Risk analysis of pulmonary metastasis of chondrosarcoma by establishing and validating a new clinical prediction model: a clinical study based on SEER database

BackgroundThe prognosis of lung metastasis (LM) in patients with chondrosarcoma was poor. The aim of this study was to construct a prognostic nomogram to predict the risk of LM, which was imperative and helpful for clinical diagnosis and treatment.MethodsData of all chondrosarcoma patients diagnosed between 2010 and 2016 was queried from the Surveillance, Epidemiology, and End Results (SEER) database. In this retrospective study, a total of 944 patients were enrolled and randomly splitting into training sets (n=644) and validation cohorts(n=280) at a ratio of 7:3. Univariate and multivariable logistic regression analyses were performed to identify the prognostic nomogram. The predictive ability of the nomogram model was assessed by calibration plots and receiver operating characteristics (ROCs) curve, while decision curve analysis (DCA) and clinical impact curve (CIC) were applied to measure predictive accuracy and clinical practice. Moreover, the nomogram was validated by the internal cohort.ResultsFive independent risk factors including age, sex, marital, tumor size, and lymph node involvement were identified by univariate and multivariable logistic regression. Calibration plots indicated great discrimination power of nomogram, while DCA and CIC presented that the nomogram had great clinical utility. In addition, receiver operating characteristics (ROCs) curve provided a predictive ability in the training sets (AUC=0.789, 95% confidence interval [CI] 0.789-0.808) and the validation cohorts (AUC=0.796, 95% confidence interval [CI] 0.744-0.841).ConclusionIn our study, the nomogram accurately predicted risk factors of LM in patients with chondrosarcoma, which may guide surgeons and oncologists to optimize individual treatment and make a better clinical decisions.Trial registrationJOSR-D-20-02045, 29 Dec 2020.

Automated summary

The study successfully constructed a prognostic nomogram to predict the risk of lung metastasis in chondrosarcoma patients, which showed great discrimination power and clinical utility. The nomogram model had a high predictive ability in both training sets and validation cohorts, providing valuable guidance for optimizing individual treatment and clinical decisions.