Distinction and Potential Prediction of Lung Metastasis in Patients with Malignant Primary Osseous Spinal Neoplasms

摘要

Study Design. Retrospective analysis. Objective. 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. Summary of Background Data. 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. Methods. 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. Results. 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 (allP < 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). Conclusion. 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.