中国中西医结合影像学杂志2024，Vol.22Issue(3)：250-254,281,6.DOI:10.3969/j.issn.1672-0512.2024.03.002
基于胰腺CT的影像组学在预测糖耐量受损人群胰岛素抵抗中的应用价值
Application value of pancreatic CT-based radiomics in predicting insulin resistance in people with impaired glucose tolerance
摘要
Abstract
Objective:To explore the application value of pancreatic CT-based radiomics in predicting insulin resistance(IR)in people with impaired glucose tolerance.Methods:A total of 381 patients initially diagnosed with impaired glucose tolerance were retrospectively collected and were divided into two groups based on homeostasis model assessment of IR(HOMA-IR),a high-IR group(191 cases)and a low-IR group(190 cases).And all patients were randomly divided into the training cohort and the validation cohort at a ratio of 8∶2.Pancreatic ROIs were sketched and radiomics features were extracted,and the optimal features were selected after dimensionality reduction and screening.Eight machine learning models were constructed,and four machine learning methods(SVM,MLP,RF and AdaBoost)were selected to construct the diagnostic prediction model.ROC curve was used to evaluate the prediction performance of each radiomics model.Results:A total of 1 834 features were extracted and 189 features were screened by Pearson's correlation analysis.The dimensionality was reduced to 23 major radiomics features by LASSO method and 5-fold cross-validation.The AUCs of the four constructed prediction models based on SVM,MLP,RF and AdaBoost for the validation cohort were 0.723,0.731,0.807 and 0.681,respectively,with the better prediction efficiency of RF.Conclusion:The RF model based on pancreatic CT radiomics features has a better potential prediction for the IR level in people with impaired glucose tolerance.关键词
胰岛素抵抗/影像组学/支持向量机/多层感知机/随机森林/自适应提升算法/体层摄影术,X线计算机Key words
Insulin resistance/Radiomics/Support vector machine/Multilayer perceptron/Random forest/Adaptive boosting algorithm/Tomography,X-ray computed引用本文复制引用
布买丽亚木·买买提艾力,陈杰..基于胰腺CT的影像组学在预测糖耐量受损人群胰岛素抵抗中的应用价值[J].中国中西医结合影像学杂志,2024,22(3):250-254,281,6.基金项目
新疆维吾尔自治区自然科学基金(2022D01C137). （2022D01C137）
