Department of Internal Medicine, Subdivision of Rheumatology, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands.
NUTRIM School for Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands.
Calcif Tissue Int. 2018 Sep;103(3):252-265. doi: 10.1007/s00223-018-0416-2. Epub 2018 Mar 29.
Most HR-pQCT studies examining cortical bone use an automatically generated endocortical contour (AUTO), which is manually corrected if it visually deviates from the apparent endocortical margin (semi-automatic method, S-AUTO). This technique may be prone to operator-related variability and is time consuming. We examined whether the AUTO instead of the S-AUTO method can be used for cortical bone analysis. Fifty scans of the distal radius and tibia from participants of The Maastricht Study were evaluated with AUTO, and subsequently with S-AUTO by three independent operators. AUTO cortical bone parameters were compared to the average parameters obtained by the three operators (S-AUTOmean). All differences in mean cortical bone parameters between AUTO and S-AUTOmean were < 5%, except for lower AUTO cortical porosity of the radius (- 16%) and tibia (- 6%), and cortical pore volume (Ct.Po.V) of the radius (- 7%). The ICC of S-AUTOmean and AUTO was > 0.90 for all parameters, except for cortical pore diameter of the radius (0.79) and tibia (0.74) and Ct.Po.V of the tibia (0.89), without systematic errors on the Bland-Altman plots. The precision errors (RMS-CV%) of the radius parameters between S-AUTOmean and AUTO were comparable to those between the individual operators, whereas the tibia RMS-CV% between S-AUTOmean and AUTO were higher than those of the individual operators. Comparison of the three operators revealed clear inter-operator variability. This study suggests that the AUTO method can be used for cortical bone analysis in a cross-sectional study, but that the absolute values-particularly of the porosity-related parameters-will be lower.
大多数研究皮质骨的 HR-pQCT 都采用自动生成的内皮质轮廓(AUTO),如果该轮廓与明显的内皮质边缘在视觉上存在偏差,就需要手动进行修正(半自动方法,S-AUTO)。这种技术可能容易受到操作人员相关的变异性的影响,并且非常耗时。我们研究了是否可以使用 AUTO 代替 S-AUTO 方法进行皮质骨分析。我们评估了 Maastricht 研究参与者的 50 个桡骨和胫骨远端扫描,使用 AUTO 进行分析,并由三位独立操作人员随后使用 S-AUTO 进行分析。将 AUTO 皮质骨参数与三位操作人员获得的平均参数(S-AUTOmean)进行比较。AUTO 和 S-AUTOmean 之间所有皮质骨参数的平均差异均<5%,除了桡骨(-16%)和胫骨(-6%)的 AUTO 皮质骨孔隙率较低,以及桡骨(-7%)和胫骨(-7%)的皮质骨孔体积(Ct.Po.V)较低。除了桡骨(0.79)和胫骨(0.74)的皮质骨孔径和胫骨(0.89)的 Ct.Po.V 外,S-AUTOmean 和 AUTO 的 ICC 均>0.90,Bland-Altman 图上没有系统误差。S-AUTOmean 和 AUTO 之间桡骨参数的精度误差(RMS-CV%)与各操作人员之间的误差相当,而 S-AUTOmean 和 AUTO 之间胫骨的 RMS-CV%则高于各操作人员之间的误差。三位操作人员之间的比较显示出明显的操作人员间变异性。本研究表明,在横断面研究中,AUTO 方法可用于皮质骨分析,但绝对值(特别是与孔隙率相关的参数)会较低。