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

Applications of Artificial Intelligence in PSMA PET/CT for Prostate Cancer Imaging

Prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) has emerged as an important imaging technique for prostate cancer. The use of PSMA PET/CT is rapidly increasing, while the number of nuclear medicine physicians and radiologists to interpret these scans is limited. Additionally, there is variability in interpretation among readers. Artificial intelligence techniques, including traditional machine learning and deep learning algorithms, are being used to address these challenges and provide additional insights from the images. The aim of this scoping review was to summarize the available research on the development and applications of AI in PSMA PET/CT for prostate cancer imaging. A systematic literature search was performed in PubMed, Embase and Cinahl according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 26 publications were included in the synthesis. The included studies focus on different aspects of artificial intelligence in PSMA PET/CT, including detection of primary tumor, local recurrence and metastatic lesions, lesion classification, tumor quantification and prediction/prognostication. Several studies show similar performances of artificial intelligence algorithms compared to human interpretation. Few artificial intelligence tools are approved for use in clinical practice. Major limitations include the lack of external validation and prospective design. Demonstrating the clinical impact and utility of artificial intelligence tools is crucial for their adoption in healthcare settings. To take the next step towards a clinically valuable artificial intelligence tool that provides quantitative data, independent validation studies are needed across institutions and equipment to ensure robustness.

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.

Assessing Therapeutic Response to Radium-223 with an Automated Bone Scan Index among Metastatic Castration-Resistant Prostate Cancer Patients: Data from Patients in the J-RAP-BSI Trial

To evaluate the usefulness of change in the automated bone scan index (aBSI) value derived from bone scintigraphy findings as an imaging biomarker for the assessment of treatment response and survival prediction in metastatic castration-resistant prostate cancer (mCRPC) patients treated with Ra-223. This study was a retrospective investigation of a Japanese cohort of 205 mCRPC patients who received Ra-223 in 14 hospitals between July 2016 and August 2020 and for whom bone scintigraphy before and after radium-223 treatment was available. Correlations of aBSI change, with changes in the serum markers alkaline phosphatase (ALP) and prostate-specific antigen (PSA) were evaluated. Additionally, the association of those changes with overall survival (OS) was assessed using the Cox proportional-hazards model and Kaplan-Meier curve results. Of the 205 patients enrolled, 165 (80.5%) completed six cycles of Ra-223. Following treatment, ALP decline (%ALP < 0%) was noted in 72.2% (148/205), aBSI decline (%aBSI < 0%) in 52.7% (108/205), and PSA decline (%PSA < 0%) in 27.8% (57/205). Furthermore, a reduction in both aBSI and ALP was seen in 87 (42.4%), a reduction in only ALP was seen in 61 (29.8%), a reduction in only aBSI was seen in 21 (10.2%), and in both aBSI and ALP increasing/stable (≥0%) was seen in 36 (17.6%) patients. Multiparametric analysis showed changes in PSA [hazard ratio (HR) 4.30, 95% confidence interval (CI) 2.32-8.77, p < 0.0001], aBSI (HR 2.22, 95%CI 1.43-3.59, p = 0.0003), and ALP (HR 2.06, 95%CI 1.35-3.14, p = 0.0008) as significant prognostic factors for OS. For mCRPC patients treated with Ra-223, aBSI change is useful as an imaging biomarker for treatment response assessment and survival prediction.

Novel nomogram developed for determining suitability of metastatic castration-resistant prostate cancer patients to receive maximum benefit from radium-223 dichloride treatment-Japanese Ra-223 Therapy in Prostate Cancer using Bone Scan Index (J-RAP-BSI) Trial

PURPOSE

To develop a novel nomogram for determining radium-223 dichloride (Ra-223) treatment suitability for metastatic castration-resistant prostate cancer (mCRPC) patients.

