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

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

doi:10.1007/s43657-022-00087-6

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

Phenomics. 2023 Jan 5;3(3):243-254. doi: 10.1007/s43657-022-00087-6. eCollection 2023 Jun.

Authors

Wenting Rui^#¹, Shengjie Zhang^#^{2

3}, Huidong Shi^#¹, Yaru Sheng¹, Fengping Zhu⁴, YiDi Yao¹, Xiang Chen^{2

3}, Haixia Cheng⁵, Yong Zhang⁶, Ababikere Aili⁷, Zhenwei Yao¹, Xiao-Yong Zhang^{2

3}, Yan Ren¹

Affiliations

¹ Department of Radiology, Huashan Hospital, Fudan University, Mid 12 Wulumuqi Road, Shanghai, 200040 China.
² Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433 China.
³ MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433 China.
⁴ Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, 200040 China.
⁵ Department of Neuropathology, Huashan Hospital, Fudan University, Shanghai, 200040 China.
⁶ GE Healthcare, MR Research, Huatuo Road, Shanghai, 201203 China.
⁷ Department of Radiology, Kuqa County People's Hospital, Xinjiang, 842000 China.

^# Contributed equally.

PMID: 37325712
PMCID: PMC10260708 (available on 2024-06-01)
DOI: 10.1007/s43657-022-00087-6

Abstract

This study aimed to explore the value of deep learning (DL)-assisted quantitative susceptibility mapping (QSM) in glioma grading and molecular subtyping. Forty-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 study. Histopathology and immunohistochemistry staining were used to determine glioma grades, and isocitrate dehydrogenase (IDH) 1 and alpha thalassemia/mental retardation syndrome X-linked gene (ATRX) subtypes. Tumor segmentation was performed manually using Insight Toolkit-SNAP program (www.itksnap.org). An inception convolutional neural network (CNN) with a subsequent linear layer was employed as the training encoder to capture multi-scale features from MRI slices. Fivefold 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 4:1:1. The performance was evaluated by the accuracy and area under the curve (AUC). With the inception CNN, single modal of QSM showed better performance 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.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-022-00087-6.

Keywords: Alpha thalassemia/mental retardation syndrome X-linked gene; Deep learning; Glioma classification; Isocitrate dehydrogenase; Quantitative susceptibility mapping.

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