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Aznarez-Sanado M, Romero-Garcia R, Li C, Morris RC, Price SJ, Manly T, Santarius T, Erez Y, Hart MG, Suckling J. Brain tumour microstructure is associated with post-surgical cognition. Sci Rep 2024; 14:5646. [PMID: 38454017 PMCID: PMC10920778 DOI: 10.1038/s41598-024-55130-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 02/20/2024] [Indexed: 03/09/2024] Open
Abstract
Brain tumour microstructure is potentially predictive of changes following treatment to cognitive functions subserved by the functional networks in which they are embedded. To test this hypothesis, intra-tumoural microstructure was quantified from diffusion-weighted MRI to identify which tumour subregions (if any) had a greater impact on participants' cognitive recovery after surgical resection. Additionally, we studied the role of tumour microstructure in the functional interaction between the tumour and the rest of the brain. Sixteen patients (22-56 years, 7 females) with brain tumours located in or near speech-eloquent areas of the brain were included in the analyses. Two different approaches were adopted for tumour segmentation from a multishell diffusion MRI acquisition: the first used a two-dimensional four group partition of feature space, whilst the second used data-driven clustering with Gaussian mixture modelling. For each approach, we assessed the capability of tumour microstructure to predict participants' cognitive outcomes after surgery and the strength of association between the BOLD signal of individual tumour subregions and the global BOLD signal. With both methodologies, the volumes of partially overlapped subregions within the tumour significantly predicted cognitive decline in verbal skills after surgery. We also found that these particular subregions were among those that showed greater functional interaction with the unaffected cortex. Our results indicate that tumour microstructure measured by MRI multishell diffusion is associated with cognitive recovery after surgery.
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Affiliation(s)
- Maite Aznarez-Sanado
- School of Education and Psychology, University of Navarra, 31009, Pamplona, Spain
| | - Rafael Romero-Garcia
- Department of Medical Physiology and Biophysics, Instituto de Biomedicina de Sevilla (IBiS), HUVR/CSIC/Universidad de Sevilla/CIBERSAM, ISCIII, 41013, Sevilla, Spain.
- Department of Psychiatry, University of Cambridge, Herchel Smith Bldg, Robinson Way, Cambridge, CB2 0SZ, UK.
| | - Chao Li
- Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Applied Mathematics and Theoretical Physics, The Centre for Mathematical Imaging in Healthcare, Cambridge, CB3 0WA, UK
- School of Medicine & School of Science and Engineering, University of Dundee, Dundee, DD1 4HN, UK
| | - Rob C Morris
- Academic Neurosurgery Division, Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Stephen J Price
- Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Thomas Manly
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, CB2 7EF, UK
| | - Thomas Santarius
- Academic Neurosurgery Division, Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Yaara Erez
- Faculty of Engineering, Bar-Ilan University, 5290002, Ramat Gan, Israel
- The Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan, Israel
| | - Michael G Hart
- St George's, University of London and St George's University Hospitals NHS Foundation Trust, Institute of Molecular and Clinical Sciences, Neurosciences Research Centre, Cranmer Terrace, London, SW17 0RE, UK
| | - John Suckling
- Department of Psychiatry, University of Cambridge, Herchel Smith Bldg, Robinson Way, Cambridge, CB2 0SZ, UK
- Cambridge and Peterborough NHS Foundation Trust, Cambridge, CB21 5EF, UK
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Teske N, Tonn JC, Karschnia P. How to evaluate extent of resection in diffuse gliomas: from standards to new methods. Curr Opin Neurol 2023; 36:564-570. [PMID: 37865849 DOI: 10.1097/wco.0000000000001212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2023]
Abstract
PURPOSE OF REVIEW Maximal safe tumor resection represents the current standard of care for patients with newly diagnosed diffuse gliomas. Recent efforts have highlighted the prognostic value of extent of resection measured as residual tumor volume in patients with isocitrate dehydrogenase (IDH)-wildtype and -mutant gliomas. Accurate assessment of such information therefore appears essential in the context of clinical trials as well as patient management. RECENT FINDINGS Current recommendations for evaluation of extent of resection rest upon standardized postoperative MRI including contrast-enhanced T1-weighted sequences, T2-weighted/fluid-attenuated-inversion-recovery sequences, and diffusion-weighted imaging to differentiate postoperative tumor volumes from ischemia and nonspecific imaging findings. In this context, correct timing of postoperative imaging within the postoperative period is of utmost importance. Advanced MRI techniques including perfusion-weighted MRI and MR-spectroscopy may add further insight when evaluating residual tumor remnants. Positron emission tomography (PET) using amino acid tracers proves beneficial in identifying metabolically active tumor beyond anatomical findings on conventional MRI. SUMMARY Future efforts will have to refine recommendations on postoperative assessment of residual tumor burden in respect to differences between IDH-wildtype and -mutant gliomas, and incorporate the emerging role of advanced imaging modalities like amino acid PET.
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Affiliation(s)
- Nico Teske
- Department of Neurosurgery, LMU University Hospital, LMU Munich
- German Cancer Consortium (DKTK), Partner Site, Munich, Germany
| | - Joerg-Christian Tonn
- Department of Neurosurgery, LMU University Hospital, LMU Munich
- German Cancer Consortium (DKTK), Partner Site, Munich, Germany
| | - Philipp Karschnia
- Department of Neurosurgery, LMU University Hospital, LMU Munich
- German Cancer Consortium (DKTK), Partner Site, Munich, Germany
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Kumar R, Shijith K, Dhanalakshmi B, Kovilapu UB, Sharma V, Debnath J, Sridhar M, Gahlot G, Das AK. Role of regional diffusion tensor imaging (DTI)-derived tensor metrics in the evaluation of intracranial gliomas and its histopathological correlation. Med J Armed Forces India 2023; 79:173-180. [PMID: 36969123 PMCID: PMC10037060 DOI: 10.1016/j.mjafi.2021.05.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 05/21/2021] [Indexed: 11/25/2022] Open
Abstract
Background The imaging of brain tumours has significantly improved with the use of advanced magnetic resonance (MR) techniques like diffusion tensor imaging (DTI). This study was conducted to analyse the utility of DTI-derived tensor metrics in the evaluation of intracranial gliomas with histopathological correlation and further adoption of these image-data analyses in clinical setting. Methods A total of 50 patients with suspected diagnosis of intracranial gliomas underwent DTI along with conventional MR examination. The study correlated various DTI parameters in the enhancing part of the tumour and the peritumoral region with the histopathological grades of the intracranial gliomas. Results The study revealed higher values of Cl (linear anisotropy), Cp (planar anisotropy), AD (axial diffusivity), FA (fractional anisotropy) and RA (relative anisotropy) and lower values of Cs (spherical anisotropy), MD (mean diffusivity) and RD (radial diffusivity) in the enhancing part of the tumour in case of high-grade gliomas. However, in the peritumoral region, the values of Cl, Cp, AD, FA and RA were less whereas values of Cs, MD and RD were more in high-grade gliomas than in the low-grade gliomas. The various cutoff values of these DTI-derived tensor metrics were found to be statistically significant. Conclusion DTI-derived tensor metrics can be a valuable tool in differentiation between high-grade and low-grade gliomas which might be accepted in clinical practice in near future.
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Affiliation(s)
- Rakesh Kumar
- Graded Specialist (Radiodiagnosis), 165 Military Hospital, C/o 99 APO, India
| | - K.P. Shijith
- Senior Advisor (Radiodiagnosis), Army Hospital (R&R), Delhi Cantt, India
| | - B. Dhanalakshmi
- Classified Specialist (Radiodiagnosis), Army Institute of Cardio Thoracic Sciences (AICTS), Pune, India
| | - Uday Bhanu Kovilapu
- Associate Professor, Department of Radiology, Armed Forces Medical College, Pune, India
| | - Vivek Sharma
- Professor (Radiodiagnosis), Bharati Vidyapeeth Medical College, Pune, India
| | - Jyotindu Debnath
- Consultant, Professor & Head (Radiodiagnosis), Army Hospital (R&R), Delhi Cantt, India
| | - M.S. Sridhar
- Deputy Commandant, Command Hospital (Air Force), Bengaluru, India
| | - G.P.S. Gahlot
- Classified Specialist (Pathology & Oncopathology), Command Hospital (Western Command), Chandimandir, India
| | - Amit Kumar Das
- Commanding Officer & Senior Advisor (Pathology), 165 Military Hospital, C/o 99 APO, India
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Proceedings of the 2022 British Neurosurgical Research Group Meeting. Br J Neurosurg 2022. [DOI: 10.1080/02688697.2022.2157943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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5
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Is Diffusion Tensor Imaging-Guided Radiotherapy the New State-of-the-Art? A Review of the Current Literature and Technical Insights. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12020816] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Despite the increasing precision of radiotherapy delivery, it is still frequently associated with neurological complications. This is in part due to damage to eloquent white matter (WM) tracts, which is made more likely by the fact they cannot be visualised on standard structural imaging. WM is additionally more vulnerable than grey matter to radiation damage. Primary brain malignancies also are known to spread along the WM. Diffusion tensor imaging (DTI) is the only in vivo method of delineating WM tracts. DTI is an imaging technique that models the direction of diffusion and therefore can infer the orientation of WM fibres. This review article evaluates the current evidence for using DTI to guide intracranial radiotherapy and whether it constitutes a new state-of-the-art technique. We provide a basic overview of DTI and its known applications in radiotherapy, which include using tractography to reduce the radiation dose to eloquent WM tracts and using DTI to detect or predict tumoural spread. We evaluate the evidence for DTI-guided radiotherapy in gliomas, metastatic disease, and benign conditions, finding that the strongest evidence is for its use in arteriovenous malformations. However, the evidence is weak in other conditions due to a lack of case-controlled trials.
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Dünger L, Seidlitz A, Jentsch C, Platzek I, Kotzerke J, Beuthien-Baumann B, Baumann M, Krause M, Troost EGC, Raschke F. Reduced diffusion in white matter after radiotherapy with photons and protons. Radiother Oncol 2021; 164:66-72. [PMID: 34537290 DOI: 10.1016/j.radonc.2021.09.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 08/30/2021] [Accepted: 09/10/2021] [Indexed: 11/30/2022]
Abstract
BACKGROUND AND PURPOSE Radio(chemo)therapy is standard in the adjuvant treatment of glioblastoma. Inevitably, brain tissue surrounding the target volume is also irradiated, potentially causing acute and late side-effects. Diffusion imaging has been shown to be a sensitive method to detect early changes in the cerebral white matter (WM) after radiation. The aim of this work was to assess possible changes in the mean diffusivity (MD) of WM after radio(chemo)therapy using Diffusion-weighted imaging (DWI) and to compare these effects between patients treated with proton and photon irradiation. MATERIALS AND METHODS 70 patients with glioblastoma underwent adjuvant radio(chemo)therapy with protons (n = 20) or photons (n = 50) at the University Hospital Dresden. MRI follow-ups were performed at three-monthly intervals and in this study were evaluated until 33 months after the end of therapy. Relative white matter MD changes between baseline and all follow-up visits were calculated in different dose regions. RESULTS We observed a significant decrease of MD (p < 0.05) in WM regions receiving more than 20 Gy. MD reduction was progressive with dose and time after radio(chemo)therapy (maximum: -7.9 ± 1.2% after 24 months, ≥50 Gy). In patients treated with photons, significant reductions of MD in the entire WM (p < 0.05) were seen at all time points. Conversely, in proton patients, whole brain MD did not change significantly. CONCLUSIONS Irradiation leads to measurable MD reduction in white matter, progressing with both increasing dose and time. Treatment with protons reduces this effect most likely due to a lower total dose in the surrounding white matter. Further investigations are needed to assess whether those MD changes correlate with known radiation induced side-effects.
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Affiliation(s)
- L Dünger
- ABX-CRO Advanced Pharmaceutical Services Forschungsgesellschaft mbH, Dresden, Germany; OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
| | - A Seidlitz
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - C Jentsch
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - I Platzek
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - J Kotzerke
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | | | - M Baumann
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany; German Cancer Research Center (DKFZ), Heidelberg, Germany; National Center for Tumor Diseases (NCT), Partner Site Heidelberg, Germany
| | - M Krause
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiooncology - OncoRay, Dresden, Germany; German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ), Heidelberg, Germany; National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany, and; Helmholtz Association / Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany
| | - E G C Troost
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiooncology - OncoRay, Dresden, Germany; German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ), Heidelberg, Germany; National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany, and; Helmholtz Association / Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany
| | - F Raschke
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
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Lin M, Wynne JF, Zhou B, Wang T, Lei Y, Curran WJ, Liu T, Yang X. Artificial intelligence in tumor subregion analysis based on medical imaging: A review. J Appl Clin Med Phys 2021; 22:10-26. [PMID: 34164913 PMCID: PMC8292694 DOI: 10.1002/acm2.13321] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 04/17/2021] [Accepted: 05/22/2021] [Indexed: 12/20/2022] Open
Abstract
Medical imaging is widely used in the diagnosis and treatment of cancer, and artificial intelligence (AI) has achieved tremendous success in medical image analysis. This paper reviews AI-based tumor subregion analysis in medical imaging. We summarize the latest AI-based methods for tumor subregion analysis and their applications. Specifically, we categorize the AI-based methods by training strategy: supervised and unsupervised. A detailed review of each category is presented, highlighting important contributions and achievements. Specific challenges and potential applications of AI in tumor subregion analysis are discussed.
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Affiliation(s)
- Mingquan Lin
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Jacob F. Wynne
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Boran Zhou
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Tonghe Wang
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Yang Lei
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Walter J. Curran
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
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Correlations between DTI-derived metrics and MRS metabolites in tumour regions of glioblastoma: a pilot study. Radiol Oncol 2020; 54:394-408. [PMID: 32990651 PMCID: PMC7585345 DOI: 10.2478/raon-2020-0055] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 07/31/2020] [Indexed: 02/08/2023] Open
Abstract
Introduction Specific correlations among diffusion tensor imaging (DTI)-derived metrics and magnetic resonance spectroscopy (MRS) metabolite ratios in brains with glioblastoma are still not completely understood. Patients and methods We made retrospective cohort study. MRS ratios (choline-to-N-acetyl aspartate [Cho/NAA], lipids and lactate to creatine [LL/Cr], and myo-inositol/creatine [mI/Cr]) were correlated with eleven DTI biomarkers: mean diffusivity (MD), fractional anisotropy (FA), pure isotropic diffusion (p), pure anisotropic diffusion (q), the total magnitude of the diffusion tensor (L), linear tensor (Cl), planar tensor (Cp), spherical tensor (Cs), relative anisotropy (RA), axial diffusivity (AD) and radial diffusivity (RD) at the same regions: enhanced rim, peritumoral oedema and normal-appearing white matter. Correlational analyses of 546 MRS and DTI measurements used Spearman coefficient. Results At the enhancing rim we found four significant correlations: FA ⇔ LL/Cr, Rs = -.364, p = .034; Cp ⇔ LL/Cr, Rs = .362, p = .035; q ⇔ LL/Cr, Rs = -.349, p = .035; RA ⇔ LL/Cr, Rs = -.357, p = .038. Another ten pairs of significant correlations were found in the peritumoral edema: AD ⇔ LL/Cr, AD ⇔ mI/Cr, MD ⇔ LL/Cr, MD ⇔ mI/Cr, p ⇔ LL/Cr, p ⇔ mI/ Cr, RD ⇔ mI/Cr, RD ⇔ mI/Cr, L ⇔ LL/Cr, L ⇔ mI/Cr. Conclusions DTI and MRS biomarkers answer different questions; peritumoral oedema represents the biggest challenge with at least ten significant correlations between DTI and MRS that need additional studies. The fact that DTI and MRS measures are not specific of one histologic type of tumour broadens their application to a wider variety of intracranial pathologies.
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Rahmat R, Saednia K, Haji Hosseini Khani MR, Rahmati M, Jena R, Price SJ. Multi-scale segmentation in GBM treatment using diffusion tensor imaging. Comput Biol Med 2020; 123:103815. [PMID: 32658776 PMCID: PMC7429988 DOI: 10.1016/j.compbiomed.2020.103815] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Revised: 05/06/2020] [Accepted: 05/07/2020] [Indexed: 10/31/2022]
Abstract
Glioblastoma (GBM) is the commonest primary malignant brain tumor in adults, and despite advances in multi-modality therapy, the outlook for patients has changed little in the last 10 years. Local recurrence is the predominant pattern of treatment failure, hence improved local therapies (surgery and radiotherapy) are needed to improve patient outcomes. Currently segmentation of GBM for surgery or radiotherapy (RT) planning is labor intensive, especially for high-dimensional MR imaging methods that may provide more sensitive indicators of tumor phenotype. Automating processing and segmentation of these images will aid treatment planning. Diffusion tensor magnetic resonance imaging is a recently developed technique (DTI) that is exquisitely sensitive to the ordered diffusion of water in white matter tracts. Our group has shown that decomposition of the tensor information into the isotropic component (p - shown to represent tumor invasion) and the anisotropic component (q - shown to represent the tumor bulk) can provide valuable prognostic information regarding tumor infiltration and patient survival. However, tensor decomposition of DTI data is not commonly used for neurosurgery or radiotherapy treatment planning due to difficulties in segmenting the resultant image maps. For this reason, automated techniques for segmentation of tensor decomposition maps would have significant clinical utility. In this paper, we modified a well-established convolutional neural network architecture (CNN) for medical image segmentation and used it as an automatic multi-sequence GBM segmentation based on both DTI image maps (p and q maps) and conventional MRI sequences (T2-FLAIR and T1 weighted post contrast (T1c)). In this proof-of-concept work, we have used multiple MRI sequences, each with individually defined ground truths for better understanding of the contribution of each image sequence to the segmentation performance. The high accuracy and efficiency of our proposed model demonstrates the potential of utilizing diffusion MR images for target definition in precision radiation treatment planning and surgery in routine clinical practice.
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Affiliation(s)
- Roushanak Rahmat
- Department of Clinical Neuroscience, University of Cambridge, UK.
| | - Khadijeh Saednia
- Department of Computer Engineering, Amirkabir University of Technology, Iran; Department Electrical Engineering and Computer Science, York University, Canada
| | | | - Mohamad Rahmati
- Department of Computer Engineering, Amirkabir University of Technology, Iran
| | - Raj Jena
- Oncology Centre, Addenbrooke's Hospital, Cambridge, UK
| | - Stephen J Price
- Department of Clinical Neuroscience, University of Cambridge, UK
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Yan JL, Li C, van der Hoorn A, Boonzaier NR, Matys T, Price SJ. A Neural Network Approach to Identify the Peritumoral Invasive Areas in Glioblastoma Patients by Using MR Radiomics. Sci Rep 2020; 10:9748. [PMID: 32546790 PMCID: PMC7297800 DOI: 10.1038/s41598-020-66691-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Accepted: 05/26/2020] [Indexed: 11/09/2022] Open
Abstract
The challenge in the treatment of glioblastoma is the failure to identify the cancer invasive area outside the contrast-enhancing tumour which leads to the high local progression rate. Our study aims to identify its progression from the preoperative MR radiomics. 57 newly diagnosed cerebral glioblastoma patients were included. All patients received 5-aminolevulinic acid (5-ALA) fluorescence guidance surgery and postoperative temozolomide concomitant chemoradiotherapy. Preoperative 3 T MRI data including structure MR, perfusion MR, and DTI were obtained. Voxel-based radiomics features extracted from 37 patients were used in the convolutional neural network to train and as internal validation. Another 20 patients of the cohort were tested blindly as external validation. Our results showed that the peritumoural progression areas had higher signal intensity in FLAIR (p = 0.02), rCBV (p = 0.038), and T1C (p = 0.0004), and lower intensity in ADC (p = 0.029) and DTI-p (p = 0.001) compared to non-progression area. The identification of the peritumoural progression area was done by using a supervised convolutional neural network. There was an overall accuracy of 92.6% in the training set and 78.5% in the validation set. Multimodal MR radiomics can demonstrate distinct characteristics in areas of potential progression on preoperative MRI.
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Affiliation(s)
- Jiun-Lin Yan
- Brain tumour imaging lab, Division of neurosurgery, Department of clinical neuroscience, University of Cambridge, Addenbrooke's hospital, Box 167, CB2 0QQ, Cambridge, United Kingdom.
- Department of neurosurgery, Chang Gung Memorial Hospital, 204, Keelung, Taiwan.
- Department of Chinese Medicine, Chang Gung University College of Medicine, 333, Taoyuan, Taiwan.
| | - Chao Li
- Brain tumour imaging lab, Division of neurosurgery, Department of clinical neuroscience, University of Cambridge, Addenbrooke's hospital, Box 167, CB2 0QQ, Cambridge, United Kingdom
| | - Anouk van der Hoorn
- Brain tumour imaging lab, Division of neurosurgery, Department of clinical neuroscience, University of Cambridge, Addenbrooke's hospital, Box 167, CB2 0QQ, Cambridge, United Kingdom
- Department of radiology, University of Cambridge, Addenbrooke's hospital, Box 218, CB2 0QQ, Cambridge, United Kingdom
- Department of radiology (EB44), University Medical Centre Groningen, University of Groningen, Box 30.001, 9700 RB, Groningen, The Netherlands
| | - Natalie R Boonzaier
- Brain tumour imaging lab, Division of neurosurgery, Department of clinical neuroscience, University of Cambridge, Addenbrooke's hospital, Box 167, CB2 0QQ, Cambridge, United Kingdom
| | - Tomasz Matys
- Department of radiology, University of Cambridge, Addenbrooke's hospital, Box 218, CB2 0QQ, Cambridge, United Kingdom
| | - Stephen J Price
- Brain tumour imaging lab, Division of neurosurgery, Department of clinical neuroscience, University of Cambridge, Addenbrooke's hospital, Box 167, CB2 0QQ, Cambridge, United Kingdom
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11
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Li C, Wang S, Yan JL, Piper RJ, Liu H, Torheim T, Kim H, Zou J, Boonzaier NR, Sinha R, Matys T, Markowetz F, Price SJ. Intratumoral Heterogeneity of Glioblastoma Infiltration Revealed by Joint Histogram Analysis of Diffusion Tensor Imaging. Neurosurgery 2019; 85:524-534. [PMID: 30239840 DOI: 10.1093/neuros/nyy388] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Accepted: 08/07/2018] [Indexed: 02/11/2024] Open
Abstract
BACKGROUND Glioblastoma is a heterogeneous disease characterized by its infiltrative growth, rendering complete resection impossible. Diffusion tensor imaging (DTI) shows potential in detecting tumor infiltration by reflecting microstructure disruption. OBJECTIVE To explore the heterogeneity of glioblastoma infiltration using joint histogram analysis of DTI, to investigate the incremental prognostic value of infiltrative patterns over clinical factors, and to identify specific subregions for targeted therapy. METHODS A total of 115 primary glioblastoma patients were prospectively recruited for surgery and preoperative magnetic resonance imaging. The joint histograms of decomposed anisotropic and isotropic components of DTI were constructed in both contrast-enhancing and nonenhancing tumor regions. Patient survival was analyzed with joint histogram features and relevant clinical factors. The incremental prognostic values of histogram features were assessed using receiver operating characteristic curve analysis. The correlation between the proportion of diffusion patterns and tumor progression rate was tested using Pearson correlation. RESULTS We found that joint histogram features were associated with patient survival and improved survival model performance. Specifically, the proportion of nonenhancing tumor subregion with decreased isotropic diffusion and increased anisotropic diffusion was correlated with tumor progression rate (P = .010, r = 0.35), affected progression-free survival (hazard ratio = 1.08, P < .001), and overall survival (hazard ratio = 1.36, P < .001) in multivariate models. CONCLUSION Joint histogram features of DTI showed incremental prognostic values over clinical factors for glioblastoma patients. The nonenhancing tumor subregion with decreased isotropic diffusion and increased anisotropic diffusion may indicate a more infiltrative habitat and potential treatment target.
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Affiliation(s)
- Chao Li
- Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom
- Department of Neurosurgery, Shanghai General Hospital (originally named "Shanghai First People's Hospital"), Shanghai Jiao Tong University School of Medicine, China
| | - Shuo Wang
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
| | - Jiun-Lin Yan
- Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom
- Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Taiwan
- Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Rory J Piper
- Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Hongxiang Liu
- Molecular Malignancy Laboratory, Hematology and Oncology Diagnostic Service, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Turid Torheim
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
- CRUK and EPSRC Cancer Imaging Centre in Cambridge and Manchester, Cambridge, United Kingdom
| | - Hyunjin Kim
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - Jingjing Zou
- Statistical laboratory, Centre for Mathematical Sciences, University of Cambridge, United Kingdom
| | - Natalie R Boonzaier
- Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom
- Developmental Imaging and Biophysics Section, Institute of Child Health, University College London, London, United Kingdom
| | - Rohitashwa Sinha
- Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Tomasz Matys
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Cancer Trials Unit Department of Oncology, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Florian Markowetz
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
- CRUK and EPSRC Cancer Imaging Centre in Cambridge and Manchester, Cambridge, United Kingdom
| | - Stephen J Price
- Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
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Increased intratumoral infiltration in IDH wild-type lower-grade gliomas observed with diffusion tensor imaging. J Neurooncol 2019; 145:257-263. [DOI: 10.1007/s11060-019-03291-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Accepted: 09/12/2019] [Indexed: 11/26/2022]
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Williams OA, Zeestraten EA, Benjamin P, Lambert C, Lawrence AJ, Mackinnon AD, Morris RG, Markus HS, Barrick TR, Charlton RA. Predicting Dementia in Cerebral Small Vessel Disease Using an Automatic Diffusion Tensor Image Segmentation Technique. Stroke 2019; 50:2775-2782. [PMID: 31510902 PMCID: PMC6756294 DOI: 10.1161/strokeaha.119.025843] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Supplemental Digital Content is available in the text. Cerebral small vessel disease (SVD) is the most common cause of vascular cognitive impairment, with a significant proportion of cases going on to develop dementia. We explore the extent to which diffusion tensor image segmentation technique (DSEG; which characterizes microstructural damage across the cerebrum) predicts both degree of cognitive decline and conversion to dementia, and hence may provide a useful prognostic procedure.
