Reza Kaboteh

A Novel Automated Platform for Quantifying the Extent of Skeletal Tumour Involvement in Prostate Cancer Patients Using the Bone Scan Index

BACKGROUND There is little consensus on a standard approach to analysing bone scan images. The Bone Scan Index (BSI) is predictive of survival in patients with progressive prostate cancer (PCa), but the popularity of this metric is hampered by the tedium of the manual calculation. OBJECTIVE Develop a fully automated method of quantifying the BSI and determining the clinical value of automated BSI measurements beyond conventional clinical and pathologic features. DESIGN, SETTING, AND PARTICIPANTS We conditioned a computer-assisted diagnosis system identifying metastatic lesions on a bone scan to automatically compute BSI measurements. A training group of 795 bone scans was used in the conditioning process. Independent validation of the method used bone scans obtained ≤3 mo from diagnosis of 384 PCa cases in two large population-based cohorts. An experienced analyser (blinded to case identity, prior BSI, and outcome) scored the BSI measurements twice. We measured prediction of outcome using pretreatment Gleason score, clinical stage, and prostate-specific antigen with models that also incorporated either manual or automated BSI measurements. MEASUREMENTS The agreement between methods was evaluated using Pearsons correlation coefficient. Discrimination between prognostic models was assessed using the concordance index (C-index). RESULTS AND LIMITATIONS Manual and automated BSI measurements were strongly correlated (ρ=0.80), correlated more closely (ρ=0.93) when excluding cases with BSI scores≥10 (1.8%), and were independently associated with PCa death (p<0.0001 for each) when added to the prediction model. Predictive accuracy of the base model (C-index: 0.768; 95% confidence interval [CI], 0.702-0.837) increased to 0.794 (95% CI, 0.727-0.860) by adding manual BSI scoring, and increased to 0.825 (95% CI, 0.754-0.881) by adding automated BSI scoring to the base model. CONCLUSIONS Automated BSI scoring, with its 100% reproducibility, reduces turnaround time, eliminates operator-dependent subjectivity, and provides important clinical information comparable to that of manual BSI scoring.

Progression of bone metastases in patients with prostate cancer - automated detection of new lesions and calculation of bone scan index

BackgroundThe objective of this study was firstly to develop and evaluate an automated method for the detection of new lesions and changes in bone scan index (BSI) in serial bone scans and secondly to evaluate the prognostic value of the method in a group of patients receiving chemotherapy.MethodsThe automated method for detection of new lesions was evaluated in a group of 266 patients using the classifications by three experienced bone scan readers as a gold standard. The prognostic value of the method was assessed in a group of 31 metastatic hormone-refractory prostate cancer patients who were receiving docetaxel. Cox proportional hazards were used to investigate the association between percentage change in BSI, number of new lesions and overall survival. Kaplan-Meier estimates of the survival function were used to indicate a significant difference between patients with an increase/decrease in BSI or those with two or more new lesions or less than two new lesions.ResultsThe automated method detected progression defined as two or more new lesions with a sensitivity of 93% and a specificity of 87%. In the treatment group, both BSI changes and the number of new metastases were significantly associated with survival. Two-year survival for patients with increasing and decreasing BSI from baseline to follow-up scans were 18% and 57% (p = 0.03), respectively. Two-year survival for patients fulfilling and not fulfilling the criterion of two or more new lesions was 35% and 38% (n.s.), respectively.ConclusionsAn automated method can be used to calculate the number of new lesions and changes in BSI in serial bone scans. These imaging biomarkers contained prognostic information in a small group of patients with prostate cancer receiving chemotherapy.

Bone Scan Index: a prognostic imaging biomarker for high-risk prostate cancer patients receiving primary hormonal therapy.

BackgroundThe objective of this study was to explore the prognostic value of the Bone Scan Index (BSI) obtained at the time of diagnosis in a group of high-risk prostate cancer patients receiving primary hormonal therapy.MethodsThis was a retrospective study based on 130 consecutive prostate cancer patients at high risk, based on clinical stage (T2c/T3/T4), Gleason score (8 to 10) and prostate-specific antigen (PSA) (> 20 ng/mL), who had undergone whole-body bone scans < 3 months after diagnosis and who received primary hormonal therapy. BSI was calculated using an automated method. Cox proportional-hazards regression models were used to investigate the association between clinical stage, Gleason score, PSA, BSI and survival. Discrimination between prognostic models was assessed using the concordance index (C-index).ResultsIn a multivariate analysis, Gleason score (p = 0.01) and BSI (p < 0.001) were associated with survival, but clinical stage (p = 0.29) and PSA (p = 0.57) were not prognostic. The C-index increased from 0.66 to 0.71 when adding BSI to a model including clinical stage, Gleason score and PSA. The 5-year probability of survival was 55% for patients without metastases, 42% for patients with BSI < 1, 31% for patients with BSI = 1 to 5, and 0% for patients with BSI > 5.ConclusionsBSI can be used as a complement to PSA to risk-stratify high-risk prostate cancer patients at the time of diagnosis. This imaging biomarker, reflecting the extent of metastatic disease, can be of value both in clinical trials and in patient management when deciding on treatment.

