NEURORADIOLOGY / ORIGINAL PAPER
Diffusion imaging in gliomas: how ADC values forecast glioma genetics
1
Department of Radiology, 10th Military Research Hospital and Polyclinic, Bydgoszcz, Poland
2
Department of Clinical Medicine, Faculty of Medicine, University of Science and Technology, Bydgoszcz, Poland
3
Department of Neurosurgery, 10th Military Research Hospital and Polyclinic, Bydgoszcz, Poland
4
Department of Internal Diseases, 10th Military Clinical Hospital and Polyclinic, Bydgoszcz, Poland
5
Laboratory of Clinical Genetics and Molecular Pathology, Department of Medical Analytics, 10th Military Research Hospital and Polyclinic, Bydgoszcz, Poland
6
Faculty of Medicine, Bydgoszcz University of Science and Technology, Bydgoszcz, Poland
Submission date: 2025-02-05
Acceptance date: 2025-02-07
Publication date: 2025-02-20
Corresponding author
Paulina Śledzińska-Bebyn
Department of Radiology, 10th Military Research Hospital and Polyclinic, 5 Powstańców Warszawy St., 85-681 Bydgoszcz, Poland
Department of Radiology, 10th Military Research Hospital and Polyclinic, 5 Powstańców Warszawy St., 85-681 Bydgoszcz, Poland
Pol J Radiol, 2025; 90: 103-113
KEYWORDS
TOPICS
ABSTRACT
Purpose:
This study investigates the relationship between diffusion-weighted imaging (DWI) and mean apparent diffusion coefficient (ADC) values in predicting the genetic and molecular features of gliomas. The goal is to enhance non-invasive diagnostic methods and support personalised treatment strategies by clarifying the association between imaging biomarkers and tumour genotypes.
Material and methods:
A total of 91 glioma patients treated between August 2023 and March 2024 were included in the analysis. All patients underwent preoperative magnetic resonance imaging (MRI), including DWI, and had available histopathological and genetic test results. Clinical data, tumour characteristics, and genetic markers such as IDH1 mutation, MGMT promoter methylation, EGFR amplification, TERT pathogenic variant, and CDKN2A deletion were collected. Statistical analysis was performed to identify correlations between ADC values, MRI perfusion parameters, and genetic characteristics.
Results:
Significant associations were found between lower ADC values and aggressive tumour features, including IDH1-wildtype, MGMT unmethylated status, TERT pathogenic variant, and EGFR amplification. Additionally, distinct ADC patterns were observed in gliomas with CDKN2A, TP53, and PTEN gene deletions. These findings were further supported by contrast enhancement and other MRI parameters, indicating their role in tumour characterisation.
Conclusions:
DWI and ADC measurements demonstrate strong potential as non-invasive tools for predicting glioma genetics. These imaging biomarkers can aid in tumour characterisation and provide valuable insights for guiding personalised treatment strategies.
This study investigates the relationship between diffusion-weighted imaging (DWI) and mean apparent diffusion coefficient (ADC) values in predicting the genetic and molecular features of gliomas. The goal is to enhance non-invasive diagnostic methods and support personalised treatment strategies by clarifying the association between imaging biomarkers and tumour genotypes.
Material and methods:
A total of 91 glioma patients treated between August 2023 and March 2024 were included in the analysis. All patients underwent preoperative magnetic resonance imaging (MRI), including DWI, and had available histopathological and genetic test results. Clinical data, tumour characteristics, and genetic markers such as IDH1 mutation, MGMT promoter methylation, EGFR amplification, TERT pathogenic variant, and CDKN2A deletion were collected. Statistical analysis was performed to identify correlations between ADC values, MRI perfusion parameters, and genetic characteristics.
Results:
Significant associations were found between lower ADC values and aggressive tumour features, including IDH1-wildtype, MGMT unmethylated status, TERT pathogenic variant, and EGFR amplification. Additionally, distinct ADC patterns were observed in gliomas with CDKN2A, TP53, and PTEN gene deletions. These findings were further supported by contrast enhancement and other MRI parameters, indicating their role in tumour characterisation.
Conclusions:
DWI and ADC measurements demonstrate strong potential as non-invasive tools for predicting glioma genetics. These imaging biomarkers can aid in tumour characterisation and provide valuable insights for guiding personalised treatment strategies.
REFERENCES (25)
1.
Weller M, Wick W, Aldape K, Brada M, Berger M, Pfister SM, et al. Glioma. Nat Rev Dis Primer 2015; 1: 15017. DOI: https://doi.org/10.1038/nrdp.2....
2.
Hagmann P, Jonasson L, Maeder P, Thiran JP, Wedeen VJ, Meuli R Understanding diffusion MR imaging techniques: from scalar diffusion-weighted imaging to diffusion tensor imaging and beyond. Radiogr Rev Publ Radiol Soc N Am Inc 2006; 26 Suppl 1: S205-S223. DOI: https://doi.org/10.1148/rg.26s....
3.
Zhang L, Min Z, Tang M, Chen S, Lei X, Zhang X. The utility of diffusion MRI with quantitative ADC measurements for differentiating high-grade from low-grade cerebral gliomas: evidence from a meta-analysis. J Neurol Sci 2017; 373: 9-15.
4.
Xing Z, Yang X, She D, Lin Y, Zhang Y, Cao D. Noninvasive assessment of IDH mutational status in World Health Organization grade II and III astrocytomas using DWI and DSC-PWI combined with conventional MR imaging. Am J Neuroradiol 2017; 38: 1138-1144.
