Advanced Radiological Diagnosis and Malignant Transformation Radiology in Gliomas
Onur AKÇAa
aAnkara Güven Hospital, Clinic of Radiology, Ankara, Türkiye
Akça O. Advanced radiological diagnosis and malignant transformation radiology in gliomas. In: Uğur HÇ, Bayatlı E, eds. Glial Tumours: Expectations from Today-Promises of the Future. 1st ed. Ankara: Türkiye Klinikleri; 2024. p.24-32.
ABSTRACT
Primary brain tumours are a complex group, both histologically and radiologically—a het- erogeneous family that may resemble each other in radiologic appearance but differ in clinical behaviour. Although various classification methods have been developed historically, the latest classification pub- lished under the auspices of the World Health Organization separates this heterogeneous group accord- ing to their genetic and molecular bases. With this distinction, there have been major changes in radiology. Rather than focusing solely on tumour size and progression, radiological examinations have been developed to help devise personalized treatments that can predict progression. As a result, the data expected from MR perfusion, spectroscopy, and diffusion examinations-which have been used clinically for a long time-have evolved, and the era of radiological differentiation of tumours with different genetic bases has begun. Today, in addition to classical MR imaging, spectroscopy, perfusion, and diffusion imaging, radiomics, radiogenomics, and artificial intelligence applications can help us determine the ge- netic subtypes of gliomas based on new classifications and make predictions about their progression.
Keywords: Magnetic resonance spectroscopy; perfusion imaging; glioma; diffusion magnetic resonance imaging
Citation
Referanslar
- whobluebooks.iarc.fr. Objective and Historical perspective. 2024 [cited 2024 06/07]; Available from: [Link]
- Leu K, Ott GA, Lai A, Nghiemphu PL, Pope WB, Yong WH, et al. Perfusion and diffusion MRI signatures in histologic and genetic subtypes of WHO grade II-III diffuse gliomas. J Neurooncol. 2017;134(1):177-88. [Crossref] [PubMed] [PMC]
- Johnson DR, Giannini C, Vaubel RA, Morris JM, Eckel LJ, Kaufmann TJ, et al. A Radiologist's Guide to the 2021 WHO Central Nervous System Tumor Classification: Part I-Key Concepts and the Spectrum of Diffuse Gliomas. Radiology. 2022;304(3):494-508. Erratum in: Radiology. 2023;306(2):e229036. [Crossref] [PubMed]
- Oz G, Alger JR, Barker PB, Bartha R, Bizzi A, Boesch C, et al.; MRS Consensus Group. Clinical proton MR spectroscopy in central nervous system disorders. Radiology. 2014;270(3):658-79. [Crossref] [PubMed] [PMC]
- Ellingson BM, Bendszus M, Boxerman J, Barboriak D, Erickson BJ, Smits M, et al.; Jumpstarting Brain Tumor Drug Development Coalition Imaging Standardization Steering Committee. Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials. Neuro Oncol. 2015;17(9):1188-98. [PubMed]
- Pant I, Chaturvedi S, Jha DK, Kumari R, Parteki S. Central nervous system tumors: Radiologic pathologic correlation and diagnostic approach. J Neurosci Rural Pract. 2015;6(2):191-7. [Crossref] [PubMed] [PMC]
