Advances in diffuse glial tumors diagnosis

Abstract

In recent decades, there have been significant advances in the diagnosis of diffuse gliomas, driven by the integration of novel technologies. These advancements have deepened our understanding of tumor oncogenesis, enabling a more refined stratification of the biological behavior of these neoplasms. This progress culminated in the fifth edition of the WHO classification of central nervous system (CNS) tumors in 2021. This comprehensive review article aims to elucidate these advances within a multidisciplinary framework, contextualized within the backdrop of the new classification. This article will explore morphologic pathology and molecular/genetics techniques (immunohistochemistry, genetic sequencing, and methylation profiling), which are pivotal in diagnosis, besides the correlation of structural neuroimaging radiophenotypes to pathology and genetics. It briefly reviews the usefulness of tractography and functional neuroimaging in surgical planning. Additionally, the article addresses the value of other functional imaging techniques such as perfusion MRI, spectroscopy, and nuclear medicine in distinguishing tumor progression from treatment-related changes. Furthermore, it discusses the advantages of evolving diagnostic techniques in classifying these tumors, as well as their limitations in terms of availability and utilization. Moreover, the expanding domains of data processing, artificial intelligence, radiomics, and radiogenomics hold great promise and may soon exert a substantial influence on glioma diagnosis. These innovative technologies have the potential to revolutionize our approach to these tumors. Ultimately, this review underscores the fundamental importance of multidisciplinary collaboration in employing recent diagnostic advancements, thereby hoping to translate them into improved quality of life and extended survival for glioma patients.

Resumo

Nas últimas décadas, houve avanços significativos no diagnóstico de gliomas difusos, impulsionados pela integração de novas tecnologias. Esses avanços aprofundaram nossa compreensão da oncogênese tumoral, permitindo uma estratificação mais refinada do comportamento biológico dessas neoplasias. Esse progresso culminou na quinta edição da classificação da OMS de tumores do sistema nervoso central (SNC) em 2021. Esta revisão abrangente tem como objetivo elucidar esses avanços de forma multidisciplinar, no contexto da nova classificação. Este artigo irá explorar a patologia morfológica e as técnicas moleculares/genéticas (imuno-histoquímica, sequenciamento genético e perfil de metilação), que são fundamentais no diagnóstico, além da correlação dos radiofenótipos da neuroimagem estrutural com a patologia e a genética. Aborda sucintamente a utilidade da tractografia e da neuroimagem funcional no planejamento cirúrgico. Destacaremos o valor de outras técnicas de imagem funcional, como ressonância magnética de perfusão, espectroscopia e medicina nuclear, na distinção entre a progressão do tumor e as alterações relacionadas ao tratamento. Discutiremos as vantagens das diferentes técnicas de diagnóstico na classificação desses tumores, bem como suas limitações em termos de disponibilidade e utilização. Além disso, os crescentes avanços no processamento de dados, inteligência artificial, radiômica e radiogenômica têm grande potencial e podem em breve exercer uma influência substancial no diagnóstico de gliomas. Essas tecnologias inovadoras têm o potencial de revolucionar nossa abordagem a esses tumores. Em última análise, esta revisão destaca a importância fundamental da colaboração multidisciplinar na utilização dos recentes avanços diagnósticos, com a esperança de traduzi-los em uma melhor qualidade de vida e uma maior sobrevida.

Keywords

Glioma - Neuroimaging - Biomarkers - Genetic Profile - Pathology - Precision Medicine - Machine Learning

Palavras-chave

Glioma - Neuroimagem - Biomarcadores - Perfil Genético - Patologia - Medicina de Precisão - Aprendizado de Máquina

Authors' Contributions

LFSG, VRP: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data, study concept or design; ASA, GAB, RAM, FCCH, FABS, FN, GCCN, AFG, LTL: drafting/revision of the manuscript for content, including medical writing for content; EAJ: drafting/revision of the manuscript for content, including medical writing for content, major role in the acquisition of data; RJY: drafting/revision of the manuscript for content, including medical writing for content; SMFM: drafting/revision of the manuscript for content, including medical writing for content; major role in the study concept or design.

