Abstract
BACKGROUND: Nutritional and immune status is paramount for the overall survival (OS) of patients with advanced osteosarcoma. Comprehensive prognostic predictors based on the two indices are scarce. This study aimed to construct and validate individualized web dynamic nomograms based on CONUT score or/and peripheral blood CD4+/CD8+ ratio for OS in patients with advanced osteosarcoma. MATERIALS AND METHODS: The clinical data of 376 advanced osteosarcoma patients from January 2000 to December 2019 were retrospectively collected. Data from the 301 patients (diagnosed in the first 15 years) were used as the development set and data from the remaining 75 patients were assigned as the validation set. Multivariate Cox regression analyses were conducted and three prediction models were constructed, namely, CD4+/CD8+ ratio univariate model (model 1), CONUT score univariate model (model 2), and CD4+/CD8+ ratio plus CONUT score (model 3). These models were visualized by conventional nomograms and individualized web dynamic nomograms, and their performances were further evaluated by C-index, calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA), respectively. RESULTS: In multivariate Cox analysis, age, metastasis, ALP, CD4+/CD8+ ratio, chemotherapy, and CONUT score were identified as independent prognostic factors for OS. The calibration curves of the three models all showed good agreement between the actual observation and nomogram prediction for 1-year overall survival. In the development set, the C-index and area under the curve (AUC) of model 3 (0.837, 0.848) were higher than that of model 1 (0.765, 0.773) and model 2 (0.712, 0.749). Similar trends were observed in the validation set. The net benefits of model 3 were better than the other two models within the threshold probability of 36-80% in DCA. CONCLUSION: CONUT score and peripheral CD4+/CD8+ ratio are easily available, reliable, and economical prognostic predictors for survival prediction and stratification in patients with advanced osteosarcoma, but the two predictors combined can establish a better prognosis prediction model.