南方医科大学学报2024，Vol.44Issue(8)：1561-1570,10.DOI:10.12122/j.issn.1673-4254.2024.08.15
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
摘要
Abstract
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 two-dimensional 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 multimodal data.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Key words
sequence deletion/feature imputation/representation learning/high-grade glioma/low-grade glioma引用本文复制引用
吴垂杏,钟伟雄,谢金城,杨蕊梦,吴元魁,许乙凯,王琳婧,甄鑫..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[J].南方医科大学学报,2024,44(8):1561-1570,10.基金项目
国家自然科学基金(82371908) （82371908）
国家自然科学基金青年基金(62106058) （62106058）
广东省自然科学基金(2022A1515011410,2024A1515012 177,2024A1515012100)Supported by National Natural Science Foundation of China(82371908). （2022A1515011410,2024A1515012 177,2024A1515012100）
