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Zhang Y, Luo X, Zhu Y, Zhang Q, Liu B. Differentiation between primary central nervous system lymphomas and gliomas according to pharmacokinetic parameters derived from dynamic contrast-enhanced magnetic resonance imaging. Heliyon 2024; 10:e32619. [PMID: 38952379 PMCID: PMC11215271 DOI: 10.1016/j.heliyon.2024.e32619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 05/15/2024] [Accepted: 06/06/2024] [Indexed: 07/03/2024] Open
Abstract
Purpose It is difficult to differentiate between primary central nervous system lymphoma and primary glioblastoma due to their similar MRI findings. This study aimed to assess whether pharmacokinetic parameters derived from dynamic contrast-enhanced MRI could provide valuable insights for differentiation. Methods Seventeen cases of primary central nervous system lymphoma and twenty-one cases of glioblastoma as confirmed by pathology, were retrospectively analyzed. Pharmacokinetic parameters, including Ktrans, Kep, Ve, and the initial area under the Gd concentration curve, were measured from the enhancing tumor parenchyma, peritumoral parenchyma, and contralateral normal parenchyma. Statistical comparisons were made using Mann-Whitney U tests for Ve and Matrix Metallopeptidase-2, while independent samples t-tests were used to compare pharmacokinetic parameters in the mentioned regions and pathological indicators of enhancing tumor parenchyma, such as vascular endothelial growth factor and microvessel density. The pharmacokinetic parameters with statistical differences were evaluated using receiver-operating characteristics analysis. Except for the Wilcoxon rank sum test for Ve, the pharmacokinetic parameters were compared within the enhancing tumor parenchyma, peritumoral parenchyma, and contralateral normal parenchyma of the primary central nervous system lymphomas and glioblastomas using variance analysis and the least-significant difference method. Results Statistical differences were observed in Ktrans and Kep within the enhancing tumor parenchyma and in Kep within the peritumoral parenchyma between these two tumor types. Differences were also found in Matrix Metallopeptidase-2, vascular endothelial growth factor, and microvessel density within the enhancing tumor parenchyma of these tumors. When compared with the contralateral normal parenchyma, pharmacokinetic parameters within the peritumoral parenchyma and enhancing tumor parenchyma exhibited variations in glioblastoma and primary central nervous system lymphoma, respectively. Moreover, the receiver-operating characteristics analysis showed that the diagnostic efficiency of Kep in the peritumoral parenchyma was notably higher. Conclusion Pharmacokinetic parameters derived from dynamic contrast-enhanced MRI can differentiate primary central nervous system lymphoma and glioblastoma, especially Kep in the peritumoral parenchyma.
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Affiliation(s)
- Yu Zhang
- Department of Radiology, 901st Hospital of the Chinese People's Liberation Army Joint Logistics Support Force, Hefei, 230031, PR China
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, PR China
| | - Xiangwei Luo
- Department of Radiology, 901st Hospital of the Chinese People's Liberation Army Joint Logistics Support Force, Hefei, 230031, PR China
| | - Youzhi Zhu
- Department of Radiology, 901st Hospital of the Chinese People's Liberation Army Joint Logistics Support Force, Hefei, 230031, PR China
| | - Qian Zhang
- Department of Radiology, 901st Hospital of the Chinese People's Liberation Army Joint Logistics Support Force, Hefei, 230031, PR China
| | - Bin Liu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, PR China
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Oh G, Moon Y, Moon WJ, Ye JC. Unpaired deep learning for pharmacokinetic parameter estimation from dynamic contrast-enhanced MRI without AIF measurements. Neuroimage 2024; 291:120571. [PMID: 38518829 DOI: 10.1016/j.neuroimage.2024.120571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 02/28/2024] [Accepted: 03/08/2024] [Indexed: 03/24/2024] Open
Abstract
DCE-MRI provides information about vascular permeability and tissue perfusion through the acquisition of pharmacokinetic parameters. However, traditional methods for estimating these pharmacokinetic parameters involve fitting tracer kinetic models, which often suffer from computational complexity and low accuracy due to noisy arterial input function (AIF) measurements. Although some deep learning approaches have been proposed to tackle these challenges, most existing methods rely on supervised learning that requires paired input DCE-MRI and labeled pharmacokinetic parameter maps. This dependency on labeled data introduces significant time and resource constraints and potential noise in the labels, making supervised learning methods often impractical. To address these limitations, we present a novel unpaired deep learning method for estimating pharmacokinetic parameters and the AIF using a physics-driven CycleGAN approach. Our proposed CycleGAN framework is designed based on the underlying physics model, resulting in a simpler architecture with a single generator and discriminator pair. Crucially, our experimental results indicate that our method does not necessitate separate AIF measurements and produces more reliable pharmacokinetic parameters than other techniques.
