗的非小细胞肺癌患者的预后。它为临床医生提供了有价值的预测性见解，协助制定治疗计划和监测疾病进展。
■收集175例患者的临床病理特征和预后生存数据。单变量和多变量Cox回归分析，采用最小绝对收缩和选择算子（LASSO）回归分析来识别变量并构建相应的模型。整合这些变量以建立岭回归模型。对模型的鉴别和校准进行了评估，并在内部验证后选择了最佳模型。将最优模型的风险评分或分组与临床因素进行比较分析，以探讨其潜在的临床应用价值。
■单变量回归分析确定吸烟，完全病理反应（CPR），和主要病理反应（MPR）作为保护因素。相反，T分期,D-二聚体/白细胞比值(DWBCR),D-二聚体/纤维蛋白原比值（DFR），D-二聚体/分钟通气量实际比值(DMVAR)为危险因素。对这些模型的评估证实了它们准确预测患者预后的能力，表现出理想的辨别和校准，岭回归模型是最优的。生存分析表明，高危组（HRG）的无病生存期（DFS）明显短于低危组（LRG）（P=2.57×10-13）。时间依赖性受试者工作特征（ROC）曲线表明，1年时的曲线下面积（AUC）值，2年,3年分别为0.74、0.81和0.79。临床相关分析显示,男性肺鳞状细胞癌或慢性阻塞性肺疾病(COPD)患者以LRG为主,提示更好的预后，并可能确定该治疗组合的受益人群。
■本研究开发的预后模型可有效预测接受新辅助化学免疫治疗的非小细胞肺癌患者的预后。它为临床医生提供了有价值的预测性见解，协助制定治疗计划和监测疾病进展。
