Yujie Liu, Minglei Yang, Bo Li, Kehan Xu, Xin Gao, Jialin Li, Haifeng Wei, Quan Huang, Wei Xu, Jianru Xiao


June 2019, Volume 28, Issue 6, pp 1491 - 1501 Original Article Read Full Article 10.1007/s00586-019-05879-5

First Online: 18 January 2019

Development of a novel model for predicting survival of patients with spine metastasis from colorectal cancer

Objective

To develop a novel nomogram for predicting survival of patients with spine metastasis from colorectal cancer (SMCRC) based on the clinical characteristics and prognostic factors.

Methods

Included in this study were 93 SMCRC patients who received treatments in our institute between 2006 and 2017, whose clinical data were analyzed retrospectively by univariate and multivariate analysis to identify independent variables that could predict prognosis. A nomogram for survival prediction was established on the basis of preoperative independent factors, and then subjected to bootstrap re-samples for internal validation. The discrimination was measured by concordance index (C-index). We used ROC analysis with the corresponding AUROC to compare the prediction accuracy of Changzheng Nomogram with three existing prognostic systems (Tomita, Tokuhashi and Bauer).

Results

The high and median degrees of primary tumor differentiation, primary tumor surgery, carcinoembryonic antigen ≤ 5 ng/ml, no visceral metastases and ECOG-PS (0–2) were favorable prognostic factors for CRC metastases in the spine. These five preoperative independent factors were identified and entered into the nomogram with the C-index of 0.786 (0.739–0.833). The calibration curves for probability of 12- and 24-month overall survival (OS) showed good agreement between the predictive risk and the actual risk, and calibration was assessed. Compared with the previous prognostic systems, Changzheng Nomogram reported in this study showed higher accuracy in predicting OS of patients with SMCRC spinal metastases (p < 0.05).

Conclusion

By using this novel predictive model, clinicians could more precisely estimate the survival outcome of individual patients by evaluating clinical characteristics and identify subgroups of patients who are in need of a specific individual treatment strategy.

Graphical abstract

These slides can be retrieved under Electronic Supplementary Material.[Figure not available: see fulltext.]


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