Publikationsverlauf

Eingereicht: 06. September 2023

Angenommen: 27. Oktober 2023

Artikel online veröffentlicht:
29. Dezember 2023

• References

• 1 Martin-Villalba A, Okuducu AF, von Deimling A. The evolution of our understanding on glioma. Brain Pathol 2008; 18 (03) 455-463
• 2 Board TWCoTE. WHO classification of tumours series, 5th ed.; vol. 6. In: Board TWCoTE. ed. 5th ed.. International Agency for Research on Cancer (IARC); 2021. (WHO classification of tumours series; )
• 3 Brat DJ, Aldape K, Colman H. et al. cIMPACT-NOW update 5: recommended grading criteria and terminologies for IDH-mutant astrocytomas. Acta Neuropathol 2020; 139 (03) 603-608
• 4 Brat DJ, Aldape K, Colman H. et al. cIMPACT-NOW update 3: recommended diagnostic criteria for “Diffuse astrocytic glioma, IDH-wildtype, with molecular features of glioblastoma, WHO grade IV”. Acta Neuropathol 2018; 136 (05) 805-810
• 5 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 (08) 1231-1251
• 6 Porter AB, Wen PY, Polley MC. Molecular Profiling in Neuro-Oncology: Where We Are, Where We're Heading, and How We Ensure Everyone Can Come Along. Am Soc Clin Oncol Educ Book 2023; 43: e389322
• 7 Wen PY, Packer RJ. The 2021 WHO Classification of Tumors of the Central Nervous System: clinical implications. Neuro-oncol 2021; 23 (08) 1215-1217
• 8 Fabbri VP, Caporalini C, Asioli S, Buccoliero A. Paediatric-type diffuse low-grade gliomas: a clinically and biologically distinct group of tumours with a favourable outcome. Pathologica 2022; 114 (06) 410-421
• 9 Armocida D, Berra LV, Frati A, Santoro A. Radiological and surgical aspects of polymorphous low-grade neuroepithelial tumor of the young (PLNTY). Acta Neurol Belg 2023; 123 (02) 327-340
• 10 Guerreiro Stucklin AS, Ryall S, Fukuoka K. et al. Alterations in ALK/ROS1/NTRK/MET drive a group of infantile hemispheric gliomas. Nat Commun 2019; 10 (01) 4343
• 11 Philteos J, Karmur BS, Mansouri A. MGMT Testing in Glioblastomas: Pitfalls and Opportunities. Am J Clin Oncol 2019; 42 (02) 117-122
• 12 Osborn AG, Louis DN, Poussaint TY, Linscott LL, Salzman KL. The 2021 World Health Organization Classification of Tumors of the Central Nervous System: What Neuroradiologists Need to Know. AJNR Am J Neuroradiol 2022; 43 (07) 928-937
• 13 Petersen JK, Boldt HB, Sørensen MD. et al. Targeted next-generation sequencing of adult gliomas for retrospective prognostic evaluation and up-front diagnostics. Neuropathol Appl Neurobiol 2021; 47 (01) 108-126
• 14 Robinson C, Kleinschmidt-DeMasters BK. IDH1-Mutation in Diffuse Gliomas in Persons Age 55 Years and Over. J Neuropathol Exp Neurol 2017; 76 (02) 151-154
• 15 Lasocki A, Gaillard F, Gorelik A, Gonzales M. MRI Features Can Predict 1p/19q Status in Intracranial Gliomas. AJNR Am J Neuroradiol 2018; 39 (04) 687-692
• 16 Patel SH, Poisson LM, Brat DJ. et al. T2-FLAIR Mismatch, an Imaging Biomarker for IDH and 1p/19q Status in Lower-grade Gliomas: A TCGA/TCIA Project. Clin Cancer Res 2017; 23 (20) 6078-6085
• 17 Batchala PP, Muttikkal TJE, Donahue JH. et al. Neuroimaging-Based Classification Algorithm for Predicting 1p/19q-Codeletion Status in IDH-Mutant Lower Grade Gliomas. AJNR Am J Neuroradiol 2019; 40 (03) 426-432
• 18 Johnson DR, Kaufmann TJ, Patel SH, Chi AS, Snuderl M, Jain R. There is an exception to every rule-T2-FLAIR mismatch sign in gliomas. Neuroradiology 2019; 61 (02) 225-227
• 19 Wagner MW, Nobre L, Namdar K. et al. T2-FLAIR Mismatch Sign in Pediatric Low-Grade Glioma. AJNR Am J Neuroradiol 2023; 44 (07) 841-845
• 20 Juratli TA, Tummala SS, Riedl A. et al. Radiographic assessment of contrast enhancement and T2/FLAIR mismatch sign in lower grade gliomas: correlation with molecular groups. J Neurooncol 2019; 141 (02) 327-335
