Department of Laboratory Medicine, Lund University, Skåne University Hospital, Malmö, Sweden.
Eur Urol. 2012 Jul;62(1):78-84. doi: 10.1016/j.eururo.2012.01.037. Epub 2012 Jan 27.
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.
背景：目前对于骨扫描图像分析尚无标准方法。骨扫描指数（BSI）可预测进展性前列腺癌（PCa）患者的生存率，但由于手动计算繁琐，该指标的普及受到阻碍。
目的：开发一种全自动量化 BSI 的方法，并确定自动化 BSI 测量在常规临床和病理特征之外的临床价值。
设计、地点和参与者：我们对计算机辅助诊断系统进行了条件设置，使其能够在骨扫描上识别转移病灶，从而自动计算 BSI 测量值。在该方法的验证过程中，使用了一个包含 795 例骨扫描的训练组。该方法的独立验证使用了来自两个大型基于人群队列的 384 例 PCa 病例的≤3 个月诊断的骨扫描。一位经验丰富的分析人员（对病例身份、先前的 BSI 和结果均不知情）对 BSI 测量值进行了两次评分。我们使用预处理的 Gleason 评分、临床分期和前列腺特异性抗原来测量结局预测，同时纳入手动或自动化 BSI 测量值的模型。
测量：使用 Pearson 相关系数评估方法之间的一致性。使用一致性指数（C 指数）评估预后模型的区分度。
结果和局限性：手动和自动化 BSI 测量值高度相关（ρ=0.80），当排除 BSI 评分≥10（1.8%）的病例时相关性更紧密（ρ=0.93），并且在添加到预测模型后与 PCa 死亡独立相关（每种情况下 p<0.0001）。基础模型的预测准确性（C 指数：0.768；95%置信区间 [CI]，0.702-0.837）通过添加手动 BSI 评分增加到 0.794（95% CI，0.727-0.860），通过将自动化 BSI 评分添加到基础模型增加到 0.825（95% CI，0.754-0.881）。
结论：自动化 BSI 评分具有 100%的可重复性，可缩短周转时间，消除操作员依赖性的主观性，并提供与手动 BSI 评分相当的重要临床信息。
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