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Doig D, Thorne L, Rees J, Fersht N, Kosmin M, Brandner S, Jäger HR, Thust S. Clinical, Imaging and Neurogenetic Features of Patients with Gliomatosis Cerebri Referred to a Tertiary Neuro-Oncology Centre. J Pers Med 2023; 13:jpm13020222. [PMID: 36836456 PMCID: PMC9960048 DOI: 10.3390/jpm13020222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 01/19/2023] [Indexed: 01/31/2023] Open
Abstract
INTRODUCTION Gliomatosis cerebri describes a rare growth pattern of diffusely infiltrating glioma. The treatment options are limited and clinical outcomes remain poor. To characterise this population of patients, we examined referrals to a specialist brain tumour centre. METHODS We analysed demographic data, presenting symptoms, imaging, histology and genetics, and survival in individuals referred to a multidisciplinary team meeting over a 10-year period. RESULTS In total, 29 patients fulfilled the inclusion criteria with a median age of 64 years. The most common presenting symptoms were neuropsychiatric (31%), seizure (24%) or headache (21%). Of 20 patients with molecular data, 15 had IDH wild-type glioblastoma, with an IDH1 mutation most common in the remainder (5/20). The median length of survival from MDT referral to death was 48 weeks (IQR 23 to 70 weeks). Contrast enhancement patterns varied between and within tumours. In eight patients who had DSC perfusion studies, five (63%) had a measurable region of increased tumour perfusion with rCBV values ranging from 2.8 to 5.7. A minority of patients underwent MR spectroscopy with 2/3 (66.6%) false-negative results. CONCLUSIONS Gliomatosis imaging, histological and genetic findings are heterogeneous. Advanced imaging, including MR perfusion, could identify biopsy targets. Negative MR spectroscopy does not exclude the diagnosis of glioma.
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Affiliation(s)
- David Doig
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, UK
- Correspondence: ; Tel.: +44-20-3456-7890
| | - Lewis Thorne
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, UK
| | - Jeremy Rees
- Department of Neurology, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, UK
| | - Naomi Fersht
- Department of Neuro-Oncology, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, UK
| | - Michael Kosmin
- Department of Neuro-Oncology, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, UK
| | - Sebastian Brandner
- Department of Neurodegenerative Disease, UCL Institute of Neurology and Division of Neuropathology, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
| | - Hans Rolf Jäger
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, UK
- Neuroradiological Academic Unit, Department of Brain Rehabilitation and Repair, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
- Imaging Department, University College Hospital, London WC1N 3BG, UK
| | - Stefanie Thust
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, UK
- Neuroradiological Academic Unit, Department of Brain Rehabilitation and Repair, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
- Imaging Department, University College Hospital, London WC1N 3BG, UK
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Zhang Y, Lin Y, Xing Z, Yao S, Cao D, Miao WB. Non-invasive assessment of heterogeneity of gliomas using diffusion and perfusion MRI: correlation with spatially co-registered PET. Acta Radiol 2022; 63:664-671. [PMID: 33858207 DOI: 10.1177/02841851211006913] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
BACKGROUND Heterogeneity of gliomas challenges the neuronavigated biopsy and oncological therapy. Diffusion and perfusion magnetic resonance imaging (MRI) can reveal the cellular and hemodynamic heterogeneity of tumors. Integrated positron emission tomography (PET)/MRI is expected to be a non-invasive imaging approach to characterizing glioma. PURPOSE To evaluate the value of apparent diffusion coefficient (ADC), cerebral blood volume (CBV), and spatially co-registered maximal standard uptake value (SUVmax) for tissue characterization and glioma grading. MATERIAL AND METHODS Thirty-seven consecutive patients with pathologically confirmed gliomas were retrospectively investigated. The relative minimum ADC (rADCmin), relative maximal ADC (rADCmax), relative maximal rCBV (rCBVmax), the relative minimum rCBV (rCBVmin), and the corresponding relative SUVmax (rSUVmax) were measured. The paired t-test was used to compare the quantitative parameters between different regions to clarify tumor heterogeneity. Imaging parameters between WHO grade IV and grade II/III gliomas were compared by t-test. The diagnostic efficiency of multiparametric PET/MRI was analyzed by receiver operating characteristic (ROC) curve. RESULTS The values of rSUVmax were significantly different between maximal diffusion/perfusion area and minimum diffusion/perfusion area (P < 0.001/P < 0.001) within tumor. The values of rADCmin (P < 0.001), rCBVmax (P = 0.002), and corresponding rSUVmax (P = 0.001/P < 0.001) could be used for grading gliomas. The areas under the ROC curves of rSUVmax defined by rADCmin and rCBVmax were 0.89 and 0.91, respectively. CONCLUSION Diffusion and perfusion MRI can detect glioma heterogeneity with excellent molecular imaging correlations. Regions with rCBVmax suggest tissues with the highest metabolism and malignancy for guiding glioma grading and tissue sampling.
