Peritumoral ADC Values Correlate with the MGMT Methylation Status in Patients with Glioblastoma.
Valentin Karl LadenhaufMalik GalijaševićJohannes KerschbaumerChristian Franz FreyschlagMartha NowosielskiAnna Maria Birkl-ToeglhoferJohannes HaybaeckElke Ruth GizewskiStephanie MangesiusAstrid Ellen GramsPublished in: Cancers (2023)
Different results have been reported concerning the relationship of the apparent diffusion coefficient (ADC) values and the status of methylation as the promoter gene for the enzyme methylguanine-DNA methyltransferase (MGMT) in patients with glioblastomas (GBs). The aim of this study was to investigate if there were correlations between the ADC values of the enhancing tumor and peritumoral areas of GBs and the MGMT methylation status. In this retrospective study, we included 42 patients with newly diagnosed unilocular GB with one MRI study prior to any treatment and histopathological data. After co-registration of ADC maps with T1-weighted sequences after contrast administration and dynamic susceptibility contrast (DSC) perfusion, we manually selected one region-of-interest (ROI) in the enhancing and perfused tumor and one ROI in the peritumoral white matter. Both ROIs were mirrored in the healthy hemisphere for normalization. In the peritumoral white matter, absolute and normalized ADC values were significantly higher in patients with MGMT-unmethylated tumors, as compared to patients with MGMT-methylated tumors (absolute values p = 0.002, normalized p = 0.0007). There were no significant differences in the enhancing tumor parts. The ADC values in the peritumoral region correlated with MGMT methylation status, confirmed by normalized ADC values. In contrast to other studies, we could not find a correlation between the ADC values or the normalized ADC values and the MGMT methylation status in the enhancing tumor parts.
Keyphrases
- diffusion weighted imaging
- contrast enhanced
- diffusion weighted
- white matter
- magnetic resonance imaging
- dna methylation
- magnetic resonance
- genome wide
- computed tomography
- newly diagnosed
- gene expression
- multiple sclerosis
- machine learning
- big data
- electronic health record
- cell free
- combination therapy
- deep learning
- circulating tumor