Assessment of the bone scan index in a randomized placebo-controlled trial of tasquinimod in men with metastatic castration-resistant prostate cancer (mCRPC)

INTRODUCTION Drug development and clinical decision making for patients with metastatic prostate cancer (PC) have been hindered by a lack of quantitative methods of assessing changes in bony disease burden that are associated with overall survival (OS). Bone scan index (BSI), a quantitative imaging biomarker of bone tumor burden, is prognostic in men with metastatic PC. We evaluated an automated method for BSI calculation for the association between BSI over time with clinical outcomes in a randomized double-blind trial of tasquinimod (TASQ) in men with metastatic castration-resistant PC (mCRPC). METHODS Bone scans collected during central review from the TASQ trial were analyzed retrospectively using EXINIbone(BSI), an automated software package for BSI calculation. Associations between BSI and other prognostic biomarkers, progression-free survival, OS, and treatment were evaluated over time. RESULTS Of 201 men (57 TASQ and 28 placebo), 85 contributed scans at baseline and week 12 of sufficient quality. Baseline BSI correlated with prostate-specific antigen and alkaline phosphatase levels and was associated with OS in univariate (hazard ratio [HR] = 1.42, P = 0.013) and multivariate (HR = 1.64, P<0.001) analyses. BSI worsening at 12 weeks was prognostic for progression-free survival (HR = 2.14 per BSI doubling, P<0.001) and OS (HR = 1.58, P = 0.033) in multivariate analyses including baseline BSI and TASQ treatment. TASQ delayed BSI progression. CONCLUSIONS BSI and BSI changes over time were independently associated with OS in men with mCRPC. A delay in objective radiographic bone scan progression with TASQ is suggested; prospective evaluation of BSI progression and response criteria in phase 3 trials of men with mCRPC is warranted.

Analytical Validation of the Automated Bone Scan Index as an Imaging Biomarker to Standardize the Quantitative Changes in Bone Scans of Patients with Metastatic Prostate Cancer.

A reproducible and quantitative imaging biomarker is needed to standardize the evaluation of changes in bone scans of prostate cancer patients with skeletal metastasis. We performed a series of analytic validation studies to evaluate the performance of the automated bone scan index (BSI) as an imaging biomarker in patients with metastatic prostate cancer. Methods: Three separate analytic studies were performed to evaluate the accuracy, precision, and reproducibility of the automated BSI. Simulation study: bone scan simulations with predefined tumor burdens were created to assess accuracy and precision. Fifty bone scans were simulated with a tumor burden ranging from low to high disease confluence (0.10–13.0 BSI). A second group of 50 scans was divided into 5 subgroups, each containing 10 simulated bone scans, corresponding to BSI values of 0.5, 1.0, 3.0, 5.0, and 10.0. Repeat bone scan study: to assess the reproducibility in a routine clinical setting, 2 repeat bone scans were obtained from metastatic prostate cancer patients after a single 600-MBq 99mTc-methylene diphosphonate injection. Follow-up bone scan study: 2 follow-up bone scans of metastatic prostate cancer patients were analyzed to determine the interobserver variability between the automated BSIs and the visual interpretations in assessing changes. The automated BSI was generated using the upgraded EXINI boneBSI software (version 2). The results were evaluated using linear regression, Pearson correlation, Cohen κ measurement, coefficient of variation, and SD. Results: Linearity of the automated BSI interpretations in the range of 0.10–13.0 was confirmed, and Pearson correlation was observed at 0.995 (n = 50; 95% confidence interval, 0.99–0.99; P < 0.0001). The mean coefficient of variation was less than 20%. The mean BSI difference between the 2 repeat bone scans of 35 patients was 0.05 (SD = 0.15), with an upper confidence limit of 0.30. The interobserver agreement in the automated BSI interpretations was more consistent (κ = 0.96, P < 0.0001) than the qualitative visual assessment of the changes (κ = 0.70, P < 0.0001) was in the bone scans of 173 patients. Conclusion: The automated BSI provides a consistent imaging biomarker capable of standardizing quantitative changes in the bone scans of patients with metastatic prostate cancer.