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Affiliation(s)
- Ying Zhang
- Department of Nuclear Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, PR China
| | - Yu Lin
- Department of Radiology, Zhongshan Hospital Affiliated to Xiamen University, Xiamen, Fujian, PR China
| | - Zhen Xing
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, PR China
| | - Shaobo Yao
- Department of Nuclear Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, PR China
| | - Dairong Cao
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, PR China
| | - Wei-bing Miao
- Department of Nuclear Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, PR China
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Sawlani V, Patel MD, Davies N, Flintham R, Wesolowski R, Ughratdar I, Pohl U, Nagaraju S, Petrik V, Kay A, Jacob S, Sanghera P, Wykes V, Watts C, Poptani H. Multiparametric MRI: practical approach and pictorial review of a useful tool in the evaluation of brain tumours and tumour-like lesions. Insights Imaging 2020; 11:84. [PMID: 32681296 PMCID: PMC7367972 DOI: 10.1186/s13244-020-00888-1] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 06/24/2020] [Indexed: 12/17/2022] Open
Abstract
MRI has a vital role in the assessment of intracranial lesions. Conventional MRI has limited specificity and multiparametric MRI using diffusion-weighted imaging, perfusion-weighted imaging and magnetic resonance spectroscopy allows more accurate assessment of the tissue microenvironment. The purpose of this educational pictorial review is to demonstrate the role of multiparametric MRI for diagnosis, treatment planning and for assessing treatment response, as well as providing a practical approach for performing and interpreting multiparametric MRI in the clinical setting. A variety of cases are presented to demonstrate how multiparametric MRI can help differentiate neoplastic from non-neoplastic lesions compared to conventional MRI alone.
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Affiliation(s)
- Vijay Sawlani
- University Hospitals Birmingham NHS Foundation Trust, Queen Elizabeth Hospital Birmingham, Mindelsohn Way, Edgbaston, Birmingham, B15 2TH, UK.
- University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
| | - Markand Dipankumar Patel
- University Hospitals Birmingham NHS Foundation Trust, Queen Elizabeth Hospital Birmingham, Mindelsohn Way, Edgbaston, Birmingham, B15 2TH, UK
- University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Nigel Davies
- University Hospitals Birmingham NHS Foundation Trust, Queen Elizabeth Hospital Birmingham, Mindelsohn Way, Edgbaston, Birmingham, B15 2TH, UK
| | - Robert Flintham
- University Hospitals Birmingham NHS Foundation Trust, Queen Elizabeth Hospital Birmingham, Mindelsohn Way, Edgbaston, Birmingham, B15 2TH, UK
| | - Roman Wesolowski
- University Hospitals Birmingham NHS Foundation Trust, Queen Elizabeth Hospital Birmingham, Mindelsohn Way, Edgbaston, Birmingham, B15 2TH, UK
| | - Ismail Ughratdar
- University Hospitals Birmingham NHS Foundation Trust, Queen Elizabeth Hospital Birmingham, Mindelsohn Way, Edgbaston, Birmingham, B15 2TH, UK
| | - Ute Pohl
- University Hospitals Birmingham NHS Foundation Trust, Queen Elizabeth Hospital Birmingham, Mindelsohn Way, Edgbaston, Birmingham, B15 2TH, UK
| | - Santhosh Nagaraju
- University Hospitals Birmingham NHS Foundation Trust, Queen Elizabeth Hospital Birmingham, Mindelsohn Way, Edgbaston, Birmingham, B15 2TH, UK
| | - Vladimir Petrik
- University Hospitals Birmingham NHS Foundation Trust, Queen Elizabeth Hospital Birmingham, Mindelsohn Way, Edgbaston, Birmingham, B15 2TH, UK
| | - Andrew Kay
- University Hospitals Birmingham NHS Foundation Trust, Queen Elizabeth Hospital Birmingham, Mindelsohn Way, Edgbaston, Birmingham, B15 2TH, UK
| | - Saiju Jacob
- University Hospitals Birmingham NHS Foundation Trust, Queen Elizabeth Hospital Birmingham, Mindelsohn Way, Edgbaston, Birmingham, B15 2TH, UK
- University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Paul Sanghera