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Affiliation(s)
- Gyutaek Oh
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-ro, Yuseong-gu, 34141, Daejeon, Republic of Korea
| | - Yeonsil Moon
- Department of Neurology, Konkuk University Medical Center, 120-1, Neungdong-ro, Gwangjin-gu, 05030, Seoul, Republic of Korea
| | - Won-Jin Moon
- Department of Radiology, Konkuk University Medical Center, 120-1, Neungdong-ro, Gwangjin-gu, 05030, Seoul, Republic of Korea.
| | - Jong Chul Ye
- Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-ro, Yuseong-gu, 34141, Daejeon, Republic of Korea.
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Kang KM, Song J, Choi Y, Park C, Park JE, Kim HS, Park SH, Park CK, Choi SH. MRI Scoring Systems for Predicting Isocitrate Dehydrogenase Mutation and Chromosome 1p/19q Codeletion in Adult-type Diffuse Glioma Lacking Contrast Enhancement. Radiology 2024; 311:e233120. [PMID: 38713025 DOI: 10.1148/radiol.233120] [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: 05/08/2024]
Abstract
Background According to 2021 World Health Organization criteria, adult-type diffuse gliomas include glioblastoma, isocitrate dehydrogenase (IDH)-wildtype; oligodendroglioma, IDH-mutant and 1p/19q-codeleted; and astrocytoma, IDH-mutant, even when contrast enhancement is lacking. Purpose To develop and validate simple scoring systems for predicting IDH and subsequent 1p/19q codeletion status in gliomas without contrast enhancement using standard clinical MRI sequences. Materials and Methods This retrospective study included adult-type diffuse gliomas lacking contrast at contrast-enhanced MRI from two tertiary referral hospitals between January 2012 and April 2022 with diagnoses confirmed at pathology. IDH status was predicted primarily by using T2-fluid-attenuated inversion recovery (FLAIR) mismatch sign, followed by 1p/19q codeletion prediction. A visual rating of MRI features, apparent diffusion coefficient (ADC) ratio, and relative cerebral blood volume was measured. Scoring systems were developed through univariable and multivariable logistic regressions and underwent calibration and discrimination, including internal and external validation. Results For the internal validation cohort, 237 patients were included (mean age, 44.4 years ± 14.4 [SD]; 136 male patients; 193 patients in IDH prediction and 163 patients in 1p/19q prediction). For the external validation cohort, 35 patients were included (46.1 years ± 15.3; 20 male patients; 28 patients in IDH prediction and 24 patients in 1p/19q prediction). The T2-FLAIR mismatch sign demonstrated 100% specificity and 100% positive predictive value for IDH mutation. IDH status prediction scoring system for tumors without mismatch sign included age, ADC ratio, and morphologic characteristics, whereas 1p/19q codeletion prediction for IDH-mutant gliomas included ADC ratio, cortical involvement, and mismatch sign. For IDH status and 1p/19q codeletion prediction, bootstrap-corrected areas under the receiver operating characteristic curve were 0.86 (95% CI: 0.81, 0.90) and 0.73 (95% CI: 0.65, 0.81), respectively, whereas at external validation they were 0.99 (95% CI: 0.98, 1.0) and 0.88 (95% CI: 0.63, 1.0). Conclusion The T2-FLAIR mismatch sign and scoring systems using standard clinical MRI predicted IDH and 1p/19q codeletion status in gliomas lacking contrast enhancement. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Badve and Hodges in this issue.
