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Mohammadi S, Ghaderi S, Jouzdani AF, Azinkhah I, Alibabaei S, Azami M, Omrani V. Differentiation Between High-Grade Glioma and Brain Metastasis Using Cerebral Perfusion-Related Parameters (Cerebral Blood Volume and Cerebral Blood Flow): A Systematic Review and Meta-Analysis of Perfusion-weighted MRI Techniques. J Magn Reson Imaging 2024. [PMID: 38899965 DOI: 10.1002/jmri.29473] [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: 04/23/2024] [Revised: 05/21/2024] [Accepted: 05/22/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND Distinguishing high-grade gliomas (HGGs) from brain metastases (BMs) using perfusion-weighted imaging (PWI) remains challenging. PWI offers quantitative measurements of cerebral blood flow (CBF) and cerebral blood volume (CBV), but optimal PWI parameters for differentiation are unclear. PURPOSE To compare CBF and CBV derived from PWIs in HGGs and BMs, and to identify the most effective PWI parameters and techniques for differentiation. STUDY TYPE Systematic review and meta-analysis. POPULATION Twenty-four studies compared CBF and CBV between HGGs (n = 704) and BMs (n = 488). FIELD STRENGTH/SEQUENCE Arterial spin labeling (ASL), dynamic susceptibility contrast (DSC), dynamic contrast-enhanced (DCE), and dynamic susceptibility contrast-enhanced (DSCE) sequences at 1.5 T and 3.0 T. ASSESSMENT Following the PRISMA guidelines, four major databases were searched from 2000 to 2024 for studies evaluating CBF or CBV using PWI in HGGs and BMs. STATISTICAL TESTS Standardized mean difference (SMD) with 95% CIs was used. Risk of bias (ROB) and publication bias were assessed, and I2 statistic was used to assess statistical heterogeneity. A P-value<0.05 was considered significant. RESULTS HGGs showed a significant modest increase in CBF (SMD = 0.37, 95% CI: 0.05-0.69) and CBV (SMD = 0.26, 95% CI: 0.01-0.51) compared with BMs. Subgroup analysis based on region, sequence, ROB, and field strength for CBF (HGGs: 375 and BMs: 222) and CBV (HGGs: 493 and BMs: 378) values were conducted. ASL showed a considerable moderate increase (50% overlapping CI) in CBF for HGGs compared with BMs. However, no significant difference was found between ASL and DSC (P = 0.08). DATA CONCLUSION ASL-derived CBF may be more useful than DSC-derived CBF in differentiating HGGs from BMs. This suggests that ASL may be used as an alternative to DSC when contrast medium is contraindicated or when intravenous injection is not feasible. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY Stage 2.
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Affiliation(s)
- Sana Mohammadi
- Neuromuscular Research Center, Department of Neurology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Sadegh Ghaderi
- Neuromuscular Research Center, Department of Neurology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Ali Fathi Jouzdani
- Neuroscience and Artificial Intelligence Research Group (NAIRG), Department of Neuroscience, School of Science and Advanced Technologies in Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Iman Azinkhah
- Medical Physics Department, Faculty of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Sanaz Alibabaei
- Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Mobin Azami
- Kurdistan University of Medical Sciences, Sanandaj, Iran
| | - Vida Omrani
- School Medical Physics Department, School of paramedical Sciences, Bushehr University of Medical Sciences, Bushehr, Iran
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Fioni F, Chen SJ, Lister INE, Ghalwash AA, Long MZ. Differentiation of high grade glioma and solitary brain metastases by measuring relative cerebral blood volume and fractional anisotropy: a systematic review and meta-analysis of MRI diagnostic test accuracy studies. Br J Radiol 2023; 96:20220052. [PMID: 36278795 PMCID: PMC10997014 DOI: 10.1259/bjr.20220052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 09/26/2022] [Accepted: 10/03/2022] [Indexed: 12/13/2022] Open
Abstract
OBJECTIVE This study aims to research the efficacy of MRI (I) for differentiating high-grade glioma (HGG) (P) with solitary brain metastasis (SBM) (C) by creating a combination of relative cerebral blood volume (rCBV) (O) and fractional anisotropy (FA) (O) in patients with intracerebral tumors. METHODS Searches were conducted on September 2021 with no publication date restriction, using an electronic search for related articles published in English, from PubMed (1994 to September 2021), Scopus (1977 to September 2021), Web of Science (1985 to September 2021), and Cochrane (1997 to September 2021). A total of 1056 studies were found, with 23 used for qualitative and quantitative data synthesis. Inclusion criteria were: patients diagnosed with HGG and SBM without age, sex, or race restriction; MRI examination of rCBV and FA; reliable histopathological diagnostic method as the gold-standard for all conditions of interest; observational and clinical studies. Newcastle-Ottawa quality assessment Scale (NOS) and Cochrane risk of bias tool (ROB) for observational and clinical trial studies were managed to appraise the quality of individual studies included. Data extraction results were managed using Mendeley and Excel, pooling data synthesis was completed using the Review Manager 5.4 software with random effect model to discriminate HGG and SBM, and divided into four subgroups. RESULTS There were 23 studies included with a total sample size of 597 HGG patients and 373 control groups/SBM. The analysis was categorized into four subgroups: (1) the subgroup with rCBV values in the central area of the tumor/intratumoral (399 HGG and 232 SBM) shows that HGG patients are not significantly different from SBM/controls group (SMD [95% CI] = -0.27 [-0.66, 0.13]), 2) the subgroup with rCBV values in the peritumoral area (452 HGG and 274 SBM) shows that HGG patients are significantly higher than SBM (SMD [95% CI] = -1.23 [-1.45 to -1.01]), (3) the subgroup with FA values in the central area of the tumor (249 HGG and 156 SBM) shows that HGG patients are significantly higher than SBM (SMD [95% CI] = - 0.44 [-0.84,-0.04]), furthermore (4) the subgroup with FA values in the peritumoral area (261 HGG and 168 SBM) shows that the HGG patients are significantly higher than the SBM (SMD [95% CI] = -0.59 [-1.02,-0.16]). CONCLUSION Combining rCBV and FA measurements in the peritumoral region and FA in the intratumoral region increase the accuracy of MRI examination to differentiate between HGG and SBM patients effectively. Confidence in the accuracy of our results may be influenced by major interstudy heterogeneity. Whereas the I2 for the rCBV in the intratumoral subgroup was 80%, I2 for the rCBV in the peritumoral subgroup was 39%, and I2 for the FA in the intratumoral subgroup was 69%, and I2 for the FA in the peritumoral subgroup was 74%. The predefined accurate search criteria, and precise selection and evaluation of methodological quality for included studies, strengthen this studyOur study has no funder, no conflict of interest, and followed an established PROSPERO protocol (ID: CRD42021279106). ADVANCES IN KNOWLEDGE The combination of rCBV and FA measurements' results is promising in differentiating HGG and SBM.
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Affiliation(s)
- Fioni Fioni
- Department of Radiology, Nanjing Medical University, first
affiliated hospital (Jiangsu Provincial People’s
Hospital), Jiangsu, China
| | - Song Jia Chen
- Department of Radiology, Nanjing Medical University, first
affiliated hospital (Jiangsu Provincial People’s
Hospital), Jiangsu, China
| | - I Nyoman Ehrich Lister
- Medicine, Universitas Prima Indonesia and Royal Prima
Hospital, Medan, North Sumatera, Indoneisa
| | | | - Ma Zhan Long
- Department of Radiology, Nanjing Medical University, first
affiliated hospital (Jiangsu Provincial People’s
Hospital), Jiangsu, China
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Diffusion tensor imaging derived metrics in high grade glioma and brain metastasis differentiation. ARCHIVE OF ONCOLOGY 2022. [DOI: 10.2298/aoo210828007b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
Background: Pretreatment differentiation between glioblastoma and metastasis
is a frequently encountered dilemma in neurosurgical practice. Distinction
is required for precise planning of resection or radiotherapy, and also for
defining further diagnostic procedures. Morphology and spectroscopy imaging
features are not specific and frequently overlap. This limitation of
magnetic resonance imaging and magnetic resonance spectroscopy was the
reason to initiate this study. The aim of the present study was to determine
whether the dataset of diffusion tensor imaging metrics contains information
which may be used for the distinction between primary and secondary
intra-axial neoplasms. Methods: Two diffusion tensor imaging parameters were
measured in 81 patients with an expansive, ring-enhancing, intra-axial
lesion on standard magnetic resonance imaging (1.5 T system). All tumors
were histologically verified glioblastoma or secondary deposit. For
qualitative analysis, two regions of interest were defined: intratumoral and
immediate peritumoral region (locations 1 and 2, respectively). Fractional
anisotropy and mean difusivity values of both groups were compared.
Additional test was performed to determine if there was a significant
difference in mean values between two locations. Results: A statistically
significant difference was found in fractional anisotropy values among two
locations, with decreasing values in the direction of neoplastic
infiltration, although such difference was not observed in fractional
anisotropy values in the group with secondary tumors. Mean difusivity values
did not appear helpful in differentiation between these two entities. In
both groups there was no significant difference in mean difusivity values,
neither in intratumoral nor in peritumoral location. Conclusion: The results
of our study justify associating the diffusion tensor imaging technique to
conventional morphologic magnetic resonance imaging as an additional
diagnostic tool for the distinction between primary and secondary
intra-axial lesions. Quantitative analysis of diffusion tensor imaging
metric, in particular measurement of fractional anisotropy in peritumoral
edema facilitates accurate diagnosis.
