文章：

深度学习在胶质母细胞瘤每日磁共振直线加速器放疗中的自动分割方法

A Deep Learning Approach for Automatic Segmentation during Daily MRI-Linac Radiotherapy of Glioblastoma

原文发布日期：31 October 2023

DOI: 10.3390/cancers15215241

类型: Article

开放获取: 是

英文摘要：

Glioblastoma changes during chemoradiotherapy are inferred from high-field MRI before and after treatment but are rarely investigated during radiotherapy. The purpose of this study was to develop a deep learning network to automatically segment glioblastoma tumors on daily treatment set-up scans from the first glioblastoma patients treated on MRI-linac. Glioblastoma patients were prospectively imaged daily during chemoradiotherapy on 0.35T MRI-linac. Tumor and edema (tumor lesion) and resection cavity kinetics throughout the treatment were manually segmented on these daily MRI. Utilizing a convolutional neural network, an automatic segmentation deep learning network was built. A nine-fold cross-validation schema was used to train the network using 80:10:10 for training, validation, and testing. Thirty-six glioblastoma patients were imaged pre-treatment and 30 times during radiotherapy (n= 31 volumes, total of 930 MRIs). The average tumor lesion and resection cavity volumes were 94.56 ± 64.68 cc and 72.44 ± 35.08 cc, respectively. The average Dice similarity coefficient between manual and auto-segmentation for tumor lesion and resection cavity across all patients was 0.67 and 0.84, respectively. This is the first brain lesion segmentation network developed for MRI-linac. The network performed comparably to the only other published network for auto-segmentation of post-operative glioblastoma lesions. Segmented volumes can be utilized for adaptive radiotherapy and propagated across multiple MRI contrasts to create a prognostic model for glioblastoma based on multiparametric MRI.

摘要翻译： 

胶质母细胞瘤在放化疗期间的变化通常通过治疗前后的高场强磁共振成像进行推断，但很少在放疗期间进行研究。本研究旨在开发一种深度学习网络，用于在首例接受磁共振直线加速器治疗的胶质母细胞瘤患者的日常治疗摆位扫描中自动分割肿瘤。研究前瞻性地对胶质母细胞瘤患者在0.35T磁共振直线加速器放化疗期间进行每日成像，并在这些日常磁共振图像上手动分割整个治疗过程中的肿瘤与水肿区域（肿瘤病灶）以及切除腔动态变化。利用卷积神经网络构建了自动分割深度学习网络，采用九折交叉验证方案，按80:10:10的比例划分训练集、验证集和测试集进行网络训练。36例胶质母细胞瘤患者在治疗前及放疗期间接受30次成像（共31组体积数据，总计930次磁共振扫描）。肿瘤病灶与切除腔的平均体积分别为94.56±64.68立方厘米和72.44±35.08立方厘米。所有患者的肿瘤病灶与切除腔手动分割与自动分割的平均戴斯相似系数分别为0.67和0.84。这是首个针对磁共振直线加速器开发的脑部病灶分割网络，其性能与目前唯一已发表的术后胶质母细胞瘤病灶自动分割网络相当。分割后的体积数据可用于适应性放疗，并可通过多模态磁共振对比度传播，建立基于多参数磁共振的胶质母细胞瘤预后模型。

原文链接：

A Deep Learning Approach for Automatic Segmentation during Daily MRI-Linac Radiotherapy of Glioblastoma