Risk stratification system and web-based nomogram constructed for predicting the overall survival of primary osteosarcoma patients after surgical resection

Background: Previous prediction models of osteosarcoma have not focused on survival in patients undergoing surgery, nor have they distinguished and compared prognostic differences among amputation, radical and local resection. This study aimed to establish and validate the first reliable prognostic nomogram to accurately predict overall survival (OS) after surgical resection in patients with osteosarcoma. On this basis, we constructed a risk stratification system and a web-based nomogram. Methods: We enrolled all patients with primary osteosarcoma who underwent surgery between 2004 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database. In patients with primary osteosarcoma after surgical resection, univariate and multivariate cox proportional hazards regression analyses were utilized to identify independent prognostic factors and construct a novel nomogram for the 1-, 3-, and 5-year OS. Then the nomogram's predictive performance and clinical utility were evaluated by the concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Result: This study recruited 1,396 patients in all, with 837 serving as the training set (60%) and 559 as the validation set (40%). After COX regression analysis, we identified seven independent prognostic factors to develop the nomogram, including age, primary site, histological type, disease stage, AJCC stage, tumor size, and surgical method. The C-index indicated that this nomogram is considerably more accurate than the AJCC stage in predicting OS [Training set (HR: 0.741, 95% CI: 0.726-0.755) vs. (HR: 0.632, 95% CI: 0.619-0.645); Validation set (HR: 0.735, 95% CI: 0.718-0.753) vs. (HR: 0.635, 95% CI: 0.619-0.652)]. Moreover, the area under ROC curves, the calibration curves, and DCA demonstrated that this nomogram was significantly superior to the AJCC stage, with better predictive performance and more net clinical benefits. Conclusion: This study highlighted that radical surgery was the first choice for patients with primary osteosarcoma since it provided the best survival prognosis. We have established and validated a novel nomogram that could objectively predict the overall survival of patients with primary osteosarcoma after surgical resection. Furthermore, a risk stratification system and a web-based nomogram could be applied in clinical practice to assist in therapeutic decision-making

AJCC staging system · Cancer · Machine learning · Multivariate analysis · Multivariate statistics · Nomogram · Osteosarcoma · Overall survival · Pathology · Proportional hazards model · Receiver operating characteristic · Staging system · Surveillance, Epidemiology, and End Results · Survival analysis · T-stage · Univariate · Univariate analysis · Applied Advanced Technologies · Bone Tumor Diagnosis and Treatments · Computer Science · Medicine · Sarcoma Diagnosis and Treatment · Epidemiology · Internal Medicine · Oncology · Surgery

• Cancer statistics, 2022

  Open Access•Rebecca L Siegel, Kimberly D Miller et al.•CA: A Cancer Journal for Clinicians•2022

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