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Nowakowski A, Lahijanian Z, Panet-Raymond V, Siegel PM, Petrecca K, Maleki F, Dankner M. Radiomics as an emerging tool in the management of brain metastases. Neurooncol Adv 2022; 4:vdac141. [PMID: 36284932 PMCID: PMC9583687 DOI: 10.1093/noajnl/vdac141] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Brain metastases (BM) are associated with significant morbidity and mortality in patients with advanced cancer. Despite significant advances in surgical, radiation, and systemic therapy in recent years, the median overall survival of patients with BM is less than 1 year. The acquisition of medical images, such as computed tomography (CT) and magnetic resonance imaging (MRI), is critical for the diagnosis and stratification of patients to appropriate treatments. Radiomic analyses have the potential to improve the standard of care for patients with BM by applying artificial intelligence (AI) with already acquired medical images to predict clinical outcomes and direct the personalized care of BM patients. Herein, we outline the existing literature applying radiomics for the clinical management of BM. This includes predicting patient response to radiotherapy and identifying radiation necrosis, performing virtual biopsies to predict tumor mutation status, and determining the cancer of origin in brain tumors identified via imaging. With further development, radiomics has the potential to aid in BM patient stratification while circumventing the need for invasive tissue sampling, particularly for patients not eligible for surgical resection.
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Affiliation(s)
- Alexander Nowakowski
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Québec, Canada
| | - Zubin Lahijanian
- McGill University Health Centre, Department of Diagnostic Radiology, McGill University, Montreal, Québec, Canada
| | - Valerie Panet-Raymond
- McGill University Health Centre, Department of Diagnostic Radiology, McGill University, Montreal, Québec, Canada
| | - Peter M Siegel
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Québec, Canada
| | - Kevin Petrecca
- Montreal Neurological Institute-Hospital, McGill University, Montreal, Québec, Canada
| | - Farhad Maleki
- Department of Computer Science, University of Calgary, Calgary, Alberta, Canada
| | - Matthew Dankner
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Québec, Canada
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Wu J, Liang F, Wei R, Lai S, Lv X, Luo S, Wu Z, Chen H, Zhang W, Zeng X, Ye X, Wu Y, Wei X, Jiang X, Zhen X, Yang R. A Multiparametric MR-Based RadioFusionOmics Model with Robust Capabilities of Differentiating Glioblastoma Multiforme from Solitary Brain Metastasis. Cancers (Basel) 2021; 13:cancers13225793. [PMID: 34830943 PMCID: PMC8616314 DOI: 10.3390/cancers13225793] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 11/13/2021] [Accepted: 11/13/2021] [Indexed: 11/24/2022] Open
Abstract
Simple Summary Glioblastoma multiforme (GBM) and solitary brain metastasis (SBM) are common brain tumors in adults. The two tumors often pose a diagnostic dilemma owing to their similar features on conventional magnetic resonance imaging (MRI). Ability to discriminate the two tumors is critical as it informs clinical treatment strategies. This pilot study attempts to employ the machine learning technique to identify GBM and SBM by fusing radiomics features of multiple MRI sequences and multiple models. A multiparametric MR-based RadioFusionOmics (RFO) model was developed and has demonstrated promising prediction accuracy for the identifications of GBM and SBM. Abstract This study aimed to evaluate the diagnostic potential of a novel RFO model in differentiating GBM and SBM with multiparametric MR sequences collected from 244 (131 GBM and 113 SBM) patients. Three basic volume of interests (VOIs) were delineated on the conventional axial MR images (T1WI, T2WI, T2_FLAIR, and CE_T1WI), including volumetric non-enhanced tumor (nET), enhanced tumor (ET), and peritumoral edema (pTE). Using the RFO model, radiomics features extracted from different multiparametric MRI sequence(s) and VOI(s) were fused and the best sequence and VOI, or possible combinations, were determined. A multi-disciplinary team (MDT)-like fusion was performed to integrate predictions from the high-performing models for the final discrimination of GBM vs. SBM. Image features extracted from the volumetric ET (VOIET) had dominant predictive performances over features from other VOI combinations. Fusion of VOIET features from the T1WI and T2_FLAIR sequences via the RFO model achieved a discrimination accuracy of AUC = 0.925, accuracy = 0.855, sensitivity = 0.856, and specificity = 0.853, on the independent testing cohort 1, and AUC = 0.859, accuracy = 0.836, sensitivity = 0.708, and specificity = 0.919 on the independent testing cohort 2, which significantly outperformed three experienced radiologists (p = 0.03, 0.01, 0.02, and 0.01, and p = 0.02, 0.01, 0.45, and 0.02, respectively) and the MDT-decision result of three experienced experts (p = 0.03, 0.02, 0.03, and 0.02, and p = 0.03, 0.02, 0.44, and 0.03, respectively).
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Affiliation(s)
- Jialiang Wu
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China; (J.W.); (R.W.); (S.L.); (Z.W.); (H.C.); (W.Z.); (X.W.); (X.J.)
