Prognostic risk factor of major salivary gland carcinomas and survival prediction model based on random survival forests.

ABSTRACT: Salivary gland malignancies are rare and are often acompanied by poor prognoses. So, identifying the populations with risk factors and timely intervention to avoid disease progression is significant. This study provides an effective prediction model to screen the target patients and is helpful to construct a cost-effective follow-up strategy. We enrolled 249 patients diagnosed with salivary gland tumors and analyzed prognostic risk factors using Cox proportional hazard univariable and multivariable regression models. The patients' data were split into training and validation sets on a 7:3 ratio, and the random survival forest (RSF) model was established using the training sets and validated using the validation sets. The maximally selected rank statistics method was used to determine a cut

SUBMITTER: Chen Y 

PROVIDER: S-EPMC10225223 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature