中央型腰椎管狭窄责任节段的CT脊髓造影影像组学与机器学习研究

Predicting the culprit level of central lumbar stenosis based on radiomic features of CT myelograoms and machine learning

目的:多节段中央型腰椎管狭窄的责任节段定位对于微创减压手术非常重要。本研究旨在基于腰椎管狭窄症病人的CT脊髓造影(CT myelograoms,CTM)图像识别具备预测责任节段价值的影像组学特征,同时基于机器学习(machine learning,ML)算法构建预测模型并探索性能最优的算法组合。方法:纳入2015年1月至2021年12月接受CTM检查的腰椎管狭窄症病人的资料,并在3D Slicer上勾画腰椎硬脊膜并提取影像组学特征。将影像组学特征数据分为三个子数据集(训练集:验证集:测试集=6:2:2)。从6×12算法组合中开发了总共72个预测模型,然后从交叉验证中择优选出最优预测模型,在测试集评估其预测性能,受试者工作特征曲线下面积(area under the curve,AUC)为主要预测性能评价指标。结果:共219名病人纳入分析,研究发现递归特征消除(recursive feature elimination,RFE)为最优的特征选择方法,其所筛选的15个影像组学预测因子包含10个纹理特征、3个形状特征和2个强度一阶特征。基于此15个影像组学预测因子所建立的多数机器学习预测模型的AUC均在0.85及以上,其中逻辑回归模型、多层感知机模型和装袋模型的AUC均在0.86以上,三者的校准曲线和临床决策曲线亦无明显差别,但装袋模型的灵敏度更高。结论:本研究发现影像组学预测因子多数为医师肉眼难以辨识的纹理特征,也初步表明了机器学习定位CTM的腰椎管狭窄责任节段的辅助价值。

Objective:The diagnosis of multi-level central lumbar stenosis(CLS)with or without radicular symptoms is very important,especially for localizing the surgical candidacy of culprit level.The aim of the study was to develop machine learning(ML)models utilizing clinical features and CT myelograms(CTM)to predict the decompression levels for CLS patients.Methods:We retrospectively reviewed the medical records of CLS patients who underwent CTM examination from Jan 2015 to Dec 2021,and the interested region of the lumbar dura were extracted with radiomic features by 3D Slicer.The included patient data were divided into three sub-datasets(training:validation:testing=6:2:2).A total of 72 initial ML models from 6×12 combinations were developed and then we selected the best prediction model from cross-validation.Meanwhile,the area under the receiver operating characteristic curve(AUC)was regarded as the main indicator to assess all classifiers.Results:A total of 219 patients with CLS were included.The study found that recursive feature elimination(RFE)was the optimal feature selection method to identify the radiomic predictors.The 15 radiomic predictors screened by RFE included 10 texture features,3 shape features and 2 first-order intensity features.The AUC of most ML prediction models were 0.85 and above.The top-3 ML models were logistic regression model,multilayer perception model and bagging model.The AUC of the top-3 ML models were all above 0.86,and there were no significant differences in the calibration curves and clinical decision curves of the three models.Conclusion:The study found that the most radiomic predictors were texture features that were difficult to recognize with the naked eye.It also preliminarily confirmed the additive value of machine learning in CTM locating CLS culprit level.