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

Abstract

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 Pearson’s 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.

Integration of Bone and Computed Tomography Scans to Assess Bone Metastasis in Metastatic Castration-Resistant Prostate Cancer

“…The BS index (BSI) is the percentage of bone mass involved by lesions on BS that uses a historical standard for overall bone mass. 16,17 However, the applicability of BS progression using PCWG criteria or BSI to other classes of novel agents is unclear. Indeed, despite an improvement in BSI and radiographic PFS (which mostly consisted of BS progression) with tasquinimod (an investigational antiangiogenic and immune-modulating drug), this agent did not yield an extension of OS in a phase III trial.…”

Prognostic and Predictive Biomarkers for Castration Resistant Prostate Cancer

“…Automated methods for interpreting bone scans and calculating BSI are under development. Ulmet et al developed computer-assisted BSI measure and reported that BSI is a valuable metric for estimating metastatic burden in patients with advanced prostate cancer (Ulmert et al 2012). The same team also reported that automated BSI calculation, like its manual counterpart, is a valuable clinical parameter in patients with progressive prostate cancer.…”

Expert opinion on first-line therapy in the treatment of castration-resistant prostate cancer

“…Automated quantitative determination of bone metastases using the bone scan index may have diagnostic, prognostic and predictive value in patients with mCRPC, but it is still subject to validation [73]. Markers of tumor activity in bone as N-telopeptide and bone AP (reflecting osteolytic and osteoblastic activity) have been shown to be prognostic and survival markers [74][75][76][77].…”