- Department of Radiology, The University of Hong Kong Shenzhen Hospital, Shenzhen 518000, China
| | - Fangrong Liang
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China;
| | - Ruili Wei
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China; (J.W.); (R.W.); (S.L.); (Z.W.); (H.C.); (W.Z.); (X.W.); (X.J.)
| | - Shengsheng Lai
- School of Medical Equipment, Guangdong Food and Drug Vocational College, Guangzhou 510520, China;
| | - Xiaofei Lv
- State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China;
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Shiwei Luo
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China; (J.W.); (R.W.); (S.L.); (Z.W.); (H.C.); (W.Z.); (X.W.); (X.J.)
| | - Zhe Wu
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China; (J.W.); (R.W.); (S.L.); (Z.W.); (H.C.); (W.Z.); (X.W.); (X.J.)
| | - Huixian Chen
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China; (J.W.); (R.W.); (S.L.); (Z.W.); (H.C.); (W.Z.); (X.W.); (X.J.)
| | - Wanli Zhang
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China; (J.W.); (R.W.); (S.L.); (Z.W.); (H.C.); (W.Z.); (X.W.); (X.J.)
| | - Xiangling Zeng
- Department of Radiology, Huizhou Municipal Central Hospital, Huizhou 516001, China;
| | - Xianghua Ye
- Department of Radiation Oncology, 1st Affiliated Hospital, Zhejiang University, Hangzhou 310009, China;
| | - Yong Wu
- Department of Oncology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China;
| | - Xinhua Wei
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China; (J.W.); (R.W.); (S.L.); (Z.W.); (H.C.); (W.Z.); (X.W.); (X.J.)
| | - Xinqing Jiang
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China; (J.W.); (R.W.); (S.L.); (Z.W.); (H.C.); (W.Z.); (X.W.); (X.J.)
| | - Xin Zhen
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China;
- Correspondence: (X.Z.); (R.Y.); Tel.: +86-20-62789323 (X.Z.); +86-20-81048873 (R.Y.)
| | - Ruimeng Yang
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China; (J.W.); (R.W.); (S.L.); (Z.W.); (H.C.); (W.Z.); (X.W.); (X.J.)
- Correspondence: (X.Z.); (R.Y.); Tel.: +86-20-62789323 (X.Z.); +86-20-81048873 (R.Y.)
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Abdel Razek AAK, Alksas A, Shehata M, AbdelKhalek A, Abdel Baky K, El-Baz A, Helmy E. Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging. Insights Imaging 2021; 12:152. [PMID: 34676470 PMCID: PMC8531173 DOI: 10.1186/s13244-021-01102-6] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 09/26/2021] [Indexed: 12/15/2022] Open
Abstract
This article is a comprehensive review of the basic background, technique, and clinical applications of artificial intelligence (AI) and radiomics in the field of neuro-oncology. A variety of AI and radiomics utilized conventional and advanced techniques to differentiate brain tumors from non-neoplastic lesions such as inflammatory and demyelinating brain lesions. It is used in the diagnosis of gliomas and discrimination of gliomas from lymphomas and metastasis. Also, semiautomated and automated tumor segmentation has been developed for radiotherapy planning and follow-up. It has a role in the grading, prediction of treatment response, and prognosis of gliomas. Radiogenomics allowed the connection of the imaging phenotype of the tumor to its molecular environment. In addition, AI is applied for the assessment of extra-axial brain tumors and pediatric tumors with high performance in tumor detection, classification, and stratification of patient's prognoses.
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Affiliation(s)
| | - Ahmed Alksas
- Biomaging Lab, Department of Bioengineering, University of Louisville, Louisville, KY, 40292, USA
| | - Mohamed Shehata
- Biomaging Lab, Department of Bioengineering, University of Louisville, Louisville, KY, 40292, USA
| | - Amr AbdelKhalek
- Internship at Mansoura University Hospital, Mansoura Faculty of Medicine, Mansoura, Egypt
| | - Khaled Abdel Baky
- Department of Diagnostic Radiology, Faculty of Medicine, Port Said University, Port Said, Egypt
| | - Ayman El-Baz
- Biomaging Lab, Department of Bioengineering, University of Louisville, Louisville, KY, 40292, USA
| | - Eman Helmy
- Department of Diagnostic Radiology, Faculty of Medicine, Mansoura University, Elgomheryia Street, Mansoura, 3512, Egypt.
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Razek AAKA, Taman S, Gamal W, El Ayouty M, Ali KM, Barakat TE. Diffusion tensor imaging of vertebral bone marrow in children with Gaucher's disease type I and III: Pre- and post-therapy. Clin Imaging 2021; 79:207-212. [PMID: 34116297 DOI: 10.1016/j.clinimag.2021.06.002] [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: 10/21/2020] [Revised: 05/30/2021] [Accepted: 06/02/2021] [Indexed: 12/14/2022]
Abstract
PURPOSE To assess diffusion tensor imaging (DTI) of the vertebral bone marrow (BM) in children with Gaucher's disease (GD) types I and III before and after therapy. METHODS Prospective study was conducted upon 25 children with GD type I (n = 17) and III (n = 8) and 13 age and sex-matched controls underwent DTI of vertebral BM. Mean diffusivity (MD) and fractional anisotropy (FA) of vertebral BM was calculated and correlated with genotyping, chitotriosidase, hemoglobin (HB) and, platelet count. RESULTS There was a statistically significant difference in MD and FA of BM between patients and controls (P = 0.001 and 0.02). The area under the curve (AUC) of MD and FA used to differentiate untreated patients from controls was 0.902 and 0.68 with sensitivity, specificity, and, accuracy 92%, 84.6%, and, 89.5% respectively. There was a significant difference in MD and FA of BM between untreated and treated patients (P = 0.001 and 0.02). AUC of MD and FA used to differentiate untreated from treated patients was 0.93 and 0.649 with sensitivity, specificity, and accuracy of 92%, 80%, and 86% respectively. There was a significant difference in MD and FA (P = 0.03, 0.001 respectively) of BM in GD with homozygous L444P mutation (n = 9) and other mutations (n = 14). Chiotriptase, HB and platelet count of patients was correlated with MD (r = -0.36, 0.42, -0.41) and FA (r = -0.47, -0.37, -0.46) respectively. CONCLUSION DTI of vertebral BM can help in diagnosis and monitoring patients with GD after therapy and correlated with genotyping, and hematological biomarkers of GD.