- Park YW, Vollmuth P, Foltyn-Dumitru M, Sahm F, Ahn SS, Chang JH, et al. The 2021 WHO Classification for Gliomas and Implications on Imaging Diagnosis: Part 1-Key Points of the Fifth Edition and Summary of Imaging Findings on Adult-Type Diffuse Gliomas. J Magn Reson Imaging. 2023;58(3):677-89. [Crossref] [PubMed]
- Al-Okaili RN, Krejza J, Wang S, Woo JH, Melhem ER. Advanced MR imaging techniques in the diagnosis of intraaxial brain tumors in adults. Radiographics. 2006;26 Suppl 1:S173-89. [Crossref] [PubMed]
- Choi C, Ganji SK, DeBerardinis RJ, Hatanpaa KJ, Rakheja D, Kovacs Z, et al. 2-hydroxyglutarate detection by magnetic resonance spectroscopy in IDH-mutated patients with gliomas. Nat Med. 2012;18(4):624-9. [Crossref] [PubMed] [PMC]
- Shiroishi MS, Boxerman JL, Pope WB. Physiologic MRI for assessment of response to therapy and prognosis in glioblastoma. Neuro Oncol. 2016;18(4):467-78. [Crossref] [PubMed] [PMC]
- Yoo RE, Yun TJ, Hwang I, Hong EK, Kang KM, Choi SH, et al. Arterial spin labeling perfusion-weighted imaging aids in prediction of molecular biomarkers and survival in glioblastomas. Eur Radiol. 2020;30(2):1202-11. [Crossref] [PubMed]
- Mao J, Deng D, Yang Z, Wang W, Cao M, Huang Y, et al. Pretreatment structural and arterial spin labeling MRI is predictive for p53 mutation in high-grade gliomas. Br J Radiol. 2020;93(1115):20200661. [Crossref] [PubMed] [PMC]
- Flies CM, Snijders TJ, Van Seeters T, Smits M, De Vos FYF, Hendrikse J, et al. Perfusion imaging with arterial spin labeling (ASL)-MRI predicts malignant progression in low‑grade (WHO grade II) gliomas. Neuroradiology. 2021;63(12):2023-33. [Crossref] [PubMed] [PMC]
- Seo HS, Chang KH, Na DG, Kwon BJ, Lee DH. High b-value diffusion (b = 3000 s/mm2) MR imaging in cerebral gliomas at 3T: visual and quantitative comparisons with b = 1000 s/mm2. AJNR Am J Neuroradiol. 2008;29(3):458-63. [Crossref] [PubMed] [PMC]
- Hirschler L, Sollmann N, Schmitz-Abecassis B, Pinto J, Arzanforoosh F, Barkhof F, et al. Advanced MR Techniques for Preoperative Glioma Characterization: Part 1. J Magn Reson Imaging. 2023;57(6):1655-75. Erratum in: J Magn Reson Imaging. 2024;59(4):1467. [Crossref] [PubMed] [PMC]
- Haacke EM, Xu Y, Cheng YC, Reichenbach JR. Susceptibility weighted imaging (SWI). Magn Reson Med. 2004;52(3):612-8. [Crossref] [PubMed]
- Halefoglu AM, Yousem DM. Susceptibility weighted imaging: Clinical applications and future directions. World J Radiol. 2018;10(4):30-45. [Crossref] [PubMed] [PMC]
- Kong LW, Chen J, Zhao H, Yao K, Fang SY, Wang Z, et al. Intratumoral Susceptibility Signals Reflect Biomarker Status in Gliomas. Sci Rep. 2019;9(1):17080. [Crossref] [PubMed] [PMC]
- van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B. Radiomics in medical imaging-"how-to" guide and critical reflection. Insights Imaging. 2020; 11(1):91. [Crossref] [PubMed] [PMC]
- Singh G, Manjila S, Sakla N, True A, Wardeh AH, Beig N, et al. Radiomics and radiogenomics in gliomas: a contemporary update. Br J Cancer. 2021;125(5):641-57. [Crossref] [PubMed] [PMC]
- Suh HB, Choi YS, Bae S, Ahn SS, Chang JH, Kang SG, et al. Primary central nervous system lymphoma and atypical glioblastoma: Differentiation using radiomics approach. Eur Radiol. 2018;28(9):3832-9. [Crossref] [PubMed]