METHODS

This Japanese Ra-223 Therapy in Prostate Cancer using Bone Scan Index (J-RAP-BSI) Trial was a retrospective multicenter investigation enrolled 258 mCRPC patients in Japan with Ra-223 treatment between June 2016 and August 2020, with bone scintigraphy findings before treatment, clinical data, and survival outcome available. A nomogram was constructed using prognostic factors for overall survival (OS) based on a least absolute shrinkage and selection operator Cox regression model. A sub-analysis was also conducted for patients meeting European Medicines Agency (EMA) guidelines.

RESULTS

Within a median of 17.4 months after initial Ra-223 treatment, 124 patients (48.1%) died from prostate cancer. Predictive factors included (1) sum of prior treatment history (score 0, never prior novel androgen receptor-targeted agents (ARTA) therapy, never prior taxane-based chemotherapy, and ever prior bisphosphonate/denosumab treatment), (2) Eastern Cooperative Oncology Group (ECOG) performance status, (3) prostate-specific antigen doubling time (PSADT), (4) hemoglobin, (5) lactate dehydrogenase (LDH), and (6) alkaline phosphatase (ALP) levels, and (7) automated bone scan index (aBSI) value based on bone scintigraphy. The nomogram using those factors showed good discrimination, with apparent and optimism-corrected Harrell's concordance index values of 0.748 and 0.734, respectively. Time-dependent area under the curve values at 1, 2, and 3 years were 0.771, 0.818, and 0.771, respectively. In 227 patients meeting EMA recommendation, the nomogram with seven factors showed good discrimination, with apparent and optimism-corrected Harrell's concordance index values of 0.722 and 0.704, respectively. Time-dependent area under the curve values at 1, 2, and 3 years were 0.747, 0.790, and 0.759, respectively.

CONCLUSION

This novel nomogram including aBSI to select mCRPC patients to receive Ra-223 with significantly prolonged OS possibility was found suitable for assisting therapeutic decision-making, regardless of EMA recommendation.

Automated Classification of PET‐CT Lesions in Lung Cancer: An Independent Validation Study

Introduction

Recently, a tool called the positron emission tomography (PET)‐assisted reporting system (PARS) was developed and presented to classify lesions in PET/computed tomography (CT) studies in patients with lung cancer or lymphoma. The aim of this study was to validate PARS with an independent group of lung‐cancer patients using manual lesion segmentations as a reference standard, as well as to evaluate the association between PARS‐based measurements and overall survival (OS).

Methods

This study retrospectively included 115 patients who had undergone clinically indicated (18F)‐fluorodeoxyglucose (FDG) PET/CT due to suspected or known lung cancer. The patients had a median age of 66 years (interquartile range [IQR]: 61–72 years). Segmentations were made manually by visual inspection in a consensus reading by two nuclear medicine specialists and used as a reference. The research prototype PARS was used to automatically analyse all the PET/CT studies. The PET foci classified as suspicious by PARS were compared with the manual segmentations. No manual corrections were applied. Total lesion glycolysis (TLG) was calculated based on the manual and PARS‐based lung‐tumour segmentations. Associations between TLG and OS were investigated using Cox analysis.

Results

PARS showed sensitivities for lung tumours of 55.6% per lesion and 80.2% per patient. Both manual and PARS TLG were significantly associated with OS.

Conclusion

Automatically calculated TLG by PARS contains prognostic information comparable to manually measured TLG in patients with known or suspected lung cancer. The low sensitivity at both the lesion and patient levels makes the present version of PARS less useful to support clinical reading, reporting and staging.

Segmented linear correlations between bone scan index and prostate cancer biomarkers, alkaline phosphatase, and prostate specific antigen in patients with a Gleason score ≥7

