Prediction of genetic status and grading in glioma based on fusion of macro-and micro-imaging features
LI Zhen
SONG Peng-fei
ZHU Rui-ze
JIANG Shan
CAO Shi-wen
YU Jin-hua
SHI Zhi-feng
Abstract：Objective To develop a dual-layer feature distillation multiple instance learning(DLFD-MIL)model integrating MRI and whole slide image(WSI)features for precise prediction of IDH1 mutation,1p/19qcodeletion,and World Health Organization(WHO)grading in adult-type diffuse gliomas.Methods A retrospective cohort of 212 adult-type diffuse gliomas patients from Huashan Hospital,Fudan University(January 2021 to June 2024)and 42 cases from The Cancer Genome Atlas(TCGA)were included.Preoperative T2-FLAIR and postoperative WSI data were jointly analyzed.The DLFD-MIL model addressed the lack of instance-level labels in weakly supervised WSI learning via a pseudo-bag generation strategy.Multimodal feature fusion was achieved through Concat.Diagnostic performance for molecular subtyping and WHO grading was evaluated by comparing area under the curve(AUC)of receiver operating characteristic(ROC)curve between single-mode(WSI or MRI)and multi-mode.Results In the IDH1 mutation prediction task,AUC of the multi-mode feature fusion model surpassed single-mode WSI model(Z=2.752,P=0.006)and single-mode T2-FLAIR model(Z=5.662,P=0.000).In the 1p/19qcodeletion prediction task,no statistically significant differences in AUC were observed between the multi-mode feature fusion model and either single-mode WSI model(Z=-0.245,P=0.806)or T2-FLAIR model(Z=0.781,P=0.435).In the WHO grading prediction task,the multi-mode feature fusion model showed no significant differences in AUC compared to single-mode WSI model(Z=1.739,P=0.082),however its AUC was significantly higher than single-mode T2-FLAIR model(Z=4.830,P=0.000).Conclusions Multi-mode fusion of macro-and micro-imaging features improves prediction accuracy for IDH1 genotyping and WHO grading in gliomas,providing a reliable artificial intelligence(AI)decision-support tool for personalized clinical management.
Keywords：GliomaMagnetic resonance imagingPathologyGenesNeoplasm gradingDeep learningROC curve
Publication Date：2025-03-25
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：10( 165-174 )