The prognostic value of perfusion MRI in cerebral glioma
Introduction Cerebral glioma is the most prevalent primary brain tumour, of which the majority are high grade gliomas. High grade gliomas possess a poor prognosis, and glioblastoma patients survive less than one year after diagnosis. To date, histological grading is used as the standard technique fo...
| Main Author: | |
|---|---|
| Format: | Thesis (University of Nottingham only) |
| Language: | English |
| Published: |
2012
|
| Online Access: | https://eprints.nottingham.ac.uk/12776/ |
| _version_ | 1848791579548975104 |
|---|---|
| author | Manita, Muftah |
| author_facet | Manita, Muftah |
| author_sort | Manita, Muftah |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Introduction
Cerebral glioma is the most prevalent primary brain tumour, of which the majority are high grade gliomas. High grade gliomas possess a poor prognosis, and glioblastoma patients survive less than one year after diagnosis. To date, histological grading is used as the standard technique for diagnosis and survival prediction. Previous studies using advanced techniques such as MR Perfusion have achieved a high sensitivity but a low specificity in identifying high grade gliomas. Moreover, they have failed to distinguish glioblastoma from anaplastic glioma. The purpose of the study presented here is to assess the diagnostic and prognostic value for cerebral glioma of cerebral blood volume maps derived from MR perfusion.
Methods
This retrospective study was approved by the local research ethics committee and clinical audit office. This study included 123 patients with newly diagnosed cerebral glioma, of all grades. Histological diagnosis was used as the standard reference for all potential patients. The relative tumour blood volume (rTBVmax) derived from MR perfusion was used for radiological grading of cerebral glioma. Receiver operating characteristics (ROC) were used to define the best threshold value in distinguishing the glioma grades and in determining the accuracy values (sensitivity, specificity, and positive and negative predictive values). For survival analysis, Kaplan-Meier was used to illustrate and compare the discriminatory value of the histological and radiological classifications. A multiple Cox regression model was used to assess the prognostic value of both classifications in addition to other tested demographic and clinical variables. Finally, the influence of potential moderators was assessed using ANOVA, to assess whether the variation in rTBVmax was only due to the difference in tumour grades.
Results
A model data set (n = 50) produced a 7-fold increase of TBVmax in tumour versus white matter and provided sensitivity and specificity of 97% and 94%, respectively, in distinguishing high versus low grade glioma. Moreover, a threshold value of 9.6 provided sensitivity and specificity of 100% and 56% in differentiating glioblastoma within the group of high grade gliomas. These threshold values were applied to the second group (n = 73) and provided sensitivity and specificity of 96% and 95% in distinguishing high versus low grade glioma, and 97% and 73% in differentiating, within the high grade gliomas, glioblastoma from anaplastic glioma. Using these two thresholds for a three-tier radiological classification, both the Kaplan-Meier plots and the multiple Cox regression showed that radiological classification was the most independent predictor of survival and tumour progression. The proposed radiological classification system was better than histological classification in predicting glioma patients survival especially noted in a group of moderately hyperaemic rTBVmax.
Conclusion
MR perfusion is a non-invasive and robust technique in glioma grading and survival prediction. The diagnostic value of rTBVmax derived from MR perfusion in differentiating high versus low grade glioma is promising. It may have a role in the future in defining the appropriate treatment. However, the proposed radiological classification was inferior in differentiating anaplastic glioma from glioblastoma multiforme. In the future, a more advanced multimodal MR, such as MR spectroscopy and MR diffusion, may be studied, besides MR perfusion, in order to improve this diagnostic accuracy. |
| first_indexed | 2025-11-14T18:30:45Z |
| format | Thesis (University of Nottingham only) |
| id | nottingham-12776 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T18:30:45Z |
| publishDate | 2012 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-127762025-02-28T11:21:18Z https://eprints.nottingham.ac.uk/12776/ The prognostic value of perfusion MRI in cerebral glioma Manita, Muftah Introduction Cerebral glioma is the most prevalent primary brain tumour, of which the majority are high grade gliomas. High grade gliomas possess a poor prognosis, and glioblastoma patients survive less than one year after diagnosis. To date, histological grading is used as the standard technique for diagnosis and survival prediction. Previous studies using advanced techniques such as MR Perfusion have achieved a high sensitivity but a low specificity in identifying high grade gliomas. Moreover, they have failed to distinguish glioblastoma from anaplastic glioma. The purpose of the study presented here is to assess the diagnostic and prognostic value for cerebral glioma of cerebral blood volume maps derived from MR perfusion. Methods This retrospective study was approved by the local research ethics committee and clinical audit office. This study included 123 patients with newly diagnosed cerebral glioma, of all grades. Histological diagnosis was used as the standard reference for all potential patients. The relative tumour blood volume (rTBVmax) derived from MR perfusion was used for radiological grading of cerebral glioma. Receiver operating characteristics (ROC) were used to define the best threshold value in distinguishing the glioma grades and in determining the accuracy values (sensitivity, specificity, and positive and negative predictive values). For survival analysis, Kaplan-Meier was used to illustrate and compare the discriminatory value of the histological and radiological classifications. A multiple Cox regression model was used to assess the prognostic value of both classifications in addition to other tested demographic and clinical variables. Finally, the influence of potential moderators was assessed using ANOVA, to assess whether the variation in rTBVmax was only due to the difference in tumour grades. Results A model data set (n = 50) produced a 7-fold increase of TBVmax in tumour versus white matter and provided sensitivity and specificity of 97% and 94%, respectively, in distinguishing high versus low grade glioma. Moreover, a threshold value of 9.6 provided sensitivity and specificity of 100% and 56% in differentiating glioblastoma within the group of high grade gliomas. These threshold values were applied to the second group (n = 73) and provided sensitivity and specificity of 96% and 95% in distinguishing high versus low grade glioma, and 97% and 73% in differentiating, within the high grade gliomas, glioblastoma from anaplastic glioma. Using these two thresholds for a three-tier radiological classification, both the Kaplan-Meier plots and the multiple Cox regression showed that radiological classification was the most independent predictor of survival and tumour progression. The proposed radiological classification system was better than histological classification in predicting glioma patients survival especially noted in a group of moderately hyperaemic rTBVmax. Conclusion MR perfusion is a non-invasive and robust technique in glioma grading and survival prediction. The diagnostic value of rTBVmax derived from MR perfusion in differentiating high versus low grade glioma is promising. It may have a role in the future in defining the appropriate treatment. However, the proposed radiological classification was inferior in differentiating anaplastic glioma from glioblastoma multiforme. In the future, a more advanced multimodal MR, such as MR spectroscopy and MR diffusion, may be studied, besides MR perfusion, in order to improve this diagnostic accuracy. 2012-12-15 Thesis (University of Nottingham only) NonPeerReviewed application/pdf en arr https://eprints.nottingham.ac.uk/12776/1/DM_thesis.pdf Manita, Muftah (2012) The prognostic value of perfusion MRI in cerebral glioma. DM thesis, University of Nottingham. |
| spellingShingle | Manita, Muftah The prognostic value of perfusion MRI in cerebral glioma |
| title | The prognostic value of perfusion MRI in cerebral glioma |
| title_full | The prognostic value of perfusion MRI in cerebral glioma |
| title_fullStr | The prognostic value of perfusion MRI in cerebral glioma |
| title_full_unstemmed | The prognostic value of perfusion MRI in cerebral glioma |
| title_short | The prognostic value of perfusion MRI in cerebral glioma |
| title_sort | prognostic value of perfusion mri in cerebral glioma |
| url | https://eprints.nottingham.ac.uk/12776/ |