A Transformer-Based Deep Learning Model for Prediction of Temozolomide Resistance in Glioblastoma Using Pretreatment MRI Images.

Objective

Glioblastoma (GBM) is a lethal tumor where temozolomide (TMZ) chemotherapy often faces resistance. We develop a deep learning model based on a Vision Transformer (ViT) architecture for pretreatment prediction of TMZ resistance.

Methods

We retrospectively enrolled 314 GBM patients across four centers between January 2021 and December 2024. The cohort was divided into a training cohort, an internal validation cohort, and two external validation cohorts. Patients were categorized into resistant and sensitive groups based on the best radiological response after Stupp chemoradiotherapy. A ViT-based deep learning network (DLN) was developed by selecting the optimal peritumoral expansion margin. A fusion model was subsequently constructed by concatenating the DLN with MGMT methylation status. Model performance was evaluated using calibration and decision curve analysis (DCA).

Results

An expansion distance of 10 mm was selected as optimal for constructing the DLN model. The fusion model outperformed both the DLN and the MGMT in the training, internal validation, and external validation cohorts, demonstrating robust performance in predicting TMZ resistance with Area Under the Curve (AUC) values of 0.855 (95% CI, 0.802-0.905), 0.917 (95% CI, 0.737-1.000), 0.806 (95% CI, 0.632-0.937), and 0.848 (95% CI, 0.673-0.964), respectively. Decision curve analysis indicated that the fusion model provided a high net clinical benefit.

Conclusion

The fusion model provides a precise and personalized risk assessment for TMZ resistance in GBM. This approach has the potential to spare resistant patients from unnecessary chemotherapy while identifying high-risk individuals who may benefit from more aggressive treatment strategies.