Deep mutual learning on hybrid amino acid PET predicts H3K27M mutations in midline gliomas

Predicting H3K27M mutation status in midline gliomas non-invasively is of considerable interest, particularly using deep learning with 11C-methionine (MET) and 18F-fluoroethyltyrosine (FET) positron emission tomography (PET). To optimise prediction efficiency, we derived an assistance training (AT) scheme to allow mutual benefits between MET and FET learning to boost the predictability but still only require either PET as inputs for predictions. Our method significantly surpassed conventional convolutional neural network (CNN), radiomics-based, and MR-based methods, achieved an area under the curve (AUC) of 0.9343 for MET, and an AUC of 0.8619 for FET during internal cross-validation (n = 90). The performance remained high in hold-out testing (n = 19) and consecutive testing cohorts (n = 21), with AUCs of 0.9205 and 0.7404. The clinical feasibility of the proposed method was confirmed by the agreements to multi-departmental decisions and outcomes in pathology-uncertain cases. The findings positions our method as a promising tool for aiding treatment decisions in midline glioma.

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Publication Details

Journal
npj Precision Oncology
Published
2024-11-25
DOI
https://doi.org/10.1038/s41698-024-00760-1
Citations
2
Primary Topic
Glioma Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.88

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article

Deep mutual learning on hybrid amino acid PET predicts H3K27M mutations in midline gliomas

Shuhao Mei, Jianping Song, Yi‐Cheng Hsu, Fang Xie et al.
2 citations
npj Precision Oncology
Glioma Diagnosis and Treatment
0.88
article

Deep mutual learning on hybrid amino acid PET predicts H3K27M mutations in midline gliomas

Shuhao Mei, Jianping Song, Yi‐Cheng Hsu, Fang Xie, Mianxin Liu, Danyang Feng, Ying‐Hua Chu, Yifan Yuan, Jie Hu, Ming-Tao Hu, Chaolin Li, Yue Qi, Yihui Guan, Guanglei Li, Ying Mao
article en
2 citations

Abstract

Predicting H3K27M mutation status in midline gliomas non-invasively is of considerable interest, particularly using deep learning with 11C-methionine (MET) and 18F-fluoroethyltyrosine (FET) positron emission tomography (PET). To optimise prediction efficiency, we derived an assistance training (AT) scheme to allow mutual benefits between MET and FET learning to boost the predictability but still only require either PET as inputs for predictions. Our method significantly surpassed conventional convolutional neural network (CNN), radiomics-based, and MR-based methods, achieved an area under the curve (AUC) of 0.9343 for MET, and an AUC of 0.8619 for FET during internal cross-validation (n = 90). The performance remained high in hold-out testing (n = 19) and consecutive testing cohorts (n = 21), with AUCs of 0.9205 and 0.7404. The clinical feasibility of the proposed method was confirmed by the agreements to multi-departmental decisions and outcomes in pathology-uncertain cases. The findings positions our method as a promising tool for aiding treatment decisions in midline glioma.

npj Precision OncologyVol. 8(1)
Fujian Medical University (CN), Shanghai Medical College of Fudan University (CN), Shanghai Lixin University of Accounting and Finance (CN), Fudan University (CN), Guangzhou University (CN), Shanghai Institute for Science of Science (CN), Beijing Academy of Artificial Intelligence (CN), National Center for Spinal Disorders (HU), First Affiliated Hospital of Fujian Medical University (CN), Shanghai Clinical Research Center (CN), Neurological Surgery (US), Huashan Hospital (CN), Shanghai Center for Brain Science and Brain-Inspired Technology (CN), Shanghai Artificial Intelligence Laboratory (CN), Siemens (China) (CN)
Shanghai Science and Technology Development Foundation, National Natural Science Foundation of China, Science and Technology Commission of Shanghai Municipality, Shanghai Hospital Development Center, National Science and Technology Major Project, Guangzhou Municipal Science and Technology Project, National Key Research and Development Program of China
Zero hunger
Openalex Percentile: Top 23%
Glioma Diagnosis and Treatment
0.88
DOIOpen Access PDFOpenAlex
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