Fan Guoxin, Wang Dongdong, Li Yufeng, Xu Zhipeng, Wang Hong, Liu Huaqing, Liao Xiang
Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518056, China.
Department of Spine Surgery, Third Affiliated Hospital, Sun Yat-sen University, Guangzhou 510630, China.
Diagnostics (Basel). 2023 Dec 26;14(1):53. doi: 10.3390/diagnostics14010053.
The accurate preoperative identification of decompression levels is crucial for the success of surgery in patients with multi-level lumbar spinal stenosis (LSS). The objective of this study was to develop machine learning (ML) classifiers that can predict decompression levels using computed tomography myelography (CTM) data from LSS patients.
A total of 1095 lumbar levels from 219 patients were included in this study. The bony spinal canal in CTM images was manually delineated, and radiomic features were extracted. The extracted data were randomly divided into training and testing datasets (8:2). Six feature selection methods combined with 12 ML algorithms were employed, resulting in a total of 72 ML classifiers. The main evaluation indicator for all classifiers was the area under the curve of the receiver operating characteristic (ROC-AUC), with the precision-recall AUC (PR-AUC) serving as the secondary indicator. The prediction outcome of ML classifiers was decompression level or not.
The embedding linear support vector (embeddingLSVC) was the optimal feature selection method. The feature importance analysis revealed the top 5 important features of the 15 radiomic predictors, which included 2 texture features, 2 first-order intensity features, and 1 shape feature. Except for shape features, these features might be eye-discernible but hardly quantified. The top two ML classifiers were embeddingLSVC combined with support vector machine (EmbeddingLSVC_SVM) and embeddingLSVC combined with gradient boosting (EmbeddingLSVC_GradientBoost). These classifiers achieved ROC-AUCs over 0.90 and PR-AUCs over 0.80 in independent testing among the 72 classifiers. Further comparisons indicated that EmbeddingLSVC_SVM appeared to be the optimal classifier, demonstrating superior discrimination ability, slight advantages in the Brier scores on the calibration curve, and Net benefits on the Decision Curve Analysis.
ML successfully extracted valuable and interpretable radiomic features from the spinal canal using CTM images, and accurately predicted decompression levels for LSS patients. The EmbeddingLSVC_SVM classifier has the potential to assist surgical decision making in clinical practice, as it showed high discrimination, advantageous calibration, and competitive utility in selecting decompression levels in LSS patients using canal radiomic features from CTM.
准确术前识别减压节段对于多节段腰椎管狭窄症（LSS）患者手术的成功至关重要。本研究的目的是开发机器学习（ML）分类器，其能够使用来自LSS患者的计算机断层扫描脊髓造影（CTM）数据预测减压节段。
本研究纳入了219例患者的总共1095个腰椎节段。手动勾勒CTM图像中的骨性椎管，并提取影像组学特征。提取的数据被随机分为训练和测试数据集（8:2）。采用六种特征选择方法与12种ML算法相结合，共产生72个ML分类器。所有分类器的主要评估指标是受试者操作特征曲线下面积（ROC-AUC），精确召回率AUC（PR-AUC）作为次要指标。ML分类器的预测结果是减压节段与否。
嵌入线性支持向量（embeddingLSVC）是最佳特征选择方法。特征重要性分析揭示了15个影像组学预测因子中的前5个重要特征，其中包括2个纹理特征、2个一阶强度特征和1个形状特征。除形状特征外，这些特征可能肉眼可辨但难以量化。前两个ML分类器是嵌入线性支持向量机（EmbeddingLSVC_SVM）和嵌入线性支持向量机结合梯度提升（EmbeddingLSVC_GradientBoost）。在72个分类器的独立测试中，这些分类器的ROC-AUC超过0.90，PR-AUC超过0.80。进一步比较表明，EmbeddingLSVC_SVM似乎是最佳分类器，在判别能力、校准曲线上的Brier评分方面有轻微优势，在决策曲线分析中有净效益优势。
ML成功地使用CTM图像从椎管中提取了有价值且可解释的影像组学特征，并准确预测了LSS患者的减压节段。EmbeddingLSVC_SVM分类器在临床实践中具有辅助手术决策的潜力，因为它在使用CTM的椎管影像组学特征选择LSS患者的减压节段时表现出高判别能力、有利的校准和有竞争力的效用。
