MGMT promoter methylation prediction based on multiparametric MRI via vision graph neural network

Mingzhe Hu; Kailin Yang; Jing Wang; Richard L. J. Qiu; Justin Roper; Shannon Kahn; Hui-Kuo Shu; Xiaofeng Yang

doi:10.1117/1.JMI.11.1.014503

16 February 2024 MGMT promoter methylation prediction based on multiparametric MRI via vision graph neural network

Mingzhe Hu, Kailin Yang, Jing Wang, Richard L. J. Qiu, Justin Roper, Shannon Kahn, Hui-Kuo Shu, Xiaofeng Yang

Author Affiliations +

Journal of Medical Imaging, Vol. 11, Issue 1, 014503 (February 2024). https://doi.org/10.1117/1.JMI.11.1.014503

Abstract

Purpose

Glioblastoma (GBM) is aggressive and malignant. The methylation status of the O6‐methylguanine‐DNA methyltransferase (MGMT) promoter in GBM tissue is considered an important biomarker for developing the most effective treatment plan. Although the standard method for assessing the MGMT promoter methylation status is via bisulfite modification and deoxyribonucleic acid (DNA) sequencing of biopsy or surgical specimens, a secondary automated method based on medical imaging may improve the efficiency and accuracy of those tests.

Approach

We propose a deep vision graph neural network (ViG) using multiparametric magnetic resonance imaging (MRI) to predict the MGMT promoter methylation status noninvasively. Our model was compared to the RSNA radiogenomic classification winners. The dataset includes 583 usable patient cases. Combinations of MRI sequences were compared. Our multi-sequence fusion strategy was compared with those using single MR sequences.

Results

Our best model [Fluid Attenuated Inversion Recovery (FLAIR), T1-weighted pre-contrast (T1w), T2-weighted (T2)] outperformed the winning models with a test area under the curve (AUC) of 0.628, an accuracy of 0.632, a precision of 0.646, a recall of 0.677, a specificity of 0.581, and an F1 score of 0.661. Compared to the winning models with single MR sequences, our ViG utilizing fused-MRI showed a significant improvement statistically in AUC scores, which are FLAIR (p=0.042), T1w (p=0.017), T1wCE (p=0.001), and T2 (p=0.018).

Conclusions

Our model is superior to challenge champions. A graph representation of the medical images enabled good handling of complexity and irregularity. Our work provides an automatic secondary check pipeline to ensure the correctness of MGMT methylation status prediction.

Citation Download Citation

Mingzhe Hu, Kailin Yang, Jing Wang, Richard L. J. Qiu, Justin Roper, Shannon Kahn, Hui-Kuo Shu, and Xiaofeng Yang "MGMT promoter methylation prediction based on multiparametric MRI via vision graph neural network," Journal of Medical Imaging 11(1), 014503 (16 February 2024). https://doi.org/10.1117/1.JMI.11.1.014503

Received: 30 May 2023; Accepted: 29 January 2024; Published: 16 February 2024

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Magnetic resonance imaging

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