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Affiliation(s)
- Owen A Williams
- From the Neurosciences Research Centre, Molecular and Clinical Sciences Research Institute, St George's University of London, United Kingdom (O.A.W., E.A.Z., C.L., T.R.B.)
| | - Eva A Zeestraten
- From the Neurosciences Research Centre, Molecular and Clinical Sciences Research Institute, St George's University of London, United Kingdom (O.A.W., E.A.Z., C.L., T.R.B.)
| | - Philip Benjamin
- Department of Radiology, Charing Cross Hospital campus, Imperial College NHS Trust, United Kingdom (P.B.)
| | - Christian Lambert
- From the Neurosciences Research Centre, Molecular and Clinical Sciences Research Institute, St George's University of London, United Kingdom (O.A.W., E.A.Z., C.L., T.R.B.).,Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom (C.L.)
| | - Andrew J Lawrence
- Stroke Research Group, Clinical Neurosciences, University of Cambridge, United Kingdom (A.J.L., H.S.M.)
| | - Andrew D Mackinnon
- Atkinson Morley Regional Neuroscience Centre, St George's NHS Healthcare Trust, London, United Kingdom (A.G.M.)
| | - Robin G Morris
- Department of Psychology, King's College Institute of Psychiatry, Psychology, and Neuroscience, London, United Kingdom (R.G.M.)
| | - Hugh S Markus
- Stroke Research Group, Clinical Neurosciences, University of Cambridge, United Kingdom (A.J.L., H.S.M.)
| | - Thomas R Barrick
- From the Neurosciences Research Centre, Molecular and Clinical Sciences Research Institute, St George's University of London, United Kingdom (O.A.W., E.A.Z., C.L., T.R.B.)
| | - Rebecca A Charlton
- Department of Psychology, Goldsmiths University of London, United Kingdom (R.A.C.)
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14
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Li C, Wang S, Serra A, Torheim T, Yan JL, Boonzaier NR, Huang Y, Matys T, McLean MA, Markowetz F, Price SJ. Multi-parametric and multi-regional histogram analysis of MRI: modality integration reveals imaging phenotypes of glioblastoma. Eur Radiol 2019; 29:4718-4729. [PMID: 30707277 PMCID: PMC6682853 DOI: 10.1007/s00330-018-5984-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Accepted: 12/18/2018] [Indexed: 12/11/2022]
Abstract
OBJECTIVES Integrating multiple imaging modalities is crucial for MRI data interpretation. The purpose of this study is to determine whether a previously proposed multi-view approach can effectively integrate the histogram features from multi-parametric MRI and whether the selected features can offer incremental prognostic values over clinical variables. METHODS Eighty newly-diagnosed glioblastoma patients underwent surgery and chemoradiotherapy. Histogram features of diffusion and perfusion imaging were extracted from contrast-enhancing (CE) and non-enhancing (NE) regions independently. An unsupervised patient clustering was performed by the multi-view approach. Kaplan-Meier and Cox proportional hazards regression analyses were performed to evaluate the relevance of patient clustering to survival. The metabolic signatures of patient clusters were compared using multi-voxel spectroscopy analysis. The prognostic values of histogram features were evaluated by survival and ROC curve analyses. RESULTS Two patient clusters were generated, consisting of 53 and 27 patients respectively. Cluster 2 demonstrated better overall survival (OS) (p = 0.007) and progression-free survival (PFS) (p < 0.001) than Cluster 1. Cluster 2 displayed lower N-acetylaspartate/creatine ratio in NE region (p = 0.040). A higher mean value of anisotropic diffusion in NE region was associated with worse OS (hazard ratio [HR] = 1.40, p = 0.020) and PFS (HR = 1.36, p = 0.031). The seven features selected by this approach showed significantly incremental value in predicting 12-month OS (p = 0.020) and PFS (p = 0.022). CONCLUSIONS The multi-view clustering method can provide an effective integration of multi-parametric MRI. The histogram features selected may be used as potential prognostic markers. KEY POINTS • Multi-parametric magnetic resonance imaging captures multi-faceted tumor physiology. • Contrast-enhancing and non-enhancing tumor regions represent different tumor components with distinct clinical relevance. • Multi-view data analysis offers a method which can effectively select and integrate multi-parametric and multi-regional imaging features.
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Affiliation(s)
- Chao Li
- Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Box 167 Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
- Department of Neurosurgery, Shanghai General Hospital (originally named "Shanghai First People's Hospital"), Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- The Centre for Mathematical Imaging in Healthcare, Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, UK.
| | - Shuo Wang
- The Centre for Mathematical Imaging in Healthcare, Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, UK
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Angela Serra
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Institute of Biosciences and Medical Technologies (BioMediTech), Tampere, Finland
- NeuRoNe Lab, DISA-MIS, University of Salerno, Fisciano, SA, Italy
| | - Turid Torheim
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- CRUK & EPSRC Cancer Imaging Centre in Cambridge and Manchester, Cambridge, UK
| | - Jiun-Lin Yan
- Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Box 167 Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
- Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Taiwan
- Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Natalie R Boonzaier
- Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Box 167 Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
- Developmental Imaging and Biophysics Section, Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Yuan Huang
- The Centre for Mathematical Imaging in Healthcare, Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, UK
| | - Tomasz Matys
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Mary A McLean
- Department of Radiology, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Florian Markowetz
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- CRUK & EPSRC Cancer Imaging Centre in Cambridge and Manchester, Cambridge, UK
| | - Stephen J Price
- Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Box 167 Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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15
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Raschke F, Wesemann T, Wahl H, Appold S, Krause M, Linn J, Troost EGC. Reduced diffusion in normal appearing white matter of glioma patients following radio(chemo)therapy. Radiother Oncol 2019; 140:110-115. [PMID: 31265941 DOI: 10.1016/j.radonc.2019.06.022] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 06/04/2019] [Accepted: 06/14/2019] [Indexed: 02/08/2023]
Abstract
BACKGROUND AND PURPOSE Standard treatment of high grade gliomas includes gross tumour resection followed by radio(chemo)therapy. Radiotherapy inevitably leads to irradiation of normal brain tissue. The goal of this prospective, longitudinal study was to use MRI to quantify normal appearing white and grey matter changes following radiation treatment as a function of dose and time after radiotherapy. MATERIALS AND METHODS Pre-radiotherapy (proton or photon therapy) MRI and follow-up MRIs collected in 3 monthly intervals thereafter were analysed for 22 glioma patients and included diffusion tensor imaging, quantitative T1, T2* and proton density mapping. Abnormal tissue was excluded from analysis. MR signal changes were quantified within different dose bin regions for grey and white matter and subsequently for whole brain white matter. RESULTS We found significant reductions in mean diffusivity, radial diffusivity, axial diffusivity and T2* in normal appearing white matter regions receiving a radiation dose as low as 10-20 Gy within the observational period of up to 18 months. The magnitude of these changes increased with the received radiation dose and progressed with time after radiotherapy. Whole brain white matter also showed a significant reduction in radial diffusivity as a function of radiation dose and time after radiotherapy. No significant changes were observed in grey matter. CONCLUSION Diffusion tensor imaging and T2* imaging revealed normal appearing white matter changes following radiation treatment. The changes were dose dependant and progressed over time. Further work is needed to understand the underlying tissue changes and to correlate the observed diffusion changes with late brain malfunctions.
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Affiliation(s)
- F Raschke
- Institute of Radiooncology - OncoRay, Helmholtz-Zentrum Dresden-Rossendorf, Rossendorf, Germany; OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Germany; National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany, and; Helmholtz Association / Helmholtz-Zentrum Dresden -Rossendorf (HZDR), Dresden, Germany.
| | - T Wesemann
- Institute of Neuroradiology, University Hospital Carl Gustav Carus and Medical Faculty of Technische Universität, Dresden, Germany
| | - H Wahl
- Institute of Neuroradiology, University Hospital Carl Gustav Carus and Medical Faculty of Technische Universität, Dresden, Germany
| | - S Appold
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany
| | - M Krause
- Institute of Radiooncology - OncoRay, Helmholtz-Zentrum Dresden-Rossendorf, Rossendorf, Germany; OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Germany; National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany, and; Helmholtz Association / Helmholtz-Zentrum Dresden -Rossendorf (HZDR), Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany; German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - J Linn
- Institute of Neuroradiology, University Hospital Carl Gustav Carus and Medical Faculty of Technische Universität, Dresden, Germany
| | - E G C Troost
- Institute of Radiooncology - OncoRay, Helmholtz-Zentrum Dresden-Rossendorf, Rossendorf, Germany; OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Germany; National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany, and; Helmholtz Association / Helmholtz-Zentrum Dresden -Rossendorf (HZDR), Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany; German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ), Heidelberg, Germany
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16
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Yan JL, Li C, Boonzaier NR, Fountain DM, Larkin TJ, Matys T, van der Hoorn A, Price SJ. Multimodal MRI characteristics of the glioblastoma infiltration beyond contrast enhancement. Ther Adv Neurol Disord 2019; 12:1756286419844664. [PMID: 31205490 PMCID: PMC6535707 DOI: 10.1177/1756286419844664] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 03/25/2019] [Indexed: 11/17/2022] Open
Abstract
Our inability to identify the invasive margin of glioblastomas hampers attempts to achieve local control. Diffusion tensor imaging (DTI) has been implemented clinically to delineate the margin of the tumor infiltration, its derived anisotropic (q) values can extend beyond the contrast-enhanced area and correlates closely with the tumor. However, its correlation with tumor infiltration shown on multivoxel proton magnetic resonance spectroscopy1 (MRS) and perfusion magnetic resonance imaging (MRI) should be investigated. In this study, we aimed to show tissue characteristics of the q-defined peritumoral invasion on MRS and perfusion MRI. Patients with a primary glioblastoma were included (n = 51). Four regions of interest were analyzed; the contrast-enhanced lesion, peritumoral abnormal q region, peritumoral normal q region, and contralateral normal-appearing white matter. MRS, including choline (Cho)/creatinine (Cr), Cho/N-acetyl-aspartate (NAA) and NAA/Cr ratios, and the relative cerebral blood volume (rCBV) were analyzed. Our results showed an increase in the Cho/NAA (p = 0.0346) and Cho/Cr (p = 0.0219) ratios in the peritumoral abnormal q region, suggestive of tumor invasion. The rCBV was marginally elevated (p = 0.0798). Furthermore, the size of the abnormal q regions was correlated with survival; patients with larger abnormal q regions showed better progression-free survival (median 287 versus 53 days, p = 0.001) and overall survival (median 464 versus 274 days, p = 0.006) than those with smaller peritumoral abnormal q regions of interest. These results support how the DTI q abnormal area identifies tumor activity beyond the contrast-enhanced area, especially correlating with MRS.
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Affiliation(s)
- Jiun-Lin Yan
- Department of Neurosurgery, Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Chao Li
- Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery and Wolfson Brain Imaging Center, Department of Clinical Neuroscience, University of Cambridge, Addenbrooke's Hospital, Box 167, CB2 0QQ, Cambridge, UK
| | - Natalie R Boonzaier
- Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery and Wolfson Brain Imaging Center, Department of Clinical Neuroscience, University of Cambridge, Addenbrooke's Hospital, Box 167, CB2 0QQ, Cambridge, UK
| | - Daniel M Fountain
- School of Clinical Medicine, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Timothy J Larkin
- Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery and Wolfson Brain Imaging Center, Department of Clinical Neuroscience, University of Cambridge, Addenbrooke's Hospital, Box 167, CB2 0QQ, Cambridge, UK
| | - Tomasz Matys
- Department of Radiology, University of Cambridge, Cambridge, UK
| | | | - Stephen J Price
- Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery and Wolfson Brain Imaging Center, Department of Clinical Neuroscience, University of Cambridge, Addenbrooke's Hospital, Box 167, CB2 0QQ, Cambridge, UK
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17
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Li C, Yan JL, Torheim T, McLean MA, Boonzaier NR, Zou J, Huang Y, Yuan J, van Dijken BRJ, Matys T, Markowetz F, Price SJ. Low perfusion compartments in glioblastoma quantified by advanced magnetic resonance imaging and correlated with patient survival. Radiother Oncol 2019; 134:17-24. [PMID: 31005212 PMCID: PMC6486398 DOI: 10.1016/j.radonc.2019.01.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Revised: 12/10/2018] [Accepted: 01/09/2019] [Indexed: 12/02/2022]
Abstract
BACKGROUND AND PURPOSE Glioblastoma exhibits profound intratumoral heterogeneity in perfusion. Particularly, low perfusion may induce treatment resistance. Thus, imaging approaches that define low perfusion compartments are crucial for clinical management. MATERIALS AND METHODS A total of 112 newly diagnosed glioblastoma patients were prospectively recruited for maximal safe resection. The apparent diffusion coefficient (ADC) and relative cerebral blood volume (rCBV) were calculated from diffusion and perfusion imaging, respectively. Based on the overlapping regions of lowest rCBV quartile (rCBVL) with the lowest ADC quartile (ADCL) and highest ADC quartile (ADCH) in each tumor, two low perfusion compartments (ADCH-rCBVL and ADCL-rCBVL) were identified for volumetric analysis. Lactate and macromolecule/lipid levels were determined from multivoxel MR spectroscopic imaging. Progression-free survival (PFS) and overall survival (OS) were analyzed using Kaplan-Meier's and multivariate Cox regression analyses, to evaluate the effects of compartment volume and lactate level on survival. RESULTS Two compartments displayed higher lactate and macromolecule/lipid levels compared to contralateral normal-appearing white matter (each P < 0.001). The proportion of the ADCL-rCBVL compartment in the contrast-enhancing tumor was associated with a larger infiltration on FLAIR (P < 0.001, rho = 0.42). The minimally invasive phenotype displayed a lower proportion of the ADCL-rCBVL compartment than the localized (P = 0.031) and diffuse phenotypes (not significant). Multivariate Cox regression showed higher lactate level in the ADCL-rCBVL compartment was associated with worsened survival (PFS: HR 2.995, P = 0.047; OS: HR 4.974, P = 0.005). CONCLUSIONS Our results suggest that the ADCL-rCBVL compartment may potentially indicate a clinically measurable resistant compartment.
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Affiliation(s)
- Chao Li
- Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neuroscience, University of Cambridge, UK; Department of Neurosurgery, Shanghai General Hospital (originally named "Shanghai First People's Hospital"), Shanghai Jiao Tong University School of Medicine, China; EPSRC Centre for Mathematical Imaging in Healthcare, University of Cambridge, UK.
| | - Jiun-Lin Yan
- Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neuroscience, University of Cambridge, UK; Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Taiwan; Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Turid Torheim
- Cancer Research UK Cambridge Institute, University of Cambridge, UK; CRUK & EPSRC Cancer Imaging Centre in Cambridge and Manchester, Cambridge, UK
| | - Mary A McLean
- Cancer Research UK Cambridge Institute, University of Cambridge, UK
| | - Natalie R Boonzaier
- Developmental Imaging and Biophysics Section, Great Ormond Street Institute of Child Health, University College London, UK
| | - Jingjing Zou
- Statistical Laboratory, Centre for Mathematical Sciences, University of Cambridge, UK
| | - Yuan Huang
- EPSRC Centre for Mathematical Imaging in Healthcare, University of Cambridge, UK; Department of Radiology, University of Cambridge, UK
| | - Jianmin Yuan
- Department of Radiology, University of Cambridge, UK
| | - Bart R J van Dijken
- Department of Radiology, University Medical Center Groningen, University of Groningen, the Netherlands
| | - Tomasz Matys
- Statistical Laboratory, Centre for Mathematical Sciences, University of Cambridge, UK; Cancer Trials Unit Department of Oncology, Addenbrooke's Hospital, Cambridge, UK
| | - Florian Markowetz
- Cancer Research UK Cambridge Institute, University of Cambridge, UK; CRUK & EPSRC Cancer Imaging Centre in Cambridge and Manchester, Cambridge, UK
| | - Stephen J Price
- Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neuroscience, University of Cambridge, UK; Wolfson Brain Imaging Centre, Department of Clinical Neuroscience, University of Cambridge, UK
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Li C, Wang S, Yan JL, Torheim T, Boonzaier NR, Sinha R, Matys T, Markowetz F, Price SJ. Characterizing tumor invasiveness of glioblastoma using multiparametric magnetic resonance imaging. J Neurosurg 2019; 132:1465-1472. [PMID: 31026822 DOI: 10.3171/2018.12.jns182926] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Accepted: 12/26/2018] [Indexed: 12/16/2022]
Abstract
OBJECTIVE The objective of this study was to characterize the abnormalities revealed by diffusion tensor imaging (DTI) using MR spectroscopy (MRS) and perfusion imaging, and to evaluate the prognostic value of a proposed quantitative measure of tumor invasiveness by combining contrast-enhancing (CE) and DTI abnormalities in patients with glioblastoma. METHODS Eighty-four patients with glioblastoma were recruited preoperatively. DTI was decomposed into isotropic (p) and anisotropic (q) components. The relative cerebral blood volume (rCBV) was calculated from the dynamic susceptibility contrast imaging. Values of N-acetylaspartate, myoinositol, choline (Cho), lactate (Lac), and glutamate + glutamine (Glx) were measured from multivoxel MRS and normalized as ratios to creatine (Cr). Tumor regions of interest (ROIs) were manually segmented from the CE T1-weighted (CE-ROI) and DTI-q (q-ROI) maps. Perfusion and metabolic characteristics of these ROIs were measured and compared. The relative invasiveness coefficient (RIC) was calculated as a ratio of the characteristic radii of CE-ROI and q-ROI. The prognostic significance of RIC was tested using Kaplan-Meier and multivariate Cox regression analyses. RESULTS The Cho/Cr, Lac/Cr, and Glx/Cr in q-ROI were significantly higher than CE-ROI (p = 0.004, p = 0.005, and p = 0.007, respectively). CE-ROI had significantly higher rCBV values than q-ROI (p < 0.001). A higher RIC was associated with worse survival in a multivariate overall survival (OS) model (hazard ratio [HR] 1.40, 95% confidence interval [CI] 1.06-1.85, p = 0.016) and progression-free survival (PFS) model (HR 1.55, 95% CI 1.16-2.07, p = 0.003). An RIC cutoff value of 0.89 significantly predicted shorter OS (median 384 vs 605 days, p = 0.002) and PFS (median 244 vs 406 days, p = 0.001). CONCLUSIONS DTI-q abnormalities displayed higher tumor load and hypoxic signatures compared with CE abnormalities, whereas CE regions potentially represented the tumor proliferation edge. Integrating the extents of invasion visualized by DTI-q and CE images into clinical practice may lead to improved treatment efficacy.
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Affiliation(s)
- Chao Li
- 1Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences
- 2Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | | | - Jiun-Lin Yan
- 1Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences
- 4Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Taiwan
- 5Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Turid Torheim
- 6Cancer Research UK Cambridge Institute, and
- 7CRUK & EPSRC Cancer Imaging Centre in Cambridge and Manchester, Cambridge
| | - Natalie R Boonzaier
- 1Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences
- 8Developmental Imaging and Biophysics Section, Great Ormond Street Institute of Child Health, University College London; and
| | - Rohitashwa Sinha
- 1Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences
| | - Tomasz Matys
- 3Department of Radiology
- 9Cancer Trials Unit, Department of Oncology, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Florian Markowetz
- 6Cancer Research UK Cambridge Institute, and
- 7CRUK & EPSRC Cancer Imaging Centre in Cambridge and Manchester, Cambridge
| | - Stephen J Price
- 1Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences
- 10Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, United Kingdom
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19
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Tissue-type mapping of gliomas. NEUROIMAGE-CLINICAL 2018; 21:101648. [PMID: 30630760 PMCID: PMC6411966 DOI: 10.1016/j.nicl.2018.101648] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Revised: 11/05/2018] [Accepted: 12/22/2018] [Indexed: 11/24/2022]
Abstract
Purpose To develop a statistical method of combining multimodal MRI (mMRI) of adult glial brain tumours to generate tissue heterogeneity maps that indicate tumour grade and infiltration margins. Materials and methods We performed a retrospective analysis of mMRI from patients with histological diagnosis of glioma (n = 25). 1H Magnetic Resonance Spectroscopic Imaging (MRSI) was used to label regions of “pure” low- or high-grade tumour across image types. Normal brain and oedema characteristics were defined from healthy controls (n = 10) and brain metastasis patients (n = 10) respectively. Probability density distributions (PDD) for each tissue type were extracted from intensity normalised proton density and T2-weighted images, and p and q diffusion maps. Superpixel segmentation and Bayesian inference was used to produce whole-brain tissue-type maps. Results Total lesion volumes derived automatically from tissue-type maps correlated with those from manual delineation (p < 0.001, r = 0.87). Large high-grade volumes were determined in all grade III & IV (n = 16) tumours, in grade II gemistocytic rich astrocytomas (n = 3) and one astrocytoma with a histological diagnosis of grade II. For patients with known outcome (n = 20), patients with survival time < 2 years (3 grade II, 2 grade III and 10 grade IV) had a high-grade volume significantly greater than zero (Wilcoxon signed rank p < 0.0001) and also significantly greater high grade volume than the 5 grade II patients with survival >2 years (Mann Witney p = 0.0001). Regions classified from mMRI as oedema had non-tumour-like 1H MRS characteristics. Conclusions 1H MRSI can label tumour tissue types to enable development of a mMRI tissue type mapping algorithm, with potential to aid management of patients with glial tumours. Non-Gaussian multimodal MRI characteristics of high and low grade glioma tissue. Bayesian inference of multimodal MRI derives whole brain tumour tissue-type maps. Automated segmentation of normal and tumour tissue volumes. Visualisation of glioma heterogeneity, infiltration, necrosis and vasogenic oedema.
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Beigi M, Safari M, Ameri A, Moghadam MS, Arbabi A, Tabatabaeefar M, SalighehRad H. Findings of DTI-p maps in comparison with T 2/T 2-FLAIR to assess postoperative hyper-signal abnormal regions in patients with glioblastoma. Cancer Imaging 2018; 18:33. [PMID: 30227891 PMCID: PMC6145209 DOI: 10.1186/s40644-018-0166-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 09/07/2018] [Indexed: 01/23/2023] Open
Abstract
PURPOSE The aim of this study was to compare diffusion tensor imaging (DTI) isotropic map (p-map) with current radiographically (T2/T2-FLAIR) methods based on abnormal hyper-signal size and location of glioblastoma tumor using a semi-automatic approach. MATERIALS AND METHODS Twenty-five patients with biopsy-proved diagnosis of glioblastoma participated in this study. T2, T2-FLAIR images and diffusion tensor imaging (DTI) were acquired 1 week before radiotherapy. Hyper-signal regions on T2, T2-FLAIR and DTI p-map were segmented by means of semi-automated segmentation. Manual segmentation was used as ground truth. Dice Scores (DS) were calculated for validation of semiautomatic method. Discordance Index (DI) and area difference percentage between the three above regions from the three modalities were calculated for each patient. RESULTS Area of abnormality in the p-map was smaller than the corresponding areas in the T2 and T2-FLAIR images in 17 patients; with mean difference percentage of 30 ± 0.15 and 35 ± 0.15, respectively. Abnormal region in the p-map was larger than the corresponding areas in the T2-FLAIR and T2 images in 4 patients; with mean difference percentage of 26 ± 0.17 and 29 ± 0.28, respectively. This region in the p-map was larger than the one in the T2 image and smaller than the one in the T2-FLAIR image in 3 patients; with mean difference percentage of 34 ± 0.08 and 27 ± 0.06, respectively. Lack of concordance was observed ranged from 0.214-0.772 for T2-FLAIR/p-map (average: 0.462 ± 0.18), 0.266-0.794 for T2 /p-map (average: 0.468 ± 0.13) and 0.123-0.776 for T2/ T2-FLAIR (average: 0.423 ± 0.2). These regions on three modalities were segmented using a semi-automatic segmentation method with over 86% sensitivity, 90% specificity and 89% dice score for three modalities. CONCLUSION It is noted that T2, T2-FLAIR and DTI p-maps represent different but complementary information for delineation of glioblastoma tumor margins. Therefore, this study suggests DTI p-map modality as a candidate to improve target volume delineation based on conventional modalities, which needs further investigations with follow-up data to be confirmed.
