Evaluation of bone scan index change over time on automated calculation in bone scintigraphy

Evaluation of two-dimensional total bone uptake (2D-TBU) and bone scan index (BSI) extracted from active bone metastatic burden on the bone scintigraphy in patients with radium-223 treatment

OBJECTIVE

Radium-223 is a first alpha-emitting radionuclide treatment for metastatic castration-resistant prostate cancer (mCRPC) patients with bone metastases. Although the spread-based bone scan index (BSI) and novel index of the intensity-based two-dimensional total bone uptake (2D-TBU) from bone scintigraphy may provide useful input in radium-223 treatment, they have not been evaluated in detail yet. This study aimed to fill this gap by evaluating BSI and 2D-TBU in patients treated with radium-223.

METHODS

Twenty-seven Japanese patients with mCRPC treated with radium-223 were retrospectively analyzed. The patients were evaluated via blood tests and bone scans at baseline and 3 cycles intervals of treatment. BSI and 2D-TBU were analyzed via VSBONE BSI in terms of correlations, response to radium-223 treatment, association with treatment completion, and the Kaplan-Meier survival analysis was performed.

RESULTS

Nineteen patients (70.4%) completed six cycles of radium-223 treatment, whereas eight patients (29.6%) did not complete the treatment regimen. A significant difference in baseline BSI and 2D-TBU was observed between these groups of patients. Both BSI and 2D-TBU were highly correlated (r = 0.96, p < 0.001). Univariate analysis showed an association between radium-223 completion in median BSI and 2D-TBU values (p = 0.015) and completion percentage differences (91.7% vs. 45.5%; p = 0.027). The Kaplan-Meier product limit estimator showed that the median overall survival was 25.2 months (95% CI 14.0-33.6 months) in the completion group and 7.5 months (95% CI 3.3-14.2 months) in the without completion group (p < 0.001). The overall survival based on median cutoff levels showed a significant difference in 2D-TBU (p = 0.007), but not in BSI (p = 0.15).

CONCLUSIONS

The 2D-TBU may offer advantages over BSI in classifying patients towards radium-223 treatment based on the degree of progression of bone metastases. This study supports the importance of preliminary assessment of bone metastasis status using BSI and 2D-TBU extracted from VSBONE BSI for radium-223 treatment decisions.

Stability of standardized uptake values for quantitative bone SPECT for jawbone lesions: a single-center cross-sectional study

BACKGROUND

The long time required for bone uptake of radiopharmaceutical material after injection for bone scintigraphy is a burden for patients with poor health. Thus, to assess whether the uptake time could be reduced for single-photon emission computed tomography (SPECT) of the jawbone, this study evaluated differences in maximum standardized uptake values (SUVmax) within patients using SPECT imaging at 2 and 3 hours after radiopharmaceutical injection.

METHODS

A total of 33 patients undergoing treatment or in post-treatment follow-up for medication-related osteonecrosis of the jaw, who visited our hospital between July 2020 and August 2021 and could receive SPECT twice on the same day, were enrolled in the study. Patients were injected with technetium-99 m hydroxymethylene diphosphonate (Tc-99 m HMDP) intravenously. The SUVmax for healthy parietal bones and jawbone lesions were calculated from the SPECT images using quantitative analysis software, and the SUVmax were compared between 2- and 3-hour uptake times.

RESULTS

After exclusion, 30 patients were included in the study. In the 2-hour and 3-hour images, the median SUVmax of the parietal bones were 1.90 and 1.81, respectively, and those of the jawbone lesions were 9.25 and 9.39, respectively. The limits of agreement (LOA) ranged from - 0.33 to 0.25 in the parietal bones, and the %LOA ranged from - 9.8 to 17.3% in the jawbone lesions, showing high equivalence between the two uptake durations. The SUVmax showed no clinical differences between the 2- and 3-hour uptake durations for Tc-99 m HMDP SPECT of the jawbone.

CONCLUSIONS

The results of this study justify a 2-3-hour uptake window when performing quantitative SPECT of the jawbone. Therefore, the minimum uptake time can potentially be reduced to only 2 hours.