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Affiliation(s)
- Koung Mi Kang
- From the Department of Radiology (K.M.K., J.S., S.H.C.), Biomedical Research Institute (C.P., C.K.P.), Department of Pathology (S.H.P.), and Department of Neurosurgery (C.K.P.), Seoul National University Hospital, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (K.M.K., S.H.C.); Division of Medical Statistics, Medical Research Collaborating Center, Seoul, Republic of Korea (Y.C.); and Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea (J.E.P., H.S.K.)
| | - Jiyoung Song
- From the Department of Radiology (K.M.K., J.S., S.H.C.), Biomedical Research Institute (C.P., C.K.P.), Department of Pathology (S.H.P.), and Department of Neurosurgery (C.K.P.), Seoul National University Hospital, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (K.M.K., S.H.C.); Division of Medical Statistics, Medical Research Collaborating Center, Seoul, Republic of Korea (Y.C.); and Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea (J.E.P., H.S.K.)
| | - Yunhee Choi
- From the Department of Radiology (K.M.K., J.S., S.H.C.), Biomedical Research Institute (C.P., C.K.P.), Department of Pathology (S.H.P.), and Department of Neurosurgery (C.K.P.), Seoul National University Hospital, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (K.M.K., S.H.C.); Division of Medical Statistics, Medical Research Collaborating Center, Seoul, Republic of Korea (Y.C.); and Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea (J.E.P., H.S.K.)
| | - Chanrim Park
- From the Department of Radiology (K.M.K., J.S., S.H.C.), Biomedical Research Institute (C.P., C.K.P.), Department of Pathology (S.H.P.), and Department of Neurosurgery (C.K.P.), Seoul National University Hospital, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (K.M.K., S.H.C.); Division of Medical Statistics, Medical Research Collaborating Center, Seoul, Republic of Korea (Y.C.); and Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea (J.E.P., H.S.K.)
| | - Ji Eun Park
- From the Department of Radiology (K.M.K., J.S., S.H.C.), Biomedical Research Institute (C.P., C.K.P.), Department of Pathology (S.H.P.), and Department of Neurosurgery (C.K.P.), Seoul National University Hospital, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (K.M.K., S.H.C.); Division of Medical Statistics, Medical Research Collaborating Center, Seoul, Republic of Korea (Y.C.); and Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea (J.E.P., H.S.K.)
| | - Ho Sung Kim
- From the Department of Radiology (K.M.K., J.S., S.H.C.), Biomedical Research Institute (C.P., C.K.P.), Department of Pathology (S.H.P.), and Department of Neurosurgery (C.K.P.), Seoul National University Hospital, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (K.M.K., S.H.C.); Division of Medical Statistics, Medical Research Collaborating Center, Seoul, Republic of Korea (Y.C.); and Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea (J.E.P., H.S.K.)
| | - Sung-Hye Park
- From the Department of Radiology (K.M.K., J.S., S.H.C.), Biomedical Research Institute (C.P., C.K.P.), Department of Pathology (S.H.P.), and Department of Neurosurgery (C.K.P.), Seoul National University Hospital, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (K.M.K., S.H.C.); Division of Medical Statistics, Medical Research Collaborating Center, Seoul, Republic of Korea (Y.C.); and Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea (J.E.P., H.S.K.)
| | - Chul-Kee Park
- From the Department of Radiology (K.M.K., J.S., S.H.C.), Biomedical Research Institute (C.P., C.K.P.), Department of Pathology (S.H.P.), and Department of Neurosurgery (C.K.P.), Seoul National University Hospital, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (K.M.K., S.H.C.); Division of Medical Statistics, Medical Research Collaborating Center, Seoul, Republic of Korea (Y.C.); and Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea (J.E.P., H.S.K.)
| | - Seung Hong Choi
- From the Department of Radiology (K.M.K., J.S., S.H.C.), Biomedical Research Institute (C.P., C.K.P.), Department of Pathology (S.H.P.), and Department of Neurosurgery (C.K.P.), Seoul National University Hospital, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (K.M.K., S.H.C.); Division of Medical Statistics, Medical Research Collaborating Center, Seoul, Republic of Korea (Y.C.); and Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea (J.E.P., H.S.K.)