5.
Śledzińska P, Bebyn MG, Furtak J, Kowalewski J, Lewandowska MA. Prognostic and predictive biomarkers in gliomas. Int J Mol Sci 2021; 22: 10373. DOI: https://doi.org/10.3390/ijms22....
6.
Śledzińska P, Bebyn M, Szczerba E, Furtak J, Harat M, Olszewska N, et al. Glioma 2021 WHO classification: the superiority of NGS over IHC in routine diagnostics. Mol Diagn Ther 2022; 26: 699-713.
7.
VASARI Research Project – The Cancer Imaging Archive (TCIA) Public Access – Cancer Imaging Archive Wiki n.d. Available at: https://wiki.cancerimagingarch... (Accessed: 10.08.2024).
8.
Louis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella-Branger D, et al. The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro Oncol 2021; 23: 1231-1251.
9.
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: 177-188.
10.
Thust SC, Hassanein S, Bisdas S, Rees JH, Hyare H, Maynard JA, et al. Apparent diffusion coefficient for molecular subtyping of non-gadolinium-enhancing WHO grade II/III glioma: volumetric segmentation versus two-dimensional region of interest analysis. Eur Radiol 2018; 28: 3779-3788.
11.
Maynard J, Okuchi S, Wastling S, Busaidi AA, Almossawi O, Mbatha W, et al. World Health Organization grade II/III glioma molecular status: prediction by MRI morphologic features and apparent diffusion coefficient. Radiology 2020; 296: 111-121.
12.
Kim M, Jung SY, Park JE, Jo Y, Park SY, Nam SJ, et al. Diffusion- and perfusion-weighted MRI radiomics model may predict isocitrate dehydrogenase (IDH) mutation and tumor aggressiveness in diffuse lower grade glioma. Eur Radiol 2020; 30: 2142-2151.
13.
Romano A, Calabria LF, Tavanti F, Minniti G, Rossi-Espagnet MC, Coppola V, et al. Apparent diffusion coefficient obtained by magnetic resonance imaging as a prognostic marker in glioblastomas: correlation with MGMT promoter methylation status. Eur Radiol 2013; 23: 513-520.
14.
Moon WJ, Choi JW, Roh HG, Lim SD, Koh YC. Imaging parameters of high grade gliomas in relation to the MGMT promoter methylation status: the CT, diffusion tensor imaging, and perfusion MR imaging. Neuroradiology 2012; 54: 555-563.
15.
Pope WB, Lai A, Mehta R, Kim HJ, Qiao J, Young JR, et al. Apparent diffusion coefficient histogram analysis stratifies progression-free survival in newly diagnosed bevacizumab-treated glioblastoma. AJNR Am J Neuroradiol 2011; 32: 882-889.
16.
Ellingson BM. Radiogenomics and imaging phenotypes in glioblastoma: novel observations and correlation with molecular characteristics. Curr Neurol Neurosci Rep 2015; 15: 506. DOI: https://doi.org/10.1007/s11910....
17.
Hegi ME, Diserens AC, Gorlia T, Hamou MF, de Tribolet N, Weller M, et al. MGMT gene silencing and benefit from temozolomide in glioblastoma. N Engl J Med 2005; 352: 997-1003.
18.
Weller M, Stupp R, Reifenberger G, Brandes AA, van den Bent MJ, Wick W, et al. MGMT promoter methylation in malignant gliomas: ready for personalized medicine? Nat Rev Neurol 2010; 6: 39-51.
19.
Aibaidula A, Chan AKY, Shi Z, Li Y, Zhang R, Yang R, et al. Adult IDH wild-type lower-grade gliomas should be further stratified. Neuro Oncol 2017; 19: 1327-1337.
20.
Brat DJ, Aldape K, Colman H, Holland EC, Louis DN, Jenkins RB, 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 (Berl) 2018; 136: 805-810.
21.
Park YW, Park JE, Ahn SS, Kim EH, Kang SG, Chang JH, et al. Magnetic resonance imaging parameters for noninvasive prediction of epidermal growth factor receptor amplification in isocitrate dehydrogenase-wild-type lower-grade gliomas: a multicenter study. Neurosurgery 2021; 89: 257-265.
22.
Appay R, Dehais C, Maurage CA, Alentorn A, Carpentier C, Colin C, et al. CDKN2A homozygous deletion is a strong adverse prognosis factor in diffuse malignant IDH-mutant gliomas. Neuro Oncol 2019; 21: 1519-1528.
23.
Park YW, Park KS, Park JE, Ahn SS, Park I, Kim HS, et al. Qualitative and quantitative magnetic resonance imaging phenotypes may predict CDKN2A/B homozygous deletion status in isocitrate dehydrogenase-mutant astrocytomas: a multicenter study. Korean J Radiol 2023; 24: 133-144.
24.
Chiang GC, Pisapia DJ, Liechty B, Magge R, Ramakrishna R, Knisely J, et al. The prognostic value of MRI subventricular zone involvement and tumor genetics in lower grade gliomas. J Neuroimaging 2020; 30: 901-909.
25.
Chen L, Chen R, Li T, Huang L, Tang C, Li Y, et al. MRI radiomics model for predicting TERT promoter mutation status in glioblastoma. Brain Behav 2023; 13: e3324. DOI: https://doi.org/10.1002/brb3.3....
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
