Predicting MGMT Promoter Methylation of Glioblastoma from Dynamic Susceptibility Contrast Perfusion: A Radiomic Approach
Autor: | Girolamo Crisi, Silvano Filice |
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Rok vydání: | 2020 |
Předmět: |
Adult
Male Oncology medicine.medical_specialty Methyltransferase Contrast Media 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Internal medicine Promoter methylation medicine Humans Radiology Nuclear Medicine and imaging Promoter Regions Genetic DNA Modification Methylases Aged Retrospective Studies medicine.diagnostic_test Brain Neoplasms business.industry Tumor Suppressor Proteins Significant difference Magnetic resonance imaging DNA Methylation Middle Aged medicine.disease Magnetic Resonance Imaging Perfusion DNA Repair Enzymes Cerebral blood flow Female Neurology (clinical) Glioblastoma business 030217 neurology & neurosurgery Dynamic susceptibility |
Zdroj: | Journal of Neuroimaging. 30:458-462 |
ISSN: | 1552-6569 1051-2284 |
Popis: | Background and purpose This study aims to investigate whether radiomic quantitative image features (IFs) from perfusion dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) retain sufficient strength to predict O6-methylguanine-DNA methyltransferase promoter methylation (MGMT_pm) in newly diagnosed glioblastoma (GB) patients. Methods We retrospectively reviewed the perfusion DSC-MRI of 59 patients with GB. Patients were classified into three groups: (1) unmethylated if MGMT_pm ≤ 9% (UM); (2) intermediate-methylated if MGMT_pm ranged between 10% and 29% (IM); (3) methylated if MGMT_pm ≥ 30% (M). A total of 92 quantitative IFs were obtained from relative cerebral blood volume and relative cerebral blood flow maps. The Mann-Whitney U-test was applied to assess whether there were statistical differences in IFs between patient groups. Those IFs showing significant difference between two patient groups were termed relevant IFs (rIFs). rIFs were uploaded to a machine learning model to predict the MGMT_pm. Results No rIFs were found between UM and IM groups. Fourteen rIFs were found among UM-M, IM-M, and (UM + IM)-M groups. We built a multilayer perceptron deep learning model that classified patients as belonging to UM + IM and M group. The model performed well with 75% sensitivity, 85% specificity, and an area under the receiver-operating curve of .84. Conclusion rIFs from perfusion DSC-MRI are potential biomarkers in GBs with a ≥30% MGMT_pm. Otherwise, unmethylated and intermediate-methylated GBs lack of rIFs. Five of 14 rIFs show sufficient strength to build an accurate prediction model of MGMT_pm. |
Databáze: | OpenAIRE |
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