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

作者：Mingzhe Hu, Kailin Yang, Jing Wang, Richard L. J. Qiu, Justin Roper, Shannon Kahn, Hui‐Kuo G. Shu, Xiaofeng Yang · 发表于：Journal of medical imaging · 年份：2024 · DOI：10.1117/1.jmi.11.1.014503 · 被引用次数：6 · 研究领域：Glioma Diagnosis and Treatment、Radiomics and Machine Learning in Medical Imaging、Epigenetics and DNA Methylation

PurposeGlioblastoma (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.ApproachWe 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.ResultsOur 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).Co...