Chen Hao, Xu Jiangjiang, Zhang Qiang, Chen Pengfei, Liu Qiuxia, Guo Lianyi, Xu Bindong
Department of Thoracic and Cardiovascular Surgery of the Affiliated Hospital of Putian University, Putian, Fujian, China.
Fuding Hospital, Fujian University of Traditional Chinese Medicine, Fuding, Fujian, China.
Front Oncol. 2024 Oct 24;14:1482374. doi: 10.3389/fonc.2024.1482374. eCollection 2024.
Oxidative stress plays a significant role in aging and cancer, yet there is currently a lack of research utilizing machine learning models to examine the relationship between oxidative stress and prognosis in elderly non-small cell lung cancer (NSCLC) patients.
This study included elderly NSCLC patients who underwent radical lung cancer resection from January 2012 to April 2018, exploring the relationship between Oxidative Stress Score (OSS) and prognosis. Machine learning techniques, including Decision Trees (DT), Random Forest (RF), and Support Vector Machine (SVM), were employed to develop predictive models for 5-year overall survival (OS).
The datasets consisted of 1647 patients in the training set, 705 in the internal validation set, and 516 in the external validation set. An OSS was formulated from six systemic oxidative stress biomarkers, such as albumin, total bilirubin, and blood urea nitrogen, among others. Boruta variable importance analysis identified low OSS as a key indicator of poor prognosis. The OSS was subsequently integrated into the DT, RF, and SVM models for training. These models, optimized through hyperparameter tuning on the training set, were then evaluated on the internal and external validation sets. The RF model demonstrated the highest predictive performance, with an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.794 in the internal validation set, compared to AUCs of 0.711 and 0.760 for the DT and SVM models, respectively. Similarly, in the external validation set, the RF model achieved an AUC of 0.784, outperforming the DT and SVM models, which had AUCs of 0.699 and 0.730, respectively. Calibration plots confirmed the RF model's superior calibration, followed by the SVM model, with the DT model performing the poorest.
The OSS-based clinical prediction model, constructed using machine learning methodologies, effectively predicts the prognosis of elderly NSCLC patients post-radical surgery.
氧化应激在衰老和癌症中起着重要作用，但目前缺乏利用机器学习模型来研究老年非小细胞肺癌（NSCLC）患者氧化应激与预后之间关系的研究。
本研究纳入了2012年1月至2018年4月期间接受根治性肺癌切除术的老年NSCLC患者，探讨氧化应激评分（OSS）与预后之间的关系。采用机器学习技术，包括决策树（DT）、随机森林（RF）和支持向量机（SVM），来开发5年总生存期（OS）的预测模型。
数据集包括训练集中的1647例患者、内部验证集中的705例患者和外部验证集中的516例患者。OSS由六种全身氧化应激生物标志物制定而成，如白蛋白、总胆红素和血尿素氮等。Boruta变量重要性分析确定低OSS是预后不良的关键指标。随后将OSS纳入DT、RF和SVM模型进行训练。这些通过在训练集上进行超参数调整而优化的模型，然后在内部和外部验证集上进行评估。RF模型表现出最高的预测性能，在内部验证集中，受试者工作特征曲线下面积（AUC）为0.794，而DT和SVM模型的AUC分别为0.711和0.760。同样，在外部验证集中，RF模型的AUC为0.784，优于DT和SVM模型，后者的AUC分别为0.699和0.730。校准图证实了RF模型具有更好的校准效果，其次是SVM模型，DT模型表现最差。
使用机器学习方法构建的基于OSS的临床预测模型，能有效预测老年NSCLC患者根治性手术后的预后。
