Tran Nate, Luks Tracy L, Li Yan, Jakary Angela, Ellison Jacob, Liu Bo, Adegbite Oluwaseun, Nair Devika, Kakhandiki Pranav, Molinaro Annette M, Villanueva-Meyer Javier E, Butowski Nicholas, Clarke Jennifer L, Chang Susan M, Braunstein Steve E, Morin Olivier, Lin Hui, Lupo Janine M
Department of Radiology & Biomedical Imaging, University of California, San Francisco, CA, USA.
UCSF/UC Berkeley Graduate Program in Bioengineering, University of California, San Francisco & Berkeley, CA, USA.
NPJ Digit Med. 2025 Aug 7;8(1):508. doi: 10.1038/s41746-025-01861-2.
The current standard-of-care (SOC) practice for defining the clinical target volume (CTV) for radiation therapy (RT) in patients with glioblastoma still employs an isotropic 1-2 cm expansion of the T2-hyperintensity lesion, without considering the heterogeneous infiltrative nature of these tumors. This study aims to improve RT CTV definition in patients with glioblastoma by incorporating biologically relevant metabolic and physiologic imaging acquired before RT along with a deep learning model that can predict regions of subsequent tumor progression by either the presence of contrast-enhancement or T2-hyperintensity. The results were compared against two standard CTV definitions. Our multi-parametric deep learning model significantly outperformed the uniform 2 cm expansion of the T2-lesion CTV in terms of specificity (0.89 ± 0.05 vs 0.79 ± 0.11; p = 0.004), while also achieving comparable sensitivity (0.92 ± 0.11 vs 0.95 ± 0.08; p = 0.10), sparing more normal brain. Model performance was significantly enhanced by incorporating lesion size-weighted loss functions during training and including metabolic images as inputs.
目前，胶质母细胞瘤患者放射治疗（RT）临床靶区（CTV）定义的标准治疗（SOC）方法仍采用T2高信号病变各向同性扩大1-2厘米，而未考虑这些肿瘤的异质性浸润特性。本研究旨在通过纳入放疗前获取的具有生物学相关性的代谢和生理影像，以及一个能够通过对比增强或T2高信号的存在来预测后续肿瘤进展区域的深度学习模型，改善胶质母细胞瘤患者的RT CTV定义。将结果与两种标准CTV定义进行比较。我们的多参数深度学习模型在特异性方面显著优于T2病变CTV均匀扩大2厘米（0.89±0.05对0.79±0.11；p = 0.004），同时敏感性相当（0.92±0.11对0.95±0.08；p = 0.10），从而使更多正常脑组织得到保护。在训练过程中纳入病变大小加权损失函数并将代谢影像作为输入，显著提高了模型性能。