Technetium-99m methyl diphosphonate bone scintigraphy is relatively easily accessible for detecting bone metastases in prostate cancer patients. However, it is subjective and can be challenging to compare images taken at different time points. The bone scan index (BSI) is a more objective evaluation and allows for better comparison of images. Its correlation with other biomarkers of prostate cancer metastases such as prostate specific antigen (PSA) and alkaline phosphatase (ALP) is not clearly understood. This study thus aimed to compare the BSI correlation to PSA against that of BSI to ALP levels in patients with a Gleason score ≥7.A retrospective analysis of the medical records of 50 prostate cancer patients with a Gleason score of ≥7 referred for a bone scan between January 1, 2015 and December 31, 2018 was undertaken. Bone scans were interpreted visually, and using a semi-automated computer programme to quantify the BSI and its relation to PSA and ALP measurements.For the metastasis positive measurements, there was a statistically significant moderate positive overall linear correlation between BSI and PSA. For ALP and BSI, there were 2 segmented strong positive linear relationships between them. The first segment consisted of ALP < 375 IU/L and BSI >10%, where ALP and BSI were strongly and positively correlated. The other segment tended to have generally low BSI measurements (<10%) and also had a strong and positive correlation.The BSI was found to be better linearly correlated with ALP than PSA.

Automated Bone Scan Index to Optimize Prostate Cancer Working Group Radiographic Progression Criteria for Men With Metastatic Castration-Resistant Prostate Cancer

INTRODUCTION

Radiographic progression-free survival (rPFS) by Prostate Cancer Working Group (PCWG) criteria is a radiographic endpoint. The automated bone scan index (aBSI) quantifies osseous disease burden on bone scintigraphy as a percentage of total skeletal weight. Using the aBSI, we sought to quantify increase in tumor burden represented by PCWG progression criteria, and to determine the interval increase that best associates with overall survival (OS).

PATIENT AND METHODS

Retrospective analysis of trials using androgen receptor axis-targeted drugs for metastatic castration resistant prostate cancer patients (mCRPC). aBSI increase in bone disease was assessed from baseline scan to time-to-progression (per PCWG criteria). Threshold for time to aBSI increase were explored and the association between each time-to-threshold and OS was computed.

RESULTS

A total of 169 mCPRC patients had bone scans available for aBSI analysis. Of these, 90 (53%) had progression in bone meeting PCWG criteria. Total aBSI increase in patients meeting PCWG criteria was 1.22 (interquartile range [IQR]: 0.65-2.49), with a median relative increase of 109% (IQR: 40%-377%). Median aBSI at baseline was 3.1 (IQR: 1.3-7.1). The best association between OS and time-to-progression occurred with an absolute increase in aBSI equal to 0.6 (Kendall's tau 0.52).

CONCLUSION

An absolute increase of 0.6 or more in aBSI from the first follow-up scan results in the highest association with OS in patients with mCRPC. The rPFS by PCWG, identified progression at nearly twice this tumor burden, suggesting that aBSI may be used to further develop the PCWG criteria without degrading its association with OS.

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.

Prostate-specific membrane antigen (PSMA)-ligand positron emission tomography and radioligand therapy (RLT) of prostate cancer

From a clinical perspective, prostate-specific membrane antigen (PSMA) is a valuable target for both diagnosis and radioligand therapy (RLT) of prostate cancer. The term ‘specific’ has been used to characterize a histologic hallmark of overexpression in the membrane of most prostate cancer. Many PSMA ligands have been developed since the previous decade and have been used in several clinical trials and clinical studies. However, procedure, specification, protocol, interpretation criteria, radiation dose, and cost-effectiveness of PSMA ligands have not been fully explained. Regardless of worldwide use of promising PSMA-ligand PET and RLT, it has not been approved in Japan. Expedited introduction of PSMA-ligand PET and RLT to Japan and implementation of clinical study are eager for many patients with prostate cancer.

The Automated Bone Scan Index as a Predictor of Response to Prostate Radiotherapy in Men with Newly Diagnosed Metastatic Prostate Cancer: An Exploratory Analysis of STAMPEDE's "M1|RT Comparison"

Background

Prostate radiotherapy (RT) is a first-line option for newly diagnosed men with low-burden metastatic prostate cancer. The current criterion to define this clinical state is based on manual bone metastasis counts, but enumeration of bone metastases is limited by interobserver variations, and it does not account for metastasis volume or lesional coalescence. The automated bone scan index (aBSI) is a quantitative method of evaluating bone metastatic burden in a standardised and reproducible manner.

