A machine learning-based model for clinical prediction of distal metastasis in chondrosarcoma: a multicenter, retrospective study.

ABSTRACT:

Background

The occurrence of distant metastases (DM) limits the overall survival (OS) of patients with chondrosarcoma (CS). Early diagnosis and treatment of CS remains a great challenge in clinical practice. The aim of this study was to investigate metastatic factors and develop a risk stratification model for clinicians' decision-making.

Methods

Six machine learning (ML) algorithms, including logistic regression (LR), plain Bayesian classifier (NBC), decision tree (DT), random forest (RF), gradient boosting machine (GBM) and extreme gradient boosting (XGBoost). A 10-fold cross-validation was performed for each model separately, multicenter data was used as external validation, and the best (highest AUC) model was selected to build the network calculator.

Results

A to

SUBMITTER: Wei J 

PROVIDER: S-EPMC10734410 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature