Evaluating the efficacy of advanced imaging techniques in differentiating histological and molecular glioblastomas from adult diffuse gliomas: PWI,DWI,and MRS

Abstract

Background

The fifth edition of the World Health Organization (WHO) CNS Tumors (CNS5) introduced a molecular framework for glioma classification, emphasizing the IDH gene and MGMT methylation status.

Purpose

To evaluate the effectiveness of dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI), diffusion-weighted imaging (DWI), and multi-voxel magnetic resonance spectroscopy (MRS) in distinguishing histological glioblastomas (GBMhis) and molecular glioblastomas (GBMmol) from adult diffuse gliomas, while also differentiating oligodendrogliomas (ODGs) and assessing the impact of MGMT methylation.

Material and Methods

We conducted a retrospective analysis of 141 adult diffuse glioma patients. Imaging techniques included DSC-PWI, DWI, and MRS, analyzed for their ability to differentiate GBMhis, GBMmol, and ODGs from adult diffuse gliomas. Pathological and molecular data, including IDH, 1p19q, and MGMT status, were collected to correlate imaging findings with prognostic outcomes.

Results

DSC-PWI and DWI effectively distinguished GBMhis from diffuse gliomas. DWI-ADC was the only technique capable of identifying ODGs. Although MGMT methylation positively impacted prognosis, it was not directly reflected in imaging parameters. Significant differences in progression-free survival and overall survival were observed between groupings.

Conclusion

This study suggests that DSC-PWI and DWI can help differentiate glioma types, while multi-voxel MRS shows limited sensitivity.

Keywords

Glioblastoma isocitrate dehydrogenase O6-methylguanine-DNA methyltransferase perfusion-weighted imaging diffusion-weighted imaging magnetic resonance spectroscopy

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References

Horbinski

Berger

Packer

, et al. Clinical implications of the 2021 edition of the WHO classification of central nervous system tumours. Nat Rev Neurol 2022;18:515–529.

Wen

Packer

. The 2021 WHO classification of tumors of the central nervous system: clinical implications. Neuro Oncol 2021;23:1215–1217.

Claus

Verhaak

. Targeting IDH in low-grade glioma. N Engl J Med 2023;389:655–659.

Kinslow

Mercurio

Kumar

, et al. Association of MGMT promoter methylation with survival in low-grade and anaplastic gliomas after alkylating chemotherapy. JAMA Oncol 2023;9:919–927.

Schiff

. Headway against brain tumors with molecular targeting of IDH-mutant gliomas. N Engl J Med 2023;389:653–654.

Batchelor

Walsh

. Understanding the genetic risk of IDH-mutant glioma. N Engl J Med 2023;388:1332–1334.

Mishchenko

Turubanova

Gorshkova

, et al. Targeting immunogenic cell death for glioma immunotherapy. Trends Cancer 2024;10:8–11.

Jaspers

Taal

van Norden

, et al. Early and late contrast enhancing lesions after photon radiotherapy for IDH mutated grade 2 diffuse glioma. Radiother Oncol. 2023;184:109674.

Vander Heiden

Cantley

Thompson

. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 2009;324:1029–1033.

10.

Dagher

Liu

H-L

Ozkara

, et al. The impact of MRI-based advanced neuroimaging on neurooncologists’ clinical decision-making in patients with posttreatment high-grade glioma: a prospective survey-based study. AJR Am J Roentgenol 2024;223:e2431595.

11.

Zhang H-W, Lyu G-W, He W-J, et al. Differential diagnosis of central lymphoma and high-grade glioma: dynamic contrast-enhanced histogram. Acta Radiol 2020;61:1221–1227.

12.

Zhang H-W, Liu X-L, Zhang H-B, et al. Differentiation of meningiomas and gliomas by amide proton transfer imaging: a preliminary study of brain tumour infiltration. Front Oncol 2022;12:886968.

13.

Guerin

Kaufmann

Eckel

, et al. A radiologist’s guide to the 2021 WHO central nervous system tumor classification: part 2—newly described and revised tumor types. Radiology 2023;307:e221885.

