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Machine learning-based risk prediction model for central nervous system involvement in diffuse large B-cell lymphoma.

Rashad Ismayilov1, Murat Ozdede2, Aysegul Uner3

  • 1Department of Internal Medicine, Hacettepe University Faculty of Medicine, Ankara, Turkiye.

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|July 12, 2025
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Summary

Machine learning models accurately predict central nervous system (CNS) relapse in diffuse large B-cell lymphoma (DLBCL) patients. These models outperform traditional scores, aiding personalized risk assessment for CNS involvement.

Keywords:
Diffuse large B-cell lymphomacentral nervous system relapsegradient boosting machine, competing risk analysismachine learningrandom survival forest

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Area of Science:

  • Oncology
  • Biostatistics
  • Computational Biology

Background:

  • Accurate prediction of central nervous system (CNS) relapse in diffuse large B-cell lymphoma (DLBCL) is crucial but challenging.
  • Existing prognostic models like the International Prognostic Index (IPI) and CNS-IPI have limitations in predicting CNS involvement.

Purpose of the Study:

  • To develop and validate a machine learning (ML)-based prognostic model for predicting CNS relapse in DLBCL patients.
  • To compare the performance of ML models against traditional prognostic scores.

Main Methods:

  • Retrospective analysis of 664 R-CHOP-treated DLBCL patients.
  • Development and validation of ML models, including Random Survival Forests (RSF) and Gradient Boosting Machines (GBM).
  • Evaluation of model performance using C-index and Integrated Brier Score (IBS), and comparison with IPI and CNS-IPI.

Main Results:

  • ML models demonstrated high discriminative ability (RSF: C-index 0.91, IBS 0.057; GBM: C-index 0.88, IBS 0.042).
  • Both ML models significantly outperformed traditional scores (IPI, CNS-IPI).
  • Key predictors identified include extranodal site number and high-risk organ involvement.

Conclusions:

  • ML-based models offer superior predictive accuracy for CNS relapse in DLBCL compared to conventional methods.
  • These advanced models can support personalized risk stratification and treatment strategies for DLBCL patients at risk of CNS dissemination.