Glioma is the main type of malignant brain tumor in adults, and the status of isocitrate dehydrogenase (IDH) mutation highly affects the diagnosis, treatment, and prognosis of gliomas. Radiographic medical imaging provides a noninvasive platform for sampling both inter and intralesion heterogeneity of gliomas, and previous research has shown that the IDH genotype can be predicted from the fusion of multimodality radiology images. The features of medical images and IDH genotype are vital for medical treatment; however, it still lacks a multitask framework for the segmentation of the lesion areas of gliomas and the prediction of IDH genotype. In this paper, we propose a novel three-dimensional (3D) multitask deep learning model for segmentation and genotype prediction (SGPNet). The residual units are also introduced into the SGPNet that allows the output blocks to extract hierarchical features for different tasks and facilitate the information propagation. Our model reduces 26.6% classification error rates comparing with previous models on the datasets of Multimodal Brain Tumor Segmentation Challenge (BRATS) 2020 and The Cancer Genome Atlas (TCGA) gliomas’ databases. Furthermore, we first practically investigate the influence of lesion areas on the performance of IDH genotype prediction by setting different groups of learning targets. The experimental results indicate that the information of lesion areas is more important for the IDH genotype prediction. Our framework is effective and generalizable, which can serve as a highly automated tool to be applied in clinical decision making.

中文翻译：

---

SGPNet：用于胶质瘤分割和IDH基因型预测的三维多任务残差框架

胶质瘤是成人恶性脑肿瘤的主要类型，异柠檬酸脱氢酶（IDH）突变的状态在很大程度上影响胶质瘤的诊断，治疗和预后。放射医学成像为神经胶质瘤的病灶内和病灶内异质性采样提供了一个非侵入性平台，先前的研究表明，IDH基因型可以通过多模态放射学图像融合来预测。医学图像和IDH基因型的特征对于医学治疗至关重要；然而，它仍然缺乏用于胶质瘤病变区域的分割和IDH基因型预测的多任务框架。在本文中，我们提出了一种用于分割和基因型预测的新型三维（3D）多任务深度学习模型（SGPNet）。剩余单元也被引入到SGPNet中，SGPNet允许输出块提取用于不同任务的分层功能并促进信息传播。与多模式脑肿瘤分割挑战（BRATS）2020和癌症基因组图谱（TCGA）胶质瘤数据库的数据集相比，我们的模型与以前的模型相比，分类错误率降低了26.6％。此外，我们首先通过设置不同的学习目标组来实际研究病变区域对IDH基因型预测性能的影响。实验结果表明，病变区域的信息对于IDH基因型的预测更为重要。我们的框架是有效且可概括的，可以用作临床决策中使用的高度自动化的工具。