Developing an Improved Statistical Approach for Survival Estimation in Bone Metastases Management: The Bone Metastases Ensemble Trees for Survival (BMETS) Model.

ABSTRACT:

Purpose

To determine whether a machine learning approach optimizes survival estimation for patients with symptomatic bone metastases (SBM), we developed the Bone Metastases Ensemble Trees for Survival (BMETS) to predict survival using 27 prognostic covariates. To establish its relative clinical utility, we compared BMETS with 2 simpler Cox regression models used in this setting.

Methods and materials

For 492 bone sites in 397 patients evaluated for palliative radiation therapy (RT) for SBM from January 2007 to January 2013, data for 27 clinical variables were collected. These covariates and the primary outcome of time from consultation to death were used to build BMETS using random survival forests. We then performed Cox regressions as per 2 validated models: Chow's 3-item (

SUBMITTER: Alcorn SR 

PROVIDER: S-EPMC7954525 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature