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December 8, 2025Blood

Development of a machine learning model to predict overall survival in patients with peripheral T-cell lymphoma in a minority enriched population

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Authors

NLNicholas LiECEmma CordoverSHSung Chul Hwang

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Overview

Machine learning predicts overall survival in patients with peripheral T-cell lymphoma, indicating a need for improved clinical decision-making strategies.

Key Points

  • The machine learning model achieved a C-index of 0.86 on the full dataset, revealing strong predictive performance for overall survival.
  • Brier scores were calculated at 30, 365, 730, and 1095 days, confirming good calibration over time for survival predictions.
  • Random forest survival model was applied on demographic and clinical data collected from 97 patients diagnosed with peripheral T-cell lymphoma between 2010-2022.
  • Further external validation and model refinement are essential to enhance clinical applicability and robustness in predicting survival outcomes.

Cite This Study

Li et al. (2025) studied this question.

synapsesocial.com/papers/69362f514fa91c937236d97ehttps://doi.org/10.1182/blood-2025-2568
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