Establishment and validation of nomogram model for survival predicting in patients with spinal metastases secondary to lung cancer

To evaluate the prognostic effect of pre-treatment factors in patients with spinal metastases secondary to lung cancer, and establish a novel predicting nomogram for predicting the survival probability. A total of 209 patients operated for spinal metastases from lung cancer were consecutively enrolled, and divided into the training and validation samples with a ratio of 7:3, for model establishing and validating, respectively. Basing on the training sample, univariate and multivariate COX proportional hazard models were used for identifying the prognostic effect of pre-treatment factors, following which significant prognostic factors would be listed as items in nomogram to calculate the survival probabilities at 3, 6, 12 and 18 months. Then, the C-indexes and the calibration curves would be figured out to evaluate the discrimination ability and accuracy of the model both for the training and validation samples. In the multivariate COX analysis, the gender, smoking history, location of spinal metastasis, visceral metastasis, Karnofsky performance status (KPS), adjuvant therapy, lymphocyte percentage and globulin were found to be significantly associated with the overall survival, and a novel nomogram was generated basing on these independent predictors. The C-indexes for the training and validation samples were 0.761 and 0.732, respectively. Favorable consistencies between the predicted and actual survival rates were demonstrated both in the internal and external validations. Pre-treatment characteristics, including gender, smoking history, location of spinal metastasis, visceral metastasis, KPS, adjuvant therapy, percentage of lymphocyte, and serum globulin level, were identified to be significantly associated with overall survival of patients living with spinal metastases derived from lung cancer, and a user-friendly nomogram was established using these independent predictors.

为评估肺癌继发脊柱转移瘤患者治疗前因素的预后价值，并构建一款新型生存概率预测列线图（nomogram）。本研究连续纳入209例因肺癌继发脊柱转移瘤接受手术治疗的患者，按7:3的比例划分为训练集与验证集，分别用于模型构建与性能验证。基于训练集，本研究采用单因素及多因素COX比例风险回归模型分析患者治疗前因素的预后效应，将筛选得到的显著预后因素作为列线图的预测条目，用于计算患者3、6、12及18个月的生存概率。随后通过计算C指数（C-index）与校准曲线，分别评估训练集与验证集上模型的区分能力与预测准确性。多因素COX回归分析结果显示，性别、吸烟史、脊柱转移瘤部位、内脏转移、卡诺夫斯基体能状态（KPS）、辅助治疗、淋巴细胞百分比及球蛋白水平均与患者总生存期显著相关；基于上述独立预测因子，本研究构建了新型列线图。训练集与验证集的C指数分别为0.761与0.732。内部验证与外部验证结果均显示，模型预测生存率与实际生存率之间具有良好的一致性。本研究证实，肺癌继发脊柱转移瘤患者的多项治疗前特征（包括性别、吸烟史、脊柱转移瘤部位、内脏转移、KPS、辅助治疗、淋巴细胞百分比及血清球蛋白水平）均与患者总生存期显著相关；基于上述独立预测因子，本研究构建了一款易用型列线图。