Bone Scan Index as a prognostic imaging biomarker during androgen deprivation therapy

BackgroundBone Scan Index (BSI) is a quantitative measurement of tumour burden in the skeleton calculated from bone scan images. When analysed at the time of diagnosis,it has been shown to provide prognostic information on survival in men with metastatic prostate cancer (PCa). In this study, we evaluated the prognostic value of BSI during androgen deprivation therapy (ADT).MethodsProstate cancer patients who were at high risk of a poor outcome and who had undergone bone scan at the time of diagnosis and during ADT were recruited from two university hospitals for a retrospective study. BSI at baseline and follow-up were calculated using an automated software package (EXINIbonebsi).Associations between BSI, other prognostic biomarkers and overall survival (OS)were evaluated using a Cox proportional hazards regression model.ResultsOne hundred forty-six PCa patients were included in the study. A total of 102patient deaths were registered, with a median survival time after the follow-up bone scan of 2.4 years (interquartile range (IQR) =0.8 to 4.4). Both at baseline and during ADT, BSI was significantly associated with OS in univariate and multivariate analyses. When BSI was added to a prognostic base model including age, prostate-specific antigen, clinical tumour stage and Gleason score, the concordance index increased from 0.73 to 0.77 (p =0.0005) at baseline and from 0.77 to 0.82 (p <0.0001) during ADT.ConclusionsAutomated BSI during ADT is an independent prognostic indicator of OS in PCa patients with bone metastasis. It represents an emerging imaging biomarker that can be used in a prognostic model for risk stratification of PCa patients at the time of diagnosis and at later stages of the disease. BSI could then help physicians identify patients who could benefit from more aggressive therapies.

Bone Scan Index as an Imaging Biomarker in Metastatic Castration-resistant Prostate Cancer: A Multicentre Study Based on Patients Treated with Abiraterone Acetate (Zytiga) in Clinical Practice

BACKGROUND Abiraterone acetate (AA) prolongs survival in metastatic castration-resistant prostate cancer (mCRPC) patients. To measure treatment response accurately in bone, quantitative methods are needed. The Bone Scan Index (BSI), a prognostic imaging biomarker, reflects the tumour burden in bone as a percentage of the total skeletal mass calculated from bone scintigraphy. OBJECTIVE To evaluate the value of BSI as a biomarker for outcome evaluation in mCRPC patients on treatment with AA according to clinical routine. DESIGN, SETTING, AND PARTICIPANTS We retrospectively studied 104 mCRPC patients who received AA following disease progression after chemotherapy. All patients underwent whole-body bone scintigraphy before and during AA treatment. Baseline and follow-up BSI data were obtained using EXINI BoneBSI software (EXINI Diagnostics AB, Lund, Sweden). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Associations between change in BSI, clinical parameters at follow-up, and overall survival (OS) were evaluated using the Cox proportional hazards regression models and Kaplan-Meier estimates. Discrimination between variables was assessed using the concordance index (C-index). RESULTS AND LIMITATIONS Patients with an increase in BSI at follow-up of at most 0.30 (n=54) had a significantly longer median survival time than those with an increase of BSI >0.30 (n=50) (median: 16 vs 10 mo; p=0.001). BSI change was also associated with OS in a multivariate Cox analysis including commonly used clinical parameters for prognosis (C-index=0.7; hazard ratio: 1.1; p=0.03). The retrospective design was a limitation. CONCLUSIONS Change in BSI was significantly associated with OS in mCRPC patients undergoing AA treatment following disease progression in a postchemotherapy setting. BSI may be a useful imaging biomarker for outcome evaluation in this group of patients, and it could be a valuable complementary tool in monitoring patients with mCRPC on second-line therapies. PATIENT SUMMARY Bone Scan Index (BSI) change is related to survival time in metastatic castration-resistant prostate cancer (mCRPC) patients on abiraterone acetate. BSI may be a valuable complementary decision-making tool supporting physicians monitoring patients with mCRPC on second-line therapies.

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.

Automated decision support for bone scintigraphy

A quantitative analysis of metastatic bone involvement can be an important prognostic indicator of survival or a tool in monitoring treatment response in patients with cancer. The purpose of this study was to develop a completely automated decision support system for whole-body bone scans using image analysis and artificial neural networks. The study population consisted of 795 whole-body bone scans. The decision support system first detects and classifies individual hotspots as being metastatic or not. A second prediction model then classifies the scan regarding metastatic disease on a patient level. The test set sensitivity and specificity was 95% and 64% respectively, corresponding to 95% area under the receiver operating characteristics curve.

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.