AUTHOR=Wang Huaizhi , Liu Haichao , Du Fang , Wang Di , Huo Xianhao , Tian Jihui , Song Lijuan 

TITLE=MDPNet: a dual-path parallel fusion network for multi-modal MRI glioma genotyping

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1574861

DOI=10.3389/fonc.2025.1574861

ISSN=2234-943X

ABSTRACT=BackgroundGlioma stands as one of the most lethal brain tumors in humans, and its accurate diagnosis is critical for patient treatment and prognosis. Magnetic Resonance Imaging (MRI) has been widely utilized for glioma diagnosis and research due to its non-invasive nature and clinical accessibility. According to the 2021 World Health Organization Central Nervous System Tumor Classification guidelines, glioma subtypes can be determined through molecular status information of Isocitrate Dehydrogenase (IDH), Chromosome 1p/19q codeletion (1p/19q), and Alpha Thalassemia/Mental Retardation Syndrome X-linked (ATRX) genes.MethodIn this study, we propose a dual-path parallel fusion network (MDPNet) designed to comprehensively extract heterogeneous features across different MRI modalities while simultaneously predicting the molecular status of IDH, 1p/19q, and ATRX. To mitigate the impact of data imbalance, we developed a cross-gene feature-sharing classifier and implemented an adaptive weighted loss function, substantially enhancing the model’s predictive performance.ResultsIn this study, each gene classification task was formulated as a binary classification problem. Experiments conducted on public datasets demonstrate that our method outperforms existing approaches in accuracy, Area Under the Curve (AUC), sensitivity, and specificity. The achieved classification accuracies for IDH, ATRX, and 1p/19q reach 86.7%, 92.0%, and 89.3%, respectively. The source code of this study can be viewed at https://github.com/whz847/MDPNet.ConclusionThe proposed framework exhibits significant advantages in integrating heterogeneous features from multi-modal MRI data. Experimental results from internal datasets further validate the model’s superior generalizability and clinical utility in assisting glioma diagnosis, highlighting its potential for real-world clinical applications.