Abstract：
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
摘要：
目的：本研究旨在比较基于双能CT（DECT）的体积材料分解与基于Hounsfield单位（HU）的体积材料分解值和皮质厚度比对预测2年骨质疏松相关骨折风险的诊断价值。
方法：111例患者的L1椎骨（55例男性，56名妇女；平均年龄，回顾性分析在2015年1月至2018年12月期间接受DECT的62年）。对于无幻影骨矿物质密度（BMD）评估，使用了采用材料分解的专用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率。使用接受者操作特征（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Logistic回归模型来确定各种预测指标与骨质疏松性骨折发生的关联。
结果：在2年的时间间隔内发生一次或多次骨质疏松相关骨折的患者与没有发生此类骨折的患者（中位年龄50.5岁[IQR38.5-69.5岁]）相比，年龄明显增大（中位年龄74.5岁[IQR57-83岁]）。根据逻辑回归模型，DECT衍生的BMD是与所有年龄段的骨质疏松性骨折发生显着相关的唯一预测参数。ROC和PR曲线分析证实了基于DECT的BMD的最高诊断准确性，ROC曲线的曲线下面积（AUC）为0.95[95%CI：0.89-0.98]，PR曲线的AUC为0.96[95%CI：0.85-0.99]。
结论：基于DECT的BMD在预测骨质疏松性骨折2年风险方面的诊断性能高于基于HU的指标和皮质厚度比。基于DECT的BMD值对于识别有骨质疏松性骨折风险的患者非常有价值。
方法：111例患者的L1椎骨（55例男性，56名妇女；平均年龄，回顾性分析在2015年1月至2018年12月期间接受DECT的62年）。对于无幻影骨矿物质密度（BMD）评估，使用了采用材料分解的专用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率。使用接受者操作特征（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Logistic回归模型来确定各种预测指标与骨质疏松性骨折发生的关联。
结果：在2年的时间间隔内发生一次或多次骨质疏松相关骨折的患者与没有发生此类骨折的患者（中位年龄50.5岁[IQR38.5-69.5岁]）相比，年龄明显增大（中位年龄74.5岁[IQR57-83岁]）。根据逻辑回归模型，DECT衍生的BMD是与所有年龄段的骨质疏松性骨折发生显着相关的唯一预测参数。ROC和PR曲线分析证实了基于DECT的BMD的最高诊断准确性，ROC曲线的曲线下面积（AUC）为0.95[95%CI：0.89-0.98]，PR曲线的AUC为0.96[95%CI：0.85-0.99]。
结论：基于DECT的BMD在预测骨质疏松性骨折2年风险方面的诊断性能高于基于HU的指标和皮质厚度比。基于DECT的BMD值对于识别有骨质疏松性骨折风险的患者非常有价值。
