Automatic survival prediction of gliomas from brain magnetic resonance imaging (MRI) volumes is an essential step for a patient's prognosis analysis. Radiomics research delivers beneficial feature information from MRI imaging which is substantially required by clinicians and oncologists for predicting disease prognosis for precise surgical treatment and planning. In recent years, the success of deep learning has been vast in the field of medical imaging, and it shows state-of-the-art performance in applications like segmentation, classification, regression, and detection. Therefore, in this paper, we proposed a collective method using deep learning and radiomics techniques for the survival prediction of brain tumor patients. We first propose a hierarchical channel attention (HAM) module and a multi-scale-aware feature enhancement (MSAFE) to efficiently fuse adjacent hierarchical features in the proposed segmentation model. After segmentation, deep/latent features (LCNN) are extracted from the bottom layer of the proposed segmentation model. Later, we extracted selected radiomics features (histogram, location, and shape) using input images and segmented masks from the proposed segmentation model. Further, the 3D deep learning regressor has been trained for 3D regressor-based deep feature extraction. We proposed the method of overall survival prediction for the brain tumor patients by combining all the meaningful features including clinical features (age) that also favorably contribute to the survival days prediction for the glioma's patients. To predict the survival days for each patient, the selected features are trained to analyze the performance of various regression techniques like random forest (RF), decision tree (DT), and XGBoost. Our proposed combined feature-based method achieved the highest performance for survival days prediction over the state-of-the-art methods. We also perform extensive experiments to show the effectiveness of each feature extraction method. The experimental results infer that deep learning-based features along with radiomic features and clinical features are truly vital paradigms to estimate survival days.

中文翻译：

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使用注意力引导的 3D 卷积神经网络和多模态磁共振成像放射组学方法对脑肿瘤患者进行基于深度学习的生存预测

根据脑磁共振成像 (MRI) 体积自动预测神经胶质瘤的生存率是患者预后分析的重要步骤。放射组学研究从 MRI 成像中提供有益的特征信息，这是临床医生和肿瘤学家预测疾病预后以进行精确手术治疗和规划所必需的。近年来，深度学习在医学成像领域取得了巨大的成功，在分割、分类、回归和检测等应用中展现出了最先进的性能。因此，在本文中，我们提出了一种利用深度学习和放射组学技术来预测脑肿瘤患者生存的集体方法。我们首先提出了分层通道注意（HAM）模块和多尺度感知特征增强（MSAFE），以有效地融合所提出的分割模型中的相邻分层特征。分割后，从所提出的分割模型的底层提取深层/潜在特征（LCNN）。随后，我们使用输入图像和来自所提出的分割模型的分割掩模提取了选定的放射组学特征（直方图、位置和形状）。此外，3D 深度学习回归器已针对基于 3D 回归器的深度特征提取进行了训练。我们提出了脑肿瘤患者总体生存预测的方法，通过结合所有有意义的特征，包括临床特征（年龄），这些特征也有利于神经胶质瘤患者的生存天数预测。为了预测每位患者的生存天数，对选定的特征进行训练，以分析随机森林 (RF)、决策树 (DT) 和 XGBoost 等各种回归技术的性能。我们提出的基于特征的组合方法在生存天数预测方面比最先进的方法实现了最高性能。我们还进行了大量的实验来展示每种特征提取方法的有效性。实验结果表明，基于深度学习的特征以及放射组学特征和临床特征是估计生存天数的真正重要范例。