Prediction of early death in peripheral T-cell lymphoma-NOS patients based on machine learning

Peripheral T-cell lymphoma-not otherwise specified (PTCL-NOS) is a highly aggressive and heterogeneous lymphoma subtype with a poor prognosis. This study aims to develop a machine learning-based model to predict early death (within 3 months of diagnosis) in PTCL-NOS patients using data from the SEER database (2016-2021). A total of 1,156 patients were included and randomly divided into training (n = 809) and validation (n = 347) sets. Key predictive factors were identified through Boruta and LASSO algorithms, including chemotherapy, radiotherapy, age, B symptoms, primary tumor site, Summary Stage, and Ann Arbor Stage. Seven machine learning models were constructed and evaluated using AUROC, AUPRC, calibration curves, Brier scores, and decision curve analysis. XGBoost demonstrated the best predictive performance (AUROC = 0.842 in training and 0.774 in validation). This study provides a novel and interpretable predictive tool that can aid in early risk stratification and personalized treatment planning for PTCL-NOS patients, ultimately improving clinical outcomes.