Application of SPECT and PET / CT with computer-aided diagnosis in bone metastasis of prostate cancer: a review

Bone metastasis has a significant influence on the prognosis of prostate cancer(PCa) patients. In this review, we discussed the current application of PCa bone metastasis diagnosis with single-photon emission computed tomography (SPECT) and positron emission tomography/computed tomography (PET/CT) computer-aided diagnosis(CAD) systems. A literature search identified articles concentrated on PCa bone metastasis and PET/CT or SPECT CAD systems using the PubMed database. We summarized the previous studies focused on CAD systems and manual quantitative markers calculation, and the coincidence rate was acceptable. We also analyzed the quantification methods, advantages, and disadvantages of CAD systems. CAD systems can detect abnormal lesions of PCa patients’ 99mTc-MDP-SPECT, 18F-FDG-PET/CT, 18F-NaF-PET/CT, and 68 Ga-PSMA PET/CT images automated or semi-automated. CAD systems can also calculate the quantitative markers, which can quantify PCa patients’ whole-body bone metastasis tumor burden accurately and quickly and give a standardized and objective result. SPECT and PET/CT CAD systems are potential tools to monitor and quantify bone metastasis lesions of PCa patients simply and accurately, the future clinical application of CAD systems in diagnosing PCa bone metastasis lesions is necessary and feasible.

Artificial intelligence for nuclear medicine in oncology

As in all other medical fields, artificial intelligence (AI) is increasingly being used in nuclear medicine for oncology. There are many articles that discuss AI from the viewpoint of nuclear medicine, but few focus on nuclear medicine from the viewpoint of AI. Nuclear medicine images are characterized by their low spatial resolution and high quantitativeness. It is noted that AI has been used since before the emergence of deep learning. AI can be divided into three categories by its purpose: (1) assisted interpretation, i.e., computer-aided detection (CADe) or computer-aided diagnosis (CADx). (2) Additional insight, i.e., AI provides information beyond the radiologist's eye, such as predicting genes and prognosis from images. It is also related to the field called radiomics/radiogenomics. (3) Augmented image, i.e., image generation tasks. To apply AI to practical use, harmonization between facilities and the possibility of black box explanations need to be resolved.

Test-retest repeatability of quantitative bone SPECT/CT

OBJECTIVE

Technological innovations in single-photon emission computed tomography (SPECT) have enabled a more accurate quantitative evaluation of the uptake, and the standardized uptake value (SUV) can be measured as a semi-quantitative value, as in positron emission tomography. Nevertheless, the reliability of the SUV of bone SPECT has not been well established. The purpose of this study is to evaluate the test-retest repeatability of the SUV of bone SPECT/CT in clinical settings.

METHODS

This prospective study recruited patients with prostate cancer planning to receive bone SPECT/CT for the evaluation of bone abnormality between August 2017 and September 2019. Bone images were acquired twice by an integrated SPECT/CT scanner (Symbia Intevo, Siemens) within a 4- to 10-day interval. The maximum SUV (SUVmax) and peak SUV (SUVpeak) were calculated for the volumes of interests on the normal bone areas, degeneration/fracture lesions, and metastatic lesions. To determine repeatability, we calculated statistical indicators, including intraclass correlation coefficient (ICC), repeatability coefficient (RC), and mean absolute percentage difference (MAPD). For the ICC, the 95% confidential interval (CI) was also calculated, and an ICC of ≥ 0.8 was defined as an almost perfect correlation.

RESULTS

Twelve male patients were enrolled in the study (58-86 years; median, 71 years), and a total of 229 volumes of the interest were included in the analyses. The ICCs were 0.968 [95% CI (0.959, 0.975)] for SUVmax and 0.976 [95% CI (0.969, 0.981)] for SUVpeak. The RCs of the relative difference were 30.7% for SUVmax and 27.6% for SUVpeak, and the MAPDs (± standardized deviation) of all lesions were 12.3 ± 9.9% for SUVmax and 11.5 ± 8.3% for SUVpeak. The RCs and the MAPDs showed comparable value with the previous report regarding repeatability studies on PET.

CONCLUSION

An almost perfect correlation was demonstrated by repeated SUVmax and SUVpeak measured by quantitative integrated SPECT/CT. The quantitative values could be reliable indicators in patient management.

