Glioblastoma is an aggressive central nervous system tumor characterized by diffuse infiltration. Despite substantial advances in oncology, survival outcomes have shown little improvement over the past three decades. Radiotherapy remains a cornerstone of treatment; however, it faces several challenges, including considerable inter-observer variability in clinical target volume delineation, dose constraints associated with adjacent organs at risk, and the persistently poor prognosis of affected patients. Recent advances in artificial intelligence, particularly deep learning, have shown promise in automating radiation therapy mapping to improve consistency, accuracy, and efficiency. This narrative review explores current auto segmentation frameworks, dose mapping, and biologically informed radiotherapy planning guided by multimodal imaging and mathematical modeling. Studies have demonstrated reproducible tumor segmentations with DSCs exceeding 0.90, reduced planning within minutes, and emerging predictive capabilities for treatment response. Radiogenomic integration has enabled imaging-based classification of critical biomarkers with high accuracy, reinforcing the potential of deep learning models in personalized radiotherapy. Despite these innovations, deployment into clinical practice remains limited, primarily due to insufficient external validation and single-institution training datasets. This review emphasizes the importance of large, annotated imaging datasets, multi-institutional collaboration, and biologically explainable modeling to successfully translate deep learning into glioblastoma radiation planning and longitudinal monitoring.
胶质母细胞瘤是一种具有弥漫性浸润特征的侵袭性中枢神经系统肿瘤。尽管肿瘤学领域已取得显著进展，但过去三十年来患者生存结局改善甚微。放射治疗仍是该疾病的核心治疗手段，然而其临床应用面临多重挑战：包括临床靶区勾画存在显著的观察者间差异、邻近危及器官的剂量限制约束，以及患者预后持续不佳等问题。近年来人工智能特别是深度学习技术的突破，为放射治疗规划的自动化提供了新的可能，有望提升治疗的一致性、精确性与效率。本文通过叙述性综述，系统探讨了基于多模态影像与数学建模的自动分割框架、剂量映射及生物信息引导的放疗规划研究进展。现有研究表明，深度学习可实现DSC值超过0.90的可重复肿瘤分割，在数分钟内完成治疗规划，并展现出预测治疗反应的新兴能力。影像基因组学整合技术已能对关键生物标志物实现高精度影像学分类，进一步强化了深度学习模型在个体化放疗中的应用潜力。尽管取得这些创新成果，相关技术向临床实践的转化仍显不足，主要受限于外部验证的缺乏及单中心训练数据集的局限性。本综述强调，构建大规模标注影像数据集、开展多机构协作研究以及发展具有生物学可解释性的模型，对于推动深度学习在胶质母细胞瘤放疗规划及纵向监测中的成功转化具有重要意义。
Advances in Artificial Intelligence for Glioblastoma Radiotherapy Planning and Treatment
肿瘤(癌症)患者之家