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Affiliation(s)
- Manijeh Beigi
- Quantitative MR Imaging and Spectroscopy Group, Research Center for Cellular and Molecular Imaging, Institute for Advanced Medical Imaging, Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
| | - Mojtaba Safari
- Department of Energy Engineering, Sharif University of Technology, Tehran, Iran
| | - Ahmad Ameri
- Department of Clinical Oncology, Shahid Beheshti University of Medical Science, Tehran, Iran
| | | | - Azim Arbabi
- Department of Medical Physics, Shahid Beheshti University of Medical Science, Tehran, Iran
| | - Morteza Tabatabaeefar
- Department of Clinical Oncology, Shahid Beheshti University of Medical Science, Tehran, Iran
| | - Hamidreza SalighehRad
- Quantitative MR Imaging and Spectroscopy Group, Research Center for Cellular and Molecular Imaging, Institute for Advanced Medical Imaging, Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran.
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Soltaninejad M, Yang G, Lambrou T, Allinson N, Jones TL, Barrick TR, Howe FA, Ye X. Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 157:69-84. [PMID: 29477436 DOI: 10.1016/j.cmpb.2018.01.003] [Citation(s) in RCA: 80] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 01/03/2018] [Accepted: 01/09/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND Accurate segmentation of brain tumour in magnetic resonance images (MRI) is a difficult task due to various tumour types. Using information and features from multimodal MRI including structural MRI and isotropic (p) and anisotropic (q) components derived from the diffusion tensor imaging (DTI) may result in a more accurate analysis of brain images. METHODS We propose a novel 3D supervoxel based learning method for segmentation of tumour in multimodal MRI brain images (conventional MRI and DTI). Supervoxels are generated using the information across the multimodal MRI dataset. For each supervoxel, a variety of features including histograms of texton descriptor, calculated using a set of Gabor filters with different sizes and orientations, and first order intensity statistical features are extracted. Those features are fed into a random forests (RF) classifier to classify each supervoxel into tumour core, oedema or healthy brain tissue. RESULTS The method is evaluated on two datasets: 1) Our clinical dataset: 11 multimodal images of patients and 2) BRATS 2013 clinical dataset: 30 multimodal images. For our clinical dataset, the average detection sensitivity of tumour (including tumour core and oedema) using multimodal MRI is 86% with balanced error rate (BER) 7%; while the Dice score for automatic tumour segmentation against ground truth is 0.84. The corresponding results of the BRATS 2013 dataset are 96%, 2% and 0.89, respectively. CONCLUSION The method demonstrates promising results in the segmentation of brain tumour. Adding features from multimodal MRI images can largely increase the segmentation accuracy. The method provides a close match to expert delineation across all tumour grades, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management.
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Affiliation(s)
| | - Guang Yang
- National Heart and Lung Institute, Imperial College London, London SW7 2AZ, UK; Neurosciences Research Centre, Molecular and Clinical Sciences Institute, St. George's, University of London, London SW17 0RE, UK.
| | - Tryphon Lambrou
- School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK.
| | - Nigel Allinson
- School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK.
| | - Timothy L Jones
- Academic Neurosurgery Unit, St. George's, University of London, London SW17 0RE, UK.
| | - Thomas R Barrick
- Neurosciences Research Centre, Molecular and Clinical Sciences Institute, St. George's, University of London, London SW17 0RE, UK.
| | - Franklyn A Howe
- Neurosciences Research Centre, Molecular and Clinical Sciences Institute, St. George's, University of London, London SW17 0RE, UK.
| | - Xujiong Ye
- School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK.
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22
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Keong NC, Pena A, Price SJ, Czosnyka M, Czosnyka Z, DeVito EE, Housden CR, Sahakian BJ, Pickard JD. Diffusion tensor imaging profiles reveal specific neural tract distortion in normal pressure hydrocephalus. PLoS One 2017; 12:e0181624. [PMID: 28817574 PMCID: PMC5560677 DOI: 10.1371/journal.pone.0181624] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2016] [Accepted: 07/05/2017] [Indexed: 12/02/2022] Open
Abstract
Background The pathogenesis of normal pressure hydrocephalus (NPH) remains unclear which limits both early diagnosis and prognostication. The responsiveness to intervention of differing, complex and concurrent injury patterns on imaging have not been well-characterized. We used diffusion tensor imaging (DTI) to explore the topography and reversibility of white matter injury in NPH pre- and early after shunting. Methods Twenty-five participants (sixteen NPH patients and nine healthy controls) underwent DTI, pre-operatively and at two weeks post-intervention in patients. We interrogated 40 datasets to generate a full panel of DTI measures and corroborated findings with plots of isotropy (p) vs. anisotropy (q). Results Concurrent examination of DTI measures revealed distinct profiles for NPH patients vs. controls. PQ plots demonstrated that patterns of injury occupied discrete white matter districts. DTI profiles for different white matter tracts showed changes consistent with i) predominant transependymal diffusion with stretch/ compression, ii) oedema with or without stretch/ compression and iii) predominant stretch/ compression. Findings were specific to individual tracts and dependent upon their proximity to the ventricles. At two weeks post-intervention, there was a 6·7% drop in axial diffusivity (p = 0·022) in the posterior limb of the internal capsule, compatible with improvement in stretch/ compression, that preceded any discernible changes in clinical outcome. On PQ plots, the trajectories of the posterior limb of the internal capsule and inferior longitudinal fasciculus suggested attempted ‘round trips’. i.e. return to normality. Conclusion DTI profiling with p:q correlation may offer a non-invasive biomarker of the characteristics of potentially reversible white matter injury.
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Affiliation(s)
- Nicole C Keong
- Department of Neurosurgery, National Neuroscience Institute and Duke-NUS Medical School, Singapore, Singapore.,Neurosurgical Division, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Alonso Pena
- SDA Bocconi School of Management, Milan, Italy
| | - Stephen J Price
- Neurosurgical Division, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Marek Czosnyka
- Neurosurgical Division, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Zofia Czosnyka
- Neurosurgical Division, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Elise E DeVito
- Department of Psychiatry and MRC/ Wellcome Trust Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, United Kingdom.,Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, United States of America
| | - Charlotte R Housden
- Department of Psychiatry and MRC/ Wellcome Trust Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, United Kingdom
| | - Barbara J Sahakian
- Department of Psychiatry and MRC/ Wellcome Trust Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, United Kingdom
| | - John D Pickard
- Neurosurgical Division, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
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23
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Williams OA, Zeestraten EA, Benjamin P, Lambert C, Lawrence AJ, Mackinnon AD, Morris RG, Markus HS, Charlton RA, Barrick TR. Diffusion tensor image segmentation of the cerebrum provides a single measure of cerebral small vessel disease severity related to cognitive change. Neuroimage Clin 2017; 16:330-342. [PMID: 28861335 PMCID: PMC5568143 DOI: 10.1016/j.nicl.2017.08.016] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2017] [Revised: 07/05/2017] [Accepted: 08/12/2017] [Indexed: 02/02/2023]
Abstract
Cerebral small vessel disease (SVD) is the primary cause of vascular cognitive impairment and is associated with decline in executive function (EF) and information processing speed (IPS). Imaging biomarkers are needed that can monitor and identify individuals at risk of severe cognitive decline. Recently there has been interest in combining several magnetic resonance imaging (MRI) markers of SVD into a unitary score to describe disease severity. Here we apply a diffusion tensor image (DTI) segmentation technique (DSEG) to describe SVD related changes in a single unitary score across the whole cerebrum, to investigate its relationship with cognitive change over a three-year period. 98 patients (aged 43-89) with SVD underwent annual MRI scanning and cognitive testing for up to three years. DSEG provides a vector of 16 discrete segments describing brain microstructure of healthy and/or damaged tissue. By calculating the scalar product of each DSEG vector in reference to that of a healthy ageing control we generate an angular measure (DSEG θ) describing the patients' brain tissue microstructural similarity to a disease free model of a healthy ageing brain. Conventional MRI markers of SVD brain change were also assessed including white matter hyperintensities, cerebral atrophy, incident lacunes, cerebral-microbleeds, and white matter microstructural damage measured by DTI histogram parameters. The impact of brain change on cognition was explored using linear mixed-effects models. Post-hoc sample size analysis was used to assess the viability of DSEG θ as a tool for clinical trials. Changes in brain structure described by DSEG θ were related to change in EF and IPS (p < 0.001) and remained significant in multivariate models including other MRI markers of SVD as well as age, gender and premorbid IQ. Of the conventional markers, presence of new lacunes was the only marker to remain a significant predictor of change in EF and IPS in the multivariate models (p = 0.002). Change in DSEG θ was also related to change in all other MRI markers (p < 0.017), suggesting it may be used as a surrogate marker of SVD damage across the cerebrum. Sample size estimates indicated that fewer patients would be required to detect treatment effects using DSEG θ compared to conventional MRI and DTI markers of SVD severity. DSEG θ is a powerful tool for characterising subtle brain change in SVD that has a negative impact on cognition and remains a significant predictor of cognitive change when other MRI markers of brain change are accounted for. DSEG provides an automatic segmentation of the whole cerebrum that is sensitive to a range of SVD related structural changes and successfully predicts cognitive change. Power analysis shows DSEG θ has potential as a monitoring tool in clinical trials. As such it may provide a marker of SVD severity from a single imaging modality (i.e. DTIs).
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Affiliation(s)
- Owen A. Williams
- Neuroscience Research Centre, Molecular and Clinical Sciences Research Institute, St George's University of London, London, UK
| | - Eva A. Zeestraten
- Neuroscience Research Centre, Molecular and Clinical Sciences Research Institute, St George's University of London, London, UK
| | - Philip Benjamin
- Department of Radiology, Charing Cross Hospital Campus, Imperial College NHS Trust, London, UK
| | - Christian Lambert
- Neuroscience Research Centre, Molecular and Clinical Sciences Research Institute, St George's University of London, London, UK
| | - Andrew J. Lawrence
- Stroke Research Group, Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Andrew D. Mackinnon
- Atkinson Morley Regional Neuroscience Centre, St George's NHS Healthcare Trust, London, UK
| | - Robin G. Morris
- Department of Psychology, King's College Institute of Psychiatry, Psychology, and Neuroscience, London, UK
| | - Hugh S. Markus
- Stroke Research Group, Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | | | - Thomas R. Barrick
- Neuroscience Research Centre, Molecular and Clinical Sciences Research Institute, St George's University of London, London, UK
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24
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Hiremath SB, Muraleedharan A, Kumar S, Nagesh C, Kesavadas C, Abraham M, Kapilamoorthy TR, Thomas B. Combining Diffusion Tensor Metrics and DSC Perfusion Imaging: Can It Improve the Diagnostic Accuracy in Differentiating Tumefactive Demyelination from High-Grade Glioma? AJNR Am J Neuroradiol 2017; 38:685-690. [PMID: 28209583 DOI: 10.3174/ajnr.a5089] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Accepted: 12/04/2016] [Indexed: 12/12/2022]
Abstract
BACKGROUND AND PURPOSE Tumefactive demyelinating lesions with atypical features can mimic high-grade gliomas on conventional imaging sequences. The aim of this study was to assess the role of conventional imaging, DTI metrics (p:q tensor decomposition), and DSC perfusion in differentiating tumefactive demyelinating lesions and high-grade gliomas. MATERIALS AND METHODS Fourteen patients with tumefactive demyelinating lesions and 21 patients with high-grade gliomas underwent brain MR imaging with conventional, DTI, and DSC perfusion imaging. Imaging sequences were assessed for differentiation of the lesions. DTI metrics in the enhancing areas and perilesional hyperintensity were obtained by ROI analysis, and the relative CBV values in enhancing areas were calculated on DSC perfusion imaging. RESULTS Conventional imaging sequences had a sensitivity of 80.9% and specificity of 57.1% in differentiating high-grade gliomas (P = .049) from tumefactive demyelinating lesions. DTI metrics (p:q tensor decomposition) and DSC perfusion demonstrated a statistically significant difference in the mean values of ADC, the isotropic component of the diffusion tensor, the anisotropic component of the diffusion tensor, the total magnitude of the diffusion tensor, and rCBV among enhancing portions in tumefactive demyelinating lesions and high-grade gliomas (P ≤ .02), with the highest specificity for ADC, the anisotropic component of the diffusion tensor, and relative CBV (92.9%). Mean fractional anisotropy values showed no significant statistical difference between tumefactive demyelinating lesions and high-grade gliomas. The combination of DTI and DSC parameters improved the diagnostic accuracy (area under the curve = 0.901). Addition of a heterogeneous enhancement pattern to DTI and DSC parameters improved it further (area under the curve = 0.966). The sensitivity increased from 71.4% to 85.7% after the addition of the enhancement pattern. CONCLUSIONS DTI and DSC perfusion add profoundly to conventional imaging in differentiating tumefactive demyelinating lesions and high-grade gliomas. The combination of DTI metrics and DSC perfusion markedly improved diagnostic accuracy.