Clinical Use of Quantitative Analysis of Bone Scintigraphy to Assess the Involvement of Arthritis Diseases in Patients with Joint Symptoms

We aimed to compare the diagnostic ability of quantitative analysis of bone scintigraphy with that of visual analysis for identifying arthritis disease involvement in patients with joint symptoms. We retrospectively included 93 patients with joint symptoms who underwent Tc-99m methylene diphosphonate bone scintigraphy for evaluating arthritis disease involvement. Bone scintigraphy images were visually and quantitatively analyzed using an in-house software by two reviewers. On quantitative analysis, joint uptake ratio was measured for 64 joints in 14 joints areas. The inter-rater agreement of visual and quantitative analyses was assessed, and diagnostic abilities were compared based on the area under the receiver operating characteristic (ROC) curve (AUC) values. Regarding visual analysis, there was a moderate degree of inter-rater agreement (kappa coefficient of 0.597), while there was a substantial inter-rater agreement (concordance correlation coefficient of 0.987) in the measurement of the joint uptake ratio. The comparisons of ROC curves for the total 5941 joints revealed that the joint uptake ratio had a significantly higher AUC value (0.789) to detect the affected joint than that of the visual analysis (p < 0.001). Quantitative analysis using joint uptake ratio showed substantial reproducibility and higher diagnostic ability to detect joints involving arthritis diseases than visual analysis on bone scintigraphy.

Analysis of Bone Scans in Various Tumor Entities Using a Deep-Learning-Based Artificial Neural Network Algorithm-Evaluation of Diagnostic Performance

The bone scan index (BSI), initially introduced for metastatic prostate cancer, quantifies the osseous tumor load from planar bone scans. Following the basic idea of radiomics, this method incorporates specific deep-learning techniques (artificial neural network) in its development to provide automatic calculation, feature extraction, and diagnostic support. As its performance in tumor entities, not including prostate cancer, remains unclear, our aim was to obtain more data about this aspect. The results of BSI evaluation of bone scans from 951 consecutive patients with different tumors were retrospectively compared to clinical reports (bone metastases, yes/no). Statistical analysis included entity-specific receiver operating characteristics to determine optimized BSI cut-off values. In addition to prostate cancer (cut-off = 0.27%, sensitivity (SN) = 87%, specificity (SP) = 99%), the algorithm used provided comparable results for breast cancer (cut-off 0.18%, SN = 83%, SP = 87%) and colorectal cancer (cut-off = 0.10%, SN = 100%, SP = 90%). Worse performance was observed for lung cancer (cut-off = 0.06%, SN = 63%, SP = 70%) and renal cell carcinoma (cut-off = 0.30%, SN = 75%, SP = 84%). The algorithm did not perform satisfactorily in melanoma (SN = 60%). For most entities, a high negative predictive value (NPV ≥ 87.5%, melanoma 80%) was determined, whereas positive predictive value (PPV) was clinically not applicable. Automatically determined BSI showed good sensitivity and specificity in prostate cancer and various other entities. Particularly, the high NPV encourages applying BSI as a tool for computer-aided diagnostic in various tumor entities.

Fully automated analysis for bone scintigraphy with artificial neural network: usefulness of bone scan index (BSI) in breast cancer

OBJECTIVE

Artificial neural network (ANN) technology has been developed for clinical use to analyze bone scintigraphy with metastatic bone tumors. It has been reported to improve diagnostic accuracy and reproducibility especially in cases of prostate cancer. The aim of this study was to evaluate the diagnostic usefulness of quantitative bone scintigraphy with ANN in patients having breast cancer.

PATIENTS AND METHODS

We retrospectively evaluated 88 patients having breast cancer who underwent both bone scintigraphy and ¹⁸F-fluorodeoxyglucose (FDG) positron-emission computed tomography/X-ray computed tomography (PET/CT) within an interval of 8 weeks between both examinations for comparison. The whole-body bone images were analyzed with fully automated software that was customized according to a Japanese multicenter database. The region of interest for FDG-PET was set to bone lesions in patients with bone metastasis, while the bone marrow of the ilium and the vertebra was used in patients without bone metastasis.

RESULTS

Thirty of 88 patients had bone metastasis. Extent of disease, bone scan index (BSI) which indicate severity of bone metastasis, the maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and serum tumor markers in patients with bone metastasis were significantly higher than those in patients without metastasis. The Kaplan-Meier survival curve showed that the overall survival of the lower BSI group was longer than that with the higher BSI group in patients with visceral metastasis. In the multivariate Cox proportional hazard model, BSI (hazard ratio (HR): 19.15, p = 0.0077) and SUVmax (HR: 10.12, p = 0.0068) were prognostic factors in patients without visceral metastasis, while the BSI was only a prognostic factor in patients with visceral metastasis (HR: 7.88, p = 0.0084), when dividing the sample into two groups with each mean value in patients with bone metastasis.

CONCLUSION

BSI, an easily and automatically calculated parameter, was a well prognostic factor in patients with visceral metastasis as well as without visceral metastasis from breast cancer.

Measuring the unmeasurable: automated bone scan index as a quantitative endpoint in prostate cancer clinical trials

BACKGROUND

Up to 90% of men with metastatic castration-resistant prostate cancer (mCRPC) will have a distribution of disease that includes bone metastases demonstrated on a Technetium-99m (^99mTc-MDP) bone scan. The Prostate Cancer Working Group 2 and 3 Consensus Criteria standardized the criteria for assessing progression based on the development of new lesions. These criteria have been recognized by regulatory authorities for drug approval. The bone scan index (BSI) is a method to quantitatively measure the burden of bony disease, and can assess both disease progression and regression. The automated BSI (aBSI) is a method of computer analysis to assess BSI, and is being qualified as a clinical trials endpoint.

METHODS

Manual searching was used to identify the literature on BSI and aBSI. We summarize the most relevant aspects of the retrospective and prospective studies evaluating aBSI measurements, and provide a critical discussion on the potential advantages and caveats of aBSI.

RESULTS

The development of neural artificial networks (EXINI boneBSI) to automatically determine the BSI reduces the turnaround time for assessing BSI with high reproducibility and accuracy. Several studies showed that the concordance between aBSI and BSI, as well as the interobserver concordance of aBSI, was >0.95. In a phase 3 assessment of aBSI, a doubling value increased the risk of death in 20%, pre-treatment aBSI values independently correlated with overall survival (OS) and time to symptomatic progression. Retrospective studies suggest that a decrease in aBSI after treatment may correlate with higher survival when compared with increasing aBSI.

CONCLUSIONS

aBSI provides a quantitative measurement that is feasible, reproducible, and in analyses to date correlates with OS and symptomatic progression. These findings support the aBSI to risk-stratify men with mCRPC for clinical trial enrollment. Future studies quantifying aBSI change over time as an intermediate endpoint for evaluating new systemic therapies are needed.

Assessment of Skeletal Tumor Load in Metastasized Castration-Resistant Prostate Cancer Patients: A Review of Available Methods and an Overview on Future Perspectives

Metastasized castration-resistant prostate cancer (mCRPC), is the most advanced form of prostate neoplasia, where massive spread to the skeletal tissue is frequent. Patients with this condition are benefiting from an increasing number of treatment options. However, assessing tumor response in patients with multiple localizations might be challenging. For this reason, many computational approaches have been developed in the last decades to quantify the skeletal tumor burden and treatment response. In this review, we analyzed the progressive development and diffusion of such approaches. A computerized literature search of the PubMed/Medline was conducted, including articles between January 2008 and March 2018. The search was expanded by manually reviewing the reference list of the chosen articles. Thirty-five studies were identified. The number of eligible studies greatly increased over time. Studies could be categorized in the following categories: automated analysis of 2D scans, SUV-based thresholding, hybrid CT- and SUV-based thresholding, and MRI-based thresholding. All methods are discussed in detail. Automated analysis of bone tumor burden in mCRPC is a growing field of research; when choosing the appropriate method of analysis, it is important to consider the possible advantages as well as the limitations thoroughly.

A prospective study to evaluate the intra-individual reproducibility of bone scans for quantitative assessment in patients with metastatic prostate cancer

BACKGROUND

The Bone Scan Index (BSI) is used to quantitatively assess the total tumour burden in bone scans of patients with metastatic prostate cancer. The clinical utility of BSI has recently been validated as a prognostic imaging biomarker. However, the clinical utility of the on-treatment change in BSI is dependent on the reproducibility of bone scans. The objective of this prospective study is to evaluate the intra-patient reproducibility of two bone scan procedures performed at a one-week interval.

METHODS

We prospectively studied prostate cancer patients who were referred for bone scintigraphy at our centres according to clinical routine. All patients underwent two whole-body bone scans: one for clinical routine purposes and a second one as a repeated scan after approximately one week. BSI values were obtained for each bone scintigraph using EXINI bone^BSI software.

RESULTS

A total of 20 patients were enrolled. There was no statistical difference between the BSI values of the first (median = 0.66, range 0-40.77) and second (median = 0.63, range 0-22.98) bone scans (p = 0.41). The median difference in BSI between the clinical routine and repeated scans was - 0.005 (range - 17.79 to 0). The 95% confidence interval for the median value was - 0.1 to 0. A separate analysis was performed for patients with BSI ≤ 10 (n = 17). Differences in BSI were smaller for patients with BSI ≤ 10 compared to the whole cohort (median - 0.1, range - 2.2-0, 95% confidence interval - 0.1 to 0).

CONCLUSIONS

The automated BSI demonstrated high intra-individual reproducibility for BSI ≤ 10 in the two repeated bone scans of patients with prostate cancer. The study supports the use of BSI as a quantitative parameter to evaluate the change in total tumour burden in bone scans.

Evaluation of changes in Bone Scan Index at different acquisition time-points in bone scintigraphy

Bone Scan Index (BSI) is a validated imaging biomarker to objectively assess tumour burden in bone in patients with prostate cancer, and can be used to monitor treatment response. It is not known if BSI is significantly altered when images are acquired at a time difference of 1 h. The aim of this study was to investigate if automatic calculation of BSI is affected when images are acquired 1 hour apart, after approximately 3 and 4 h. We prospectively studied patients with prostate cancer who were referred for bone scintigraphy according to clinical routine. The patients performed a whole-body bone scan at approximately 3 h after injection of radiolabelled bisphosphonate and a second 1 h after the first. BSI values for each bone scintigraphy were obtained using EXINI boneBSI software. A total of 25 patients were included. Median BSI for the first acquisition was 0·05 (range 0-11·93) and for the second acquisition 0·21 (range 0-13·06). There was a statistically significant increase in BSI at the second image acquisition compared to the first (P<0·001). In seven of 25 patients (28%) and in seven of 13 patients with BSI > 0 (54%), a clinically significant increase (>0·3) was observed. The time between injection and scanning should be fixed when changes in BSI are important, for example when monitoring therapeutic efficacy.

Evaluation of bone metastatic burden by bone SPECT/CT in metastatic prostate cancer patients: defining threshold value for total bone uptake and assessment in radium-223 treated patients

Objectives

To establish a new three-dimensional quantitative evaluation method for bone metastasis, we applied bone single photon emission tomography with computed tomography (SPECT/CT). The total bone uptake (TBU), which measures active bone metastatic burden, was calculated as the sum of [mean uptake obtained as standardized uptake value (SUV) above a cut-off level] × (the volume of the lesion) in the trunk using bone SPECT/CT. We studied the threshold value and utility of TBU in prostate cancer patients treated with radium-223 (Ra-223) therapy.

Methods

To establish the threshold value of TBU, we compared bone metastatic and non-metastatic regions in 61 prostate cancer patients with bone metastasis and 69 without. Five fixed sites in each patient were selected as evaluation points and divided into bone metastatic and non-metastatic sites. Sensitivity and specificity analysis was applied to establish the threshold level. Using the obtained threshold value, we then calculated the TBU in nine prostate cancer patients who received Ra-223 therapy, and compared the results with the bone scan index (BSI) by BONENAVI^® and visual evaluation of bone scintigraphy.

Results

Uptake was significantly lower in non-metastatic sites in patients with bone metastasis than in patients without metastasis. Sensitivity and specificity analysis revealed SUV = 7.0 as the threshold level. There was a discrepancy between TBU and BSI change in two of the nine patients, in whom TBU change correlated with visual judgement, but BSI change did not. In two patients, BSI was nearly 0 throughout the course, but the TBU was positive and changed, although the change was not large. These results suggest that TBU may be more accurate and sensitive than BSI for quantitative evaluation of active bone metastatic burden.

Conclusion

We established a threshold value (SUV > 7.0) for three-dimensional TBU for evaluating active bone metastatic burden in prostate cancer patients using bone SPECT/CT. Despite the small number of patients, we expect the change in TBU could be more accurate and sensitive than the change in BSI among patients who received Ra-223.

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

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.