A novel automated platform for quantifying the extent of skeletal tumour involvement in prostate cancer patients using the bone scan index Journal Article

Authors: Ulmert, D.; Kaboteh, R.; Fox, J. J.; Savage, C.; Evans, M. J.; Lilja, H.; Abrahamsson, P. A.; Björk, T.; Gerdtsson, A.; Bjartell, A.; Gjertsson, P.; Höglund, P.; Lomsky, M.; Ohlsson, M.; Richter, J.; Sadik, M.; Morris, M. J.; Scher, H. I.; Sjöstrand, K.; Yu, A.; Suurküla, M.; Edenbrandt, L.; Larson, S. M.
Article Title: 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. © 2012 European Association of Urology.
Keywords: adult; aged; bone neoplasms; bone tumor; major clinical study; cancer staging; sensitivity and specificity; prostate specific antigen; reproducibility of results; metastasis; cohort studies; image analysis; image interpretation, computer-assisted; clinical assessment; cohort analysis; automation; prediction; prostate cancer; gleason score; prostate-specific antigen; prostatic neoplasms; computer assisted diagnosis; whole body imaging; scoring system; bone and bones; radionuclide imaging; automated detection; bone scintiscanning; bone metastases; technetium tc 99m medronate; risk prediction; bone scan index; neoplasm grading; automated quantification
Journal Title: European Urology
Volume: 62
Issue: 1
ISSN: 0302-2838
Publisher: Elsevier Science, Inc.  
Date Published: 2012-07-01
Start Page: 78
End Page: 84
Language: English
DOI: 10.1016/j.eururo.2012.01.037
PROVIDER: scopus
PUBMED: 22306323
PMCID: PMC3402084
Notes: --- - "Cited By (since 1996): 3" - "Export Date: 2 July 2012" - "CODEN: EUURA" - "Source: Scopus"
Citation Impact
MSK Authors
  1. Josef J Fox
    54 Fox
  2. Caroline Savage
    80 Savage
  3. Michael Morris
    361 Morris
  4. Hans Gosta Lilja
    307 Lilja
  5. Hans David Staffan Ulmert
    51 Ulmert
  6. Michael John Evans
    22 Evans
  7. Steven M Larson
    879 Larson
  8. Alice Yu
    3 Yu
  9. Howard Scher
    991 Scher