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Deep Learning‑Assisted Quantitative Susceptibility Mapping as a Tool for Grading and Molecular Subtyping of Gliomas

Phenomics 2023年第3期3卷 243-254页

作者： Wenting Rui Shengjie Zhang Huidong Shi Yaru Sheng Fengping Zhu YiDi Yao xiang Chen Haixia Cheng Yong Zhang Ababikere Aili Zhenwei Yao xiao‑Yong Zhang Yan Ren Department of Radiology Huashan HospitalFudan UniversityMid 12 Wulumuqi RoadShanghai 200040China Institute of Science and Technology for Brain‑Inspired Intelligence Fudan UniversityShanghai 200433China MOE Key Laboratory of Computational Neuroscience and Brain‑Inspired Intelligence MOE Frontiers Center for Brain ScienceFudan UniversityShanghai 200433China Department of Neurosurgery Huashan HospitalFudan UniversityShanghai 200040China Department of Neuropathology Huashan HospitalFudan UniversityShanghai 200040China GE Healthcare MR ResearchHuatuo RoadShanghai 201203China Department of Radiology Kuqa County People’s HospitalXinjiang 842000China

This study aimed to explore the value of deep learning(DL)-assisted quantitative susceptibility mapping(QSM)in glioma grading and molecular ***-two patients with gliomas,who underwent preoperative T2 fluid-attenuated inversion recovery(T2 FLAIR),contrast-enhanced T1-weighted imaging(T1WI+C),and QSM scanning at 3.0T magnetic resonance imaging(MRI)were included in this *** and immunohistochemistry staining were used to determine glioma grades,and isocitrate dehydrogenase(IDH)1 and alpha thalassemia/mental retardation syndrome x-linked gene(ATRx)*** segmentation was performed manually using Insight Toolkit-SNAP program(***).An inception convolutional neural network(CNN)with a subsequent linear layer was employed as the training encoder to capture multi-scale features from MRI *** cross-validation was utilized as the training strategy(seven samples for each fold),and the ratio of sample size of the training,validation,and test dataset was 4:1:*** performance was evalu-ated by the accuracy and area under the curve(AUC).With the inception CNN,single modal of QSM showed better perfor-mance in differentiating glioblastomas(GBM)and other grade gliomas(OGG,grade II–III),and predicting IDH1 mutation and ATRx loss(accuracy:0.80,0.77,0.60)than either T2 FLAIR(0.69,0.57,0.54)or T1WI+C(0.74,0.57,0.46).When combining three modalities,compared with any single modality,the best AUC/accuracy/F1-scores were reached in grading gliomas(OGG and GBM:0.91/0.89/0.87,low-grade and high-grade gliomas:0.83/0.86/0.81),predicting IDH1 mutation(0.88/0.89/0.85),and predicting ATRx loss(0.78/0.71/0.67).As a supplement to conventional MRI,DL-assisted QSM is a promising molecular imaging method to evaluate glioma grades,IDH1 mutation,and ATRx loss.

关键词： Quantitative susceptibility mapping Glioma classification Isocitrate dehydrogenase alpha thalassemia/mental retardation syndrome x-linked gene Deep learning

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