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已发表论文
基于多模态磁共振成像与深度学习融合的神经胶质瘤分析：分割、分子分型及临床转化路径的技术创新
Authors Yi G, Ma W, Yu Z, Bai H, Zhang H, Wang Y, Huang C
Received 18 July 2025
Accepted for publication 23 October 2025
Published 28 October 2025 Volume 2025:16 Pages 1989—2001
DOI https://doi.org/10.2147/AMEP.S554692
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Sateesh B Arja
Guangming Yi,1 Wenhui Ma,2 Zhenni Yu,2 Hong Bai,3 Hengsheng Zhang,2 Yujun Wang,4 Cong Huang2
1Department of Oncology, the Third Hospital of Mianyang (Sichuan Mental Health Center), Mianyang, Sichuan, 621000, People’s Republic of China; 2Department of Radiology, No. 926 Hospital, Joint Logistics Support Force of PLA, Kaiyuan, Yunnan, 661699, People’s Republic of China; 3Department of Neurology, No. 926 Hospital, Joint Logistics Support Force of PLA, Kaiyuan, Yunnan, 661699, People’s Republic of China; 4Department of Radiology, the First Affiliated Hospital of Zhejiang Chinese Medicine University (Zhejiang Provincial Hospital of Chinese Medicine), ZheJiang, Hangzhou, 310006, People’s Republic of China
Correspondence: Yujun Wang, Email 981861280@qq.com Cong Huang, Email magichc401@163.com
Abstract: The integration of multimodal MRI and deep learning is reshaping glioma diagnosis and treatment, shifting from experience-dependent to data-driven paradigms. Conventional radiology, limited by subjective qualitative assessment, fails to fully quantify glioma heterogeneity, whereas deep learning addresses multidimensional data complexity through cross-modal feature fusion—particularly via Transformer-3D CNN hybrid models with cross-modal attention mechanisms. These models have enhanced glioma segmentation accuracy to a Dice coefficient of 0.92 and enabled noninvasive prediction of critical molecular markers (eg, IDH mutation), while uncovering biological links between imaging features and EGFR/PI3K-AKT signaling pathways. Clinically, this framework predicts glioma recurrence 3– 6 months earlier and traces metastatic brain tumor primary lesions with 87.5% accuracy. However, challenges remain, including data heterogeneity, poor model interpretability, and ethical constraints, which demand standardized protocols for clinical translation. Future efforts will focus on integrating multi-omics data, developing real-time decision systems, and establishing evidence-based medical frameworks via interdisciplinary collaboration to achieve personalized whole-process glioma management. This review systematically synthesizes recent advances in multimodal MRI-deep learning fusion for glioma care, clarifies technical development trajectories, addresses core bottlenecks (eg, cross-center data discrepancies, clinical translation latency), and provides a theoretical basis for translating these technologies into clinical practice.
Keywords: glioblastoma, multimodal MRI, deep learning, radiomics, molecular subtyping, anatomical-molecular co-optimization, IDH mutation, dynamic modality adaptation, 3D Transformer, multicenter validation
1Department of Oncology, the Third Hospital of Mianyang (Sichuan Mental Health Center), Mianyang, Sichuan, 621000, People’s Republic of China; 2Department of Radiology, No. 926 Hospital, Joint Logistics Support Force of PLA, Kaiyuan, Yunnan, 661699, People’s Republic of China; 3Department of Neurology, No. 926 Hospital, Joint Logistics Support Force of PLA, Kaiyuan, Yunnan, 661699, People’s Republic of China; 4Department of Radiology, the First Affiliated Hospital of Zhejiang Chinese Medicine University (Zhejiang Provincial Hospital of Chinese Medicine), ZheJiang, Hangzhou, 310006, People’s Republic of China
Correspondence: Yujun Wang, Email 981861280@qq.com Cong Huang, Email magichc401@163.com
Abstract: The integration of multimodal MRI and deep learning is reshaping glioma diagnosis and treatment, shifting from experience-dependent to data-driven paradigms. Conventional radiology, limited by subjective qualitative assessment, fails to fully quantify glioma heterogeneity, whereas deep learning addresses multidimensional data complexity through cross-modal feature fusion—particularly via Transformer-3D CNN hybrid models with cross-modal attention mechanisms. These models have enhanced glioma segmentation accuracy to a Dice coefficient of 0.92 and enabled noninvasive prediction of critical molecular markers (eg, IDH mutation), while uncovering biological links between imaging features and EGFR/PI3K-AKT signaling pathways. Clinically, this framework predicts glioma recurrence 3– 6 months earlier and traces metastatic brain tumor primary lesions with 87.5% accuracy. However, challenges remain, including data heterogeneity, poor model interpretability, and ethical constraints, which demand standardized protocols for clinical translation. Future efforts will focus on integrating multi-omics data, developing real-time decision systems, and establishing evidence-based medical frameworks via interdisciplinary collaboration to achieve personalized whole-process glioma management. This review systematically synthesizes recent advances in multimodal MRI-deep learning fusion for glioma care, clarifies technical development trajectories, addresses core bottlenecks (eg, cross-center data discrepancies, clinical translation latency), and provides a theoretical basis for translating these technologies into clinical practice.
Keywords: glioblastoma, multimodal MRI, deep learning, radiomics, molecular subtyping, anatomical-molecular co-optimization, IDH mutation, dynamic modality adaptation, 3D Transformer, multicenter validation