MRI characteristics of H3 G34–mutant diffuse hemispheric gliomas and possible differentiation from IDH–wild-type glioblastomas in adolescents and young adults

Hanbing Shao Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu;

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Jing Gong Department of Pathology, West China Hospital of Sichuan University, Chengdu;

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Xiaorui Su Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu;

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Ni Chen Department of Pathology, West China Hospital of Sichuan University, Chengdu;
Huaxi Glioma Center, West China Hospital of Sichuan University, Chengdu;

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Shuang Li Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu;

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Xibiao Yang Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu;
Department of Radiology, West China Hospital of Sichuan University, Chengdu;

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Simin Zhang Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu;

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Zhangfeng Huang Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu;

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Wei Hu Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu;

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Qiyong Gong Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu;
Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian; and

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Yaou Liu Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China

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Qiang Yue Department of Radiology, West China Hospital of Sichuan University, Chengdu;
Huaxi Glioma Center, West China Hospital of Sichuan University, Chengdu;

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OBJECTIVE

H3 G34–mutant diffuse hemispheric gliomas (G34m-DHGs) are rare and constitute a new infiltrating brain tumor entity whose characteristics require elucidation, and their difference from isocitrate dehydrogenase–wild-type glioblastomas (IDH-WT-GBMs) needs to be clarified. In this study, the authors report the demographic, clinical, and neuroradiological features of G34m-DHG and investigate the capability of quantitative MRI features in differentiating them.

METHODS

Twenty-three patients with G34m-DHG and 30 patients with IDH-WT-GBM were included in this retrospective study. The authors reviewed the clinical, radiological, and molecular data of G34m-DHGs and compared their neuroimaging features with those of IDH-WT-GBMs in adolescents and young adults. Visually Accessible Rembrandt Images (VASARI) features were extracted, and the Kruskal-Wallis test was performed. A logistic regression model was constructed to evaluate the diagnostic performance for differentiating between G34m-DHG and IDH-WT-GBM. Subsequently, FeAture Explorer (FAE) was used to generate the machine learning pipeline and select important radiomics features that had been extracted with PyRadiomics. Estimates of the performance were supplied by metrics such as sensitivity, specificity, accuracy, and area under the curve (AUC).

RESULTS

The mean age of the 23 patients with G34m-DHG was 23.7 years (range 11–45 years), younger than the mean age of patients with IDH-WT-GBM (30.96 years, range 5–43 years). All tumors were hemispheric. Most cases were immunonegative for ATRX (95%) and Olig2 (100%), were immunopositive for p53 (95%), and exhibited MGMT promoter methylation (81%). The radiological presentations of G34m-DHG were different from those of IDH-WT-GBM. The majority of the G34m-DHGs were in the frontal, parietal, and temporal lobes and demonstrated no or only faint contrast enhancement (74%), while IDH-WT-GBMs were mostly seen in the frontal lobe and showed marked contrast enhancement in 83% of cases. The FAE-generated model, based on radiomics features (AUC 0.925) of conventional MR images, had better discriminatory performance between G34m-DHG and IDH-WT-GBM than VASARI feature analysis (AUC 0.843).

CONCLUSIONS

G34m-DHGs most frequently occur in the frontal, parietal, and temporal lobes in adolescent and young adults and are associated with radiological characteristics distinct from those of IDH-WT-GBMs. Successful identification can be achieved by using either VASARI features or radiomics signatures, which may contribute to prognostic evaluation and assist in clinical settings.

ABBREVIATIONS

ADC = apparent diffusion coefficient; AUC = area under the curve; DWI = diffusion-weighted imaging; FAE = FeAture Explorer; GBM = glioblastoma; GLDM = gray-level dependence matrix; GLN = gray-level nonuniformity; GLRLM = gray-level run length matrix; G34m-DHG = H3 G34–mutant diffuse hemispheric glioma; G34R = glycine 34 to arginine; G34V = glycine 34 to valine; IDH = isocitrate dehydrogenase; IDH-WT-GBM = IDH–wild-type GBM; ML = machine learning; NAA = N-acetyl-aspartate; nCET = nonenhancing tumor; ROC = receiver operating characteristic; T1WI = T1-weighted imaging; T2WI = T2-weighted imaging; VASARI = Visually Accessible Rembrandt Images.