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Lee J, Chen MM, Liu HL, Ucisik FE, Wintermark M, Kumar VA. MR Perfusion Imaging for Gliomas. Magn Reson Imaging Clin N Am 2024; 32:73-83. [PMID: 38007284 DOI: 10.1016/j.mric.2023.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2023]
Abstract
Accurate diagnosis and treatment evaluation of patients with gliomas is imperative to make clinical decisions. Multiparametric MR perfusion imaging reveals physiologic features of gliomas that can help classify them according to their histologic and molecular features as well as distinguish them from other neoplastic and nonneoplastic entities. It is also helpful in distinguishing tumor recurrence or progression from radiation necrosis, pseudoprogression, and pseudoresponse, which is difficult with conventional MR imaging. This review provides an update on MR perfusion imaging for the diagnosis and treatment monitoring of patients with gliomas following standard-of-care chemoradiation therapy and other treatment regimens such as immunotherapy.
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Affiliation(s)
- Jina Lee
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA
| | - Melissa M Chen
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA
| | - Ho-Ling Liu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA
| | - F Eymen Ucisik
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA
| | - Max Wintermark
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA
| | - Vinodh A Kumar
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA.
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Tseng CH, Jaspers J, Romero AM, Wielopolski P, Smits M, van Osch MJP, Vos F. Improved reliability of perfusion estimation in dynamic susceptibility contrast MRI by using the arterial input function from dynamic contrast enhanced MRI. NMR IN BIOMEDICINE 2024; 37:e5038. [PMID: 37712359 DOI: 10.1002/nbm.5038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 08/02/2023] [Accepted: 08/23/2023] [Indexed: 09/16/2023]
Abstract
The arterial input function (AIF) plays a crucial role in estimating quantitative perfusion properties from dynamic susceptibility contrast (DSC) MRI. An important issue, however, is that measuring the AIF in absolute contrast-agent concentrations is challenging, due to uncertainty in relation to the measuredR 2 ∗ -weighted signal, signal depletion at high concentration, and partial-volume effects. A potential solution could be to derive the AIF from separately acquired dynamic contrast enhanced (DCE) MRI data. We aim to compare the AIF determined from DCE MRI with the AIF from DSC MRI, and estimated perfusion coefficients derived from DSC data using a DCE-driven AIF with perfusion coefficients determined using a DSC-based AIF. AIFs were manually selected in branches of the middle cerebral artery (MCA) in both DCE and DSC data in each patient. In addition, a semi-automatic AIF-selection algorithm was applied to the DSC data. The amplitude and full width at half-maximum of the AIFs were compared statistically using the Wilcoxon rank-sum test, applying a 0.05 significance level. Cerebral blood flow (CBF) was derived with different AIF approaches and compared further. The results showed that the AIFs extracted from DSC scans yielded highly variable peaks across arteries within the same patient. The semi-automatic DSC-AIF had significantly narrower width compared with the manual AIFs, and a significantly larger peak than the manual DSC-AIF. Additionally, the DCE-based AIF provided a more stable measurement of relative CBF and absolute CBF values estimated with DCE-AIFs that were compatible with previously reported values. In conclusion, DCE-based AIFs were reproduced significantly better across vessels, showed more realistic profiles, and delivered more stable and reasonable CBF measurements. The DCE-AIF can, therefore, be considered as an alternative AIF source for quantitative perfusion estimations in DSC MRI.
