Optimizing HR-pQCT workflow: a comparison of bias and precision error for quantitative bone analysis

Manual correction of automatically generated contours for high-resolution peripheral quantitative computed tomography can be time consuming and introduces precision error. However, bias related to the automated protocol is unknown. This study provides insight into error bias that is present when using uncorrected contours and inter-operator precision error based on operator training.

INTRODUCTION

High-resolution peripheral quantitative computed tomography workflow includes manually correcting contours generated by the manufacturer's automated protocol. There is interest in minimizing corrections to save time and reduce precision error; however, bias related to the automated protocol is unknown. This study quantifies error bias when contours are uncorrected and identifies the impact of operator training on bias and precision error.

METHODS

Forty-five radii and tibiae scans across a representative range of bone density were analyzed using the automated and manually corrected contours of three operators, with training ranging from beginner to expert, and compared with a "ground truth" to estimate bias. Inter-operator precision was measured across operators.

RESULTS

The tibia had greater error bias than the radius when contours were uncorrected, with compartmental bone mineral densities and cortical microarchitecture having greatest biases, which could have significant implications for interpretation of studies using this skeletal site. Bias and precision error were greatest when contours were corrected by the beginner operator; however, when this operator was removed, bias was no longer present and inter-operator precision was between 0.01 and 3.74% for all parameters except cortical porosity.

CONCLUSION

These findings establish the need for manual correction and provide guidance on operator training needed to maximize workflow efficiency.

Reliability of HR-pQCT Derived Cortical Bone Structural Parameters When Using Uncorrected Instead of Corrected Automatically Generated Endocortical Contours in a Cross-Sectional Study: The Maastricht Study

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.