Department of Neurosurgery, Fujian Medical University Union Hospital, No. 29, Xin Quan Road, Fuzhou City, 350001, Fujian Province, China.
Eur Radiol. 2022 Nov;32(11):8019-8026. doi: 10.1007/s00330-022-08805-4. Epub 2022 Apr 30.
Undiagnosed osteoporosis may lead to severe complications after spinal surgery. This study aimed to construct and validate a radiomic signature based on CT scans to screen for lumbar spine osteoporosis.
Using a stratified random sample method, 386 vertebral bodies were randomly divided into a training set (n = 270) and a test set (n = 116). A total of 1040 radiomics features were automatically retracted from lumbar spine CT scans using the 3D slicer pyradiomics module, and a radiomic signature was created. The sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) of the Hounsfield and radiomics signature models were calculated. The AUCs of the two models were compared using the DeLong test. Their clinical usefulness was assessed using a decision curve analysis.
Twelve features were chosen to establish the radiomic signature. The AUCs of the radiomics signature and Hounsfield models were 0.96 and 0.88 in the training set and 0.92 and 0.84 in the test set, respectively. According to the DeLong test, the AUCs of the two models were significantly different (p < 0.05). The radiomics signature model indicated a higher overall net benefit than the Hounsfield model, as determined by decision curve analysis.
The CT-based radiomic signature can differentiate patients with/without osteoporosis prior to lumbar spinal surgery. Without additional medical cost and radiation exposure, the radiomics method may provide valuable information facilitating surgical decision-making.
• The goal of the study was to evaluate the efficacy of a radiomics signature model based on routine preoperative lumbar spine CT scans in screening osteoporosis. • The radiomics signature model demonstrated excellent prediction performance in both the training and test sets. • This radiomics method may provide valuable information and facilitate surgical decision-making without additional medical costs and radiation exposure.
未诊断的骨质疏松症可能导致脊柱手术后出现严重并发症。本研究旨在构建和验证一种基于 CT 扫描的放射组学特征，以筛查腰椎骨质疏松症。
采用分层随机抽样方法，随机将 386 个椎体分为训练集（n = 270）和测试集（n = 116）。使用 3D slicer pyradiomics 模块从腰椎 CT 扫描中自动提取 1040 个放射组学特征，并创建放射组学特征。计算 Hounsfield 和放射组学特征模型的灵敏度、特异度、准确性和受试者工作特征曲线（ROC）下面积（AUC）。使用 DeLong 检验比较两个模型的 AUC。使用决策曲线分析评估它们的临床实用性。
选择 12 个特征建立放射组学特征。在训练集中，放射组学特征和 Hounsfield 模型的 AUC 分别为 0.96 和 0.88，在测试集中分别为 0.92 和 0.84。根据 DeLong 检验，两个模型的 AUC 有显著差异（p < 0.05）。通过决策曲线分析，放射组学特征模型的总体净获益高于 Hounsfield 模型。
基于 CT 的放射组学特征可在腰椎脊柱手术前区分骨质疏松症患者和非骨质疏松症患者。放射组学方法无需额外的医疗费用和辐射暴露，可能为手术决策提供有价值的信息。
本研究旨在评估基于常规术前腰椎 CT 扫描的放射组学特征模型在筛查骨质疏松症中的效果。
放射组学特征模型在训练集和测试集中均具有出色的预测性能。
这种放射组学方法无需额外的医疗费用和辐射暴露，可为手术决策提供有价值的信息。