Objective

To evaluate whether aBSI has utility as a predictive imaging biomarker to define a newly diagnosed metastatic prostate cancer population that might benefit from the addition of prostate RT to standard of care (SOC) systemic therapy.

Design, setting, and participants

This is an exploratory analysis of men with newly diagnosed metastatic prostate cancer randomised in a 1:1 ratio to either SOC or SOC + prostate RT within the STAMPEDE “M1|RT comparison”.

Intervention

The SOC was lifelong androgen deprivation therapy, with up-front docetaxel permitted from December 2015. Men allocated RT received either a daily or a weekly schedule that was nominated before randomisation.

Outcome measurements and statistical analysis

Baseline bone scans were evaluated retrospectively to calculate aBSI. We used overall (OS) and failure-free (FFS) survival as the end points. Treatment-aBSI interaction was evaluated using the multivariable fractional polynomial interaction (MFPI) and subpopulation treatment effect pattern plot. Further analysis was done in aBSI quartiles using Cox regression models adjusted for stratification factors.

Results and limitations

: Baseline bone scans for 660 (SOC: 323 and SOC + RT: 337) of 2061 men randomised within the “M1|RT comparison” met the software requirements for aBSI calculation. The median age was 68 yr, median PSA was 100 ng/mL, median aBSI was 0.9, and median follow-up was 39 mo. Baseline patient characteristics including aBSI were balanced between the treatment groups. Using the MFPI procedure, there was evidence of aBSI-treatment interaction for OS (p = 0.04, MFPI procedure) and FFS (p <  0.01, MFPI procedure). Graphical evaluation of estimated treatment effect plots showed that the OS and FFS benefit from prostate RT was greatest in patients with a low aBSI. Further analysis in quartiles based on aBSI supported this finding.

Conclusions

A low automated bone scan index is predictive of survival benefit associated with prostate RT in men with newly diagnosed metastatic prostate cancer.

Patient summary

The widely used bone scan can be evaluated using an automated technique to potentially select men with newly diagnosed metastatic prostate cancer who might benefit from prostate radiotherapy.

Assessing Radiographic Response to 223Ra with an Automated Bone Scan Index in Metastatic Castration-Resistant Prostate Cancer Patients

For effective clinical management of patients being treated with ²²³Ra, there is a need for radiographic response biomarkers to minimize disease progression and to stratify patients for subsequent treatment options. The objective of this study was to evaluate an automated bone scan index (aBSI) as a quantitative assessment of bone scans for radiographic response in patients with metastatic castration-resistant prostate cancer (mCRPC). Methods: In a multicenter retrospective study, bone scans from patients with mCRPC treated with monthly injections of ²²³Ra were collected from 7 hospitals in Sweden. Patients with available bone scans before treatment with ²²³Ra and at treatment discontinuation were eligible for the study. The aBSI was generated at baseline and at treatment discontinuation. The Spearman rank correlation was used to correlate aBSI with the baseline covariates: alkaline phosphatase (ALP) and prostate-specific antigen (PSA). The Cox proportional-hazards model and Kaplan-Meier curve were used to evaluate the association of covariates at baseline and their change at treatment discontinuation with overall survival (OS). The concordance index (C-index) was used to evaluate the discriminating strength of covariates in predicting OS. Results: Bone scan images at baseline were available from 156 patients, and 67 patients had both a baseline and a treatment discontinuation bone scan (median, 5 doses; interquartile range, 3-6 doses). Baseline aBSI (median, 4.5; interquartile range, 2.4-6.5) was moderately correlated with ALP (r = 0.60, P < 0.0001) and with PSA (r = 0.38, P = 0.003). Among baseline covariates, aBSI (P = 0.01) and ALP (P = 0.001) were significantly associated with OS, whereas PSA values were not (P = 0.059). After treatment discontinuation, 36% (24/67), 80% (54/67), and 13% (9/67) of patients demonstrated a decline in aBSI, ALP, and PSA, respectively. As a continuous variable, the relative change in aBSI after treatment, compared with baseline, was significantly associated with OS (P < 0.0001), with a C-index of 0.67. Median OS in patients with both aBSI and ALP decline (median, 134 wk) was significantly longer than in patients with ALP decline only (median, 77 wk; P = 0.029). Conclusion: Both aBSI at baseline and its change at treatment discontinuation were significant parameters associated with OS. The study warrants prospective validation of aBSI as a quantitative imaging response biomarker to predict OS in patients with mCRPC treated with ²²³Ra.