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Affiliation(s)
- S B Hiremath
- From the Departments of Imaging Sciences and Interventional Radiology (S.B.H., A.M., S.K., C.N., C.K., T.R.K., B.T.)
| | - A Muraleedharan
- From the Departments of Imaging Sciences and Interventional Radiology (S.B.H., A.M., S.K., C.N., C.K., T.R.K., B.T.)
| | - S Kumar
- From the Departments of Imaging Sciences and Interventional Radiology (S.B.H., A.M., S.K., C.N., C.K., T.R.K., B.T.)
| | - C Nagesh
- From the Departments of Imaging Sciences and Interventional Radiology (S.B.H., A.M., S.K., C.N., C.K., T.R.K., B.T.)
| | - C Kesavadas
- From the Departments of Imaging Sciences and Interventional Radiology (S.B.H., A.M., S.K., C.N., C.K., T.R.K., B.T.)
| | - M Abraham
- Neurosurgery (M.A.), Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, Kerala, India
| | - T R Kapilamoorthy
- From the Departments of Imaging Sciences and Interventional Radiology (S.B.H., A.M., S.K., C.N., C.K., T.R.K., B.T.)
| | - B Thomas
- From the Departments of Imaging Sciences and Interventional Radiology (S.B.H., A.M., S.K., C.N., C.K., T.R.K., B.T.)
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Yan JL, van der Hoorn A, Larkin TJ, Boonzaier NR, Matys T, Price SJ. Extent of resection of peritumoral diffusion tensor imaging–detected abnormality as a predictor of survival in adult glioblastoma patients. J Neurosurg 2017; 126:234-241. [DOI: 10.3171/2016.1.jns152153] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
OBJECTIVE
Diffusion tensor imaging (DTI) has been shown to detect tumor invasion in glioblastoma patients and has been applied in surgical planning. However, the clinical value of the extent of resection based on DTI is unclear. Therefore, the correlation between the extent of resection of DTI abnormalities and patients' outcome was retrospectively reviewed.
METHODS
A review was conducted of 31 patients with newly diagnosed supratentorial glioblastoma who underwent standard 5-aminolevulinic acid–aided surgery with the aim of maximal resection of the enhancing tumor component. All patients underwent presurgical MRI, including volumetric postcontrast T1-weighted imaging, DTI, and FLAIR. Postsurgical anatomical MR images were obtained within 72 hours of resection. The diffusion tensor was split into an isotropic (p) and anisotropic (q) component. The extent of resection was measured for the abnormal area on the p, q, FLAIR, and postcontrast T1-weighted images. Data were analyzed in relation to patients' outcome using univariate and multivariate Cox regression models controlling for possible confounding factors including age, O6-methylguanine-DNA-methyltrans-ferase methylation status, and isocitrate dehydrogenase–1 mutation.
RESULTS
Complete resection of the enhanced tumor shown on the postcontrast T1-weighted images was achieved in 24 of 31 patients (77%). The mean extent of resection of the abnormal p, q, and FLAIR areas was 57%, 83%, and 59%, respectively. Increased resection of the abnormal p and q areas correlated positively with progression-free survival (p = 0.009 and p = 0.006, respectively). Additionally, a larger, residual, abnormal q volume predicted significantly shorter time to progression (p = 0.008). More extensive resection of the abnormal q and contrast-enhanced area improved overall survival (p = 0.041 and 0.050, respectively).
CONCLUSIONS
Longer progression-free survival and overall survival were seen in glioblastoma patients in whom more DTI-documented abnormality was resected, which was previously shown to represent infiltrative tumor. This highlights the potential usefulness and the importance of an extended resection based on DTI-derived maps.
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Affiliation(s)
- Jiun-Lin Yan
- 1Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery,
- 2Wolfson Brain Imaging Centre, Department of Clinical Neuroscience, and
- 4Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung
- 5Chang Gung University College of Medicine, Taoyuan, Taiwan; and
| | - Anouk van der Hoorn
- 1Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery,
- 3Department of Radiology, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom
- 6Department of Radiology, University Medical Centre Groningen, University of Groningen, The Netherlands
| | - Timothy J. Larkin
- 1Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery,
| | | | - Tomasz Matys
- 3Department of Radiology, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Stephen J. Price
- 1Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery,
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26
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Price SJ, Allinson K, Liu H, Boonzaier NR, Yan JL, Lupson VC, Larkin TJ. Less Invasive Phenotype Found in Isocitrate Dehydrogenase-mutated Glioblastomas than in Isocitrate Dehydrogenase Wild-Type Glioblastomas: A Diffusion-Tensor Imaging Study. Radiology 2016; 283:215-221. [PMID: 27849434 DOI: 10.1148/radiol.2016152679] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Purpose To explore the diffusion-tensor (DT) imaging-defined invasive phenotypes of both isocitrate dehydrogenase (IDH-1)-mutated and IDH-1 wild-type glioblastomas. Materials and Methods Seventy patients with glioblastoma were prospectively recruited and imaged preoperatively. All patients provided signed consent, and the local research ethics committee approved the study. Patients underwent surgical resection, and tumor samples underwent immunohistochemistry for IDH-1 R132H mutations. DT imaging data were coregistered to the anatomic magnetic resonance study and reconstructed to provide the anisotropic and isotropic components of the DT. The invasive phenotype was determined by using previously published criteria and correlated with IDH-1 mutation status by using the Freeman-Halton extension of the Fisher exact probability test. Results Nine patients had an IDH-1 mutation and 61 had IDH-1 wild type. All of the patients with IDH-1 mutation had a minimally invasive DT imaging phenotype. Among the IDH-1 wild-type tumors, 42 of 61 (69%) were diffusively invasive glioblastomas, 14 of 61 (23%) were locally invasive, and five of 61 (8%) were minimally invasive (P < .001). Conclusion IDH-mutated glioblastomas have a less invasive phenotype compared with IDH wild type. This finding may have implications for individualizing the extent of surgical resection and radiation therapy volumes.
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Affiliation(s)
- Stephen J Price
- From the Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery (S.J.P., N.R.B., J.L.Y., T.J.L.), and Wolfson Brain Imaging Centre, Department of Clinical Neurosciences (S.J.P., N.R.B., V.C.L., T.J.L.), University of Cambridge, Cambridge Biomedical Campus, Cambridge CB2 0QQ, England; and Department of Histopathology (H.L.) and Molecular Malignancy Laboratory (K.A., H.L.), Addenbrooke's Hospital, Cambridge, England
| | - Kieren Allinson
- From the Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery (S.J.P., N.R.B., J.L.Y., T.J.L.), and Wolfson Brain Imaging Centre, Department of Clinical Neurosciences (S.J.P., N.R.B., V.C.L., T.J.L.), University of Cambridge, Cambridge Biomedical Campus, Cambridge CB2 0QQ, England; and Department of Histopathology (H.L.) and Molecular Malignancy Laboratory (K.A., H.L.), Addenbrooke's Hospital, Cambridge, England
| | - Hongxiang Liu
- From the Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery (S.J.P., N.R.B., J.L.Y., T.J.L.), and Wolfson Brain Imaging Centre, Department of Clinical Neurosciences (S.J.P., N.R.B., V.C.L., T.J.L.), University of Cambridge, Cambridge Biomedical Campus, Cambridge CB2 0QQ, England; and Department of Histopathology (H.L.) and Molecular Malignancy Laboratory (K.A., H.L.), Addenbrooke's Hospital, Cambridge, England
| | - Natalie R Boonzaier
- From the Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery (S.J.P., N.R.B., J.L.Y., T.J.L.), and Wolfson Brain Imaging Centre, Department of Clinical Neurosciences (S.J.P., N.R.B., V.C.L., T.J.L.), University of Cambridge, Cambridge Biomedical Campus, Cambridge CB2 0QQ, England; and Department of Histopathology (H.L.) and Molecular Malignancy Laboratory (K.A., H.L.), Addenbrooke's Hospital, Cambridge, England
| | - Jiun-Lin Yan
- From the Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery (S.J.P., N.R.B., J.L.Y., T.J.L.), and Wolfson Brain Imaging Centre, Department of Clinical Neurosciences (S.J.P., N.R.B., V.C.L., T.J.L.), University of Cambridge, Cambridge Biomedical Campus, Cambridge CB2 0QQ, England; and Department of Histopathology (H.L.) and Molecular Malignancy Laboratory (K.A., H.L.), Addenbrooke's Hospital, Cambridge, England
| | - Victoria C Lupson
- From the Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery (S.J.P., N.R.B., J.L.Y., T.J.L.), and Wolfson Brain Imaging Centre, Department of Clinical Neurosciences (S.J.P., N.R.B., V.C.L., T.J.L.), University of Cambridge, Cambridge Biomedical Campus, Cambridge CB2 0QQ, England; and Department of Histopathology (H.L.) and Molecular Malignancy Laboratory (K.A., H.L.), Addenbrooke's Hospital, Cambridge, England
| | - Timothy J Larkin
- From the Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery (S.J.P., N.R.B., J.L.Y., T.J.L.), and Wolfson Brain Imaging Centre, Department of Clinical Neurosciences (S.J.P., N.R.B., V.C.L., T.J.L.), University of Cambridge, Cambridge Biomedical Campus, Cambridge CB2 0QQ, England; and Department of Histopathology (H.L.) and Molecular Malignancy Laboratory (K.A., H.L.), Addenbrooke's Hospital, Cambridge, England
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Srinivasan K, Thomas B, Shah D, Kannath SK, Menon G, Sandhyamani S, Kesavadas C, Kapilamoorthy TR. Quantification of diffusion and anisotropy in intracranial epidermoids using diffusion tensor metrics and p: q tensor decomposition. J Neuroradiol 2016; 43:363-370. [PMID: 27318387 DOI: 10.1016/j.neurad.2016.02.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2015] [Revised: 02/22/2016] [Accepted: 02/24/2016] [Indexed: 10/21/2022]
Abstract
PURPOSE To quantitatively evaluate the diffusion tensor metrics p, q, L and fractional anisotropy in intracranial epidermoids in comparison with normal white matter in the splenium of the corpus callosum. METHODS This retrospective study included 20 consecutive patients referred to our institute. All patients had a magnetic resonance imaging (MRI) study on a 1.5-Tesla MR system. A spin-echo echo-planar DTI sequence with diffusion gradients along 30 non-collinear directions was performed. The eigen values (λ1, λ2, λ3) were computed for each voxel and, using p: q tensor decomposition, the DTI metrics p, q and L-values and fractional anositropy (FA) were calculated. The region of interest (ROI) (6 pixels each) was placed within the lesion in all the cases and in the splenium of the corpus callosum. RESULTS The mean FA in the lesion and splenium were 0.50 and 0.88 respectively, with a statistically significant difference between them (P<0.01). On p: q tensor decomposition, the mean p-value in the epidermoid was 1.55±0.24 and 1.35±0.20 in the splenium; the mean q-values in the epidermoid was 0.67±0.13 and 1.27±0.17 in the splenium; the differences were statistically significant (P=0.01 and <0.01 respectively). The significant difference between p- and q-values in epidermoids compared with the splenium of callosum was probably due to structural and orientation differences in the keratin flakes in epidermoids and white matter bundles in the callosum. However, no significant statistical difference in L-values was noted (P=0.44). CONCLUSION DTI metrics p and q have the potential to quantify the diffusion and anisotropy in various tissues thereby gaining information about their internal architecture. The results also suggest that significant differences of DTI metrics p and q between epidermoid and the splenium of the corpus callosum are due to the difference in structural organization within them.
