Journal article
MGMT promoter methylation prediction based on multiparametric MRI via vision graph neural network
Journal of medical imaging (Bellingham, Wash.), Vol.11(1), pp.014503-014503
01/01/2024
DOI: 10.1117/1.JMI.11.1.014503
PMCID: PMC10869845
PMID: 38370421
Abstract
Glioblastoma (GBM) is aggressive and malignant. The methylation status of the
-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.
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.
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 (
), T1w (
), T1wCE (
), and T2 (
).
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.
Details
- Title: Subtitle
- MGMT promoter methylation prediction based on multiparametric MRI via vision graph neural network
- Creators
- Mingzhe Hu - Emory UniversityKailin Yang - Cleveland ClinicJing Wang - Emory UniversityRichard L J Qiu - Emory UniversityJustin Roper - Emory UniversityShannon Kahn - Emory UniversityHui-Kuo Shu - Emory UniversityXiaofeng Yang - Emory University
- Resource Type
- Journal article
- Publication Details
- Journal of medical imaging (Bellingham, Wash.), Vol.11(1), pp.014503-014503
- DOI
- 10.1117/1.JMI.11.1.014503
- PMID
- 38370421
- PMCID
- PMC10869845
- NLM abbreviation
- J Med Imaging (Bellingham)
- ISSN
- 2329-4302
- eISSN
- 2329-4310
- Language
- English
- Date published
- 01/01/2024
- Academic Unit
- Radiation Oncology
- Record Identifier
- 9984696712302771
Metrics
6 Record Views