- University Hospitals Birmingham NHS Foundation Trust, Queen Elizabeth Hospital Birmingham, Mindelsohn Way, Edgbaston, Birmingham, B15 2TH, UK
| | - Victoria Wykes
- University Hospitals Birmingham NHS Foundation Trust, Queen Elizabeth Hospital Birmingham, Mindelsohn Way, Edgbaston, Birmingham, B15 2TH, UK
- University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Colin Watts
- University Hospitals Birmingham NHS Foundation Trust, Queen Elizabeth Hospital Birmingham, Mindelsohn Way, Edgbaston, Birmingham, B15 2TH, UK
- University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Harish Poptani
- Centre for Pre-Clinical Imaging, Department of Cellular and Molecular Physiology, University of Liverpool, Crown Street, Liverpool, L69 3BX, UK
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Yang Y, Yang Y, Wu X, Pan Y, Zhou D, Zhang H, Chen Y, Zhao J, Mo Z, Huang B. Adding DSC PWI and DWI to BT-RADS can help identify postoperative recurrence in patients with high-grade gliomas. J Neurooncol 2020; 146:363-371. [PMID: 31902040 DOI: 10.1007/s11060-019-03387-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Accepted: 12/27/2019] [Indexed: 12/22/2022]
Abstract
BACKGROUND The Brain Tumor Reporting and Data System (BT-RADS) category 3 is suitable for identifying cases with intermediate probability of tumor recurrence that do not meet the Response Assessment in Neuro-Oncology (RANO) criteria for progression. The aim of this study was to evaluate the added value of dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC PWI) and diffusion-weighted imaging (DWI) to BT-RADS for differentiating tumor recurrence from non-recurrence in postoperative high-grade glioma (HGG) patients with category 3 lesions. METHODS Patients with BT-RADS category 3 lesions were included. The maximal relative cerebral blood volume (rCBVmax) and the mean apparent diffusion coefficient (ADCmean) values were measured. The added value of DSC PWI and DWI to BT-RADS was evaluated by receiver operating characteristic (ROC) curve analysis. RESULTS Fifty-one of 91 patients had tumor recurrence, and 40 patients did not. There were significant differences in rCBVmax and ADCmean between the tumor recurrence group and non-recurrence group. Compared to BT-RADS alone, the addition of DSC PWI to BT-RADS increased the area under curve (AUC) from 0.76 (95% confidence interval [CI] 0.66-0.84) to 0.90 (95% CI 0.81-0.95) for differentiating tumor recurrence from non-recurrence. The addition of DWI to BT-RADS increased the AUC from 0.76 (95% CI 0.66-0.84) to 0.88 (95% CI 0.80-0.94). The combination of BT-RADS, DSC PWI, and DWI exhibited the best diagnostic performance (AUC = 0.95; 95% CI 0.88-0.98) for differentiating tumor recurrence from non-recurrence. CONCLUSION Adding DSC PWI and DWI to BT-RADS can significantly improve the diagnostic performance for differentiating tumor recurrence from non-recurrence in BT-RADS category 3 lesions.
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Affiliation(s)
- Yuelong Yang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Road, Guangzhou, 510080, China
| | - Yunjun Yang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Road, Guangzhou, 510080, China
| | - Xiaoling Wu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Road, Guangzhou, 510080, China
| | - Yi Pan
- Department of Radiotherapy, Cancer Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Dong Zhou
- Department of Neurosurgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Hongdan Zhang
- Department of Radiotherapy, Cancer Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Yonglu Chen
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Road, Guangzhou, 510080, China
| | - Jiayun Zhao
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Road, Guangzhou, 510080, China
| | - Zihua Mo
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Road, Guangzhou, 510080, China
| | - Biao Huang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Road, Guangzhou, 510080, China.
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