基于氧化应激的老年食管鳞癌患者 3 年生存预测的机器学习方法比较。

Comparison of machine learning methods for Predicting 3-Year survival in elderly esophageal squamous cancer patients based on oxidative stress.

机构信息

Department of Cardiothoracic Surgery, The Affiliated Hospital of Putian University, No.999 Dongzhen Road, Fujian, 351100, China.

Department of Gastroenterology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, 121001, China.

出版信息

BMC Cancer. 2024 Nov 21;24(1):1432. doi: 10.1186/s12885-024-13115-7.

DOI:10.1186/s12885-024-13115-7

PMID:39574068

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11580478/

Abstract

BACKGROUND

Oxidative stress process plays a key role in aging and cancer; however, currently, there is paucity of machine-learning model studies investigating the relationship between oxidative stress and prognosis of elderly patients with esophageal squamous cancer (ESCC).

METHODS

This study included elderly patients with ESCC who underwent curative ESCC resection surgery continuously from January 2013 to December 2020 and were stratified into the training and external validation cohorts. Using Cox stepwise regression analysis based on Akaike information criterion, the relationship between oxidative stress biomarkers and prognosis was explored, and a geriatric ESCC-related oxidative stress score (OSS) was constructed. To construct a predictive model for 3-year overall survival (OS), machine-learning strategies including decision tree (DT), random forest (RF), and support vector machine (SVM) were employed. These machine-learning strategies play a key role in data mining and pattern recognition tasks. Each model was tested in the external validation cohort through 1000 resampling iterations. Validation was conducted using receiver operating characteristic area under the curve (AUC) and calibration plots.

RESULTS

The training cohort and validation cohort consisted of 340 and 145 patients, respectively. In the training cohort, the 3-year OS rate for patients was 59.2%. We constructed the OSS based on systemic oxidative stress biomarkers using the training cohort. The study found that pathological N stage, pathological T stage, tumor histological type, lymphovascular invasion, CEA, OSS, CA 19 - 9, and the amount of bleeding were the most important factors influencing the 3-year OS. These eight important features were included in training the RF, DT, and SVM and trained on the training cohort and validated cohort, respectively. In the training cohort, the RF model demonstrated the highest predictive performance with an AUC of 0.975 (0.962-0.987), while the DT model is 0.784 (0.739-0.830) and the SVM is 0.879 (0.843-0.916). In the external validation cohort, the RF model again exhibited the highest performance with an AUC of 0.791 (0.717-0.864), compared to the DT model with an AUC of 0.717 (0.640-0.794) and 0.779 (0.702-0.856) in SVM.

CONCLUSIONS

The random forest clinical prediction model constructed based on OSS can effectively predict the prognosis of elderly patients with ESCC after curative surgery.

摘要

背景

氧化应激过程在衰老和癌症中起着关键作用；然而，目前，基于机器学习模型研究探讨氧化应激与老年食管癌患者预后之间关系的研究还很少。

方法

本研究纳入了 2013 年 1 月至 2020 年 12 月连续接受根治性食管癌切除术的老年食管癌患者，并将其分为训练队列和外部验证队列。采用基于赤池信息量准则（Akaike information criterion）的 Cox 逐步回归分析，探讨氧化应激生物标志物与预后的关系，并构建老年食管癌相关氧化应激评分（OSS）。为构建 3 年总生存（OS）的预测模型，采用决策树（DT）、随机森林（RF）和支持向量机（SVM）等机器学习策略。这些机器学习策略在数据挖掘和模式识别任务中起着关键作用。每个模型均通过 1000 次重采样迭代在外部验证队列中进行测试。验证使用接收器工作特征曲线下面积（AUC）和校准图进行。

结果

训练队列和验证队列分别纳入 340 例和 145 例患者。在训练队列中，患者的 3 年 OS 率为 59.2%。我们基于系统氧化应激生物标志物构建了 OSS。研究发现，病理 N 分期、病理 T 分期、肿瘤组织学类型、淋巴血管侵犯、CEA、OSS、CA 19-9 和出血量是影响 3 年 OS 的最重要因素。这 8 个重要特征被纳入 RF、DT 和 SVM 的训练，并分别在训练队列和验证队列中进行训练。在训练队列中，RF 模型的预测性能最高，AUC 为 0.975（0.962-0.987），而 DT 模型为 0.784（0.739-0.830），SVM 为 0.879（0.843-0.916）。在外部验证队列中，RF 模型再次表现出最高的性能，AUC 为 0.791（0.717-0.864），而 DT 模型的 AUC 为 0.717（0.640-0.794），SVM 的 AUC 为 0.779（0.702-0.856）。