• 21 van Lent DI, van Baarsen KM, Snijders TJ, Robe PAJT. Radiological differences between subtypes of WHO 2016 grade II-III gliomas: a systematic review and meta-analysis. Neurooncol Adv 2020; 2 (01) vdaa044
• 22 Patel SH, Batchala PP, Muttikkal TJE. et al. Fluid attenuation in non-contrast-enhancing tumor (nCET): an MRI Marker for Isocitrate Dehydrogenase (IDH) mutation in Glioblastoma. J Neurooncol 2021; 152 (03) 523-531
• 23 Li M, Wang J, Chen X. et al. The sinuous, wave-like intratumoral-wall sign is a sensitive and specific radiological biomarker for oligodendrogliomas. Eur Radiol 2023; 33 (06) 4440-4452
• 24 Alexander BM, Cloughesy TF. Adult Glioblastoma. J Clin Oncol 2017; 35 (21) 2402-2409
• 25 Mesny E, Barritault M, Izquierdo C. et al. Gyriform infiltration as imaging biomarker for molecular glioblastomas. J Neurooncol 2022; 157 (03) 511-521
• 26 Nam YK, Park JE, Park SY. et al. Reproducible imaging-based prediction of molecular subtype and risk stratification of gliomas across different experience levels using a structured reporting system. Eur Radiol 2021; 31 (10) 7374-7385
• 27 Jang EB, Kim HS, Park JE. et al. Diffuse glioma, not otherwise specified: imaging-based risk stratification achieves histomolecular-level prognostication. Eur Radiol 2022; 32 (11) 7780-7788
• 28 Da-Veiga MA, Rogister B, Lombard A, Neirinckx V, Piette C. Glioma Stem Cells in Pediatric High-Grade Gliomas: From Current Knowledge to Future Perspectives. Cancers (Basel) 2022; 14 (09) 2296
• 29 Castel D, Philippe C, Calmon R. et al. Histone H3F3A and HIST1H3B K27M mutations define two subgroups of diffuse intrinsic pontine gliomas with different prognosis and phenotypes. Acta Neuropathol 2015; 130 (06) 815-827
• 30 Buczkowicz P, Bartels U, Bouffet E, Becher O, Hawkins C. Histopathological spectrum of paediatric diffuse intrinsic pontine glioma: diagnostic and therapeutic implications. Acta Neuropathol 2014; 128 (04) 573-581
• 31 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 (01) vdaa142
• 32 Schreck KC, Ranjan S, Skorupan N. et al. Incidence and clinicopathologic features of H3 K27M mutations in adults with radiographically-determined midline gliomas. J Neurooncol 2019; 143 (01) 87-93
• 33 Law M, Yang S, Wang H. et al. Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging. AJNR Am J Neuroradiol 2003; 24 (10) 1989-1998
• 34 Kickingereder P, Sahm F, Radbruch A. et al. IDH mutation status is associated with a distinct hypoxia/angiogenesis transcriptome signature which is non-invasively predictable with rCBV imaging in human glioma. Sci Rep 2015; 5: 16238
• 35 van Santwijk L, Kouwenberg V, Meijer F, Smits M, Henssen D. A systematic review and meta-analysis on the differentiation of glioma grade and mutational status by use of perfusion-based magnetic resonance imaging. Insights Imaging 2022; 13 (01) 102
• 36 Le Fèvre C, Constans JM, Chambrelant I. et al. Pseudoprogression versus true progression in glioblastoma patients: A multiapproach literature review. Part 2 - Radiological features and metric markers. Crit Rev Oncol Hematol 2021; 159: 103230
• 37 Kapoor GS, Gocke TA, Chawla S. et al. Magnetic resonance perfusion-weighted imaging defines angiogenic subtypes of oligodendroglioma according to 1p19q and EGFR status. J Neurooncol 2009; 92 (03) 373-386
• 38 Patel P, Baradaran H, Delgado D. et al. MR perfusion-weighted imaging in the evaluation of high-grade gliomas after treatment: a systematic review and meta-analysis. Neuro-oncol 2017; 19 (01) 118-127
• 39 Andronesi OC, Kim GS, Gerstner E. et al. Detection of 2-hydroxyglutarate in IDH-mutated glioma patients by in vivo spectral-editing and 2D correlation magnetic resonance spectroscopy. Sci Transl Med 2012; 4 (116) 116ra4
