论文标题
图像和几何学的协作学习用于预测神经胶质瘤的异急塞脱氢酶状态
Collaborative learning of images and geometrics for predicting isocitrate dehydrogenase status of glioma
论文作者
论文摘要
异位酸脱氢酶（IDH）基因突变状态是神经胶质瘤患者的重要生物标志物。 IDH突变检测的黄金标准需要通过侵入性方法获得的肿瘤组织，通常很昂贵。放射基因组学的最新进展提供了一种基于MRI预测IDH突变的非侵入性方法。同时，肿瘤的几何形式包含用于肿瘤表型的关键信息。在这里，我们提出了一个协作学习框架，该框架分别使用卷积神经网络（CNN）和图神经网络（GNN）学习肿瘤图像和肿瘤几何技术。我们的结果表明，所提出的模型优于3D-Densenet121的基线模型。此外，协作学习模型比单独的CNN或GNN实现更好的性能。该模型的解释表明，CNN和GNN可以确定IDH突变预测的共同和独特的感兴趣区域。总之，协作图像和几何学习者提供了一种预测基因型和表征神经胶质瘤的新方法。
The isocitrate dehydrogenase (IDH) gene mutation status is an important biomarker for glioma patients. The gold standard of IDH mutation detection requires tumour tissue obtained via invasive approaches and is usually expensive. Recent advancement in radiogenomics provides a non-invasive approach for predicting IDH mutation based on MRI. Meanwhile, tumor geometrics encompass crucial information for tumour phenotyping. Here we propose a collaborative learning framework that learns both tumor images and tumor geometrics using convolutional neural networks (CNN) and graph neural networks (GNN), respectively. Our results show that the proposed model outperforms the baseline model of 3D-DenseNet121. Further, the collaborative learning model achieves better performance than either the CNN or the GNN alone. The model interpretation shows that the CNN and GNN could identify common and unique regions of interest for IDH mutation prediction. In conclusion, collaborating image and geometric learners provides a novel approach for predicting genotype and characterising glioma.
