Department of Orthopedic Surgery, Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine, Wenzhou, Zhejiang, China.
Department of Orthopedic Surgery, Ningbo Yinzhou Second Hospital, Ningbo Zhejiang, China.
Spine (Phila Pa 1976). 2020 Jun 15;45(12):E713-E720. doi: 10.1097/BRS.0000000000003404.
Retrospective analysis.
Our goal was to provide a predictive model and a risk classification system that predicts cancer-specific survival (CSS) from spinal and pelvic tumors.
Primary bone tumors of the spinal and pelvic are rare, thus limiting the understanding of the manifestations and survival from these tumors. Nomograms are the graphical representation of mathematical relationships or laws that accurately predict individual survival.
A total of 1033 patients with spinal and pelvic bone tumors between 2004 and 2016 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Multivariate Cox analysis was used on the training set to select significant predictors to build a nomogram that predicted 3- and 5-year CSS. We validate the precision of the nomogram by discrimination and calibration, and the clinical value of nomogram was assessed by making use of a decision curve analyses (DCA).
Data from 1033 patients with initially-diagnosed spinal and pelvic tumors were extracted from the SEER database. Multivariate analysis of the training cohort, predictors included in the nomogram were age, pathological type, tumor stage, and surgery. The value of C-index was 0.711 and 0.743 for the internal and external validation sets, respectively, indicating good agreement with actual CSS. The internal and external calibration curves revealed good correlation of CSS between the actual observation and the nomogram. Then, the DCA showed greater net benefits than that of treat-all or treat-none at all time points. A novel risk grouping system was established for CSS that can readily divide all patients into three distinct risk groups.
The proposed nomogram obtained more precision prognostic prediction for patients with initially-diagnosed primary spinal and pelvic tumors.
回顾性分析。
我们的目标是提供一个预测模型和风险分类系统，用于预测脊柱和骨盆肿瘤的癌症特异性生存（CSS）。
脊柱和骨盆的原发性骨肿瘤很少见，因此限制了对这些肿瘤的表现和生存的了解。列线图是数学关系或定律的图形表示，可以准确预测个体生存。
从监测、流行病学和最终结果（SEER）数据库中选择了 2004 年至 2016 年期间的 1033 名脊柱和骨盆骨肿瘤患者。使用多变量 Cox 分析对训练集进行分析，以选择显著预测因子来构建预测 3 年和 5 年 CSS 的列线图。我们通过区分度和校准来验证列线图的精度，并通过决策曲线分析（DCA）评估列线图的临床价值。
从 SEER 数据库中提取了 1033 名最初诊断为脊柱和骨盆肿瘤的患者数据。对训练队列的多变量分析表明，列线图中包含的预测因子包括年龄、病理类型、肿瘤分期和手术。内部和外部验证集的 C 指数值分别为 0.711 和 0.743，表明与实际 CSS 具有良好的一致性。内部和外部校准曲线显示 CSS 与实际观察结果之间存在良好的相关性。然后，DCA 表明在所有时间点，净收益均大于治疗全部或治疗全部。建立了一个新的 CSS 风险分组系统，可以方便地将所有患者分为三个不同的风险组。
所提出的列线图为最初诊断为原发性脊柱和骨盆肿瘤的患者提供了更精确的预后预测。
3 级。
