摘要
目的探讨基于MRI影像组学联合临床特征的不同机器学习模型对宫颈鳞癌组织学分级的预测价值。方法回顾性分析经病理活检证实的150例宫颈鳞癌患者,按4∶1的比例随机分为训练集和验证集。从T2加权像脂肪抑制序列(FS-T2WI)和增强T1WI(延迟期)的感兴趣区中提取特征。经过降维和筛选特征后,使用Logistic回归(LR)、支持向量机(SVM)、贝叶斯(NB)、随机森林(RF)、轻量级梯度提升机(LightGBM)、K-最近邻法(KNN)构建预测宫颈鳞癌组织学分级的影像组学模型。采用受试者操作特征(ROC)曲线下面积(AUC)评估6种模型的预测性能。采用单因素及多因素Logistic回归分析预测独立危险因素,并建立临床及影像组学联合模型。通过AUC比较各个模型的差异,决策曲线(DCA)评估模型的临床价值。结果在影像组学模型中,LightGBM模型AUC下面积最大(训练集为0.910,验证集为0.839)。临床特征联合LightGBM模型的AUC面积最大(训练集0.935,验证集0.888),高于临床模型(AUC训练集为0.762,验证集为0.710)和LightGBM影像组学模型。结论LightGBM模型在影像组学模型中预测价值较高。联合模型的DCA效果最佳,具有最好的临床净获益。结合影像组学和临床特征的联合预测模型对宫颈鳞癌低分化具有良好的预测价值,可为临床决策提供一种无创、高效的方法。
Objective To explore the predictive value of different machine learning models based on MRI radiomics combined with clinical features for histological grade of cervical squamous cell carcinoma.Methods Clinical data of 150 patients with cervical squamous cell carcinoma confirmed by pathological biopsy were retrospectively analyzed.They were randomly divided into the training set and validation set at a ratio of 4∶1.Features were extracted from the regions of interest of T2WI fat suppression sequence(FS-T2WI)and enhanced T1WI(delayed phase).After dimensionality reduction and feature selection,logistic regression(LR),support vector machine(SVM),naïve Bayes(NB),random forest(RF),Light Gradient Boosting Machine(LightGBM),K-nearest neighbor(KNN)were used to construct a radiomics model for predicting the histological grade of cervical squamous cell carcinoma.The area under the receiver operating characteristic(ROC)curve(AUC)was used to evaluate the predictive performance of the six models.Univariate and multivariate logistic regression analyses were performed to predict the independent risk factors,and a combined model of clinical and radiomics was established.The differences of each model were compared by AUC,and the clinical value of the model was evaluated by decision curve(DCA).Results In the radiomics model,the LightGBM model had the largest AUC(0.910 in the training set,and 0.839 in the validation set).The AUC of clinical features combined with LightGBM model was the largest(0.935 in the training set,and 0.888 in the validation set),which was higher than those of clinical model(0.762 in the training set,and 0.710 in the validation set)and LightGBM radiomics model.Conclusions The LightGBM model has a high predictive value in the radiomics model.The combined model has the optimal DCA effect and the highest clinical net benefit.The combined prediction model combining radiomics and clinical features has good predictive value for cervical squamous cell carcinoma with low differentiation,providing a noninvasive and efficient method for clinical decision-making.
作者
王贺真
边芳
童玉洁
段亚楠
翟冬枝
Wang Hezhen;Bian Fang;Tong Yujie;Duan Yanan;Zhai Dongzhi(Department of Medical Imaging,the Second Affiliated Hospital of Zhengzhou University,Zhengzhou 450014,China)
出处
《新医学》
CAS
2024年第3期176-183,共8页
Journal of New Medicine
关键词
宫颈鳞癌
影像组学
组织学分级
磁共振成像
机器学习
Cervical cancer
Radiomics
Histological grade
Magnetic resonance imaging
Machine learning