Phase 3 Assessment of the Automated Bone Scan Index as a Prognostic Imaging Biomarker of Overall Survival in Men With Metastatic Castration-Resistant Prostate Cancer: A Secondary Analysis of a Randomized Clinical Trial

Importance

Prostate cancer commonly metastasizes to bone, and bone metastases are associated with pathologic fractures, pain, and reduced survival. Bone disease is routinely visualized using the technetium Tc 99m (99mTc) bone scan; however, the standard interpretation of bone scan data relies on subjective manual assessment of counting metastatic lesion numbers. There is an unmet need for an objective and fully quantitative assessment of bone scan data.

Objective

To clinically assess in a prospectively defined analysis plan of a clinical trial the automated Bone Scan Index (aBSI) as an independent prognostic determinant of overall survival (OS) in men with metastatic castration-resistant prostate cancer (mCRPC).

Design, Setting, and Participants

This investigation was a prospectively planned analysis of the aBSI in a phase 3 multicenter randomized, double-blind, placebo-controlled clinical trial of tasquinimod (10TASQ10). Men with bone metastatic chemotherapy-naïve CRPC were recruited at 241 sites in 37 countries between March 2011 and August 2015. The statistical analysis plan to clinically evaluate the aBSI was prospectively defined and locked before unmasking of the 10TASQ10 study. The analysis of aBSI was conducted between May 25, 2016, and June 3, 2017.

Main Outcomes and Measures

The associations of baseline aBSI with OS, radiographic progression-free survival (rPFS), time to symptomatic progression, and time to opiate use for cancer pain.

Results

Of the total 1245 men enrolled, 721 were evaluable for the aBSI. The mean (SD) age (available for 719 men) was 70.6 (8.0) years (age range, 47-90 years). The aBSI population was representative of the total study population based on baseline characteristics. The aBSI (median, 1.07; range, 0-32.60) was significantly associated with OS (hazard ratio [HR], 1.20; 95% CI, 1.14-1.26; P < .001). The median OS by aBSI quartile (lowest to highest) was 34.7, 27.3, 21.7, and 13.3 months, respectively. The discriminative ability of the aBSI (C index, 0.63) in prognosticating OS was significantly higher than that of the manual lesion counting (C index, 0.60) (P = .03). In a multivariable survival model, a higher aBSI remained independently associated with OS (HR, 1.06; 95% CI, 1.01-1.11; P = .03). A higher aBSI was also independently associated with time to symptomatic progression (HR, 1.18; 95% CI, 1.13-1.23; P < .001) and time to opiate use for cancer pain (HR, 1.21; 95% CI, 1.14-1.30; P < .001).

Conclusions and Relevance

To date, this investigation is the largest prospectively analyzed study to validate the aBSI as an independent prognostic imaging biomarker of survival in mCRPC. These data support the prognostic utility of the aBSI as an objective imaging biomarker in the design and eligibility of clinical trials of systemic therapies for patients with mCRPC.

Trial Registration

ClinicalTrials.gov Identifier: NCT01234311.