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Affiliation(s)
- K Srinivasan
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology Trivandrum, India
| | - B Thomas
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology Trivandrum, India.
| | - D Shah
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology Trivandrum, India
| | - S K Kannath
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology Trivandrum, India
| | - G Menon
- Department of Neurosurgery, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, India
| | - S Sandhyamani
- Department of Pathology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, India
| | - C Kesavadas
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology Trivandrum, India
| | - T R Kapilamoorthy
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology Trivandrum, India
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Price SJ, Young AMH, Scotton WJ, Ching J, Mohsen LA, Boonzaier NR, Lupson VC, Griffiths JR, McLean MA, Larkin TJ. Multimodal MRI can identify perfusion and metabolic changes in the invasive margin of glioblastomas. J Magn Reson Imaging 2016; 43:487-94. [PMID: 26140696 PMCID: PMC5008200 DOI: 10.1002/jmri.24996] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2015] [Accepted: 06/23/2015] [Indexed: 12/13/2022] Open
Abstract
PURPOSE To use perfusion and magnetic resonance (MR) spectroscopy to compare the diffusion tensor imaging (DTI)-defined invasive and noninvasive regions. Invasion of normal brain is a cardinal feature of glioblastomas (GBM) and a major cause of treatment failure. DTI can identify invasive regions. MATERIALS AND METHODS In all, 50 GBM patients were imaged preoperatively at 3T with anatomic sequences, DTI, dynamic susceptibility perfusion MR (DSCI), and multivoxel spectroscopy. The DTI and DSCI data were coregistered to the spectroscopy data and regions of interest (ROIs) were made in the invasive (determined by DTI), noninvasive regions, and normal brain. Values of relative cerebral blood volume (rCBV), N-acetyl aspartate (NAA), myoinositol (mI), total choline (Cho), and glutamate + glutamine (Glx) normalized to creatine (Cr) and Cho/NAA were measured at each ROI. RESULTS Invasive regions showed significant increases in rCBV, suggesting angiogenesis (invasive rCBV 1.64 [95% confidence interval, CI: 1.5-1.76] vs. noninvasive 1.14 [1.09-1.18]; P < 0.001), Cho/Cr (invasive 0.42 [0.38-0.46] vs. noninvasive 0.35 [0.31-0.38]; P = 0.02) and Cho/NAA (invasive 0.54 [0.41-0.68] vs. noninvasive 0.37 [0.29-0.45]; P = < 0.03), suggesting proliferation, and Glx/Cr (invasive 1.54 [1.27-1.82] vs. noninvasive 1.3 [1.13-1.47]; P = 0.028), suggesting glutamate release; and a significantly reduced NAA/Cr (invasive 0.95 [0.85-1.05] vs. noninvasive 1.19 [1.06-1.31]; P = 0.008). The mI/Cr was not different between the three ROIs (invasive 1.2 [0.99-1.41] vs. noninvasive 1.3 [1.14-1.46]; P = 0.68). In the noninvasive regions, the values were not different from normal brain. CONCLUSION Combining DTI to identify the invasive region with perfusion and spectroscopy, we can identify changes in invasive regions not seen in noninvasive regions.
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Affiliation(s)
- Stephen J Price
- Neurosurgery Division, Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
| | - Adam M H Young
- Neurosurgery Division, Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
| | - William J Scotton
- Neurosurgery Division, Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
| | - Jared Ching
- Neurosurgery Division, Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
| | - Laila A Mohsen
- University Department of Radiology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
| | - Natalie R Boonzaier
- Neurosurgery Division, Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
| | - Victoria C Lupson
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
| | - John R Griffiths
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK
| | - Mary A McLean
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK
| | - Timothy J Larkin
- Neurosurgery Division, Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
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Cortez-Conradis D, Rios C, Moreno-Jimenez S, Roldan-Valadez E. Partial correlation analyses of global diffusion tensor imaging-derived metrics in glioblastoma multiforme: Pilot study. World J Radiol 2015; 7:405-414. [PMID: 26644826 PMCID: PMC4663379 DOI: 10.4329/wjr.v7.i11.405] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2015] [Revised: 08/13/2015] [Accepted: 10/13/2015] [Indexed: 02/06/2023] Open
Abstract
AIM: To determine existing correlates among diffusion tensor imaging (DTI)-derived metrics in healthy brains and brains with glioblastoma multiforme (GBM).
METHODS: Case-control study using DTI data from brain magnetic resonance imaging of 34 controls (mean, 41.47; SD, ± 21.94 years; range, 21-80 years) and 27 patients with GBM (mean, SD; 48.41 ± 15.18 years; range, 18-78 years). Image postprocessing using FSL software calculated eleven tensor metrics: fractional (FA) and relative anisotropy; pure isotropic (p) and anisotropic diffusions (q), total magnitude of diffusion (L); linear (Cl), planar (Cp) and spherical tensors (Cs); mean (MD), axial (AD) and radial diffusivities (RD). Partial correlation analyses (controlling the effect of age and gender) and multivariate Mancova were performed.
RESULTS: There was a normal distribution for all metrics. Comparing healthy brains vs brains with GBM, there were significant very strong bivariate correlations only depicted in GBM: [FA↔Cl (+)], [FA↔q (+)], [p↔AD (+)], [AD↔MD (+)], and [MD↔RD (+)]. Among 56 pairs of bivariate correlations, only seven were significantly different. The diagnosis variable depicted a main effect [F-value (11, 23) = 11.842, P≤ 0.001], with partial eta squared = 0.850, meaning a large effect size; age showed a similar result. The age also had a significant influence as a covariate [F (11, 23) = 10.523, P < 0.001], with a large effect size (partial eta squared = 0.834).
CONCLUSION: DTI-derived metrics depict significant differences between healthy brains and brains with GBM, with specific magnitudes and correlations. This study provides reference data and makes a contribution to decrease the underlying empiricism in the use of DTI parameters in brain imaging.
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Boonzaier NR, Piccirillo SGM, Watts C, Price SJ. Assessing and monitoring intratumor heterogeneity in glioblastoma: how far has multimodal imaging come? CNS Oncol 2015; 4:399-410. [PMID: 26497327 DOI: 10.2217/cns.15.20] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Glioblastoma demonstrates imaging features of intratumor heterogeneity that result from underlying heterogeneous biological properties. This stems from variations in cellular behavior that result from genetic mutations that either drive, or are driven by, heterogeneous microenvironment conditions. Among all imaging methods available, only T1-weighted contrast-enhancing and T2-weighted fluid-attenuated inversion recovery are used in standard clinical glioblastoma assessment and monitoring. Advanced imaging modalities are still considered emerging techniques as appropriate end points and robust methodologies are missing from clinical trials. Discovering how these images specifically relate to the underlying tumor biology may aid in improving quality of clinical trials and understanding the factors involved in regional responses to treatment, including variable drug uptake and effect of radiotherapy. Upon validation and standardization of emerging MR techniques, providing information based on the underlying tumor biology, these images may allow for clinical decision-making that is tailored to an individual's response to treatment.
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Affiliation(s)
- Natalie R Boonzaier
- Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, UK.,Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, Cambridge Biomedical Campus, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Sara G M Piccirillo
- Cambridge Centre for Brain Repair, Department of Clinical Neurosciences, Forvie Site, Robinson Way, Cambridge CB2 0PY, UK
| | - Colin Watts
- Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Stephen J Price
- Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, UK.,Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, Cambridge Biomedical Campus, University of Cambridge, Cambridge CB2 0QQ, UK
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Castellano A, Donativi M, Rudà R, De Nunzio G, Riva M, Iadanza A, Bertero L, Rucco M, Bello L, Soffietti R, Falini A. Evaluation of low-grade glioma structural changes after chemotherapy using DTI-based histogram analysis and functional diffusion maps. Eur Radiol 2015; 26:1263-73. [PMID: 26318368 DOI: 10.1007/s00330-015-3934-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2015] [Revised: 07/16/2015] [Accepted: 07/20/2015] [Indexed: 12/13/2022]
Abstract
OBJECTIVES To explore the role of diffusion tensor imaging (DTI)-based histogram analysis and functional diffusion maps (fDMs) in evaluating structural changes of low-grade gliomas (LGGs) receiving temozolomide (TMZ) chemotherapy. METHODS Twenty-one LGG patients underwent 3T-MR examinations before and after three and six cycles of dose-dense TMZ, including 3D-fluid-attenuated inversion recovery (FLAIR) sequences and DTI (b = 1000 s/mm(2), 32 directions). Mean diffusivity (MD), fractional anisotropy (FA), and tensor-decomposition DTI maps (p and q) were obtained. Histogram and fDM analyses were performed on co-registered baseline and post-chemotherapy maps. DTI changes were compared with modifications of tumour area and volume [according to Response Assessment in Neuro-Oncology (RANO) criteria], and seizure response. RESULTS After three cycles of TMZ, 20/21 patients were stable according to RANO criteria, but DTI changes were observed in all patients (Wilcoxon test, P ≤ 0.03). After six cycles, DTI changes were more pronounced (P ≤ 0.005). Seventy-five percent of patients had early seizure response with significant improvement of DTI values, maintaining stability on FLAIR. Early changes of the 25th percentiles of p and MD predicted final volume change (R(2) = 0.614 and 0.561, P < 0.0005, respectively). TMZ-related changes were located mainly at tumour borders on p and MD fDMs. CONCLUSIONS DTI-based histogram and fDM analyses are useful techniques to evaluate the early effects of TMZ chemotherapy in LGG patients. KEY POINTS • DTI helps to assess the efficacy of chemotherapy in low-grade gliomas. • Histogram analysis of DTI metrics quantifies structural changes in tumour tissue. • Functional diffusion maps (fDMs) spatially localize the changes of DTI metrics. • Changes in DTI histograms and fDMs precede changes in conventional MRI. • Early changes in DTI histograms and fDMs correlate with seizure response.
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Affiliation(s)
- Antonella Castellano
- Neuroradiology Unit and CERMAC, San Raffaele Scientific Institute and Vita-Salute San Raffaele University, Via Olgettina 60, 20132, Milano, Italy
| | - Marina Donativi
- Department of Mathematics and Physics "Ennio De Giorgi" and A.D.A.M. (Advanced Data Analysis in Medicine), University of Salento, Lecce, Italy
| | - Roberta Rudà
- Department of Neuro-oncology, University of Torino, Turin, Italy
| | - Giorgio De Nunzio
- Department of Mathematics and Physics "Ennio De Giorgi" and A.D.A.M. (Advanced Data Analysis in Medicine), University of Salento, Lecce, Italy
- INFN (National Institute of Nuclear Physics), Lecce, Italy
| | - Marco Riva
- Department of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, Milan, and Humanitas Research Hospital, Rozzano, MI, Italy
| | - Antonella Iadanza
- Neuroradiology Unit and CERMAC, San Raffaele Scientific Institute and Vita-Salute San Raffaele University, Via Olgettina 60, 20132, Milano, Italy
| | - Luca Bertero
- Department of Neuro-oncology, University of Torino, Turin, Italy
| | - Matteo Rucco
- School of Science and Technology, Computer Science Division, University of Camerino, Camerino, MC, Italy
| | - Lorenzo Bello
- Department of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, Milan, and Humanitas Research Hospital, Rozzano, MI, Italy
| | | | - Andrea Falini
- Neuroradiology Unit and CERMAC, San Raffaele Scientific Institute and Vita-Salute San Raffaele University, Via Olgettina 60, 20132, Milano, Italy.
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Price SJ. Imaging Markers of Isocitrate Dehydrogenase-1 Mutations in Gliomas. Neurosurgery 2015; 62 Suppl 1:166-70. [DOI: 10.1227/neu.0000000000000812] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
- Stephen J. Price
- Neurosurgery Division, Department of Clinical Neurosciences and Wolfson Brain Imaging Centre, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
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Jones TL, Byrnes TJ, Yang G, Howe FA, Bell BA, Barrick TR. Brain tumor classification using the diffusion tensor image segmentation (D-SEG) technique. Neuro Oncol 2014; 17:466-76. [PMID: 25121771 PMCID: PMC4483092 DOI: 10.1093/neuonc/nou159] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2014] [Accepted: 07/07/2014] [Indexed: 11/29/2022] Open
Abstract
Background There is an increasing demand for noninvasive brain tumor biomarkers to guide surgery and subsequent oncotherapy. We present a novel whole-brain diffusion tensor imaging (DTI) segmentation (D-SEG) to delineate tumor volumes of interest (VOIs) for subsequent classification of tumor type. D-SEG uses isotropic (p) and anisotropic (q) components of the diffusion tensor to segment regions with similar diffusion characteristics. Methods DTI scans were acquired from 95 patients with low- and high-grade glioma, metastases, and meningioma and from 29 healthy subjects. D-SEG uses k-means clustering of the 2D (p,q) space to generate segments with different isotropic and anisotropic diffusion characteristics. Results Our results are visualized using a novel RGB color scheme incorporating p, q and T2-weighted information within each segment. The volumetric contribution of each segment to gray matter, white matter, and cerebrospinal fluid spaces was used to generate healthy tissue D-SEG spectra. Tumor VOIs were extracted using a semiautomated flood-filling technique and D-SEG spectra were computed within the VOI. Classification of tumor type using D-SEG spectra was performed using support vector machines. D-SEG was computationally fast and stable and delineated regions of healthy tissue from tumor and edema. D-SEG spectra were consistent for each tumor type, with constituent diffusion characteristics potentially reflecting regional differences in tissue microstructure. Support vector machines classified tumor type with an overall accuracy of 94.7%, providing better classification than previously reported. Conclusions D-SEG presents a user-friendly, semiautomated biomarker that may provide a valuable adjunct in noninvasive brain tumor diagnosis and treatment planning.
