Fan Yonggang, Cai Mandi, Xia Lei
Department of Orthopaedic Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, P. R. China.
Spine (Phila Pa 1976). 2020 Jul 1;45(13):921-929. doi: 10.1097/BRS.0000000000003421.
Retrospective analysis.
The aim of this study was to develop and validate a nomogram for the prediction of lung metastasis in patients with malignant primary spinal tumors.
In patients with malignant primary spinal tumors, lung metastasis is usually found by computed tomography (CT) and is considered to be an essential factor affecting the prognosis and survival.
We retrospectively collected 580 malignant primary osseous spinal neoplasms patients from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic analysis were used to identify independent factors. These prognostic factors were included in the nomograms. The nomograms were validated based on its calibration, discrimination, and clinical utility. The overall survival of the patients was analyzed using the Kaplan-Meier method and the survival differences were tested by the log-rank test.
We randomly divided all these patients (n = 580) into a training cohort (n = 408) and a validation cohort (n = 172). The results showed that the risk of lung metastasis was independently influenced by histologic type, use of surgery, clinical T stage, clinical N stage, and tumor extension (all P < 0.05). The nomogram consisted of five clinical features and provided good calibration and discrimination in the training and validation cohort, with an area under the curve of 0.858 and 0.811, respectively. Decision curve analysis showed that the nomogram was clinically useful. The Kaplan-Meier curves showed a significant difference between the higher and lower risk of lung metastasis groups (P < 0.001).
Nomograms were developed to predict the risk of lung metastasis in patients with malignant primary spinal tumors. The nomogram showed favorable discrimination and calibration values, which may help optimize treatment decision-making for patients.
回顾性分析。
本研究旨在开发并验证一种用于预测恶性原发性脊柱肿瘤患者肺转移的列线图。
在恶性原发性脊柱肿瘤患者中，肺转移通常通过计算机断层扫描（CT）发现，并且被认为是影响预后和生存的重要因素。
我们回顾性收集了2010年至2015年期间来自监测、流行病学和最终结果（SEER）数据库的580例恶性原发性骨脊柱肿瘤患者。使用最小绝对收缩和选择算子（LASSO）和多因素逻辑回归分析来确定独立因素。这些预后因素被纳入列线图。基于校准、区分度和临床实用性对列线图进行验证。使用Kaplan-Meier方法分析患者的总生存期，并通过对数秩检验检验生存差异。
我们将所有这些患者（n = 580）随机分为训练队列（n = 408）和验证队列（n = 172）。结果表明，肺转移风险独立受组织学类型、手术使用情况、临床T分期、临床N分期和肿瘤扩展影响（所有P < 0.05）。列线图由五个临床特征组成，在训练队列和验证队列中提供了良好的校准和区分度，曲线下面积分别为0.858和0.811。决策曲线分析表明列线图具有临床实用性。Kaplan-Meier曲线显示肺转移高风险组和低风险组之间存在显著差异（P < 0.001）。
开发了列线图以预测恶性原发性脊柱肿瘤患者的肺转移风险。列线图显示出良好的区分度和校准值，这可能有助于优化患者的治疗决策。
4级。
