Prognosticating salvage stereotactic radiosurgery outcomes in relapsed primary central nervous system lymphoma: A machine learning-driven decision tree analysis.

ABSTRACT:

Purpose

To identify key clinical risk factors affecting therapeutic outcomes in relapsed primary central nervous system lymphoma (r-PCNSL) patients undergoing stereotactic radiosurgery salvage therapy (SRS-ST) and develop a decision tree-based predictive model.

Patients and methods

A retrospective analysis was performed on r-PCNSL patients undergoing SRS-ST at The Second Affiliated Hospital of Xuzhou Medical University between January 2012 and November 2021. The cohort was randomly divided into training and validation sets (7:3 ratio). The C5.0 algorithm was employed to develop a decision tree model for predicting treatment response. Model performance was evaluated using diagnostic metrics including accuracy (ACC), sensitivity, and specificity.

Results

A cohort of 209

SUBMITTER: Zhao H 

PROVIDER: S-EPMC12329516 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature