A Preanalytic Validation Study of Automated Bone Scan Index: Effect on Accuracy and Reproducibility Due to the Procedural Variabilities in Bone Scan Image Acquisition

Abstract

The effect of the procedural variability in image acquisition on the quantitative assessment of bone scan is unknown. Here, we have developed and performed preanalytical studies to assess the impact of the variability in scanning speed and in vendor-specific γ-camera on reproducibility and accuracy of the automated bone scan index (BSI).

Methods: Two separate preanalytical studies were performed: a patient study and a simulation study. In the patient study, to evaluate the effect on BSI reproducibility, repeated bone scans were prospectively obtained from metastatic prostate cancer patients enrolled in 3 groups (Grp). In Grp1, the repeated scan speed and the γ-camera vendor were the same as that of the original scan. In Grp2, the repeated scan was twice the speed of the original scan. In Grp3, the repeated scan used a different γ-camera vendor than that used in the original scan. In the simulation study, to evaluate the effect on BSI accuracy, bone scans of a virtual phantom with predefined skeletal tumor burden (phantom-BSI) were simulated against the range of image counts (0.2, 0.5, 1.0, and 1.5 million) and separately against the resolution settings of the γ-cameras. The automated BSI was measured with a computer-automated platform. Reproducibility was measured as the absolute difference between the repeated BSI values, and accuracy was measured as the absolute difference between the observed BSI and the phantom-BSI values. Descriptive statistics were used to compare the generated data.

Results: In the patient study, 75 patients, 25 in each group, were enrolled. The reproducibility of Grp2 (mean ± SD, 0.35 ± 0.59) was observed to be significantly lower than that of Grp1 (mean ± SD, 0.10 ± 0.13; P < 0.0001) and that of Grp3 (mean ± SD, 0.09 ± 0.10; P < 0.0001). However, no significant difference was observed between the reproducibility of Grp3 and Grp1 (P = 0.388). In the simulation study, the accuracy at 0.5 million counts (mean ± SD, 0.57 ± 0.38) and at 0.2 million counts (mean ± SD, 4.67 ± 0.85) was significantly lower than that observed at 1.5 million counts (mean ± SD, 0.20 ± 0.26; P < 0.0001). No significant difference was observed in the accuracy data of the simulation study with vendor-specific γ-cameras (P = 0.266).

Conclusion: In this study, we observed that the automated BSI accuracy and reproducibility were dependent on scanning speed but not on the vendor-specific γ-cameras. Prospective BSI studies should standardize scanning speed of bone scans to obtain image counts at or above 1.5 million.

Keywords: bone scan; bone scan index; imaging biomarker; metastatic prostate cancer; pre-analytical validation.

FIGURE 1.

Anterior and posterior (A/P) image count and associated automated BSI values in repeated bone scans of patients with same scanning speeds (Grp1) (A) and in repeated bone scans of patients with twice scanning speed of original scan (Grp2) (B). Doubling of scanning speed results in image count that is approximately half that of original scan. Lesions detected and classified as metastatic by automated platform, for BSI calculation, are highlighted in red.

FIGURE 2.

Box plots representing reproducibility data in 3 predefined patient study groups.

FIGURE 3.

Respective automated BSI reads of simulated bone scans at 0.2, 0.5, 1.0, and 1.5 million image counts with predefined tumor burden corresponding to phantom-BSI of 3.0.

FIGURE 4.

Box plot representing accuracy data under respective image count simulation.