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

## TL;DR

This study shows that preoperative language tests can predict glioma recurrence and link to molecular biomarkers, offering a noninvasive tool for risk assessment.

## Contribution

The LTC model is a novel language-based framework for predicting glioma recurrence and correlating with molecular biomarkers.

## Key findings

- Language predictors like auditory verbal comprehension and repetition achieved an AUC of 0.834 for recurrence prediction.
- The LTC model showed strong correlations with molecular biomarkers such as MGMT codeletion (AUC = 0.922).
- Language scores differed significantly between IDH1/2-mutant and wild-type glioma groups.

## Abstract

Glioma progression is often accompanied by language dysfunction, and postoperative recurrence is nearly inevitable, especially in high‐grade cases. This study aimed to identify valuable language‐based prognostic markers and improve risk management strategies.

A retrospective longitudinal cohort of 191 glioma patients (2010–2018) was analyzed. Language status was assessed using the Aphasia Battery of Chinese (ABC), adapted from the Western Aphasia Battery. Principal component analysis (PCA) addressed collinearity in language scores, and Cox regression identified predictors for a survival model. Bootstrap validation and SHapley Additive exPlanations (SHAP) were used to confirm model stability and interpretability. Logistic regression was used to test associations between predictors and pathological molecular parameters.

Auditory verbal comprehension & writing and repetition emerged as key language predictors in the Cox model, achieving an AUC of 0.834. SHAP analysis confirmed their dominant contribution, defining the model as the Language Tests Combinations (LTC) model. Two‐sample t‐tests revealed significant differences in language scores between IDH1/2‐mutant and wild‐type groups. Logistic regression showed strong correlations between language predictors and molecular parameters (MGMT codeletion: AUC = 0.922; 1p/19q: AUC = 0.813; IDH1/2: AUC = 0.748), with tumor locations included as dummy variables.

Language components were identified as robust predictors of glioma recurrence and were significantly associated with key molecular features. The LTC model represents an internally validated and interpretable exploratory prognostic framework that may complement existing risk‐stratification approaches and inform future studies on postoperative glioma management.

This study proposes a language‐based prognostic model for glioma recurrence by analyzing preoperative language performance using the Aphasia Battery of Chinese in 191 patients with WHO grade 2–4 gliomas. Principal component analysis and Cox regression identified key language predictors, which were used to construct and internally validate the language tests combinations (LTC) model. The model demonstrated strong predictive power and significant associations with molecular biomarkers, offering a noninvasive tool for risk stratification and personalized management.