Diffuse glioma diagnosis [Neurosurgery Wiki]

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====== Diffuse glioma diagnosis ======

===== Biomarkers =====

see [[Glioma biomarker]]s.

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The management of [[glioma]]s is based on precise histologic diagnosis. The tumor tissue can be obtained during open surgery or via [[stereotactic biopsy]]. Intraoperative tissue imaging could substantially improve biopsy precision and, ultimately, the [[extent of resection]].

While a change in tumor classification aims to improve patient management and treatment, it implies the requirement for invasive tissue sampling to the detriment of potentially severe biopsy and/or surgical complications, resulting in a mortality rate of 2.8% 
((Malone H, Yang J, Hershman DL, Wright JD, Bruce JN, Neugut AI. Complications Following Stereotactic Needle Biopsy of Intracranial Tumors. World Neurosurg. 2015 Oct;84(4):1084-9. doi: 10.1016/j.wneu.2015.05.025. Epub 2015 May 22. PMID: 26008141.)).

As a result, there is a high demand for non-invasive tissue analysis to initiate adequate therapy regimens without potential biopsy-associated side effects. In particular, this is true for inoperable tumors that would be solely biopsied. Over the past few years, a number of studies demonstrated the great potential of MRI-based image analyses for the tumor decoding of gliomas
((Haubold J, Demircioglu A, Gratz M, Glas M, Wrede K, Sure U, Antoch G, Keyvani K, Nittka M, Kannengiesser S, Gulani V, Griswold M, Herrmann K, Forsting M, Nensa F, Umutlu L. Non-invasive tumor decoding and phenotyping of cerebral gliomas utilizing multiparametric 18F-FET PET-MRI and MR Fingerprinting. Eur J Nucl Med Mol Imaging. 2020 Jun;47(6):1435-1445. doi: 10.1007/s00259-019-04602-2. Epub 2019 Dec 6. PMID: 31811342.)).

===== Magnetic resonance imaging =====

see [[Glioma magnetic resonance imaging]].
 


===== Radiomics =====
[[Glioma Radiomics]]

===== Histopathology =====


[[Glioma]]s are difficult to classify precisely because of [[interobserver]] variability during histopathologic [[grading]]. Identifying biological signatures of each glioma subtype through protein [[biomarker]] profiling of [[tumor]] or tumor-proximal fluids is therefore of high priority. Such profiling not only may provide clues regarding tumor [[classification]] but may identify clinical biomarkers and pathologic targets for the development of personalized treatments. 

In the past, differential proteomic profiling techniques have utilized tumor, cerebrospinal fluid, and plasma from glioma patients to identify the first candidate diagnostic, prognostic, predictive, and therapeutic response markers, highlighting the potential for glioma biomarker discovery. The number of markers identified, however, has been limited, their reproducibility between studies is unclear, and none have been validated for clinical use. 

Technological advancements in methodologies for high-throughput profiling, which provide easy access, rapid [[screening]], low sample consumption, and accurate [[protein]] identification, are anticipated to accelerate brain tumor biomarker discovery. Reliable tools for biomarker verification forecast translation of the biomarkers into clinical diagnostics in the foreseeable future
((Kalinina J, Peng J, Ritchie JC, Van Meir EG. Proteomics of gliomas: initial
biomarker discovery and evolution of technology. Neuro Oncol. 2011
Sep;13(9):926-42. doi: 10.1093/neuonc/nor078. Review. PubMed PMID: 21852429;
PubMed Central PMCID: PMC3158015.
)).

===== Screening for glioma =====

[[Screening for glioma]]


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Ferreyra Vega et al. investigated the potential utility of [[DNA methylation profiling]] to achieve molecular diagnosis in adult primary diffuse lower-grade glioma (dLGG) according to the [[World Health Organization Classification of Tumors of the Central Nervous System 2016]]. They also evaluated whether [[methylation]] profiling could provide improved molecular characterization and identify prognostic differences beyond the classical histological WHO grade together with [[IDH mutation]] status and [[1p/19q co-deletion]] status. All patients diagnosed with dLGG in the period 2007-2016 from the Västra Götaland region in [[Sweden]] were assessed for inclusion in the study.

A total of 166 dLGG cases were subjected to genome-wide DNA methylation analysis. Of these, 126 (76%) were assigned a defined diagnostic methylation class with a class prediction score ≥ 0.84 and a subclass score ≥ 0.50. The assigned methylation classes were highly associated with their IDH mutation status and 1p/19q codeletion status. IDH-wild-type gliomas were further divided into subgroups with distinct molecular features.

The stratification of the patients by methylation profiling was as effective as the integrated WHO 2016 molecular reclassification at predicting the clinical outcome of the patients. The study shows that DNA methylation profiling is a reliable and robust approach for the classification of dLGG into molecularly defined subgroups, providing accurate detection of molecular markers according to WHO 2016 classification
((Ferreyra Vega S, Olsson Bontell T, Corell A, Smits A, Jakola AS, Carén H. [[DNA methylation profiling]] for molecular classification of adult diffuse lower-grade gliomas. Clin Epigenetics. 2021 May 3;13(1):102. doi: 10.1186/s13148-021-01085-7. PMID: 33941250.)).
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Conventional genetic analyzers require surgically obtained [[tumor]] [[tissue]]s to confirm the [[molecular diagnostics]] of [[diffuse glioma]]. Recent technical breakthroughs have enabled increased utilization of [[circulating tumor DNA]] (ctDNA) in body fluids as a reliable resource for molecular diagnosis in various cancers. 

Fujioka et al. tested the application of a chip-based [[digital PCR]] system for the less invasive diagnosis (i.e., [[liquid biopsy]]) of [[diffuse glioma]] using the [[cerebrospinal fluid]] (CSF).

CSF samples from 34 patients with diffuse glioma were collected from the surgical field during [[craniotomy]]. Preoperative lumbar CSF collection was also performed in 11 patients. Extracted ctDNA was used to analyze diagnostic point mutations in IDH1 R132H, TERT promoter (C228T and C250T), and H3F3A (K27M) on the QuantStudio® 3D Digital PCR System. These results were compared with their corresponding tumor DNA samples.

They detected either of the diagnostic [[mutation]]s in tumor DNA samples from 28 of 34 patients. Among them, they achieved precise molecular diagnoses using intracranial CSF in 20 (71%). Univariate analyses revealed that the World Health Organization (WHO) grade (p = 0.0034), radiographic enhancement (p = 0.0006), and Mib1 index (p = 0.01) were significant predictors of precise CSF-based molecular diagnosis. They precisely diagnosed WHO grade III or IV diffuse gliomas using lumbar CSF obtained from 6 (87%) of 7 patients with tumors harboring any mutation.

Fujioka et al. established a novel, non-invasive [[molecular diagnostics]] using a chip-based [[digital PCR]] system targeting [[circulating tumor DNA]] derived from [[CSF]] with high [[sensitivity]] and [[specificity]], especially for [[high-grade glioma]]s
((Fujioka Y, Hata N, Akagi Y, Kuga D, Hatae R, Sangatsuda Y, Michiwaki Y, Amemiya T, Takigawa K, Funakoshi Y, Sako A, Iwaki T, Iihara K, Mizoguchi M. Molecular diagnosis of diffuse glioma using a chip-based digital PCR system to analyze IDH, TERT, and H3 mutations in the cerebrospinal fluid. J Neurooncol. 2021 Jan 8. doi: 10.1007/s11060-020-03682-7. Epub ahead of print. PMID: 33417137.)).
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[[Apparent diffusion coefficient]] (ADC) values had a better correlation with overall survival than [[relative cerebral blood flow]] (CBV) values. A preoperative prognostic model based on patient age, [[relative cerebral blood volume]], and ADC values predicted overall survival of patients with diffuse gliomas independent of pathology
((Hilario A, Sepulveda JM, Perez-Nuñez A, Salvador E, Millan JM, Hernandez-Lain 
A, Rodriguez-Gonzalez V, Lagares A, Ramos A. A Prognostic Model Based on
Preoperative MRI Predicts Overall Survival in Patients with Diffuse Gliomas. AJNR
Am J Neuroradiol. 2014 Jan 23. [Epub ahead of print] PubMed PMID: 24457819.)).