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Illustration from Caklili et al. (pp 223–235). © Savas Ceylan, published with permission.

  • 1

    Louis DN, Perry A, Wesseling P, et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol. 2021;23(8):12311251.

  • 2

    Louis DN, Wesseling P, Aldape K, et al. cIMPACT-NOW update 6: new entity and diagnostic principle recommendations of the cIMPACT-Utrecht meeting on future CNS tumor classification and grading. Brain Pathol. 2020;30(4):844856.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3

    Schwartzentruber J, Korshunov A, Liu XY, et al. Driver mutations in histone H3.3 and chromatin remodelling genes in paediatric glioblastoma. Nature. 2012;482(7384):226231.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 4

    Neumann JE, Dorostkar MM, Korshunov A, et al. Distinct histomorphology in molecular subgroups of glioblastomas in young patients. J Neuropathol Exp Neurol. 2016;75(5):408414.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5

    Roux A, Pallud J, Saffroy R, et al. High-grade gliomas in adolescents and young adults highlight histomolecular differences from their adult and pediatric counterparts. Neuro Oncol. 2020;22(8):11901202.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 6

    Yoshimoto K, Hatae R, Sangatsuda Y, et al. Prevalence and clinicopathological features of H3.3 G34-mutant high-grade gliomas: a retrospective study of 411 consecutive glioma cases in a single institution. Brain Tumor Pathol. 2017;34(3):103112.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7

    Korshunov A, Ryzhova M, Hovestadt V, et al. Integrated analysis of pediatric glioblastoma reveals a subset of biologically favorable tumors with associated molecular prognostic markers. Acta Neuropathol. 2015;129(5):669678.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8

    Mackay A, Burford A, Molinari V, et al. Molecular, pathological, radiological, and immune profiling of non-brainstem pediatric high-grade glioma from the HERBY phase II randomized trial. Cancer Cell. 2018;33(5):829842.e5.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 9

    Korshunov A, Capper D, Reuss D, et al. Histologically distinct neuroepithelial tumors with histone 3 G34 mutation are molecularly similar and comprise a single nosologic entity. Acta Neuropathol. 2016;131(1):137146.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10

    Grill J, Massimino M, Bouffet E, et al. Phase II, open-label, randomized, multicenter trial (HERBY) of bevacizumab in pediatric patients with newly diagnosed high-grade glioma. J Clin Oncol. 2018;36(10):951958.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11

    Wang L, Shao L, Li H, et al. Histone H3.3 G34-mutant diffuse gliomas in adults. Am J Surg Pathol. 2022;46(2):249257.

  • 12

    Schulte JD, Buerki RA, Lapointe S, et al. Clinical, radiologic, and genetic characteristics of histone H3 K27M-mutant diffuse midline gliomas in adults. Neurooncol Adv. 2020;2(1):a142.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 13

    Mosaab A, El-Ayadi M, Khorshed EN, et al. Histone H3K27M mutation overrides histological grading in pediatric gliomas. Sci Rep. 2020;10(1):8368.

  • 14

    Sturm D, Witt H, Hovestadt V, et al. Hotspot mutations in H3F3A and IDH1 define distinct epigenetic and biological subgroups of glioblastoma. Cancer Cell. 2012;22(4):425437.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 15

    Lim KY, Won JK, Park CK, et al. H3 G34-mutant high-grade glioma. Brain Tumor Pathol. 2021;38(1):413.

  • 16

    Vuong HG, Le HT, Dunn IF. The prognostic significance of further genotyping H3G34 diffuse hemispheric gliomas. Cancer. 2022;128(10):19071912.

  • 17

    Lucas CG, Mueller S, Reddy A, et al. Diffuse hemispheric glioma, H3 G34-mutant: genomic landscape of a new tumor entity and prospects for targeted therapy. Letter. Neuro Oncol. 2021;23(11):19741976.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 18

    Chen CCL, Deshmukh S, Jessa S, et al. Histone H3.3G34-mutant interneuron progenitors co-opt PDGFRA for gliomagenesis. Cell. 2020;183(6):16171633.e22.

  • 19

    Bjerke L, Mackay A, Nandhabalan M, et al. Histone H3.3. mutations drive pediatric glioblastoma through upregulation of MYCN. Cancer Discov. 2013;3(5):512519.

