Department of Diagnostic and Interventional Radiology, Clinic for Radiology and Nuclear Medicine, Hospital of the Goethe University Frankfurt, 60590 Frankfurt am Main, Germany.
Department of Diagnostic and Interventional Radiology, Clinic for Radiology and Nuclear Medicine, Hospital of the Goethe University Frankfurt, 60590 Frankfurt am Main, Germany.
Acad Radiol. 2024 Dec;31(12):5108-5117. doi: 10.1016/j.acra.2024.07.010. Epub 2024 Aug 8.
This study aimed to compare the diagnostic value of dual-energy CT (DECT)-based volumetric material decomposition with that of Hounsfield units (HU)-based values and cortical thickness ratio for predicting the 2-year risk of osteoporosis-associated fractures.
The L1 vertebrae of 111 patients (55 men, 56 women; median age, 62 years) who underwent DECT between 01/2015 and 12/2018 were retrospectively analyzed. For phantomless bone mineral density (BMD) assessment, a specialized DECT postprocessing software employing material decomposition was utilized. The digital records of all patients were monitored for two years after the DECT scans to track the incidence of osteoporotic fractures. Diagnostic accuracy parameters were calculated for all metrics using receiver-operating characteristic (ROC) and precision-recall (PR) curves. Logistic regression models were used to determine associations of various predictive metrics with the occurrence of osteoporotic fractures.
Patients who sustained one or more osteoporosis-associated fractures in a 2-year interval were significantly older (median age 74.5 years [IQR 57-83 years]) compared those without such fractures (median age 50.5 years [IQR 38.5-69.5 years]). According to logistic regression models, DECT-derived BMD was the sole predictive parameter significantly associated with osteoporotic fracture occurrence across all age groups. ROC and PR curve analyses confirmed the highest diagnostic accuracy for DECT-based BMD, with an area under the curve (AUC) of 0.95 [95% CI: 0.89-0.98] for the ROC curve and an AUC of 0.96 [95% CI: 0.85-0.99] for the PR curve.
The diagnostic performance of DECT-based BMD in predicting the 2-year risk of osteoporotic fractures is greater than that of HU-based metrics and the cortical thickness ratio. DECT-based BMD values are highly valuable in identifying patients at risk for osteoporotic fractures.
本研究旨在比较基于双能 CT（DECT）的容积物质分解与基于 Hounsfield 单位（HU）值和皮质厚度比预测骨质疏松性骨折 2 年风险的诊断价值。
回顾性分析了 2015 年 1 月至 2018 年 12 月间进行 DECT 的 111 例患者（男 55 例，女 56 例；中位年龄 62 岁）的 L1 椎体。为了进行无模体骨密度（BMD）评估，使用了一种采用物质分解的专用 DECT 后处理软件。在 DECT 扫描后对所有患者的数字记录进行了两年的监测，以跟踪骨质疏松性骨折的发生情况。使用受试者工作特征（ROC）和精确召回（PR）曲线计算所有指标的诊断准确性参数。使用逻辑回归模型确定各种预测指标与骨质疏松性骨折发生的关联。
在 2 年的随访期间发生 1 次或多次骨质疏松性骨折的患者年龄明显大于（中位数 74.5 岁[IQR 57-83 岁]）未发生此类骨折的患者（中位数 50.5 岁[IQR 38.5-69.5 岁]）。根据逻辑回归模型，DECT 衍生的 BMD 是唯一与所有年龄段骨质疏松性骨折发生相关的预测参数。ROC 和 PR 曲线分析证实了基于 DECT 的 BMD 具有最高的诊断准确性，ROC 曲线的 AUC 为 0.95[95%CI：0.89-0.98]，PR 曲线的 AUC 为 0.96[95%CI：0.85-0.99]。
基于 DECT 的 BMD 预测骨质疏松性骨折 2 年风险的诊断性能优于基于 HU 的指标和皮质厚度比。基于 DECT 的 BMD 值在识别骨质疏松性骨折风险患者方面具有重要价值。
