Abstract
Purpose: Osteosarcoma (OSC) is a high-morbidity bone cancer with an unsatisfactory prognosis. Timely and accurate assessment the overall survival (OS) and progression-free survival (PFS) in patients with OSC are required to guide and select the best treatment. This study aimed to develop a simple, convenient and low-cost prognostic model based on clinical characteristics and blood biomarkers for predicting OS and PFS in OSC patients. Methods: Overall, 158 patients with OSC included from the Sun Yat-sen University Cancer Center in this retrospective study. LASSO-Cox algorithm was used to shrink predictive factor size and established a prognostic risk model for predicting OS and PFS in OSC patients. The predictive ability of the survival model was compared to the Tumor Node Metastasis (TNM) stage and clinical treatment by concordance index (C-index), time-dependent receiver operating characteristic (td-ROC) curve, decision curve analysis (DCA), net reclassification improvement index (NRI), and integrated discrimination improvement index (IDI). Results: Based on results from the LASSO-Cox method, gender, family history of cancer, monocyte (M), red blood cell (RBC), lactic dehydrogenase (LDH), and cystatin C (Cys-C) were identified to construct a novel predictive model for the OSC patients. The C-index of the prognostic model to predict OS and PFS were 0.713 (95% CI = 0.630 - 0.795) and 0.636 (95% CI = 0.577 - 0.696), respectively, which were higher than the OS and PFS of TNM stage and clinical treatment. Td-ROC curve and DCA of the predictive model also demonstrated good predictive accuracy and discriminatory power of OS and PFS compared to TNM stage and treatment. Moreover, the prognostic model performed well across all time frames (1-, 3-, and 5-year) with regards to the IDI and NRI in comparison to the TNM stage, and clinical treatment. Conclusion: The simple, convenient and low-cost prognostic model we developed demonstrated favorable performance for predicting OS and PFS in OSC patients, which may serve as a useful tool for physicians to provide personalized survival prediction for OSC patients.