  • 20

    Sweha SR, Chung C, Natarajan SK, et al. Epigenetically defined therapeutic targeting in H3.3G34R/V high-grade gliomas. Sci Transl Med. 2021;13(615):eabf7860.

  • 21

    Picart T, Barritault M, Poncet D, et al. Characteristics of diffuse hemispheric gliomas, H3 G34-mutant in adults. Neurooncol Adv. 2021;3(1):vdab061.

  • 22

    Kurokawa R, Baba A, Kurokawa M, et al. Neuroimaging features of diffuse hemispheric glioma, H3 G34-mutant: a case series and systematic review. J Neuroimaging. 2022;32(1):1727.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 23

    Chen KY, Bush K, Klein RH, et al. Reciprocal H3.3 gene editing identifies K27M and G34R mechanisms in pediatric glioma including NOTCH signaling. Commun Biol. 2020;3(1):363.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 24

    Morris M, Driscoll M, Henson JW, et al. Low-grade gemistocytic morphology in H3 G34R-mutant gliomas and concurrent K27M mutation: clinicopathologic findings. J Neuropathol Exp Neurol. 2020;79(10):10381043.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 25

    Vettermann FJ, Felsberg J, Reifenberger G, et al. Characterization of diffuse gliomas with histone H3-G34 mutation by MRI and dynamic 18F-FET PET. Clin Nucl Med. 2018;43(12):895898.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 26

    Gutman DA, Cooper LA, Hwang SN, et al. MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set. Radiology. 2013;267(2):560569.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 27

    Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48(4):441446.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 28

    Mayerhoefer ME, Materka A, Langs G, et al. Introduction to radiomics. J Nucl Med. 2020;61(4):488495.

  • 29

    Gevaert O, Mitchell LA, Achrol AS, et al. Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features. Radiology. 2014;273(1):168174.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 30

    Li G, Li L, Li Y, et al. An MRI radiomics approach to predict survival and tumour-infiltrating macrophages in gliomas. Brain. 2022;145(3):11511161.

  • 31

    Wu C, Zheng H, Li J, et al. MRI-based radiomics signature and clinical factor for predicting H3K27M mutation in pediatric high-grade gliomas located in the midline of the brain. Eur Radiol. 2022;32(3):18131822.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 32

    Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. FSL. Neuroimage. 2012;62(2):782790.

  • 33

    Song Y, Zhang J, Zhang YD, et al. FeAture Explorer (FAE): a tool for developing and comparing radiomics models. PLoS One. 2020;15(8):e0237587.

  • 34

    Wang S, Dai Y, Shen J, Xuan J. Research on expansion and classification of imbalanced data based on SMOTE algorithm. Sci Rep. 2021;11(1):24039.

  • 35

    Gonçalves FG, Alves CAPF, Vossough A. Updates in pediatric malignant gliomas. Top Magn Reson Imaging. 2020;29(2):8394.

  • 36

    Wang W, Wang LM, Lu DH, et al. High-grade gliomas with H3 G34R mutation: a clinicopathological study. Article in Chinese. Zhonghua Bing Li Xue Za Zhi. 2020;49(12):12671271.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 37

    Mohammadi AM, Sullivan TB, Barnett GH, et al. Use of high-field intraoperative magnetic resonance imaging to enhance the extent of resection of enhancing and nonenhancing gliomas. Neurosurgery. 2014;74(4):339-349.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 38

    Jain R, Poisson LM, Gutman D, et al. Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: focus on the nonenhancing component of the tumor. Radiology. 2014;272(2):484493.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 39

    Li Y, Qian Z, Xu K, et al. MRI features predict p53 status in lower-grade gliomas via a machine-learning approach. Neuroimage Clin. 2017;17:306311.

  • 40

    Jakola AS, Zhang YH, Skjulsvik AJ, et al. Quantitative texture analysis in the prediction of IDH status in low-grade gliomas. Clin Neurol Neurosurg. 2018;164:114120.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 41

    Moiseev A, Snopova L, Kuznetsov S, et al. Pixel classification method in optical coherence tomography for tumor segmentation and its complementary usage with OCT microangiography. J Biophotonics. 2018;11(4):e201700072.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 42

    Zhang B, Chang K, Ramkissoon S, et al. Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas. Neuro Oncol. 2017;19(1):109117.

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