摘要
目的 探究基于胸部CT图像椎体纹理分析结合支持向量机(SVM)机器学习方法在鉴别急性及陈旧性椎体压缩性骨折的价值。方法 回顾性分析132例2018年5月至2021年5月行常规胸部CT并经MRI证实为椎体压缩性骨折患者的资料,纳入急性骨折椎体98个、陈旧性骨折椎体65个,共163个椎体。采用Mazda软件提取所有椎体轴位与矢状位纹理特征,采用IPMS软件进一步筛选、降维及建模,将新旧骨折椎体按7:3的比例随机分入训练集与验证集。通过T检验、Wilcoxon秩和检验及Pearson相关分析对训练集的纹理特征进行筛选,依次建立轴位及矢状位筛选特征的SVM模型,进一步用验证集验证模型有效性,获得受试者工作特征(ROC)曲线。结果 每个椎体矢状位及轴位分别获得294个特征。矢状位最终获得8个参数,训练集及验证集SVM模型的ROC曲线下面积(AUC)分别为0.78和0.68;轴位最终获得6个参数,训练集及验证集SVM模型的AUC分别为0.80和0.84。轴位模型效能较矢状位更高。结论 胸部CT影像组学特征结合SVM可对急性及陈旧性椎体压缩性骨折进行鉴别,以轴位模型为佳,能够为无明确外伤史的偶发急性椎体骨折提供辅助诊断从而促进早期治疗。
Objective To investigate the value of vertebral texture analysis based on thoracic CT images combined with support vector machine(SVM) machine learning method in identifying acute and old vertebral compression fractures. Methods The data of 132 patients with incidental vertebral compression fractures detected on routine chest CT and confirmed by MRI from May 2018 to May 2021 were retrospectively analyzed. 163 vertebrae including 98 acute fractures and 65 old were included. The Mazda software was used to extract texture features of each vertebra in axial and sagittal orientation.Then the IPMS software was used to further dimensionality reduction and model building. The old and acute fractured vertebrae were randomly divided into the training and validation samples according to the ratio of 7:3. For training sample,the T test,Wilcoxon rank sum test and Pearson correlation analysis were performed to screen the texture features in both orientations. SVM models were built based on the selected axial and sagittal parameters respectively. Then the diagnostic value was tested by the validation sample and the receiver operating characteristic curves(ROC) were obtained.Results For each vertebra,294 features were extracted from sagittal and axial imaging respectively.8 parameters were finally obtained for the sagittal orientation,the AUC of the SVM model was 0.78 and 0.68 for the training and validation sample,respectively;7 parameters were finally obtained for the axial orientation,the AUC of the SVM model was 0.80 and 0.84 for the training and validation sample,respectively. with higher model efficacy for the axial position than for the sagittal position. The axial model shown higher diagnostic value than sagittal model. Conclusion Radiomic based on chest CT combined with SVM method can differentiate acute from old vertebral compression fractures,the axial model shown better performance and may provide an auxiliary diagnosis for incidental acute vertebral fractures without a clear history of trauma thus facilitating early
作者
李邦凤
付玉苹
龚良庚
彭云
林华山
LI Bang-feng;FU Yu-ping;GONG Liang-geng;PENG Yun;LIN Hua-shan(Department of Radiology,The Second Affiliated Hospital of Nanchang University,Nanchang 330006,Jiangxi Province,China;The Second Clinical College of Medicine,Nanchang University,Nanchang 330006,Jiangxi Province,China;GE Pharmaceutical GE Healthcare,Changsha 410000,Hunan Province,China)
出处
《中国CT和MRI杂志》
2023年第2期149-150,174,共3页
Chinese Journal of CT and MRI
基金
国家自然科学基金(81860316)基于影像学和分子生物学探究miR-318通过激活Hippo信号通路调控心肌梗死的机制。
关键词
影像组学
椎体压缩性骨折
支持向量机
纹理分析
Radiomics
Vertebral Compression Fracture
Support Vector Machine
Texture Analysis
