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Related Experiment Video

Updated: May 6, 2026

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A Language Performance Model for Predicting Glioma Recurrence and Molecular Biomarkers: A Retrospective Cohort Study.

Hua Song1, Linghao Bu2, Chen Luo3

  • 1Department of Biostatistics, School of Public Health, Fudan University, Shanghai, China.

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|March 2, 2026
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Summary

Language tests accurately predict glioma recurrence and correlate with molecular features. The Language Tests Combinations (LTC) model offers a novel prognostic tool for glioma patients.

Keywords:
gliomalanguage disordersmolecularpathologysurvival analysis

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Area of Science:

  • Neuro-oncology
  • Clinical Linguistics
  • Biostatistics

Background:

  • Glioma progression frequently leads to language deficits.
  • Postoperative recurrence is a significant challenge, particularly in high-grade gliomas.

Purpose of the Study:

  • Identify language-based prognostic markers for glioma.
  • Enhance risk management strategies for glioma patients.

Main Methods:

  • Retrospective analysis of 191 glioma patients (2010-2018).
  • Language status assessed using the Aphasia Battery of Chinese (ABC).
  • Cox regression, bootstrap validation, and SHapley Additive exPlanations (SHAP) for prognostic modeling.

Main Results:

  • Auditory verbal comprehension, writing, and repetition were key predictors (AUC=0.834).
  • The Language Tests Combinations (LTC) model demonstrated strong predictive power.
  • Language predictors correlated significantly with molecular markers (MGMT, 1p/19q, IDH1/2).

Conclusions:

  • Language components are robust predictors of glioma recurrence.
  • The LTC model provides an interpretable prognostic framework.
  • Findings may inform postoperative glioma management and risk stratification.