Assessment of a software for semi-automatically calculating the bone scan index on bone scintigraphy scans

Evaluation of jaw pathologies of patients with medication-related osteonecrosis of the jaw using a computer program to assess the bone scan index: comparison of standardized uptake values with bone SPECT/CT

OBJECTIVES

The aim of this study is to investigate the jaw pathologies of patients with medication-related osteonecrosis of the jaw (MRONJ) using a computer program to assess the bone scan index (BSI), especially comparison of standardized uptake values (SUVs) with bone single-photon emission-computed tomography/computed tomography (SPECT/CT).

METHODS

Sixty-three patients with MRONJ underwent bone SPECT/CT in this prospective study. BSI and high-risk hot spot as bone metastases in the patients with MRONJ were evaluated using a computer program for BSI that scanned SPECT/CT and automatically defined the data. The maximum and mean SUVs with SPECT/CT were obtained using commercially available software. Statistical analyses were performed by Pearson chi-square test, Mann-Whitney U-test, or one-way analysis of variance with Tukey's honestly significant difference test. A P value lower than 0.05 was considered statistically significant.

RESULTS

The maximum and mean SUVs for a high-risk hot spot of the jaw with MRONJ [28.2 ± 10.2 and 11.7 ± 3.8; n = 6 (6/63 : 9.5%)] were significantly higher than those for a low-risk hot spot [18.5 ± 6.4 and 6.2 ± 1.9; n = 23 (23/63 : 36.5%)] and no-risk hot spot [14.2 ± 9.4 and 5.3 ± 5.1; n = 34 (34/63 : 54.0%)], respectively.

CONCLUSION

The computer program for BSI indicated that 9.5% of the jaw with MRONJ were false positive of bone metastases. The study suggests that high-risk hot spots of the jaw with MRONJ depend on the SUVs.

Skeleton Segmentation on Bone Scintigraphy for BSI Computation

Bone Scan Index (BSI) is an image biomarker for quantifying bone metastasis of cancers. To compute BSI, not only the hotspots (metastasis) but also the bones have to be segmented. Most related research focus on binary classification in bone scintigraphy: having metastasis or none. Rare studies focus on pixel-wise segmentation. This study compares three advanced convolutional neural network (CNN) based models to explore bone segmentation on a dataset in-house. The best model is Mask R-CNN, which reaches the precision, sensitivity, and F1-score: 0.93, 0.87, 0.90 for prostate cancer patients and 0.92, 0.86, and 0.88 for breast cancer patients, respectively. The results are the average of 10-fold cross-validation, which reveals the reliability of clinical use on bone segmentation.

Comparison of skeletal segmentation by deep learning-based and atlas-based segmentation in prostate cancer patients

OBJECTIVE

We aimed to compare the deep learning-based (VSBONE BSI) and atlas-based (BONENAVI) segmentation accuracy that have been developed to measure the bone scan index based on skeletal segmentation.

METHODS

We retrospectively conducted bone scans for 383 patients with prostate cancer. These patients were divided into two groups: 208 patients were injected with ^99mTc-hydroxymethylene diphosphonate processed by VSBONE BSI, and 175 patients were injected with ^99mTc-methylene diphosphonate processed by BONENAVI. Three observers classified the skeletal segmentations as either a "Match" or "Mismatch" in the following regions: the skull, cervical vertebrae, thoracic vertebrae, lumbar vertebrae, pelvis, sacrum, humerus, rib, sternum, clavicle, scapula, and femur. Segmentation error was defined if two or more observers selected "Mismatch" in the same region. We calculated the segmentation error rate according to each administration group and evaluated the presence of hot spots suspected bone metastases in "Mismatch" regions. Multivariate logistic regression analysis was used to determine the association between segmentation error and variables like age, uptake time, total counts, extent of disease, and gamma cameras.

RESULTS

The regions of "Mismatch" were more common in the long tube bones for VSBONE BSI and in the pelvis and axial skeletons for BONENAVI. Segmentation error was observed in 49 cases (23.6%) with VSBONE BSI and 58 cases (33.1%) with BONENAVI. VSBONE BSI tended that "Mismatch" regions contained hot spots suspected of bone metastases in patients with multiple bone metastases and showed that patients with higher extent of disease (odds ratio = 8.34) were associated with segmentation error in multivariate logistic regression analysis.

CONCLUSIONS

VSBONE BSI has a potential to be higher segmentation accuracy compared with BONENAVI. However, the segmentation error in VSBONE BSI occurred dependent on bone metastases burden. We need to be careful when evaluating multiple bone metastases using VSBONE BSI.