Li Meiqin, Li Zhoulei, Wei Luyong, Li Lujie, Wang Meng, He Shaofu, Peng Zhenpeng, Feng Shi-Ting
Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Quant Imaging Med Surg. 2023 Aug 1;13(8):4933-4942. doi: 10.21037/qims-22-1234. Epub 2023 May 24.
Non-invasive glycogen quantification could provide crucial information on biological processes for glycogen storage disorder. Using dual-energy computed tomography (DECT), this study aimed to assess the viability of quantifying glycogen content .
A fast kilovolt-peak switching DECT was used to scan a phantom containing 33 cylinders with different proportions of glycogen and iodine mixture at varying doses. The virtual glycogen concentration (VGC) was then measured using material composition images. Additionally, the correlations between VGC and nominal glycogen concentration (NGC) were evaluated using least-square linear regression, then the calibration curve was constructed. Quantitative estimation was performed by calculating the linearity, conversion factor (inverse of curve slope), stability, sensitivity (limit of detection/limit of quantification), repeatability (inter-class correlation coefficient), and variability (coefficient of variation).
In all conditions, excellent linear relationship between VGC and NGC were observed (P<0.001, coefficient of determination: 0.989-0.997; residual root-mean-square error of glycogen: 1.862-3.267 mg/mL). The estimated conversion factor from VGC to NGC was 3.068-3.222. In addition, no significant differences in curve slope were observed among different dose levels and iodine densities. The limit of detection and limit of quantification had respective ranges of 6.421-15.315 and 10.95-16.46 mg/mL. The data demonstrated excellent scan-repeat scan agreement (inter-class correlation coefficient, 0.977-0.991) and small variation (coefficient of variation, 0.1-0.2%).
The pilot phantom analysis demonstrated the feasibility and efficacy of detecting and quantifying glycogen using DECT and provided good quantitative performance with significant stability and reproducibility/variability. Thus, in the future, DECT could be used as a convenient method for glycogen quantification to provide more reliable information for clinical decision-making.
无创糖原定量可为糖原贮积病的生物学过程提供关键信息。本研究旨在利用双能计算机断层扫描（DECT）评估糖原含量定量的可行性。
使用快速千伏峰值切换DECT扫描一个包含33个圆柱体的体模，这些圆柱体含有不同比例的糖原和碘混合物，且剂量各异。然后使用物质成分图像测量虚拟糖原浓度（VGC）。此外，使用最小二乘线性回归评估VGC与标称糖原浓度（NGC）之间的相关性，随后构建校准曲线。通过计算线性度、转换因子（曲线斜率的倒数）、稳定性、灵敏度（检测限/定量限）、重复性（组内相关系数）和变异性（变异系数）进行定量估计。
在所有条件下，均观察到VGC与NGC之间存在良好的线性关系（P<0.001，决定系数：0.989 - 0.997；糖原的残余均方根误差：1.862 - 3.267 mg/mL）。从VGC到NGC的估计转换因子为3.068 - 3.222。此外，在不同剂量水平和碘密度之间未观察到曲线斜率有显著差异。检测限和定量限的范围分别为6.421 - 15.315和10.95 - 16.46 mg/mL。数据显示扫描 - 重复扫描一致性良好（组内相关系数，0.977 - 0.991）且变异小（变异系数，0.1 - 0.2%）。
初步体模分析证明了使用DECT检测和定量糖原的可行性和有效性，并提供了良好的定量性能，具有显著的稳定性和再现性/变异性。因此，未来DECT可作为一种方便的糖原定量方法，为临床决策提供更可靠的信息。
