论文标题
基于MRI的自动化胶质瘤分割和预测等级，IDH突变和1P19Q共同删除的管道
Automated MRI based pipeline for glioma segmentation and prediction of grade, IDH mutation and 1p19q co-deletion
论文作者
论文摘要
在WHO胶质瘤分类指南中，IDH突变和1P19Q共同删除起着核心作用，因为它们是预后和最佳治疗计划的重要标志。因此，我们提出了一个全自动的基于MRI的3D管道，用于神经胶质瘤分割和分类。设计的分割网络是一个3D U-NET，达到平均整个肿瘤骰子分数为90％。分割后，将3D肿瘤ROI提取并馈入多任务分类网络。该网络在628名患者的大型异质数据集上进行了训练和评估，该数据集是从癌症成像档案和Brats 2019数据库中收集的。此外，该网络在一个独立的数据集中进行了验证，该数据集是在根特大学医院（GUH）回顾性收购的110名患者的数据集中。 TCIA测试数据的分类AUC分数分别为0.93、0.94和0.82，在GUH数据的GUH数据，IDH和1P19Q状态分别为0.94、0.86和0.87。
In the WHO glioma classification guidelines grade, IDH mutation and 1p19q co-deletion play a central role as they are important markers for prognosis and optimal therapy planning. Therefore, we propose a fully automatic, MRI based, 3D pipeline for glioma segmentation and classification. The designed segmentation network was a 3D U-Net achieving an average whole tumor dice score of 90%. After segmentation, the 3D tumor ROI is extracted and fed into the multi-task classification network. The network was trained and evaluated on a large heterogeneous dataset of 628 patients, collected from The Cancer Imaging Archive and BraTS 2019 databases. Additionally, the network was validated on an independent dataset of 110 patients retrospectively acquired at the Ghent University Hospital (GUH). Classification AUC scores are 0.93, 0.94 and 0.82 on the TCIA test data and 0.94, 0.86 and 0.87 on the GUH data for grade, IDH and 1p19q status respectively.