- Suh CH, Kim HS, Jung SC, Choi CG, Kim SJ. Clinically Relevant Imaging Features for MGMT Promoter Methylation in Multiple Glioblastoma Studies: A Systematic Review and Meta-Analysis. AJNR Am J Neuroradiol. 2018;39(8):1439-45. [Crossref] [PubMed]
- Doniselli FM, Pascuzzo R, Mazzi F, Padelli F, Moscatelli M, Akinci D'Antonoli T, et al. Quality assessment of the MRI-radiomics studies for MGMT promoter methylation prediction in glioma: a systematic review and meta-analysis. Eur Radiol. 2024;34(9):5802-15. [Crossref] [PubMed] [PMC]
- Svensson SF, Halldórsson S, Latysheva A, Fuster-Garcia E, Hjørnevik T, Fraser-Green J, et al. MR elastography identifies regions of extracellular matrix reorganization associated with shorter survival in glioblastoma patients. Neurooncol Adv. 2023;5(1):vdad021. [Crossref] [PubMed] [PMC]
- Pepin KM, McGee KP, Arani A, Lake DS, Glaser KJ, Manduca A, et al. MR Elastography Analysis of Glioma Stiffness and IDH1-Mutation Status. AJNR Am J Neuroradiol. 2018;39(1):31-6. [Crossref] [PubMed] [PMC]
- Fløgstad Svensson S, Fuster-Garcia E, Latysheva A, Fraser-Green J, Nordhøy W, Isam Darwish O, et al. Decreased tissue stiffness in glioblastoma by MR elastography is associated with increased cerebral blood flow. Eur J Radiol. 2022;147:110136. [Crossref] [PubMed]
- Schregel K, Nowicki MO, Palotai M, Nazari N, Zane R, Sinkus R, et al. Magnetic Resonance Elastography reveals effects of anti-angiogenic glioblastoma treatment on tumor stiffness and captures progression in an orthotopic mouse model. Cancer Imaging. 2020;20(1):35. [Crossref] [PubMed] [PMC]
- Macdonald DR, Cascino TL, Schold SC Jr, Cairncross JG. Response criteria for phase II studies of supratentorial malignant glioma. J Clin Oncol. 1990;8(7):1277-80. [Crossref] [PubMed]
- Wen PY, Macdonald DR, Reardon DA, Cloughesy TF, Sorensen AG, Galanis E, et al. Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol. 2010;28(11):1963-72. [Crossref] [PubMed]
- Okada H, Weller M, Huang R, Finocchiaro G, Gilbert MR, Wick W, et al. Immunotherapy response assessment in neuro-oncology: a report of the RANO working group. Lancet Oncol. 2015;16(15):e534-e42. [Crossref] [PubMed]
- Zhang J, Liu H, Tong H, Wang S, Yang Y, Liu G, et al. Clinical Applications of Contrast-Enhanced Perfusion MRI Techniques in Gliomas: Recent Advances and Current Challenges. Contrast Media Mol Imaging. 2017;2017:7064120. [Crossref] [PubMed] [PMC]
- Baek HJ, Kim HS, Kim N, Choi YJ, Kim YJ. Percent change of perfusion skewness and kurtosis: a potential imaging biomarker for early treatment response in patients with newly diagnosed glioblastomas. Radiology. 2012;264(3):834-43. [Crossref] [PubMed]
- Matsusue E, Fink JR, Rockhill JK, Ogawa T, Maravilla KR. Distinction between glioma progression and post-radiation change by combined physiologic MR imaging. Neuroradiology. 2010;52(4):297-306. [Crossref] [PubMed]
- Jakola AS, Bouget D, Reinertsen I, Skjulsvik AJ, Sagberg LM, Bø HK, et al. Spatial distribution of malignant transformation in patients with low-grade glioma. J Neurooncol. 2020;146(2):373-80. [Crossref] [PubMed] [PMC]
- Satar Z, Hotton G, Samandouras G. Systematic review-Time to malignant transformation in low-grade gliomas: Predicting a catastrophic event with clinical, neuroimaging, and molecular markers. Neurooncol Adv. 2021;3(1):vdab101. [Crossref] [PubMed] [PMC]