A machine learning-based model to predict POD24 in follicular lymphoma: a study by the Chinese workshop on follicular lymphoma.

ABSTRACT:

Background

Disease progression within 24 months (POD24) significantly impacts overall survival (OS) in patients with follicular lymphoma (FL). This study aimed to develop a robust predictive model, FLIPI-C, using a machine learning approach to identify FL patients at high risk of POD24.

Methods

A cohort of 1,938 FL patients (FL1-3a) from seventeen centers nationwide in China was randomly divided into training and internal validation sets (2:1 ratio). XGBoost was utilized to construct the POD24-predicting model, which was internally validated in the validation set and externally validated in the GALLIUM cohort. Key predictors of POD24 included lymphocyte-to-monocyte ratio (LMR), lactate dehydrogenase (LDH) > ULN, low hemoglobin (Hb), elevated beta-2 microglobulin (β2-MG), max

SUBMITTER: Zha J 

PROVIDER: S-EPMC11697473 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature