A clinical prediction model for lung metastasis risk in osteosarcoma: A multicenter retrospective study - Zheng, Shengping; Chen, Longhao; Wang, Jiaming; Wang, Haosheng; Hu, Zhaohui; Li, Wanying; Xu, Chan; Ma, Minmin; Wang, Bing; Huang, Yangjun; Liu, Qiang; Tang, Zhi-Ri; et al. (Frontiers in Oncology, 2023)
Background: Lung metastases (LM) have a poor prognosis of osteosarcoma. This study aimed to construct a nomogram to predict the risk of LM in patients with osteosarcoma and clinic application. Methods: We analyzed 1100 patients' data from the Surveillance, Epidemiology and End Results (SEER) database between 2010 and 2016 as training cohort. Univariate and multivariate logistic regression analyses were used to identify independent prognostic factors of osteosarcoma lung metastases. 108 osteosarcoma patients from a multicentre dataset was as valiation data. The predictive power of the nomogram model was assessed by receiver operating characteristic curves (ROC) and calibration plots, and decision curve analysis (DCA) was utilized to interpret the accurate validity in clinical practice. Results: A total of 1208 patients with osteosarcoma from both the SEER database(n=1100) and the multicentre database (n=108) were analyzed. Univariate and multivariate logistic regression analyses showed that Survival time, Sex, T-stage, N-stage, Surgery, Radiation, and Bone metastases were independent risk factors for lung metastasis. We combined these factors to construct a nomogram for estimating the risk of lung metastasis. Internal and external validation showed significant predictive differences (AUC 0.779, 0.792 respectively). Calibration plots showed good performance of the nomogram model. Conclusions: In this study, a nomogram model for predicting the risk of lung metastases in osteosarcoma patients was constructed and turned out to be accurate and reliable through internal and external validation. Moreover we built a webpage calculator (https://drliwenle.shinyapps.io/OSLM/) taken into account nomogram model to help clinicians make more accurate and personalized predictions.