基于序列缺失的MRI多序列特征填补与融合互助模型:鉴别高低级别胶质瘤  

An MRI multi-sequence feature imputation and fusion mutual-aid model based on sequence deletion for differentiation of high-grade from low-grade glioma

作　　者：吴垂杏 钟伟雄 谢金城 杨蕊梦[2,3] 吴元魁 许乙凯[4] 王琳婧[5] 甄鑫 WU Chuixing;ZHONG Weixiong;XIE Jincheng;YANG Ruimeng;WU Yuankui;XU Yikai;WANG Linjing;ZHEN Xin(School of Biomedical Engineering,Southern Medical University,Guangzhou 510515,China;Department of Radiology,Guangzhou FirstPeople's Hospital,Guangzhou 510180,China;School of Medicine,South China University of Technology,Guangzhou 510006,China;Department of Medical Imaging,Nanfang Hospital,Southern Medical University,Guangzhou 510515,China;Guangzhou Institute ofCancer Research,the Affiliated Cancer Hospital,Guangzhou Medical University,Guangzhou 510095,China)

机构地区：[1]南方医科大学生物医学工程学院,广东广州510515 [2]广州市第一人民医院放射科,广东广州510180 [3]华南理工大学医学院,广东广州510006 [4]南方医科大学南方医院医学影像科,广东广州510515 [5]广州医科大学附属肿瘤医院,广东广州510095

出　　处：《南方医科大学学报》2024年第8期1561-1570,共10页Journal of Southern Medical University

基　　金：国家自然科学基金(82371908);国家自然科学基金青年基金(62106058);广东省自然科学基金(2022A1515011410,2024A1515012177,2024A1515012100)。

摘　　要：目的探讨基于序列缺失的MRI多序列特征填补与融合互助模型应用于高级别胶质瘤(HGG)与低级别胶质瘤(LGG)鉴别的性能表现。方法回顾性收集305例胶质瘤患者(189例HGG,116例LGG)的MRI图像,分别勾画出T1加权成像(T1WI)、T2加权成像(T2WI)、T2液体翻转恢复衰减(T2_FLAIR)和T1WI增强图像(CE_T1WI)的感兴趣区(ROI),提取出4个ROI的影像组学特征。利用本研究提出的基于序列缺失的MRI多序列特征填补与融合互助模型对含有缺失数据的特征矩阵进行填补与融合双向学习得到互助模型。采用五折交叉验证方法和准确率(ACC)、平衡准确率(BAcc)、ROC曲线下的面积(AUC)、特异性和灵敏度评价该模型的鉴别能力。所提模型与其他非完整多模态分类模型在鉴别HGG与LGG上进行定量比较,对本文提出的特征填补与融合方法学习得到的潜在特征进行类可分性实验,观察样本在二维平面的分类效果,采用收敛性实验验证该模型的可行性。结果模型序列缺失率为10%时,其在鉴别HGG与LGG的ACC、BAcc、AUC、特异性、灵敏度分别为:0.777、0.768、0.826、0.754和0.780,融合的潜在特征在类可分性实验中有优秀表现,该算法可迭代至收敛。缺失率为30%、50%时,分类性能也优于其他方法。结论基于序列缺失的MRI多序列特征填补与融合互助模型在HGG和LGG的分类任务中具有优异的性能表现。与其他非完整多模态分类模型相比,该模型在鉴别HGG和LGG的分类性能更优,适用于非完整模态的多模态数据的处理。Objective 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).Methods 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.Results 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%.Conclusion 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 mu

关 键 词：序列缺失 特征填补 表征学习 高级别胶质瘤 低级别胶质瘤 

分 类 号：R445.2[医药卫生—影像医学与核医学] R739.41[医药卫生—诊断学]