ABSTRACT

Objectives

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.

Methods

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.

Results

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.

Discussion

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.

中文翻译：

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肺癌继发脊柱转移瘤患者生存预测列线图模型的建立与验证

摘要

目标

评价预处理因素对肺癌继发脊柱转移患者预后的影响，并建立一种预测生存概率的新型预测列线图。

方法

连续入组209例肺癌脊柱转移瘤患者，按照7:3的比例分为训练样本和验证样本，分别进行模型建立和验证。基于训练样本，使用单变量和多变量 COX 比例风险模型来识别预处理因素的预后影响，然后将重要的预后因素列为列线图中的项目，以计算在 3、6、12 和18 个月。然后，将计算出 C 指数和校准曲线，以评估模型对训练和验证样本的区分能力和准确性。

结果

在多变量 COX 分析中，发现性别、吸烟史、脊柱转移部位、内脏转移、Karnofsky 体能状态（KPS）、辅助治疗、淋巴细胞百分比和球蛋白与总生存期显着相关，并且新的列线图是基于这些独立的预测变量生成。训练和验证样本的 C 指数分别为 0.761 和 0.732。在内部和外部验证中都证明了预测和实际存活率之间的良好一致性。

讨论

治疗前特征，包括性别、吸烟史、脊柱转移部位、内脏转移、KPS、辅助治疗、淋巴细胞百分比和血清球蛋白水平，被确定与脊柱转移患者的总生存期显着相关肺癌，并使用这些独立预测因子建立了用户友好的列线图。