Wu C, Zhong W, Xie J, Yang R, Wu Y, Xu Y, Wang L, Zhen X
School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
Department of Radiology, Guangzhou First People's Hospital, Guangzhou 510180, China.
Nan Fang Yi Ke Da Xue Xue Bao. 2024 Aug 20;44(8):1561-1570. doi: 10.12122/j.issn.1673-4254.2024.08.15.
To evaluate the performance of magnetic resonance imaging (MRI) multi-sequence feature imputation and fusion mutual model based on sequence deletion in differentiating high-grade glioma (HGG) from low-grade glioma (LGG).
We retrospectively collected multi-sequence MR images from 305 glioma patients, including 189 HGG patients and 116 LGG patients. The region of interest (ROI) of T1-weighted images (T1WI), T2-weighted images (T2WI), T2 fluid attenuated inversion recovery (T2_FLAIR) and post-contrast enhancement T1WI (CE_T1WI) were delineated to extract the radiomics features. A mutual-aid model of MRI multi-sequence feature imputation and fusion based on sequence deletion was used for imputation and fusion of the feature matrix with missing data. The discriminative ability of the model was evaluated using 5-fold cross-validation method and by assessing the accuracy, balanced accuracy, area under the ROC curve (AUC), specificity, and sensitivity. The proposed model was quantitatively compared with other non-holonomic multimodal classification models for discriminating HGG and LGG. Class separability experiments were performed on the latent features learned by the proposed feature imputation and fusion methods to observe the classification effect of the samples in twodimensional plane. Convergence experiments were used to verify the feasibility of the model.
For differentiation of HGG from LGG with a missing rate of 10%, the proposed model achieved accuracy, balanced accuracy, AUC, specificity, and sensitivity of 0.777, 0.768, 0.826, 0.754 and 0.780, respectively. The fused latent features showed excellent performance in the class separability experiment, and the algorithm could be iterated to convergence with superior classification performance over other methods at the missing rates of 30% and 50%.
The proposed model has excellent performance in classification task of HGG and LGG and outperforms other non-holonomic multimodal classification models, demonstrating its potential for efficient processing of non-holonomic multimodal data.
评估基于序列删除的磁共振成像（MRI）多序列特征插补与融合互模型在鉴别高级别胶质瘤（HGG）与低级别胶质瘤（LGG）中的性能。
我们回顾性收集了305例胶质瘤患者的多序列MR图像，其中包括189例HGG患者和116例LGG患者。勾画T1加权图像（T1WI）、T2加权图像（T2WI）、T2液体衰减反转恢复序列（T2_FLAIR）和增强后T1WI（CE_T1WI）的感兴趣区（ROI）以提取影像组学特征。使用基于序列删除的MRI多序列特征插补与融合互助模型对存在缺失数据的特征矩阵进行插补和融合。采用5折交叉验证法并通过评估准确率、平衡准确率、ROC曲线下面积（AUC）、特异性和敏感性来评估模型的判别能力。将所提出的模型与其他非完整多模态分类模型进行定量比较，以鉴别HGG和LGG。对所提出的特征插补和融合方法学习到的潜在特征进行类可分性实验，以观察样本在二维平面上的分类效果。采用收敛实验验证模型的可行性。
对于缺失率为10%的HGG与LGG鉴别，所提出的模型的准确率、平衡准确率、AUC、特异性和敏感性分别达到0.777、0.768、0.826、0.754和0.780。融合后的潜在特征在类可分性实验中表现优异，并且在缺失率为30%和50%时，该算法能够迭代收敛，分类性能优于其他方法。
所提出的模型在HGG和LGG的分类任务中具有优异性能，优于其他非完整多模态分类模型，证明了其在高效处理非完整多模态数据方面的潜力。