• 40 Choi C, Ganji SK, DeBerardinis RJ. et al. 2-hydroxyglutarate detection by magnetic resonance spectroscopy in IDH-mutated patients with gliomas. Nat Med 2012; 18 (04) 624-629
• 41 Thomas T, Thakur S, Young R. Imaging 2-hydroxyglutarate and other brain oncometabolites pertinent to critical genomic alterations in brain tumors. BJR Open 2023; 5 (01) 20210070
• 42 de la Fuente MI, Young RJ, Rubel J. et al. Integration of 2-hydroxyglutarate-proton magnetic resonance spectroscopy into clinical practice for disease monitoring in isocitrate dehydrogenase-mutant glioma. Neuro-oncol 2016; 18 (02) 283-290
• 43 Laino ME, Young R, Beal K. et al. Magnetic resonance spectroscopic imaging in gliomas: clinical diagnosis and radiotherapy planning. BJR Open 2020; 2 (01) 20190026
• 44 Law I, Albert NL, Arbizu J. et al. Joint EANM/EANO/RANO practice guidelines/SNMMI procedure standards for imaging of gliomas using PET with radiolabelled amino acids and [¹⁸F]FDG: version 1.0. Eur J Nucl Med Mol Imaging 2019; 46 (03) 540-557
• 45 Henssen D, Leijten L, Meijer FJA. et al. Head-To-Head Comparison of PET and Perfusion Weighted MRI Techniques to Distinguish Treatment Related Abnormalities from Tumor Progression in Glioma. Cancers (Basel) 2023; 15 (09) 2631
• 46 Bello L, Gambini A, Castellano A. et al. Motor and language DTI Fiber Tracking combined with intraoperative subcortical mapping for surgical removal of gliomas. Neuroimage 2008; 39 (01) 369-382
• 47 D'Andrea G, Familiari P, Di Lauro A, Angelini A, Sessa G. Safe Resection of Gliomas of the Dominant Angular Gyrus Availing of Preoperative FMRI and Intraoperative DTI: Preliminary Series and Surgical Technique. World Neurosurg 2016; 87: 627-639
• 48 Essayed WI, Zhang F, Unadkat P, Cosgrove GR, Golby AJ, O'Donnell LJ. White matter tractography for neurosurgical planning: A topography-based review of the current state of the art. Neuroimage Clin 2017; 15: 659-672
• 49 Nadkarni TN, Andreoli MJ, Nair VA. et al. Usage of fMRI for pre-surgical planning in brain tumor and vascular lesion patients: task and statistical threshold effects on language lateralization. Neuroimage Clin 2014; 7: 415-423
• 50 Duffau H. Functional Mapping before and after Low-Grade Glioma Surgery: A New Way to Decipher Various Spatiotemporal Patterns of Individual Neuroplastic Potential in Brain Tumor Patients. Cancers (Basel) 2020; 12 (09) 2611
• 51 Rudie JD, Rauschecker AM, Bryan RN, Davatzikos C, Mohan S. Emerging Applications of Artificial Intelligence in Neuro-Oncology. Radiology 2019; 290 (03) 607-618
• 52 Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology 2016; 278 (02) 563-577
• 53 Singh G, Manjila S, Sakla N. et al. Radiomics and radiogenomics in gliomas: a contemporary update. Br J Cancer 2021; 125 (05) 641-657
• 54 Lohmann P, Franceschi E, Vollmuth P. et al. Radiomics in neuro-oncological clinical trials. Lancet Digit Health 2022; 4 (11) e841-e849
• 55 van Kempen EJ, Post M, Mannil M. et al. Accuracy of Machine Learning Algorithms for the Classification of Molecular Features of Gliomas on MRI: A Systematic Literature Review and Meta-Analysis. Cancers (Basel) 2021; 13 (11) 2606
• 56 Guimaraes AR. Quantitative Imaging in Medicine: Background and Basics. AIPP BOOKS; 2021
• 57 Bizzo BC, Dasegowda G, Bridge C. et al. Addressing the Challenges of Implementing Artificial Intelligence Tools in Clinical Practice: Principles From Experience. J Am Coll Radiol 2023; 20 (03) 352-360
• 58 Kotter E, Ranschaert E. Challenges and solutions for introducing artificial intelligence (AI) in daily clinical workflow. Eur Radiol 2021; 31 (01) 5-7