南方医科大学学报2024，Vol.44Issue(1)：138-145,8.DOI:10.12122/j.issn.1673-4254.2024.01.16
基于距匹配及判别表征学习的多模态特征融合分类模型研究:高级别胶质瘤与单发性脑转移瘤的鉴别诊断
A multi-modal feature fusion classification model based on distance matching and discriminative representation learning for differentiation of high-grade glioma from solitary brain metastasis
摘要
Abstract
Objective To explore the performance of a new multimodal feature fusion classification model based on distance matching and discriminative representation learning for differentiating high-grade glioma(HGG)from solitary brain metastasis(SBM).Methods We collected multi-parametric magnetic resonance imaging(MRI)data from 61 patients with HGG and 60 with SBM,and delineated regions of interest(ROI)on T1WI,T2WI,T2-weighted fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)images.The radiomics features were extracted from each sequence using Pyradiomics and fused using a multimodal feature fusion classification model based on distance matching and discriminative representation learning to obtain a classification model.The discriminative performance of the classification model for differentiating HGG from SBM was evaluated using five-fold cross-validation with metrics of specificity,sensitivity,accuracy,and the area under the ROC curve(AUC)and quantitatively compared with other feature fusion models.Visual experiments were conducted to examine the fused features obtained by the proposed model to validate its feasibility and effectiveness.Results The five-fold cross-validation results showed that the proposed multimodal feature fusion classification model had a specificity of 0.871,a sensitivity of 0.817,an accuracy of 0.843,and an AUC of 0.930 for distinguishing HGG from SBM.This feature fusion method exhibited excellent discriminative performance in the visual experiments.Conclusion The proposed multimodal feature fusion classification model has an excellent ability for differentiating HGG from SBM with significant advantages over other feature fusion classification models in discrimination and classification tasks between HGG and SBM.关键词
特征融合/共享表征学习/判别分析/高级别胶质瘤/单发性脑转移瘤Key words
feature fusion/shared representation learning/discriminant analysis/high-grade glioma/solitary brain metastasis引用本文复制引用
张振阳,谢金城,钟伟雄,梁芳蓉,杨蕊梦,甄鑫..基于距匹配及判别表征学习的多模态特征融合分类模型研究:高级别胶质瘤与单发性脑转移瘤的鉴别诊断[J].南方医科大学学报,2024,44(1):138-145,8.基金项目
国家自然科学基金(81874216,62106058,81971574) （81874216,62106058,81971574）
广东省自然科学基金(2022A1515011410) （2022A1515011410）
广州市科技项目的资助(202201011662) （202201011662）
广州市重点实验室建设项目(202201020376) Supported by National Natural Science Foundation of China(81874216,62106058,81971574). （202201020376）