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Affiliation(s)
| | - Saher Taman
- Department of Diagnostic Radiology, Mansoura Faculty of Medicine, Mansoura, Egypt
| | - Walaa Gamal
- Gastroenterology and Hepatology Unit, Department of Pediatrics, Mansoura Faculty of Medicine, Mansoura, Egypt
| | - Mostafa El Ayouty
- Gastroenterology and Hepatology Unit, Department of Pediatrics, Mansoura Faculty of Medicine, Mansoura, Egypt
| | - Khadiga M Ali
- Department of Pathology, Mansoura Faculty of Medicine, Mansoura, Egypt
| | - Tarik Elsayed Barakat
- Gastroenterology and Hepatology Unit, Department of Pediatrics, Mansoura Faculty of Medicine, Mansoura, Egypt
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Dikaios N. Deep learning magnetic resonance spectroscopy fingerprints of brain tumours using quantum mechanically synthesised data. NMR IN BIOMEDICINE 2021; 34:e4479. [PMID: 33448078 DOI: 10.1002/nbm.4479] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 11/24/2020] [Accepted: 01/05/2021] [Indexed: 06/12/2023]
Abstract
Metabolic fingerprints are valuable biomarkers for diseases that are associated with metabolic disorders. 1H magnetic resonance spectroscopy (MRS) is a unique noninvasive diagnostic tool that can depict the metabolic fingerprint based solely on the proton signal of different molecules present in the tissue. However, its performance is severely hindered by low SNR, field inhomogeneities and overlapping spectra of metabolites, which affect the quantification of metabolites. Consequently, MRS is rarely included in routine clinical protocols and has not been proven in multi-institutional trials. This work proposes an alternative approach, where instead of quantifying metabolites' concentration, deep learning (DL) is used to model the complex nonlinear relationship between diseases and their spectroscopic metabolic fingerprint (pattern). DL requires large training datasets, acquired (ideally) with the same protocol/scanner, which are very rarely available. To overcome this limitation, a novel method is proposed that can quantum mechanically synthesise MRS data for any scanner/acquisition protocol. The proposed methodology is applied to the challenging clinical problem of differentiating metastasis from glioblastoma brain tumours on data acquired across multiple institutions. DL algorithms were trained on the augmented synthetic spectra and tested on two independent datasets acquired by different scanners, achieving a receiver operating characteristic area under the curve of up to 0.96 and 0.97, respectively.
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Affiliation(s)
- Nikolaos Dikaios
- Mathematics Research Center, Academy of Athens, Athens, Greece
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK
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Khalsa JK, Shah K. Immune Profiling of Syngeneic Murine and Patient GBMs for Effective Translation of Immunotherapies. Cells 2021; 10:491. [PMID: 33668856 PMCID: PMC7996275 DOI: 10.3390/cells10030491] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 01/30/2021] [Accepted: 02/05/2021] [Indexed: 01/15/2023] Open
Abstract
Immunotherapy for brain tumors remains elusive, unlike many other cancer types for which it is one of the most promising therapeutic options. Recent studies have comprehensively profiled the immune-landscape of the highly malignant brain tumor, glioblastoma (GBM) in patients and identified novel immune-modulatory targets. However, given that pre-clinical exploration of potential novel therapeutics is primarily performed in immune-competent mice, it is vital to compare the immune-profiling data obtained from syngeneic mouse GBM models with GBM patient samples. This will pave the way for utilizing appropriate clinically relevant mouse GBM models for evaluating novel immune-therapies in pre-clinical settings. Recent brain tumor immune-profiling studies using state-of-the-art time of flight cytometry (CyTOF) analysis compared different human and mouse GBM types and reported immunological distinctions amongst these mouse models. These studies also contrast the immune phenotype of brain tumor patients with commonly used pre-clinical immune-competent mouse models. In this perspective, we provide the outcomes of very recent brain tumor immune-profiling studies and their implications on designing and translating unique, tumor-subtype specific therapeutics.
