Ramesh Karthik K, Xu Karen M, Trivedi Anuradha G, Huang Vicki, Sharghi Vahid Khalilzad, Kleinberg Lawrence R, Mellon Eric A, Shu Hui-Kuo G, Shim Hyunsuk, Weinberg Brent D
Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA.
Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA 30332, USA.
Cancers (Basel). 2023 Aug 3;15(15):3956. doi: 10.3390/cancers15153956.
Glioblastoma (GBM) has a poor survival rate even with aggressive surgery, concomitant radiation therapy (RT), and adjuvant chemotherapy. Standard-of-care RT involves irradiating a lower dose to the hyperintense lesion in T2-weighted fluid-attenuated inversion recovery MRI (T2w/FLAIR) and a higher dose to the enhancing tumor on contrast-enhanced, T1-weighted MRI (CE-T1w). While there have been several attempts to segment pre-surgical brain tumors, there have been minimal efforts to segment post-surgical tumors, which are complicated by a resection cavity and postoperative blood products, and tools are needed to assist physicians in generating treatment contours and assessing treated patients on follow up. This report is one of the first to train and test multiple deep learning models for the purpose of post-surgical brain tumor segmentation for RT planning and longitudinal tracking. Post-surgical FLAIR and CE-T1w MRIs, as well as their corresponding RT targets (GTV1 and GTV2, respectively) from 225 GBM patients treated with standard RT were trained on multiple deep learning models including: Unet, ResUnet, Swin-Unet, 3D Unet, and Swin-UNETR. These models were tested on an independent dataset of 30 GBM patients with the Dice metric used to evaluate segmentation accuracy. Finally, the best-performing segmentation model was integrated into our longitudinal tracking web application to assign automated structured reporting scores using change in percent cutoffs of lesion volume. The 3D Unet was our best-performing model with mean Dice scores of 0.72 for GTV1 and 0.73 for GTV2 with a standard deviation of 0.17 for both in the test dataset. We have successfully developed a lightweight post-surgical segmentation model for RT planning and longitudinal tracking.
胶质母细胞瘤（GBM）即使采用积极的手术、同步放疗（RT）和辅助化疗，生存率仍很低。标准治疗放疗包括在T2加权液体衰减反转恢复磁共振成像（T2w/FLAIR）中对高强度病变照射较低剂量，在对比增强T1加权磁共振成像（CE-T1w）中对强化肿瘤照射较高剂量。虽然已经有几次对术前脑肿瘤进行分割的尝试，但对术后肿瘤进行分割的努力却很少，术后肿瘤因切除腔和术后血液产物而变得复杂，需要工具来协助医生生成治疗轮廓并在随访中评估接受治疗的患者。本报告是最早为RT规划和纵向跟踪目的训练和测试多个深度学习模型以进行术后脑肿瘤分割的报告之一。对225例接受标准RT治疗的GBM患者的术后FLAIR和CE-T1w磁共振成像及其相应的RT靶区（分别为GTV1和GTV2）在多个深度学习模型上进行训练，这些模型包括：Unet、ResUnet、Swin-Unet、3D Unet和Swin-UNETR。这些模型在30例GBM患者的独立数据集上进行测试，使用Dice指标评估分割准确性。最后，将性能最佳的分割模型集成到我们的纵向跟踪网络应用程序中，使用病变体积百分比截断值的变化来分配自动结构化报告分数。3D Unet是我们性能最佳的模型，在测试数据集中GTV1的平均Dice分数为0.72，GTV2的平均Dice分数为0.73，两者的标准差均为0.17。我们成功开发了一种用于RT规划和纵向跟踪的轻量级术后分割模型。
