双能CT诊断膝关节痛风性骨髓水肿的诊断价值

Diagnostic Value of Dual-Energy CT for Bone Marrow Edema in the Knee of Patients with Gout

摘要: 目的：评估双能计算机断层扫描(DECT)虚拟去钙(VNCa)技术在痛风性膝关节骨髓水肿(BME)诊断中的准确性。方法：前瞻性连续纳入50例男性痛风患者，所有患者均在7天内完成膝关节DECT与3.0T MRI检查。以MRI表现作为诊断BME的金标准，采用改良WORMS系统将膝关节划分为7个解剖区域。由两名高资历放射科医师对VNCa图像进行定性评分(二分类法)及定量分析(测量ROI的CT值)，评价者对临床及MRI结果设盲。通过受试者工作特征(ROC)曲线确定诊断BME的最佳CT截断值，并计算DECT诊断BME的敏感性、特异性、阳性预测值、阴性预测值及准确度。结果：在50例患者的350个解剖区域中，MRI共检出42个BME区域。定性分析显示，两名医师利用DECT-VNCa图像诊断BME的敏感性分别为83.3%和85.7%，特异性分别为95.4%和96.8%，准确度分别为94.0%和95.4%，两名医师间的诊断性能无统计学差异(p = 0.387)。定量分析显示，BME受累区与正常区之间的密度差异具有统计学意义(p < 0.001)；基于ROC曲线分析，其诊断BME的敏感性为88.1%，特异性为91.2%，准确度为90.9%。结论：DECT-VNCa成像能准确识别痛风膝关节BME。

Abstract: Objective: To evaluate the diagnostic accuracy of dual-energy computed tomography (DECT) virtual non-calcium (VNCa) imaging for detecting bone marrow edema (BME) in the knees of patients with gout. Methods: Fifty male patients with gout were prospectively and consecutively enrolled. All patients underwent both knee DECT and 3.0T MRI examinations within a 7-day interval. Using MRI findings as the gold standard, the knee joint was divided into seven anatomical regions according to the modified Whole-Organ Magnetic Resonance Imaging Score (WORMS). Two senior radiologists, blinded to clinical and MRI data, performed qualitative scoring (using a binary system) and quantitative analysis (measuring CT values in regions of interest [ROIs]) on the VNCa images. The optimal CT cutoff value for BME diagnosis was determined via receiver operating characteristic (ROC) curve analysis. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of DECT for BME detection were calculated. Results: Among 350 anatomical regions in 50 patients, 42 regions (12.0%) were identified as BME by MRI. Qualitative analysis showed that for Reader 1 and Reader 2, the sensitivities were 83.3% and 85.7%, specificities were 95.4% and 96.8%, and accuracies were 94.0% and 95.4%, respectively, with no significant difference in diagnostic performance between the two readers (p = 0.387). Quantitative analysis demonstrated a significant difference in density between BME-affected and normal regions (p < 0.001). Based on ROC curve analysis, the sensitivity, specificity, and accuracy for diagnosing BME were 88.1%, 91.2%, and 90.9%, respectively. Conclusion: DECT-VNCa imaging can accurately identify bone marrow edema in the knees of patients with gout.