Jang Kevin, Back Michael
Department of Radiation Oncology, Royal North Shore Hospital, Sydney, NSW 2065, Australia.
Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2050, Australia.
Brain Sci. 2025 May 27;15(6):576. doi: 10.3390/brainsci15060576.
Glioblastoma (GBM) often exhibits distinct anatomical patterns of relapse after radiotherapy. Tumour cell migration along myelinated white matter tracts is a key driver of disease progression. The failure of conventional imaging to capture subclinical infiltration has driven interest in advanced imaging biomarkers capable of quantifying tumour-brain interactions. Diffusion tensor imaging (DTI), radiomics, and connectomics represent a triad of innovative, non-invasive approaches that map white matter architecture, predict recurrence risk, and inform biologically guided treatment strategies. This review examines the biological rationale and clinical applications of DTI-based metrics, radiomic signatures, and tractography-informed connectomics in GBM. We discuss the integration of these modalities into machine learning frameworks and radiotherapy/surgical planning, supported by landmark studies and multi-institutional data. The implications for personalised neuro-oncology are profound, marking a shift towards risk-adaptive, tract-aware treatment strategies that may improve local control and preserve neurocognitive function.
胶质母细胞瘤（GBM）在放疗后常表现出独特的复发解剖模式。肿瘤细胞沿有髓白质束迁移是疾病进展的关键驱动因素。传统成像无法捕捉亚临床浸润，这激发了人们对能够量化肿瘤与脑相互作用的先进成像生物标志物的兴趣。扩散张量成像（DTI）、放射组学和连接组学代表了三种创新的非侵入性方法，可绘制白质结构、预测复发风险并为生物引导的治疗策略提供信息。本综述探讨了基于DTI的指标、放射组学特征和基于纤维束成像的连接组学在GBM中的生物学原理和临床应用。我们讨论了这些模式在机器学习框架以及放疗/手术规划中的整合，并得到了具有里程碑意义的研究和多机构数据的支持。对个性化神经肿瘤学的影响意义深远，标志着向风险适应性、纤维束感知治疗策略的转变，这可能会改善局部控制并保留神经认知功能。
