Nomogram
osteosarcoma
REGRESSION, OSTEOSARCOMA
All identified pediatric and young adulthood osteosarcoma patients were randomly divided into train dataset and validation dataset with a split of 7:3. The clinical characteristics comparison between train dataset and validation dataset was performed by Pearson’s chi-square test. Univariate and multivariate logistic regression analyses were used to identify risk factors associated with SLM occurrence in the train dataset. Then a nomogram for predicting the probability of SLM occurrence at diagnosis was developed based on significant factors. The discriminative ability of the nomogram and each variable were evaluated by the area under the receiver operating characteristic curve (AUC). The calibration curve was used to measure the differences between the predicted and observed outcomes.