Department of Pharmacy, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, Shanxi, China.
School of Pharmacy, Shanxi Medical University, Taiyuan, China.
Front Immunol. 2024 Aug 2;15:1431150. doi: 10.3389/fimmu.2024.1431150. eCollection 2024.
Lung cancer remains a significant global health burden, with non-small cell lung cancer (NSCLC) being the predominant subtype. Despite advancements in treatment, the prognosis for patients with advanced NSCLC remains unsatisfactory, underscoring the imperative for precise prognostic assessment models. This study aimed to develop and validate a survival prediction model specifically tailored for patients diagnosed with NSCLC.
A total of 523 patients were randomly divided into a training dataset (n=313) and a validation dataset (n=210). We conducted initial variable selection using three analytical methods: univariate Cox regression, LASSO regression, and random survival forest (RSF) analysis. Multivariate Cox regression was then performed on the variables selected by each method to construct the final predictive models. The optimal model was selected based on the highest bootstrap C-index observed in the validation dataset. Additionally, the predictive performance of the model was evaluated using time-dependent receiver operating characteristic (Time-ROC) curves, calibration plots, and decision curve analysis (DCA).
The LASSO regression model, which included N stage, neutrophil-lymphocyte ratio (NLR), D-dimer, neuron-specific enolase (NSE), squamous cell carcinoma antigen (SCC), driver alterations, and first-line treatment, achieved a bootstrap C-index of 0.668 (95% CI: 0.626-0.722) in the validation dataset, the highest among the three models tested. The model demonstrated good discrimination in the validation dataset, with area under the ROC curve (AUC) values of 0.707 (95% CI: 0.633-0.781) for 1-year survival, 0.691 (95% CI: 0.616-0.765) for 2-year survival, and 0.696 (95% CI: 0.611-0.781) for 3-year survival predictions, respectively. Calibration plots indicated good agreement between predicted and observed survival probabilities. Decision curve analysis demonstrated that the model provides clinical benefit at a range of decision thresholds.
The LASSO regression model exhibited robust performance in the validation dataset, predicting survival outcomes for patients with advanced NSCLC effectively. This model can assist clinicians in making more informed treatment decisions and provide a valuable tool for patient risk stratification and personalized management.
肺癌仍然是一个重大的全球健康负担，非小细胞肺癌（NSCLC）是主要亚型。尽管治疗取得了进展，但晚期 NSCLC 患者的预后仍不尽人意，这凸显了精确预后评估模型的必要性。本研究旨在为诊断为 NSCLC 的患者开发和验证一种生存预测模型。
共纳入 523 名患者，随机分为训练数据集（n=313）和验证数据集（n=210）。我们使用三种分析方法（单因素 Cox 回归、LASSO 回归和随机生存森林（RSF）分析）进行初始变量选择。然后，对每种方法选择的变量进行多因素 Cox 回归，构建最终预测模型。根据验证数据集中观察到的最高 bootstrap C-指数选择最佳模型。此外，还通过时间依赖性接受者操作特征（Time-ROC）曲线、校准图和决策曲线分析（DCA）评估模型的预测性能。
LASSO 回归模型包含 N 分期、中性粒细胞-淋巴细胞比值（NLR）、D-二聚体、神经元特异性烯醇化酶（NSE）、鳞状细胞癌抗原（SCC）、驱动基因突变和一线治疗，在验证数据集中的 bootstrap C-指数为 0.668（95%置信区间：0.626-0.722），在三种测试模型中最高。该模型在验证数据集中具有良好的判别能力，1 年生存率的 ROC 曲线下面积（AUC）值为 0.707（95%置信区间：0.633-0.781），2 年生存率为 0.691（95%置信区间：0.616-0.765），3 年生存率为 0.696（95%置信区间：0.611-0.781）。校准图表明预测和观察到的生存概率之间具有良好的一致性。决策曲线分析表明，该模型在一系列决策阈值下提供了临床获益。
LASSO 回归模型在验证数据集中表现出稳健的性能，有效地预测了晚期 NSCLC 患者的生存结局。该模型可以帮助临床医生做出更明智的治疗决策，并为患者风险分层和个性化管理提供有价值的工具。