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Affiliation(s)
- Jasneet Kaur Khalsa
- Center for Stem Cell Therapeutics and Imaging, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA;
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Khalid Shah
- Center for Stem Cell Therapeutics and Imaging, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA;
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Harvard Stem Cell Institute, Harvard University, Cambridge, MA 02138, USA
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Differentiating Glioblastomas from Solitary Brain Metastases Using Arterial Spin Labeling Perfusion− and Diffusion Tensor Imaging−Derived Metrics. World Neurosurg 2019; 127:e593-e598. [DOI: 10.1016/j.wneu.2019.03.213] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2018] [Revised: 03/19/2019] [Accepted: 03/20/2019] [Indexed: 12/20/2022]
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Costabile JD, Alaswad E, D'Souza S, Thompson JA, Ormond DR. Current Applications of Diffusion Tensor Imaging and Tractography in Intracranial Tumor Resection. Front Oncol 2019; 9:426. [PMID: 31192130 PMCID: PMC6549594 DOI: 10.3389/fonc.2019.00426] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2018] [Accepted: 05/07/2019] [Indexed: 01/01/2023] Open
Abstract
In the treatment of brain tumors, surgical intervention remains a common and effective therapeutic option. Recent advances in neuroimaging have provided neurosurgeons with new tools to overcome the challenge of differentiating healthy tissue from tumor-infiltrated tissue, with the aim of increasing the likelihood of maximizing the extent of resection volume while minimizing injury to functionally important regions. Novel applications of diffusion tensor imaging (DTI), and DTI-derived tractography (DDT) have demonstrated that preoperative, non-invasive mapping of eloquent cortical regions and functionally relevant white matter tracts (WMT) is critical during surgical planning to reduce postoperative deficits, which can decrease quality of life and overall survival. In this review, we summarize the latest developments of applying DTI and tractography in the context of resective surgery and highlight its utility within each stage of the neurosurgical workflow: preoperative planning and intraoperative management to improve postoperative outcomes.
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Affiliation(s)
- Jamie D Costabile
- Department of Neurosurgery, School of Medicine, University of Colorado, Aurora, CO, United States
| | - Elsa Alaswad
- Department of Neurosurgery, School of Medicine, University of Colorado, Aurora, CO, United States
| | - Shawn D'Souza
- Department of Neurosurgery, School of Medicine, University of Colorado, Aurora, CO, United States
| | - John A Thompson
- Department of Neurosurgery, School of Medicine, University of Colorado, Aurora, CO, United States
| | - D Ryan Ormond
- Department of Neurosurgery, School of Medicine, University of Colorado, Aurora, CO, United States
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Artzi M, Bressler I, Ben Bashat D. Differentiation between glioblastoma, brain metastasis and subtypes using radiomics analysis. J Magn Reson Imaging 2019; 50:519-528. [PMID: 30635952 DOI: 10.1002/jmri.26643] [Citation(s) in RCA: 107] [Impact Index Per Article: 21.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Revised: 12/19/2018] [Accepted: 12/20/2018] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Differentiation between glioblastoma and brain metastasis is highly important due to differing medical treatment strategies. While MRI is the modality of choice for the assessment of patients with brain tumors, differentiation between glioblastoma and solitary brain metastasis may be challenging due to their similar appearance on MRI. PURPOSE To differentiate between glioblastoma and brain metastasis subtypes using radiomics analysis based on conventional post-contrast T1 -weighted (T1 W) MRI. STUDY TYPE Retrospective. SUBJECTS Data were acquired from 439 patients: 212 patients with glioblastoma and 227 patients with brain metastasis (breast, lung, and others). FIELD STRENGTH/SEQUENCE Post-contrast 3D T1 W gradient echo images, acquired with 1.5 and 3.0 T MR systems. ASSESSMENT Analysis included image preprocessing, segmentation of tumor area, and features extraction including: patients' clinical information, tumor location, first- and second-order statistical, morphological, wavelet features, and bag-of-features. Following dimension reduction, classification was performed using various machine-learning algorithms including support-vector machine (SVM), k-nearest neighbor, decision trees, and ensemble classifiers. STATISTICAL TESTS For classification, the data were divided into training (80%) and testing datasets (20%). Following optimization of the classifiers, mean sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were calculated. RESULTS For the testing dataset, the best results for differentiation of glioblastoma from brain metastasis were obtained using the SVM classifier with mean accuracy = 0.85, sensitivity = 0.86, specificity = 0.85, and AUC = 0.96. The best classification results between glioblastoma and brain metastasis subtypes were obtained using SVM classifier with mean accuracy = 0.85, 0.89, 0.75, 0.90; sensitivity = 1.00, 0.60, 0.57, 0.11; specificity = 0.76, 0.92, 0.87, 0.99; and AUC = 0.98, 0.81, 0.83, 0.57 for the glioblastoma, breast, lung, and other brain metastases, respectively. DATA CONCLUSION Differentiation between glioblastoma and brain metastasis showed a high success rate based on postcontrast T1 W MRI. Classification between glioblastoma and brain metastasis subtypes may require additional MR sequences with other tissue contrasts. LEVEL OF EVIDENCE 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:519-528.
