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Prediction of H3K27M-mutant brainstem glioma by amide proton transfer–weighted imaging and its derived radiomics

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European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Purpose

H3K27M-mutant associated brainstem glioma (BSG) carries a very poor prognosis. We aimed to predict H3K27M mutation status by amide proton transfer–weighted (APTw) imaging and radiomic features.

Methods

Eighty-one BSG patients with APTw imaging at 3T MR and known H3K27M status were retrospectively studied. APTw values (mean, median, and max) and radiomic features within manually delineated 3D tumor masks were extracted. Comparison of APTw measures between H3K27M-mutant and wildtype groups was conducted by two-sample Student’s T/Mann–Whitney U test and receiver operating characteristic curve (ROC) analysis. H3K27M-mutant prediction using APTw-derived radiomics was conducted using a machine learning algorithm (support vector machine) in randomly selected train (n = 64) and test (n = 17) sets. Sensitivity analysis with additional random splits of train and test sets, 2D tumor masks, and other classifiers were conducted. Finally, a prospective cohort including 29 BSG patients was acquired for validation of the radiomics algorithm.

Results

BSG patients with H3K27M-mutant were younger and had higher max APTw values than those with wildtype. APTw-derived radiomic measures reflecting tumor heterogeneity could predict H3K27M mutation status with an accuracy of 0.88, sensitivity of 0.92, and specificity of 0.80 in the test set. Sensitivity analysis confirmed the predictive ability (accuracy range: 0.71–0.94). In the independent prospective validation cohort, the algorithm reached an accuracy of 0.86, sensitivity of 0.88, and specificity of 0.85 for predicting H3K27M-mutation status.

Conclusion

BSG patients with H3K27M-mutant had higher max APTw values than those with wildtype. APTw-derived radiomics could accurately predict a H3K27M-mutant status in BSG patients.

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Data availability

The data would be available if the corresponding author received requirement from qualified researchers.

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Funding

This study was funded by the National Science Foundation of China (Nos. 81870958 and 81571631), the Beijing Municipal Natural Science Foundation for Distinguished Young Scholars (No. JQ20035), the Special Fund of the Pediatric Medical Coordinated Development Center of Beijing Hospitals Authority (No. XTYB201831). FB was supported by the NIHR biomedical research center at UCLH.

Author information

Author notes

  1. Zhizheng Zhuo, Liying Qu, Peng Zhang, Liwei Zhang, and Yaou Liu contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China

    Zhizheng Zhuo, Liying Qu, Yunyun Duan, Dan Cheng, Xiaolu Xu, Ting Sun, Jinli Ding, Cong Xie & Yaou Liu

  2. Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 10070, China

    Peng Zhang & Liwei Zhang

  3. Department of Pathology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 10070, China

    Xing Liu

  4. Department of Imaging and Medical Informatics, University Hospitals of Geneva and Faculty of Medicine of the University of Geneva, Geneva, Switzerland

    Sven Haller

  5. UCL Institutes of Neurology and Healthcare Engineering, London, UK

    Frederik Barkhof

  6. Department of Radiology & Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands

    Frederik Barkhof

Authors
  1. Zhizheng Zhuo
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  2. Liying Qu
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Contributions

Data management, data processing, statistical analysis, and manuscript drafting: Zhizheng Zhuo. Data management and lesion segmentation: Liying Qu. Clinical information acquisition and clinical diagnosis: Peng Zhang. Data acquisition, lesion segmentation, and clinical diagnosis: Yunyun Duan. Lesion segmentation and clinical evaluation: Dan Cheng. Manuscript editing: Xiaolu Xu. Manuscript editing: Ting Sun. MRI data acquisition: Jinli Ding. MRI data acquisition: Cong Xie. Histopathological and molecular information: Xing Liu. Manuscript editing and review: Sven Haller. Manuscript editing and review: Frederik Barkhof. Patient recruitment and clinical diagnosis: Liwei Zhang. Study design, manuscript editing and final approval of this manuscript: Yaou Liu.

Corresponding authors

Correspondence to Liwei Zhang or Yaou Liu.

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This article is part of the Topical Collection on Oncology - Brain.

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Zhuo, Z., Qu, L., Zhang, P. et al. Prediction of H3K27M-mutant brainstem glioma by amide proton transfer–weighted imaging and its derived radiomics. Eur J Nucl Med Mol Imaging 48, 4426–4436 (2021). https://doi.org/10.1007/s00259-021-05455-4

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