What question did this study set out to answer?

The study aims to create a machine learning model that predicts early death in patients diagnosed with peripheral T-cell lymphoma-NOS.

June 13, 2026Open Access

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

Key Points

The study aims to create a machine learning model that predicts early death in patients diagnosed with peripheral T-cell lymphoma-NOS.
Data from the SEER database (2016-2021) was used, including 1,156 peripheral T-cell lymphoma-NOS patients; divided into training (n=809) and validation (n=347) sets.
Predictive factors were identified using Boruta and LASSO algorithms.
Seven machine learning models, including XGBoost, were evaluated using AUROC, AUPRC, calibration curves, Brier scores, and decision curve analysis.
XGBoost achieved the best AUROC of 0.842 in the training set and 0.774 in the validation set.
Key predictive factors included chemotherapy, radiotherapy, age, B symptoms, primary tumor site, Summary Stage, and Ann Arbor Stage.
The model aids in early risk stratification and personalized treatment planning.

Abstract

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.

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