摘要
目的构建及验证一种基于机器学习的用于预测慢性硬膜下血肿(cSDH)内镜治疗后复发的CT影像组学模型。方法选择自2016年10月至2024年10月于苏州大学附属第二医院神经外科接受内镜治疗的252例cSDH患者为研究对象。回顾性收集患者的临床及影像学资料,并按照7∶3比例随机将患者分为训练集(n=176)与验证集(n=76),进一步分别在训练集与验证集中依据患者出院后3个月内是否复发将其分为复发组与非复发组。研究内容包括:(1)使用3D-Slicer软件提取患者初始非增强CT图像上cSDH的影像组学特征,通过单因素分析及最小绝对收缩和选择算子(LASSO)回归分析筛选出最优特征,并基于这些最优特征,分别采用3种机器学习算法(Logistic回归、支持向量机、K-近邻)构建CT影像组学模型,应用敏感度、特异度和受试者工作特征曲线下面积(AUC)等指标比较不同CT影像组学模型间预测性能的差异,筛选出最佳模型。(2)基于患者初始非增强CT图像,依据Nakaguchi分型对血肿进行分型(均质型、层状型、分隔型和小梁型),并联合患者的临床特征(如Markwalder''s分级、双侧血肿等),通过单因素分析及多因素Logistic回归分析筛选出cSDH复发的独立风险因素,并基于这些因素,分别采用3种机器学习算法(Logistic回归、支持向量机、K-近邻)构建血肿分型-临床特征模型,应用敏感度、特异度和受试者工作特征AUC等指标比较不同血肿分型-临床特征模型间预测性能的差异,筛选出最佳模型。(3)采用DeLong''s检验比较CT影像组学模型与血肿分型-临床特征模型的ROC曲线差异,采用决策曲线分析比较CT影像组学模型与血肿分型-临床特征模型的有效范围。结果(1)经单因素分析及LASSO回归分析筛选后获得7个最优CT影像组学特征:1个灰度依赖矩阵特征、1个第一阶能量特征、2个灰度共生矩阵特征、2个灰度尺寸区域矩阵特征、1个灰度游程矩阵特征(均基于小波变换)。基于这7个最优特征构建的K-近邻模型预测cSDH复发的性能最佳,其在验证集患者中的AUC为0.845,敏感度为0.833,特异度为0.857,召回率为0.833,F1分数为0.476。(2)经单因素分析及多因素Logistic回归分析筛选出3个cSDH复发的独立风险因素:血肿Nakaguchi分型、Markwalder's分级、双侧血肿。基于这3个因素构建的Logistic回归模型预测cSDH复发的性能最佳,其在验证集患者中的AUC为0.675,敏感度为0.609,特异度为0.654,召回率为0.609,F1分数为0.311。(3)DeLong's检验显示,无论是训练集患者还是验证集患者,CT影像组学模型的AUC均明显大于血肿分型-临床特征模型,差异均有统计学意义(训练集:P=0.027;验证集:P=0.035)。决策曲线分析显示,在CT影像组学模型中,风险阈值为0.05~0.95时模型净效益均>0;在血肿分型-临床特征模型中,风险阈值为0.05~0.55时模型净效益均>0。结论本研究基于7种CT影像组学特征构建的K-近邻模型能有效预测cSDH患者内镜治疗后复发,且性能明显优于本研究构建的传统血肿分型-临床特征模型。
Objective To develop and validate CT radiomics models based on machine learning for predicting recurrence of chronic subdural hematoma(cSDH)after endoscopic treatment.Methods A retrospective study was performed;252 patients with cSDH who underwent endoscopic treatment in Department of Neurosurgery,the Second Affiliated Hospital of Soochow University from October 2016 to October 2024 were selected.The clinical and imaging data of these patients were collected,and these patients were divided into a training set(n=176)and a validation set(n=76)at a ratio of 7:3.Patients in both sets were further sub-divided into a recurrence group and a non-recurrence group based on whether they had recurrence within 3 months of discharge.(1)Radiomics features of cSDH on initial non-enhanced CT images were extracted using 3D-Slicer software.Optimal features were selected through univariate analysis and least absolute shrinkage and selection operator(LASSO)regression analysis;based on these optimal features,3 machine learning algorithms(Logistic,support vector machine[SVM],and K-nearest neighbor[KNN])were used to construct CT radiomics models.Differences in predictive performance of different radiomics models were compared by analyzing indicators such as sensitivity,specificity,and area under receiver operating characteristic(ROC)curve(AUC),and the best model was selected.(2)Based on the initial non-enhanced CT images,cSDH was classified into homogeneous type,laminar type,septated type,and trabecular type according to Nakaguchi classification system;combined these cSDH typing with clinical features(clinical Markwalder's grade and bilateral hematoma),univariate analysis and multivariate Logistic regression analysis were used to screen the independent risk factors for cSDH recurrence.Based on these factors,the 3 machine learning algorithms(Logistic,SVM,KNN)were used to construct hematoma typing-clinical feature models;differences in predictive performance of different hematoma typing-clinical feature models were compared by analyzing indicators such as sensitivity,specificity,and AUC,and the best model was selected.(3)DeLong's test was used to compare the ROC curve differences between the CT radiomics model and hematoma typing-clinical feature model.Decision curve analysis was used to compare the effective scope of the CT radiomics model and hematoma typing-clinical feature model.Results(1)Seven optimal CT radiomics features based on wavelet transform were obtained after univariate analysis and LASSO regression:one gray-level dependence matrix feature,one first-order energy feature,two gray-level co-occurrence matrix features,two gray level size zone matrix features,and one gray-level run-length matrix feature.The KNN model constructed based on these 7 optimal features had the best performance in predicting cSDH recurrence,with an AUC of 0.845,a sensitivity of 0.833,a specificity of 0.857,a recall rate of 0.833,and an F1 score of 0.476 in patients from the validation set.(2)Three independent risk factors for cSDH recurrence were screened out through univariate analysis and multivariate Logistic regression analysis:hematoma Nakaguchi classification,Markwalder's grade,and bilateral hematoma.Logistic model constructed based on these 3 factors had the best performance in predicting cSDH recurrence,with an AUC of 0.675,a sensitivity of 0.609,a specificity of 0.654,a recall rate of 0.609,and an F1 score of 0.311 in patients from the validation set.(3)DeLong's test showed that the AUC of the CT radiomics model was significantly greater than that of the hematoma typing-clinical feature model in patients from the training set and validation set(P=0.027 and P=0.035).Decision curve analysis showed that in the CT radiomics model,the net benefit of the model was>0 when the risk threshold was 0.05-0.95;in the hematoma typing-clinical feature model,the net benefit of the model was>0 when the risk threshold was 0.05-0.55.Conclusion The KNN model based on 7 CT radiomics features in this study can effectively predict the cSDH recurrence in patients after endoscopic treatment,and its performance is obviously better than that of hematoma typing-clinical feature model constructed in this study.
作者
王麒龙
吴毅
王中勇
董军
兰青
Wang Qilong;Wu Yi;Wang Zhongyong;Dong Jun;Lan Qing(Department of Neurosurgery,The Second Affiliated Hospital of Soochow University,Suzhou 215000,China)
出处
《中华神经医学杂志》
北大核心
2025年第11期1115-1124,共10页
Chinese Journal of Neuromedicine
基金
江苏省医学重点学科建设单位(JSDW202225)
苏州市姑苏卫生人才计划(GSWS2021014)
苏州大学附属第二医院神经疾病研究中心研究课题(ND2024B06)。
关键词
慢性硬膜下血肿
CT影像组学
机器学习
复发
预测模型
内镜治疗
Chronic subdural hematoma
CT radiomics
Machine learning
Recurrence
Prediction model
Endoscopic treatment
