A Comparison of Single- and Multiparametric MRI Models for Differentiation of Recurrent Glioblastoma from Treatment-Related Change
To evaluate single- and multiparametric MRI models to differentiate recurrent glioblastoma (GBM) and treatment-related changes (TRC) in clinical routine imaging. Selective and unselective apparent diffusion coefficient (ADC) and minimum, mean, and maximum cerebral blood volume (CBV) measurements in...
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| Format: | Article |
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MDPI AG
2021-12-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/11/12/2281 |
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| author | Felix Eisenhut Tobias Engelhorn Soheil Arinrad Sebastian Brandner Roland Coras Florian Putz Rainer Fietkau Arnd Doerfler Manuel A. Schmidt |
| author_facet | Felix Eisenhut Tobias Engelhorn Soheil Arinrad Sebastian Brandner Roland Coras Florian Putz Rainer Fietkau Arnd Doerfler Manuel A. Schmidt |
| author_sort | Felix Eisenhut |
| collection | DOAJ |
| description | To evaluate single- and multiparametric MRI models to differentiate recurrent glioblastoma (GBM) and treatment-related changes (TRC) in clinical routine imaging. Selective and unselective apparent diffusion coefficient (ADC) and minimum, mean, and maximum cerebral blood volume (CBV) measurements in the lesion were performed. Minimum, mean, and maximum ratios<sub>CBV</sub> (CBV<sub>lesion</sub> to CBV<sub>healthy white matter</sub>) were computed. All data were tested for lesion discrimination. A multiparametric model was compiled via multiple logistic regression using data demonstrating significant difference between GBM and TRC and tested for its diagnostic strength in an independent patient cohort. A total of 34 patients (17 patients with recurrent GBM and 17 patients with TRC) were included. ADC measurements showed no significant difference between both entities. All CBV and ratios<sub>CBV</sub> measurements were significantly higher in patients with recurrent GBM than TRC. A minimum CBV of 8.5, mean CBV of 116.5, maximum CBV of 327 and ratio<sub>CBV</sub> <sub>minimum</sub> of 0.17, ratio<sub>CBV</sub> <sub>mean</sub> of 2.26 and ratio<sub>CBV</sub> <sub>maximum</sub> of 3.82 were computed as optimal cut-off values. By integrating these parameters in a multiparametric model and testing it in an independent patient cohort, 9 of 10 patients, i.e., 90%, were classified correctly. The multiparametric model further improves radiological discrimination of GBM from TRC in comparison to single-parameter approaches and enables reliable identification of recurrent tumors. |
| first_indexed | 2024-03-10T04:18:35Z |
| format | Article |
| id | doaj.art-08c90aa142c44b209d0b05017578a22e |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T04:18:35Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-08c90aa142c44b209d0b05017578a22e2023-11-23T07:53:55ZengMDPI AGDiagnostics2075-44182021-12-011112228110.3390/diagnostics11122281A Comparison of Single- and Multiparametric MRI Models for Differentiation of Recurrent Glioblastoma from Treatment-Related ChangeFelix Eisenhut0Tobias Engelhorn1Soheil Arinrad2Sebastian Brandner3Roland Coras4Florian Putz5Rainer Fietkau6Arnd Doerfler7Manuel A. Schmidt8Department of Neuroradiology, University Hospital Erlangen, Schwabachanlage 6, 91054 Erlangen, GermanyDepartment of Neuroradiology, University Hospital Erlangen, Schwabachanlage 6, 91054 Erlangen, GermanyDepartment of Neurosurgery, University Hospital Erlangen, Schwabachanlage 6, 91054 Erlangen, GermanyDepartment of Neurosurgery, University Hospital Erlangen, Schwabachanlage 6, 91054 Erlangen, GermanyDepartment of Neuropathology, University Hospital Erlangen, Schwabachanlage 6, 91054 Erlangen, GermanyDepartment of Radiation Oncology, University Hospital Erlangen, Universitaetsstrasse 27, 91054 Erlangen, GermanyDepartment of Radiation Oncology, University Hospital Erlangen, Universitaetsstrasse 27, 91054 Erlangen, GermanyDepartment of Neuroradiology, University Hospital Erlangen, Schwabachanlage 6, 91054 Erlangen, GermanyDepartment of Neuroradiology, University Hospital Erlangen, Schwabachanlage 6, 91054 Erlangen, GermanyTo evaluate single- and multiparametric MRI models to differentiate recurrent glioblastoma (GBM) and treatment-related changes (TRC) in clinical routine imaging. Selective and unselective apparent diffusion coefficient (ADC) and minimum, mean, and maximum cerebral blood volume (CBV) measurements in the lesion were performed. Minimum, mean, and maximum ratios<sub>CBV</sub> (CBV<sub>lesion</sub> to CBV<sub>healthy white matter</sub>) were computed. All data were tested for lesion discrimination. A multiparametric model was compiled via multiple logistic regression using data demonstrating significant difference between GBM and TRC and tested for its diagnostic strength in an independent patient cohort. A total of 34 patients (17 patients with recurrent GBM and 17 patients with TRC) were included. ADC measurements showed no significant difference between both entities. All CBV and ratios<sub>CBV</sub> measurements were significantly higher in patients with recurrent GBM than TRC. A minimum CBV of 8.5, mean CBV of 116.5, maximum CBV of 327 and ratio<sub>CBV</sub> <sub>minimum</sub> of 0.17, ratio<sub>CBV</sub> <sub>mean</sub> of 2.26 and ratio<sub>CBV</sub> <sub>maximum</sub> of 3.82 were computed as optimal cut-off values. By integrating these parameters in a multiparametric model and testing it in an independent patient cohort, 9 of 10 patients, i.e., 90%, were classified correctly. The multiparametric model further improves radiological discrimination of GBM from TRC in comparison to single-parameter approaches and enables reliable identification of recurrent tumors.https://www.mdpi.com/2075-4418/11/12/2281glioblastomacerebral radiation necrosispseudoprogressiontreatment-related changesdynamic susceptibility contrast perfusion imagingapparent diffusion coefficient |
| spellingShingle | Felix Eisenhut Tobias Engelhorn Soheil Arinrad Sebastian Brandner Roland Coras Florian Putz Rainer Fietkau Arnd Doerfler Manuel A. Schmidt A Comparison of Single- and Multiparametric MRI Models for Differentiation of Recurrent Glioblastoma from Treatment-Related Change Diagnostics glioblastoma cerebral radiation necrosis pseudoprogression treatment-related changes dynamic susceptibility contrast perfusion imaging apparent diffusion coefficient |
| title | A Comparison of Single- and Multiparametric MRI Models for Differentiation of Recurrent Glioblastoma from Treatment-Related Change |
| title_full | A Comparison of Single- and Multiparametric MRI Models for Differentiation of Recurrent Glioblastoma from Treatment-Related Change |
| title_fullStr | A Comparison of Single- and Multiparametric MRI Models for Differentiation of Recurrent Glioblastoma from Treatment-Related Change |
| title_full_unstemmed | A Comparison of Single- and Multiparametric MRI Models for Differentiation of Recurrent Glioblastoma from Treatment-Related Change |
| title_short | A Comparison of Single- and Multiparametric MRI Models for Differentiation of Recurrent Glioblastoma from Treatment-Related Change |
| title_sort | comparison of single and multiparametric mri models for differentiation of recurrent glioblastoma from treatment related change |
| topic | glioblastoma cerebral radiation necrosis pseudoprogression treatment-related changes dynamic susceptibility contrast perfusion imaging apparent diffusion coefficient |
| url | https://www.mdpi.com/2075-4418/11/12/2281 |
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