14.

Kawaguchi

Sonoda

Shibahara

, et al. Impact of gross total resection in patients with WHO grade III glioma harboring the IDH 1/2 mutation without the 1p/19q co-deletion. J Neurooncol 2016;129:505–514.

15.

Pointer

Gatson

NTN

. MGMT Methylation status in grades 2 and 3 gliomas is important, but is it prognostic? JAMA Oncol 2023;9:928–929.

16.

DiNardo

Stein

de Botton

, et al. Durable remissions with ivosidenib in IDH1-mutated relapsed or refractory AML. N Engl J Med 2018;378:2386–2398.

17.

Mellinghoff

Van Den Bent

Blumenthal

, et al. Vorasidenib in IDH1-or IDH2-mutant low-grade glioma. N Engl J Med 2023;389:589–601.

18.

Park

Han

Kim

, et al. MRI Features may predict molecular features of glioblastoma in isocitrate dehydrogenase wild-type lower-grade gliomas. AJNR Am J Neuroradiol 2021;42:448–456.

19.

Chang

Chavarro

Gerstl

, et al. Recurrent glioblastoma—molecular underpinnings and evolving treatment paradigms. Int J Mol Sci 2024;25:6733.

20.

Lin

C-HA

Rhodes

Lin

, et al. Comparative analyses identify molecular signature of MRI-classified SVZ-associated glioblastoma. Cell Cycle 2017;16:765–775.

21.

Koppenol

Bounds

Dang

. Otto Warburg's contributions to current concepts of cancer metabolism. Nat Rev Cancer. 2011;11:325–337.

22.

Bhaduri

Kelly

Lesbats

, et al. Metabolic changes in glioblastomas in response to choline kinase inhibition: in vivo MRS in rodent models. NMR in Biomed 2023;36:e4855.

23.

Zhang H-W, Zhang H-B, Liu X-L, et al. Clinical Assessment of Magnetic Resonance Spectroscopy and Diffusion-Weighted Imaging in Diffuse Glioma: Insights Into Histological Grading and IDH Classification. Can Assoc Radiol J 2024;75:868–877.

24.

Wei

Feng

, et al. Stalled oligodendrocyte differentiation in IDH-mutant gliomas. Genome Med 2023;15:24.

25.

Lee

Chen

Liu

H-L

, et al. Mr perfusion imaging for gliomas. Magn Reson Imaging Clin N Am 2024;32:73–83.

26.

Zhang H-W, He W-J, Lei Y, et al. DSC and DCE histogram analyses of glioma biomarkers, including IDH, MGMT, and TERT, on differentiation and survival. Acad Radiol 2020;27:e263–e271.

27.

Yang

Xing

, et al. Prediction of Ki-67 labeling index, ATRX mutation, and MGMT promoter methylation status in IDH-mutant astrocytoma by morphological MRI, SWI, DWI, and DSC-PWI. Eur Radiol 2023;33:7003–7014.

28.

Ahn

Park

, et al. Association of dynamic susceptibility contrast-and dynamic contrast-enhanced magnetic resonance imaging parameters with molecular marker status in lower-grade gliomas: a retrospective study. Neuroradiol J 2023;36:49–58.

29.

Pei

Guan

Hong

, et al. Radiomic features from dynamic susceptibility contrast perfusion-weighted imaging improve the three-class prediction of molecular subtypes in patients with adult diffuse gliomas. Eur Radiol 2023;33:3455–3466.

30.

Bumes

Fellner

, et al. Validation study for non-invasive prediction of IDH mutation status in patients with glioma using in vivo 1H-magnetic resonance spectroscopy and machine learning. Cancers (Basel) 2022;14:2762.

31.

Wang

Song

, et al. Multimodal-based machine learning strategy for accurate and non-invasive prediction of intramedullary glioma grade and mutation status of molecular markers: a retrospective study. BMC Med 2023;21:198.

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