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Affiliation(s)
- Moran Artzi
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Idan Bressler
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Dafna Ben Bashat
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
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Holly KS, Fitz-Gerald JS, Barker BJ, Murcia D, Daggett R, Ledbetter C, Gonzalez-Toledo E, Sun H. Differentiation of High-Grade Glioma and Intracranial Metastasis Using Volumetric Diffusion Tensor Imaging Tractography. World Neurosurg 2018; 120:e131-e141. [PMID: 30165214 DOI: 10.1016/j.wneu.2018.07.230] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Revised: 07/24/2018] [Accepted: 07/25/2018] [Indexed: 11/15/2022]
Abstract
OBJECTIVE A reliable, noninvasive method to differentiate high-grade glioma (HGG) and intracranial metastasis (IM) has remained elusive. The aim of this study was to differentiate between HGG and IM using tumoral and peritumoral diffusion tensor imaging characteristics. METHODS A semiautomated script generated volumetric regions of interest (ROIs) for the tumor and a peritumoral shell at a predetermined voxel thickness. ROI differences in diffusion tensor imaging-related metrics between HGG and IM groups were estimated, including fractional anisotropy, mean diffusivity, total fiber tract counts, and tract density. RESULTS The HGG group (n = 46) had a significantly higher tumor-to-brain volume ratio than the IM group (n = 35) (P < 0.001). The HGG group exhibited significantly higher mean fractional anisotropy and significantly lower mean diffusivity within peritumoral ROI than the IM group (P < 0.05). The HGG group exhibited significantly higher total tract count and higher tract density in tumoral and peritumoral ROIs than the IM group (P < 0.05). Tumoral tract count and peritumoral tract density were the most optimal metrics to differentiate the groups based on receiver operating characteristic curve analysis. Predictive analysis using receiver operating characteristic curve thresholds was performed on 13 additional participants. Compared with correct clinical diagnoses, the 2 thresholds exhibited equal specificities (66.7%), but the tumoral tract count (85.7%) seemed more sensitive in differentiating the 2 groups. CONCLUSIONS Tract count and tract density were significantly different in tumoral and peritumoral regions between HGG and IM. Differences in microenvironmental interactions between the tumor types may cause these tract differences.
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Affiliation(s)
- Kevin S Holly
- Department of Neurosurgery, Louisiana State University Health Sciences Center Shreveport, Shreveport, Louisiana, USA
| | - Joseph S Fitz-Gerald
- Department of Neurosurgery, Louisiana State University Health Sciences Center Shreveport, Shreveport, Louisiana, USA
| | - Benjamin J Barker
- Department of Neurosurgery, Louisiana State University Health Sciences Center Shreveport, Shreveport, Louisiana, USA
| | - Derrick Murcia
- Department of Neurosurgery, Louisiana State University Health Sciences Center Shreveport, Shreveport, Louisiana, USA
| | - Rebekah Daggett
- Department of Neurosurgery, Louisiana State University Health Sciences Center Shreveport, Shreveport, Louisiana, USA
| | - Christina Ledbetter
- Department of Neurosurgery, Louisiana State University Health Sciences Center Shreveport, Shreveport, Louisiana, USA
| | - Eduardo Gonzalez-Toledo
- Department of Radiology, Louisiana State University Health Sciences Center Shreveport, Shreveport, Louisiana, USA
| | - Hai Sun
- Department of Neurosurgery, Louisiana State University Health Sciences Center Shreveport, Shreveport, Louisiana, USA.
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Soliman SH, Amer HA, Al Tawdy MH, Youssof AA, Emad-Eldin S. Correlation of diffusion tensor imaging findings and episodic memory impairment in temporal lobe epilepsy. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2018. [DOI: 10.1016/j.ejrnm.2017.12.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
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Mohamed FF, Almassry HN, Sharaf MH. ADC value as a predictor for myelin loss/Preservation in MS plaques with different enhancement pattern in correlation with disease activity. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2017. [DOI: 10.1016/j.ejrnm.2017.07.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
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Holly KS, Barker BJ, Murcia D, Bennett R, Kalakoti P, Ledbetter C, Gonzalez-Toledo E, Nanda A, Sun H. High-grade Gliomas Exhibit Higher Peritumoral Fractional Anisotropy and Lower Mean Diffusivity than Intracranial Metastases. Front Surg 2017; 4:18. [PMID: 28443285 PMCID: PMC5385351 DOI: 10.3389/fsurg.2017.00018] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2017] [Accepted: 03/16/2017] [Indexed: 11/18/2022] Open
Abstract
Differentiating high-grade gliomas and intracranial metastases through non-invasive imaging has been challenging. Here, we retrospectively compared both intratumoral and peritumoral fractional anisotropy (FA), mean diffusivity (MD), and fluid-attenuated inversion recovery (FLAIR) measurements between high-grade gliomas and metastases. Two methods were utilized to select peritumoral region of interest (ROI). The first method utilized the manual placement of four ROIs adjacent to the lesion. The second method utilized a semiautomated and proprietary MATLAB script to generate an ROI encompassing the entire tumor. The average peritumoral FA, MD, and FLAIR values were determined within the ROIs for both methods. Forty patients with high-grade gliomas and 44 with metastases were enrolled in this study. Thirty-five patients with high-grade glioma and 30 patients with metastases had FLAIR images. There was no significant difference in age, gender, or race between the two patient groups. The high-grade gliomas had a significantly higher tumor-to-brain area ratio compared to the metastases. There were no differences in average intratumoral FA, MD, and FLAIR values between the two groups. Both the manual sample method and the semiautomated peritumoral ring method resulted in significantly higher peritumoral FA and significantly lower peritumoral MD in high-grade gliomas compared to metastases (p < 0.05). No significant difference was found in FLAIR values between the two groups peritumorally. Receiver operating curve analysis revealed FA to be a more sensitive and specific metric to differentiate high-grade gliomas and metastases than MD. The differences in the peritumoral FA and MD values between high-grade gliomas and metastases seemed due to the infiltration of glioma to the surrounding brain parenchyma.
