Wang Xiyue, Wang Ruijie, Yang Sen, Zhang Jun, Wang Minghui, Zhong Dexing, Zhang Jing, Han Xiao
College of Biomedical Engineering, Sichuan University, Chengdu, China.
College of Computer Science, Sichuan University, Chengdu, China.
Front Bioeng Biotechnol. 2022 Mar 21;10:841958. doi: 10.3389/fbioe.2022.841958. eCollection 2022.
Subtype classification is critical in the treatment of gliomas because different subtypes lead to different treatment options and postoperative care. Although many radiological- or histological-based glioma classification algorithms have been developed, most of them focus on single-modality data. In this paper, we propose an innovative two-stage model to classify gliomas into three subtypes (i.e., glioblastoma, oligodendroglioma, and astrocytoma) based on radiology and histology data. In the first stage, our model classifies each image as having glioblastoma or not. Based on the obtained non-glioblastoma images, the second stage aims to accurately distinguish astrocytoma and oligodendroglioma. The radiological images and histological images pass through the two-stage design with 3D and 2D models, respectively. Then, an ensemble classification network is designed to automatically integrate the features of the two modalities. We have verified our method by participating in the MICCAI 2020 CPM-RadPath Challenge and won 1st place. Our proposed model achieves high performance on the validation set with a balanced accuracy of 0.889, Cohen's Kappa of 0.903, and an F1-score of 0.943. Our model could advance multimodal-based glioma research and provide assistance to pathologists and neurologists in diagnosing glioma subtypes. The code has been publicly available online at https://github.com/Xiyue-Wang/1st-in-MICCAI2020-CPM.
亚型分类在胶质瘤治疗中至关重要，因为不同亚型会导致不同的治疗方案和术后护理。尽管已经开发了许多基于放射学或组织学的胶质瘤分类算法，但其中大多数都专注于单模态数据。在本文中，我们提出了一种创新的两阶段模型，基于放射学和组织学数据将胶质瘤分为三种亚型（即胶质母细胞瘤、少突胶质细胞瘤和星形细胞瘤）。在第一阶段，我们的模型将每个图像分类为是否患有胶质母细胞瘤。基于获得的非胶质母细胞瘤图像，第二阶段旨在准确区分星形细胞瘤和少突胶质细胞瘤。放射学图像和组织学图像分别通过3D和2D模型的两阶段设计。然后，设计了一个集成分类网络来自动整合这两种模态的特征。我们通过参加2020年医学图像计算与计算机辅助干预国际会议（MICCAI）的CPM-RadPath挑战赛验证了我们的方法，并获得了第一名。我们提出的模型在验证集上表现出色，平衡准确率为0.889，科恩卡帕系数为0.903，F1分数为0.943。我们的模型可以推动基于多模态的胶质瘤研究，并为病理学家和神经学家诊断胶质瘤亚型提供帮助。代码已在https://github.com/Xiyue-Wang/1st-in-MICCAI2020-CPM上公开提供。
