IT Department, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China.
Department of Clinical Oncology, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China.
Technol Health Care. 2024;32(3):1431-1443. doi: 10.3233/THC-230385.
The incidence of type 2 diabetes is rapidly increasing worldwide. Studies have shown that it is also associated with cancer-related morbidities. Early detection of cancer in patients with type 2 diabetes is crucial.
This study aimed to construct a model to predict cancer risk in patients with type 2 diabetes.
This study collected clinical data from a total of 5198 patients. A cancer risk prediction model was established by analyzing 261 items from routine laboratory tests. We screened 107 risk factors from 261 clinical tests based on the importance of the characteristic variables, significance of differences between groups (P< 0.05), and minimum description length algorithm.
Compared with 16 machine learning classifiers, five classifiers based on the decision tree algorithm (CatBoost, light gradient boosting, random forest, XGBoost, and gradient boosting) had an area under the receiver operating characteristic curve (AUC) of > 0.80. The AUC for CatBoost was 0.852 (sensitivity: 79.6%; specificity: 83.2%).
The constructed model can predict the risk of cancer in patients with type 2 diabetes based on tumor biomarkers and routine tests using machine learning algorithms. This is helpful for early cancer risk screening and prevention to improve patient outcomes.
全球 2 型糖尿病的发病率正在迅速上升。研究表明，它也与癌症相关的发病率有关。早期发现 2 型糖尿病患者的癌症至关重要。
本研究旨在构建一个预测 2 型糖尿病患者癌症风险的模型。
本研究共收集了 5198 名患者的临床数据。通过分析 261 项常规实验室检查项目，建立了癌症风险预测模型。我们根据特征变量的重要性、组间差异的显著性（P<0.05）和最小描述长度算法，从 261 项临床检查中筛选出 107 个风险因素。
与 16 种机器学习分类器相比，基于决策树算法（CatBoost、light gradient boosting、random forest、XGBoost 和 gradient boosting）的五种分类器的受试者工作特征曲线下面积（AUC）>0.80。CatBoost 的 AUC 为 0.852（敏感性：79.6%；特异性：83.2%）。
该模型可以使用机器学习算法，根据肿瘤生物标志物和常规检测结果预测 2 型糖尿病患者的癌症风险。这有助于早期进行癌症风险筛查和预防，改善患者的预后。
