论文标题
使用2.5D混合多任务卷积神经网络基于MRI的IDH突变和1P/19Q代码瘤状态的分类
MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network
论文作者
论文摘要
异氯酸盐脱氢酶（IDH）突变和1p/19q编码状态是神经胶质瘤的重要预后标记。目前，它们是使用侵入性程序确定的。我们的目标是开发基于人工智能的方法，以非侵入性确定MRI的分子改变。为此，从华盛顿大学医学院（WUSM; n = 835）收集了2648例神经胶质瘤患者（II-IV级）的术前MRI扫描，并公开可用的数据集。脑肿瘤分割（BRAT; n = 378），LGG 1P/19Q（n = 159），IVY胶质母细胞瘤地图集（Ivy Gap; n = 41），癌症基因组地图集（TCGA; n = 461）和Erasmus Glioma glioma glioma glioma database（egd; egd; n = 774 = 774）。提出了一个2.5D混合卷积神经网络，以同时将肿瘤定位并通过利用MR扫描的成像特征以及临床记录和肿瘤位置的先验知识特征来对其分子状态进行分类。在一个内部（TCGA）和两个外部（WUSM和EGD）测试集上测试了模型。对于IDH，在接收器操作特性（AUROC）下达到的区域达到了0.925、0.874、0.933，并且在内部，WUSM和EGD测试集上分别为0.925、0.874、0.933，并且在Precision-Recall曲线（AUPRC）下的区域分别为0.899、0.702、0.853。对于1P/19Q，最佳模型在这三个数据分别为0.782、0.754、0.842和0.588、0.713、0.782的AUROCS分别为0.782、0.754、0.842和AUPRC。该模型在看不见的数据上的高精度展示了其概括能力，并提出了其对剪裁治疗计划和胶质瘤的整体临床管理进行“虚拟活检”的潜力。
Isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status are important prognostic markers for glioma. Currently, they are determined using invasive procedures. Our goal was to develop artificial intelligence-based methods to non-invasively determine these molecular alterations from MRI. For this purpose, pre-operative MRI scans of 2648 patients with gliomas (grade II-IV) were collected from Washington University School of Medicine (WUSM; n = 835) and publicly available datasets viz. Brain Tumor Segmentation (BraTS; n = 378), LGG 1p/19q (n = 159), Ivy Glioblastoma Atlas Project (Ivy GAP; n = 41), The Cancer Genome Atlas (TCGA; n = 461), and the Erasmus Glioma Database (EGD; n = 774). A 2.5D hybrid convolutional neural network was proposed to simultaneously localize the tumor and classify its molecular status by leveraging imaging features from MR scans and prior knowledge features from clinical records and tumor location. The models were tested on one internal (TCGA) and two external (WUSM and EGD) test sets. For IDH, the best-performing model achieved areas under the receiver operating characteristic (AUROC) of 0.925, 0.874, 0.933 and areas under the precision-recall curves (AUPRC) of 0.899, 0.702, 0.853 on the internal, WUSM, and EGD test sets, respectively. For 1p/19q, the best model achieved AUROCs of 0.782, 0.754, 0.842, and AUPRCs of 0.588, 0.713, 0.782, on those three data-splits, respectively. The high accuracy of the model on unseen data showcases its generalization capabilities and suggests its potential to perform a 'virtual biopsy' for tailoring treatment planning and overall clinical management of gliomas.
