The success of volumetric means ADC in predicting MGMT promoter hypermethylation in glioblastomas

Yıl 2023, Cilt: 6 Sayı: 4, 767 - 771, 30.07.2023

Serhat Korkmaz , Emin Demırel

https://doi.org/10.32322/jhsm.1311867

Öz

Aims: This study aimed to investigate the relationship between volumetric mean ADC values and MGMT promoter hypermethylation status in glioblastoma (GB) patients segmented into perilesional edema area, solid tumor area, and necrosis area.
Methods: The 212 GB patients in the University of California San Francisco Preoperative Diffuse Glioma MRI (UCSF-PDGM) dataset were retrieved from the Cancer Imaging Archive (TCIA). The mean volumetric ADC value was calculated in patients with shared segmentation data in the UCSF-PDGM dataset. The difference in mean volumetric ADC value was investigated in patients divided into groups based on MGMT promoter hypermethylation (MGMT+/ MGMT-).
Results: Of the patients in our study, 125 (59.0%) were male. The median age of the patients was 62 years (26-94). MGMT promoter hypermethylation was observed in 152 (71.7%) patients. Mean Survival was calculated as 574.14±345.57 days in the MGMT+ group and 484.68±301.71 days in MGMT- group. According to volumetric mean ADC values, a difference was observed in the solid tumor and perilesional edema areas according to MGMT promoter hypermethylation (p<0.001). In the ROC analysis, the AUC value was calculated as 0.897 for the edema area and 0.812 for the solid tumor area. MGMT+ group could be identified with a cut-off value of >1.14 in ADC measurements from the edema area with 72% sensitivity and 90% specificity. MGMT+ group could be determined with a sensitivity of 88% and specificity of 69% with a cut-off value of >1.01 in ADC measurements from the solid tumor area.
Conclusion: Volumetric ADC measurements from the perilesional edema and solid tumor areas revealed higher ADC values in the MGMT+ group.

Anahtar Kelimeler

Glioblastoma, diffusion magnetic resonance imaging, MGMT hypermethylation, apparent diffusion coefficient (ADC)

Kaynakça

• Vigneswaran K, Neill S, Hadjipanayis CG. Beyond the World Health Organization grading of infiltrating gliomas: advances in the molecular genetics of glioma classification. Ann Transl Med. 2015;3(7):95. doi: 10.3978/j.issn.2305-5839.2015.03.57
• McNamara C, Mankad K, Thust S, et al. 2021 WHO classification of tumours of the central nervous system: a review for the neuroradiologist. Neuroradiology. 2022;64(10):1919–1950.
• Chen R, Smith-Cohn M, Cohen AL, Colman H. Glioma subclassifications and their clinical significance. Neurotherapeutics. 2017;14(2):284–297.
• Haque W, Teh C, Butler EB, Teh BS. Prognostic and predictive impact of MGMT promoter methylation status in high risk grade II glioma. J Neurooncol. 2022;157(1):137–146.
• Wen PY, Macdonald DR, Reardon DA, et al. Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol. 2010;28(11):1963–1972.
• Watanabe M, Tanaka R, Takeda N. Magnetic resonance imaging and histopathology of cerebral gliomas. Neuroradiology. 1992;34:463–469.
• Schaefer PW, Grant PE, Gonzalez RG. Diffusion-weighted MR imaging of the brain. Radiology. 2000;217(2):331–345.
• Yamasaki F, Sugiyama K, Ohtaki M, et al. Glioblastom treated with postoperative radio-chemotherapy: prognostic value of apparent diffusion coefficient at MR imaging. Eur J Radiol. 2010;73(3):532–537.
• Qu J, Qin L, Cheng S, et al. Residual low ADC and high FA at the resection margin correlate with poor chemoradiation response and overall Survival in high-grade glioma patients. Eur J Radiol. 2016;85(3):657–664.
• Murakami R, Sugahara T, Nakamura H, et al. Malignant supratentorial astrocytoma treated with postoperative radiation therapy: prognostic value of pretreatment quantitative diffusion-weighted MR imaging. Radiology. 2007;243(2):493–499.
• Hein PA, Eskey CJ, Dunn JF, Hug EB. Diffusion-weighted imaging in the follow-up of treated high-grade gliomas: tumor recurrence versus radiation injury. Am J Neuroradiol. 2004;25(2):201–209.
• Calabrese E, Villanueva-Meyer JE, Rudie JD, et al. The University of California San Francisco Preoperative Diffuse Glioma MRI Dataset. Radiol Artif Intell. 2022;4(6):e220058.
• Clark K, Vendt B, Smith K, et al. The Cancer Imaging Archive (TCIA): Maintaining and operating a public information repository. J Digit Imaging. 2013;26(6):1045–1057.
• Weller M, Stupp R, Reifenberger G, et al. MGMT promoter methylation in malignant gliomas: ready for personalized medicine? Nat Rev Neurol. 2010;6(1):39–51.
• Gupta A, Omuro AM, Shah AD, et al. Continuing the search for MR imaging biomarkers for MGMT promoter methylation status: conventional and perfusion MRI revisited. Neuroradiology. 2012;54:641–643.
• Darbar A, Waqas M, Enam SF, Mahmood SD. Use of preoperative apparent diffusion coefficients to predict brain tumor grade. Cureus. 2018;10(3).
• Gates EDH, Lin JS, Weinberg JS, et al. Guiding the first biopsy in glioma patients using estimated Ki-67 maps derived from MRI: conventional versus advanced imaging. Neuro-Oncol. 2019;21(4):527–536.
• Wang Q, Zhang J, Xu X, Chen X, Xu B. Diagnostic performance of apparent diffusion coefficient parameters for glioma grading. J Neurooncol. 2018;139(1):61–6.
• Artzi M, Bokstein F, Blumenthal DT, et al. Differentiation between vasogenic-edema versus tumor-infiltrative area in patients with glioblastom during bevacizumab therapy: a longitudinal MRI study. Eur J Radiol. 2014;83(7):1250–1256.
• Ladenhauf VK, Galijasevic M, Kerschbaumer J, et al. Peritumoral ADC values correlate with the MGMT methylation status in patients with glioblastom. Cancers. 2023;15(5):1384.
• Xie Z, Li J, Zhang Y, et al. The diagnostic value of ADC histogram and direct ADC measurements for coexisting isocitrate dehydrogenase mutation and O6-methylguanine-DNA methyltransferase promoter methylation in glioma. Front Neurosci [Internet]. 2023 [cited 2023 Apr 2];16. Available from: https://www.frontiersin.org/articles/10.3389/fnins.2022.1099019
• Choi YS, Ahn SS, Kim DW, et al. Incremental prognostic value of ADC histogram analysis over MGMT promoter methylation status in patients with glioblastom. Radiology. 2016;281(1):175–184.
• Demirel E, Özer Gökaslan Ç, Dilek O, Ozdemir C, Boyaci M, Korkmaz S. Differential diagnosis of glioblastom and solitary brain metastasis – the success of artificial intelligence models created with radiomics data obtained by automatic segmentation from conventional MRI sequences. Čes Slov Neurol Neurochir. 2022;84:541.