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Affiliation(s)
- Timothy L Jones
- Academic Neurosurgery Unit, St. Georges, University of London, London, UK (T.L.J., T.J.B., B.A.B.); Neurosciences Research Centre, Cardio-vascular and Cell Sciences Institute, St. George's, University of London, London, UK (G.Y., F.A.H., T.R.B.)
| | - Tiernan J Byrnes
- Academic Neurosurgery Unit, St. Georges, University of London, London, UK (T.L.J., T.J.B., B.A.B.); Neurosciences Research Centre, Cardio-vascular and Cell Sciences Institute, St. George's, University of London, London, UK (G.Y., F.A.H., T.R.B.)
| | - Guang Yang
- Academic Neurosurgery Unit, St. Georges, University of London, London, UK (T.L.J., T.J.B., B.A.B.); Neurosciences Research Centre, Cardio-vascular and Cell Sciences Institute, St. George's, University of London, London, UK (G.Y., F.A.H., T.R.B.)
| | - Franklyn A Howe
- Academic Neurosurgery Unit, St. Georges, University of London, London, UK (T.L.J., T.J.B., B.A.B.); Neurosciences Research Centre, Cardio-vascular and Cell Sciences Institute, St. George's, University of London, London, UK (G.Y., F.A.H., T.R.B.)
| | - B Anthony Bell
- Academic Neurosurgery Unit, St. Georges, University of London, London, UK (T.L.J., T.J.B., B.A.B.); Neurosciences Research Centre, Cardio-vascular and Cell Sciences Institute, St. George's, University of London, London, UK (G.Y., F.A.H., T.R.B.)
| | - Thomas R Barrick
- Academic Neurosurgery Unit, St. Georges, University of London, London, UK (T.L.J., T.J.B., B.A.B.); Neurosciences Research Centre, Cardio-vascular and Cell Sciences Institute, St. George's, University of London, London, UK (G.Y., F.A.H., T.R.B.)
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Global diffusion tensor imaging derived metrics differentiate glioblastoma multiforme vs. normal brains by using discriminant analysis: introduction of a novel whole-brain approach. Radiol Oncol 2014; 48:127-36. [PMID: 24991202 PMCID: PMC4078031 DOI: 10.2478/raon-2014-0004] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2013] [Accepted: 12/21/2013] [Indexed: 02/08/2023] Open
Abstract
Background Histological behavior of glioblastoma multiforme suggests it would benefit more from a global rather than regional evaluation. A global (whole-brain) calculation of diffusion tensor imaging (DTI) derived tensor metrics offers a valid method to detect the integrity of white matter structures without missing infiltrated brain areas not seen in conventional sequences. In this study we calculated a predictive model of brain infiltration in patients with glioblastoma using global tensor metrics. Methods Retrospective, case and control study; 11 global DTI-derived tensor metrics were calculated in 27 patients with glioblastoma multiforme and 34 controls: mean diffusivity, fractional anisotropy, pure isotropic diffusion, pure anisotropic diffusion, the total magnitude of the diffusion tensor, linear tensor, planar tensor, spherical tensor, relative anisotropy, axial diffusivity and radial diffusivity. The multivariate discriminant analysis of these variables (including age) with a diagnostic test evaluation was performed. Results The simultaneous analysis of 732 measures from 12 continuous variables in 61 subjects revealed one discriminant model that significantly differentiated normal brains and brains with glioblastoma: Wilks’ λ = 0.324, χ2 (3) = 38.907, p < .001. The overall predictive accuracy was 92.7%. Conclusions We present a phase II study introducing a novel global approach using DTI-derived biomarkers of brain impairment. The final predictive model selected only three metrics: axial diffusivity, spherical tensor and linear tensor. These metrics might be clinically applied for diagnosis, follow-up, and the study of other neurological diseases.
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Smitha KA, Gupta AK, Jayasree RS. Total magnitude of diffusion tensor imaging as an effective tool for the differentiation of glioma. Eur J Radiol 2013; 82:857-61. [DOI: 10.1016/j.ejrad.2012.12.027] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2012] [Revised: 12/22/2012] [Accepted: 12/28/2012] [Indexed: 10/27/2022]
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Mohsen LA, Shi V, Jena R, Gillard JH, Price SJ. Diffusion tensor invasive phenotypes can predict progression-free survival in glioblastomas. Br J Neurosurg 2013; 27:436-41. [DOI: 10.3109/02688697.2013.771136] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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Diagnostic performance of regional DTI-derived tensor metrics in glioblastoma multiforme: simultaneous evaluation of p, q, L, Cl, Cp, Cs, RA, RD, AD, mean diffusivity and fractional anisotropy. Eur Radiol 2012; 23:1112-21. [PMID: 23085868 DOI: 10.1007/s00330-012-2688-7] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2012] [Accepted: 09/30/2012] [Indexed: 02/08/2023]
Abstract
OBJECTIVES Almost a dozen diffusion tensor-imaging (DTI) variables have been used to evaluate brain tumours with scarce information about their diagnostic ability. We aimed to perform a comprehensive evaluation of tensor metrics reported in the last decade. METHODS Retrospective case control study performed in 14 patients with glioblastoma multiforme (GBM) and 28 controls. Conventional brain MR sequences and image postprocessing of DTI allowed the calculation of: MD, FA, p, q, L, Cl, Cp, Cs, RA, RD and AD, classified into five regions: normal appearance white matter (NAWM), immediate and distant oedema, enhancing rim and cystic cavity. ANOVA and AUROC analyses were performed. RESULTS ANOVA depicted a significant difference among all metrics (p < 0.05). RA had the highest performance in the NAWM and cystic cavity; immediate and distant zones of oedema were best diagnosed by RD and Cp respectively; q was the best biomarker of the enhancing rim zone; p < 0.001 for all metrics. CONCLUSIONS FA and MD, accepted biomarkers of brain injury, were surpassed by other metrics. RA, together with Cs, Cl and CP, might be the new leaders in the evaluation of brain tumours. DTI tensor metrics depict different clinical applicability at each tumour region.
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Price SJ, Tozer DJ, Gillard JH. Methodology of diffusion-weighted, diffusion tensor and magnetisation transfer imaging. Br J Radiol 2012; 84 Spec No 2:S121-6. [PMID: 22433823 DOI: 10.1259/bjr/12789972] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
MRI offers a number of opportunities to examine characteristics of tissue well below the spatial resolution of the imaging technique. The best known of these is diffusion imaging, which allows the production of images whose contrast reflects the ability of water molecules to move through the extravascular extracellular space. Less well-known, but increasingly important, is magnetisation transfer imaging, which produces contrast based on the ability of protons to move between the free water pool and local macromolecules. Both of these techniques offer unique information about the microscopic and molecular structure of tumour tissue. This article will briefly review the underlying theory and technical aspects associated with these imaging techniques.
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Affiliation(s)
- S J Price
- Academic Neurosurgery Division, Department of Clinical Neuroscience, UCL, London, UK.
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Sun M, Yuan W, Hertzler DA, Cancelliere A, Altaye M, Mangano FT. Diffusion tensor imaging findings in young children with benign external hydrocephalus differ from the normal population. Childs Nerv Syst 2012; 28:199-208. [PMID: 22167268 DOI: 10.1007/s00381-011-1651-2] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2011] [Accepted: 11/28/2011] [Indexed: 11/26/2022]
Abstract
PURPOSE To compare a pediatric population diagnosed with benign external hydrocephalus (BEH) to normal age-matched controls using diffusion tensor imaging (DTI) techniques. METHODS We retrospectively identified 17 BEH patients by specific clinical and neuroimaging criteria. Fractional anisotropy (FA) and mean diffusivity (MD) values obtained from DTI scans were compared to a population of age-matched controls and group differences were examined by mixed model analysis. A longitudinal comparison was completed on a subset that underwent multiple scans (n = 8). RESULTS In the genu of the corpus callosum (gCC), six of 15 BEH children had an FA value above the upper limit of 95% prediction interval, nine of 15 BEH children had MD values below the lower limit of 95% prediction interval. A similar trend applied to the other regions of interest (ROIs): splenium of the corpus callosum (sCC), ALIC, and PLIC. Statistical analysis demonstrated significant differences in FA within the gCC, sCC, and PLIC and in MD within the sCC between BEH patients and controls given (P = 0.05). No statistical differences were identified at any ROIs at the later scans. CONCLUSIONS We found a significant increase in FA and decrease in MD in children with BEH compared with normal children in specific white matter (WM) ROIs, notably in the gCC and sCC; furthermore, in longitudinal comparison, DTI parameters normalized over time. The current study further demonstrates the ability of DTI to distinguish between subtle diffusion changes in periventricular white matter and establishes preliminary objective radiographic parameters for watchful observation of patients with BEH.
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Affiliation(s)
- M Sun
- Department of Neurosurgery, Division of Pediatric Neurosurgery, 3333 Burnet Avenue, Cincinnati, OH 44529, USA
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Price SJ, Gillard JH. Imaging biomarkers of brain tumour margin and tumour invasion. Br J Radiol 2011; 84 Spec No 2:S159-67. [PMID: 22433826 PMCID: PMC3473903 DOI: 10.1259/bjr/26838774] [Citation(s) in RCA: 72] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Invasion of tumour cells into the normal brain is one of the major reasons of treatment failure for gliomas. Although there is a good understanding of the molecular and cellular processes that occur during this invasion, it is not possible to detect the extent of the tumour with conventional imaging. However, there is an understanding that the degree of invasion differs with individual tumours, and yet they are all treated the same. Newer imaging techniques that probe the pathological changes within tumours may be suitable biomarkers for invasion. Imaging methods are now available that can detect subtle changes in white matter organisation (diffusion tensor imaging), tumour metabolism and cellular proliferation (using MR spectroscopy and positron emission tomography) occurring in regions of tumour that cannot be detected by conventional imaging. The role of such biomarkers of invasion should allow better delineation of tumour margins, which should improve treatment planning (especially surgery and radiotherapy) and provide information on the invasiveness of an individual tumour to help select the most appropriate therapy and help stratify patients for clinical trials.
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Affiliation(s)
- S J Price
- Academic Neurosurgery Division, Department of Clinical Neuroscience, Addenbrooke's Hospital, Cambridge, UK.
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Abstract
Imaging plays a key role in the management of low-grade gliomas. The traditional view of these tumours as non-enhancing areas of increased signal on T2-weighted imaging is now accepted as being incorrect. Using new MR and PET techniques that can probe the pathological changes with in these tumours by assessing vascularity (perfusion MR), cellularity and infiltration (diffusion weighted and diffusion tensor MR), metabolism (MR spectroscopy and FDG PET) and proliferation (MR spectroscopy, methionine PET and 18F-fluorothymidine FLT PET). These tools will allow improvements in tumour grading, biopsy/therapy guidance and earlier assessment of the response to therapy.
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Affiliation(s)
- Stephen J Price
- Academic Neurosurgery Division, Department of Clinical Neurosciences, Addenbrooke's Hospital, Cambridge, UK
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Abstracts of the 8th International Conference on Xenon CT and Related Cerebral Blood Flow Techniques: cerebral blood flow and brain metabolic imaging in clinical practice. Br J Neurosurg 2009; 20:333-58. [PMID: 17129888 DOI: 10.1080/02688690601002432] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Wang W, Steward CE, Desmond PM. Diffusion tensor imaging in glioblastoma multiforme and brain metastases: the role of p, q, L, and fractional anisotropy. AJNR Am J Neuroradiol 2008; 30:203-8. [PMID: 18842762 DOI: 10.3174/ajnr.a1303] [Citation(s) in RCA: 95] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Microinvasive tumor cells, which are not detected on conventional imaging, contribute to poor prognoses for patients diagnosed with glioblastoma multiforme (GBM, WHO grade IV). Diffusion tensor imaging (DTI) shows promise in being able to detect this infiltration. This study aims to detect a difference in diffusion properties between GBM (infiltrative) and brain metastases (noninfiltrative). MATERIALS AND METHODS For 49 tumors (30 GBM, 19 metastases), DTI measures (p, q, L, and fractional anisotropy [FA]) were calculated for regions of gross tumor (excluding hemorrhagic and necrotic core), peritumoral edema, peritumoral margin (edema most adjacent to tumor), adjacent normal-appearing white matter (NAWM), and contralateral white matter. Parametric and nonparametric statistical tests were used to determine significance, and receiver operating characteristic (ROC) curve analyses were performed. RESULTS Mean values of p, L, and FA from regions of signal-intensity abnormality differed from those of normal brain in both tumors. The mean q value did not differ significantly compared with that in normal brain in any region in metastases or in adjacent NAWM of GBM. For GBM compared with metastases, q and FA were significantly lower in gross tumor (P < .001) and q was significantly lower in peritumoral margin (P < .001), which may be due to tumor infiltration. Significant overlap was present, which was reflected in the ROC curve analyses (area under the curve values from 0.732 to 0.804). CONCLUSIONS DTI may be used to help differentiate between GBM and brain metastases. The results also suggest that DTI has the potential to assist in detecting infiltrative tumor cells in surrounding brain.
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Affiliation(s)
- W Wang
- Department of Radiology, University of Melbourne, Parkville, Australia
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BJR review of the year – 2006. Br J Radiol 2007. [DOI: 10.1259/bjr/20483383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
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