Denoising of Scintillation Camera Images Using a Deep Convolutional Neural Network: A Monte Carlo Simulation Approach

Scintillation camera images contain a large amount of Poisson noise. We have investigated whether noise can be removed in whole-body bone scans using convolutional neural networks (CNNs) trained with sets of noisy and noiseless images obtained by Monte Carlo simulation. Methods: Three CNNs were generated using 3 different sets of training images: simulated bone scan images, images of a cylindric phantom with hot and cold spots, and a mix of the first two. Each training set consisted of 40,000 noiseless and noisy image pairs. The CNNs were evaluated with simulated images of a cylindric phantom and simulated bone scan images. The mean squared error between filtered and true images was used as difference metric, and the coefficient of variation was used to estimate noise reduction. The CNNs were compared with gaussian and median filters. A clinical evaluation was performed in which the ability to detect metastases for CNN- and gaussian-filtered bone scans with half the number of counts was compared with standard bone scans. Results: The best CNN reduced the coefficient of variation by, on average, 92%, and the best standard filter reduced the coefficient of variation by 88%. The best CNN gave a mean squared error that was on average 68% and 20% better than the best standard filters, for the cylindric and bone scan images, respectively. The best CNNs for the cylindric phantom and bone scans were the dedicated CNNs. No significant differences in the ability to detect metastases were found between standard, CNN-, and gaussian-filtered bone scans. Conclusion: Noise can be removed efficiently regardless of noise level with little or no resolution loss. The CNN filter enables reducing the scanning time by half and still obtaining good accuracy for bone metastasis assessment.

Automated Bone Scan Index as an Imaging Biomarker to Predict Overall Survival in the Zometa European Study/SPCG11

BACKGROUND

Owing to the large variation in treatment response among patients with high-risk prostate cancer, it would be of value to use objective tools to monitor the status of bone metastases during clinical trials. Automated Bone Scan Index (aBSI) based on artificial intelligence has been proposed as an imaging biomarker for the quantification of skeletal metastases from bone scintigraphy.

OBJECTIVE

To investigate how an increase in aBSI during treatment may predict clinical outcome in a randomised controlled clinical trial including patients with high-risk prostate cancer.

DESIGN, SETTING, AND PARTICIPANTS

We retrospectively selected all patients from the Zometa European Study (ZEUS)/SPCG11 study with image data of sufficient quality to allow for aBSI assessment at baseline and at 48-mo follow-up. Data on aBSI were obtained using EXINIbone^BSI software, blinded for clinical data and randomisation of zoledronic acid treatment. Data on age, overall survival (OS), and prostate-specific antigen (PSA) at baseline and upon follow-up were available from the study database.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Association between clinical parameters and aBSI increase during treatment was evaluated using Cox proportional-hazards regression models, Kaplan-Meier estimates, and log-rank test. Discrimination between prognostic variables was assessed using the concordance index (C-index).

RESULTS AND LIMITATIONS

In this cohort, 176 patients with bone metastases and a change in aBSI from baseline to follow-up of ≤0.3 had a significantly longer median survival time than patients with an aBSI change of >0.3 (p<0.0001). The increase in aBSI was significantly associated with OS (p<0.01 and C-index=0.65), while age and PSA change were not.

CONCLUSIONS

The aBSI used as an objective imaging biomarker predicted outcome in prostate cancer patients in the ZEUS/SPCG11 study. An analysis of the change in aBSI from baseline to 48-mo follow-up represents a valuable tool for prognostication and monitoring of prostate cancer patients with bone metastases.

PATIENT SUMMARY

The increase in the burden of skeletal metastases, as measured by the automated Bone Scan Index (aBSI), during treatment was associated with overall survival in patients from the Zometa European Study/SPCG11 study. The aBSI may be a useful tool also in monitoring prostate cancer patients with newly developed bone metastases.

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.

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.

WITHDRAWN: Author Reply to the Editorial Comment on: Zacho HD, Gade M, Mortensen JC, Bertelsen H, Boldsen SK, Petersen LJ. Bone Scan Index Is an Independent Predictor for Time to Castration Resistant Prostate Cancer in Newly Diagnosed Prostate Cancer: A Prospective Study. Urology 2017, In Press

The Publisher regrets that this article is an accidental duplication of an article that has already been published, http://dx.doi.org/10.1016/j.urology.2017.05.060. The duplicate article has therefore been withdrawn. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.