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Affiliation(s)
- Kevin S Holly
- Department of Neurosurgery, Louisiana State University Health Sciences Center Shreveport, Shreveport, LA, USA
| | - Benjamin J Barker
- Department of Neurology, Louisiana State University Health Sciences Center Shreveport, Shreveport, LA, USA
| | - Derrick Murcia
- Department of Neurosurgery, Louisiana State University Health Sciences Center Shreveport, Shreveport, LA, USA
| | - Rebekah Bennett
- Department of Biological Sciences, Louisiana State University Shreveport, Shreveport, LA, USA
| | - Piyush Kalakoti
- Department of Neurosurgery, Louisiana State University Health Sciences Center Shreveport, Shreveport, LA, USA
| | - Christina Ledbetter
- Department of Neurosurgery, Louisiana State University Health Sciences Center Shreveport, Shreveport, LA, USA
| | - Eduardo Gonzalez-Toledo
- Department of Radiology, Louisiana State University Health Sciences Center Shreveport, Shreveport, LA, USA
| | - Anil Nanda
- Department of Neurosurgery, Louisiana State University Health Sciences Center Shreveport, Shreveport, LA, USA
| | - Hai Sun
- Department of Neurosurgery, Louisiana State University Health Sciences Center Shreveport, Shreveport, LA, USA
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El-Serougy L, Abdel Razek AAK, Ezzat A, Eldawoody H, El-Morsy A. Assessment of diffusion tensor imaging metrics in differentiating low-grade from high-grade gliomas. Neuroradiol J 2016; 29:400-7. [PMID: 27562582 DOI: 10.1177/1971400916665382] [Citation(s) in RCA: 85] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
AIM The aim of this article is to assess diffusion tensor imaging (DTI) metrics in differentiating low-grade from high-grade gliomas. PATIENTS AND METHODS A prospective study was conducted on 35 patients with gliomas who underwent DTI. Gliomas were classified into low-grade and high-grade gliomas. The fractional anisotropy (FA), mean diffusivity (MD), linear coefficient (CL), planar coefficient (CP) and spherical coefficient (CS) of the solid tumoral part and peri-tumoral regions were calculated. RESULTS There was significant difference (p = 0.001) in MD of the solid tumoral part of low-grade (1.78 ± 0.33 × 10(-3 )mm(2)/s) and high-grade (1.16 ± 0.22 × 10(-3 )mm(2)/s) gliomas. The selection of 1.42 × 10(-3 )mm(2)/s as a cutoff value of MD of the tumoral part was used to differentiate low-grade and high-grade gliomas; the best results were obtained with area under the curve (AUC) of 0.957 and accuracy of 91.4%. There was a significant difference in FA, MD, CP and CS of peri-tumoral regions of both groups with p values of 0.006, 0.042, 0.030 and 0.037, respectively. The cutoff values of MD, FA, CS and CP of the peri-tumoral region used to differentiate low-grade from high-grade gliomas were 1.24, 0.315, 0.726 and 0.321 with AUC of 0.694, 0.773, 0.734 and 0.724 and accuracy of 68.6%, 80.0%, 74.3% and 74.3%, respectively. The combined MD of the solid tumoral part and FA of the peri-tumoral region used to differentiate low-grade from high-grade gliomas revealed AUC of 0.974 and accuracy of 88.6%. CONCLUSION We conclude that the combination of MD of the solid tumoral part and FA of the peri-tumoral region is a noninvasive method to differentiate low-grade from high-grade gliomas.
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Affiliation(s)
- Lamiaa El-Serougy
- Department of Diagnostic Radiology, Mansoura Faculty of Medicine, Egypt
| | | | - Amani Ezzat
- Department of Diagnostic Radiology, Mansoura Faculty of Medicine, Egypt
| | - Hany Eldawoody
- Department of Neurosurgery, Mansoura Faculty of Medicine, Egypt
| | - Ahmad El-Morsy
- Department of Diagnostic Radiology, Mansoura Faculty of Medicine, Egypt
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