Guo Shunchao, Wang Lihui, Chen Qijian, Wang Li, Zhang Jian, Zhu Yuemin
Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, College of Computer Science and Technology, Guizhou University, Guiyang, China.
College of Computer and Information, Qiannan Normal University for Nationalities, Duyun, China.
Front Oncol. 2022 Feb 24;12:819673. doi: 10.3389/fonc.2022.819673. eCollection 2022.
Glioma is the most common primary brain tumor, with varying degrees of aggressiveness and prognosis. Accurate glioma classification is very important for treatment planning and prognosis prediction. The main purpose of this study is to design a novel effective algorithm for further improving the performance of glioma subtype classification using multimodal MRI images.
MRI images of four modalities for 221 glioma patients were collected from Computational Precision Medicine: Radiology-Pathology 2020 challenge, including T1, T2, T1ce, and fluid-attenuated inversion recovery (FLAIR) MRI images, to classify astrocytoma, oligodendroglioma, and glioblastoma. We proposed a multimodal MRI image decision fusion-based network for improving the glioma classification accuracy. First, the MRI images of each modality were input into a pre-trained tumor segmentation model to delineate the regions of tumor lesions. Then, the whole tumor regions were centrally clipped from original MRI images followed by max-min normalization. Subsequently, a deep learning-based network was designed based on a unified DenseNet structure, which extracts features through a series of dense blocks. After that, two fully connected layers were used to map the features into three glioma subtypes. During the training stage, we used the images of each modality after tumor segmentation to train the network to obtain its best accuracy on our testing set. During the inferring stage, a linear weighted module based on a decision fusion strategy was applied to assemble the predicted probabilities of the pre-trained models obtained in the training stage. Finally, the performance of our method was evaluated in terms of accuracy, area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), etc.
The proposed method achieved an accuracy of 0.878, an AUC of 0.902, a sensitivity of 0.772, a specificity of 0.930, a PPV of 0.862, an NPV of 0.949, and a Cohen's Kappa of 0.773, which showed a significantly higher performance than existing state-of-the-art methods.
Compared with current studies, this study demonstrated the effectiveness and superiority in the overall performance of our proposed multimodal MRI image decision fusion-based network method for glioma subtype classification, which would be of enormous potential value in clinical practice.
胶质瘤是最常见的原发性脑肿瘤，具有不同程度的侵袭性和预后。准确的胶质瘤分类对于治疗方案规划和预后预测非常重要。本研究的主要目的是设计一种新颖有效的算法，以利用多模态磁共振成像（MRI）图像进一步提高胶质瘤亚型分类的性能。
从“计算精准医学：放射学 - 病理学2020挑战赛”中收集了221例胶质瘤患者的四种模态的MRI图像，包括T1、T2、T1增强（T1ce）和液体衰减反转恢复（FLAIR）MRI图像，用于对星形细胞瘤、少突胶质细胞瘤和胶质母细胞瘤进行分类。我们提出了一种基于多模态MRI图像决策融合的网络，以提高胶质瘤分类的准确性。首先，将每种模态的MRI图像输入到一个预训练的肿瘤分割模型中，以勾勒出肿瘤病变区域。然后，从原始MRI图像中中心裁剪整个肿瘤区域，随后进行最大 - 最小归一化。随后，基于统一的密集连接网络（DenseNet）结构设计了一个深度学习网络，该网络通过一系列密集块提取特征。之后，使用两个全连接层将特征映射为三种胶质瘤亚型。在训练阶段，我们使用肿瘤分割后的每种模态的图像来训练网络，以在我们的测试集上获得最佳准确性。在推理阶段，应用基于决策融合策略的线性加权模块来组合在训练阶段获得的预训练模型的预测概率。最后，从准确率、曲线下面积（AUC）、敏感性、特异性、阳性预测值（PPV）、阴性预测值（NPV）等方面评估我们方法的性能。
所提出的方法实现了0.878的准确率、0.902的AUC、0.772的敏感性、0.930的特异性、0.862的PPV、0.949的NPV以及0.773的科恩卡帕系数（Cohen's Kappa），其性能显著高于现有的最先进方法。
与当前研究相比，本研究证明了我们提出的基于多模态MRI图像决策融合的网络方法在胶质瘤亚型分类的整体性能方面的有效性和优越性，这在临床实践中将具有巨大的潜在价值。
