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）即使在积极手术、联合放疗（RT）及辅助化疗下，其生存率仍不理想。标准放疗方案包括对T2加权液体衰减反转恢复磁共振成像（T2w/FLAIR）中的高信号病灶给予较低剂量照射，而对对比增强T1加权磁共振成像（CE-T1w）显示的强化肿瘤区域施加较高剂量。尽管已有多种针对术前脑肿瘤分割的尝试，但对术后肿瘤分割的研究却极为有限——术后情况因切除空腔和术后血液产物而变得复杂，目前亟需工具以辅助医生制定治疗轮廓并随访评估患者。本报告是首批为放疗规划与纵向追踪目的，训练并测试多种深度学习模型以实现术后脑肿瘤分割的研究之一。研究使用225例接受标准放疗的GBM患者的术后FLAIR与CE-T1w磁共振图像及其对应的放疗靶区（分别为GTV1和GTV2），对包括Unet、ResUnet、Swin-Unet、3D Unet及Swin-UNETR在内的多种深度学习模型进行训练。这些模型在包含30例GBM患者的独立数据集上进行测试，并采用Dice系数评估分割准确性。最终，将性能最优的分割模型集成至我们开发的纵向追踪网络应用程序中，通过病灶体积百分比变化阈值实现自动化结构化报告评分。在测试数据集中，3D Unet表现最佳，其GTV1与GTV2的平均Dice分数分别为0.72和0.73，两者标准差均为0.17。我们成功开发了一款轻量化的术后分割模型，可用于放疗规划与纵向追踪。
肿瘤(癌症)患者之家