Abstract:

Objective To construct a nomogram to predict the risk of lung metastasis in patients with extremity osteosarcomas. Methods Clinical data of 1 610 extremity osteosarcoma patients were acquired from the National Cancer Institute’s Surveillance, Epidemiology, and End Results database from 2010 to 2020. The data were assigned into the training dataset and validation dataset at a ratio of 7:3. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for the occurrence of lung metastasis in patients with extremity osteosarcomas. A nomogram to predict risk factors for lung metastasis in patients with extremity osteosarcomas was then constructed based on the results of multivariate logistic regression analysis. Its performance was assessed by the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA). Results Logistic regression analysis showed that advanced T staging, lymph node metastasis, refusal of surgery and treatment of radiotherapy and chemotherapy were independent risk factors for lung metastases in patients with extremity osteosarcomas (P<0.05). The area under the curve of the nomogram in predicting lung metastasis of extremity osteosarcomas was 0.801 and 0.640 in the training dataset and validation dataset, respectively. The calibration curve and DCA showed the good accuracy and a high net benefit over a wide range of probability thresholds, respectively. Conclusion We created a nomogram to predict lung metastasis in patients with extremity osteosarcomas, showing high accuracy and feasibility. It assists orthopedists to make a faster and more reliable prediction and provide an individualized treatment.

Key words: osteosarcoma, neoplasm metastasis, SEER data, nomogram, risk prediction