国际医学放射学杂志2025，Vol.48Issue(2)：151-158,8.DOI:10.19300/j.2025.L21648
基于能谱CT的影像组学机器学习模型及列线图在术前鉴别结直肠癌KRAS基因的应用
Application of a spectral CT-based radiomics machine learning model and nomogram for preoperative identification of KRAS gene status in colorectal cancer
摘要
Abstract
Objective To explore the diagnostic performance of a spectral CT-based radiomics machine learning model and nomogram for preoperatively identifying the KRAS gene status in patients with colorectal cancer(CRC).Methods A total of 137 CRC patients who underwent KRAS mutation detection and preoperative spectral CT examination were retrospectively included(70 cases with KRAS wild type and 67 cases with KRAS mutant type).They were randomly divided into a training set(95 cases)and a test set(42 cases)in a 7∶3 ratio.Tumor region of interest(ROI)was delineated on venous-phase 70 keV monochromatic enhanced CT images,and radiomics features were extracted and selected.A radiomics score(Rad-score)was calculated using least absolute shrinkage and selection operator(LASSO)regression.Six models were established including three radiomics models based on support vector machine(SVM),extreme gradient boosting(XGBoost),and logistic regression(LR),as well as three combined models integrating spectral CT imaging features with the Rad-score.Model performance was evaluated using the area under the receiver operating characteristic(ROC)curve(AUC),and compared using the Delong test.A radiomics nomogram was constructed based on the Rad-score and validated in the test set.Calibration curves,decision curve analysis(DCA),and clinical impact curves were used to assess calibration,clinical net benefit,and clinical utility.Results A total of 8 radiomics features and 1 spectral parameter were selected.In the test set,the LR-based combined model demonstrated the best performance,with an AUC of 0.891,outperforming the combined models based on SVM(AUC=0.796),XGBoost(AUC=0.787),and LR(AUC=0.812)(all P＜0.05),as well as the combined models based on SVM(AUC=0.889)and XGBoost(AUC=0.873)(both P＜0.05).The nomogram model achieved AUCs of 0.987 and 0.916 in the training and test sets,respectively.The calibration curve showed good agreement in the training set,while performance in the test set was slightly lower.DCA and clinical impact curves demonstrated that the nomogram provided favorable clinical net benefit and utility.Conclusion The LR-based model and nomogram,constructed using venous-phase spectral CT and radiomics features,offer valuable preoperative insights into KRAS gene status in CRC patients and may serve as a reference for clinical decision-making.关键词
结直肠癌/影像组学/机器学习模型/列线图/能谱CT/基因突变Key words
Colorectal cancer/Radiomics/Machine learning model/Nomogram/CT spectral imaging/Gene mutation分类
特种医学引用本文复制引用
李泽茂,马汝航,王雅静,陈伟彬..基于能谱CT的影像组学机器学习模型及列线图在术前鉴别结直肠癌KRAS基因的应用[J].国际医学放射学杂志,2025,48(2):151-158,8.基金项目
河北省2023年度医学科学研究课题计划(20231255) （20231255）