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Affiliation(s)
- Chih-Hsien Tseng
- Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands
- Medical Delta, Delft, the Netherlands
- Holland Proton Therapy Center Consortium-Erasmus MC, Rotterdam, Holland Proton Therapy Centre, Delft, Leiden University Medical Center, Leiden and Delft University of Technology, Delft, the Netherlands
| | - Jaap Jaspers
- Holland Proton Therapy Center Consortium-Erasmus MC, Rotterdam, Holland Proton Therapy Centre, Delft, Leiden University Medical Center, Leiden and Delft University of Technology, Delft, the Netherlands
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Alejandra Mendez Romero
- Holland Proton Therapy Center Consortium-Erasmus MC, Rotterdam, Holland Proton Therapy Centre, Delft, Leiden University Medical Center, Leiden and Delft University of Technology, Delft, the Netherlands
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Piotr Wielopolski
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Marion Smits
- Medical Delta, Delft, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Brain Tumour Center, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Matthias J P van Osch
- Medical Delta, Delft, the Netherlands
- Holland Proton Therapy Center Consortium-Erasmus MC, Rotterdam, Holland Proton Therapy Centre, Delft, Leiden University Medical Center, Leiden and Delft University of Technology, Delft, the Netherlands
- C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Frans Vos
- Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands
- Medical Delta, Delft, the Netherlands
- Holland Proton Therapy Center Consortium-Erasmus MC, Rotterdam, Holland Proton Therapy Centre, Delft, Leiden University Medical Center, Leiden and Delft University of Technology, Delft, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
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Staub-Bartelt F, Rittenauer J, Sabel M, Rapp M. Functional Outcome and Overall Survival in Patients with Primary or Secondary CNS Lymphoma after Surgical Resection vs. Biopsy. Cancers (Basel) 2023; 15:5266. [PMID: 37958439 PMCID: PMC10647498 DOI: 10.3390/cancers15215266] [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: 10/02/2023] [Revised: 10/22/2023] [Accepted: 10/31/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Central nervous system lymphoma (CNSL) is rare form of brain tumour. It manifests either as primary CNS lymphoma (pCNSL) originating within the central nervous system or as secondary CNS lymphoma (sCNSL), arising as cerebral metastases of systemic lymphoma. For a significant period, surgical resection was considered obsolete due to the favourable response to chemotherapy and the associated risk of postoperative deficits. The objective of the present study was to demonstrate the benefits of resection in CNSL patients, including extended survival and improved postoperative function. METHODS A retrospective study involving patients diagnosed with either PCNSL or SCNSL that were surgically approached at our neurosurgical department between 2010 and 2022 was conducted. Patients were categorised into three subgroups based on their neurosurgical approach: (1) stereotactical biopsy, (2) open biopsy, (3) resection. We then performed statistical analyses to assess overall survival (OS) and progression-free survival (PFS). Additionally, we examined various secondary factors such as functional outcome via Karnofsky Performance Index (KPS) and prognosis scoring. RESULTS 157 patients diagnosed with PCNSL or SCNSL were enclosed in the study. Of these, 101 underwent stereotactic biopsy, 21 had open biopsy, and 35 underwent resection. Mean age of the cohort was 64.94 years, with majority of patients being female (54.1%). The resection group showed longest OS at 44 months (open biopsy = 13 months, stereotactic biopsy = 9 months). Calculated median follow-up was 34.5 months. In the Cox regression model, postoperative KPS 70% (p < 0.001) and resection vs. stereotactic biopsy (p = 0.040) were identified as protective factors, whereas older age at diagnosis was identified as a risk factor (p < 0.001). In the one-way analysis of variance, differences in postoperative KPS were found among all groups (p = 0.021), while there was no difference in preoperative KPS among the groups. CONCLUSIONS Our data show a favourable outcome when resection is compared to either stereotactic or open biopsy. Additionally, the marginally improved postoperative functional status observed in patients who underwent resection, as opposed to in those who underwent biopsy, provides further evidence in favour of the advantages of surgical resection for enhancing neurological deficits.
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Affiliation(s)
- Franziska Staub-Bartelt
- Department of Neurosurgery, Medical Faculty, Heinrich-Heine University Düsseldorf, Moorenstraße 5, 40225 Düsseldorf, Germany (M.S.); (M.R.)
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Liu LH, Zhang HW, Zhang HB, Liu XL, Deng HZ, Lin F, Huang B. Distinctive magnetic resonance imaging features in primary central nervous system lymphoma: A case report. World J Radiol 2023; 15:274-280. [PMID: 37823021 PMCID: PMC10563853 DOI: 10.4329/wjr.v15.i9.274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 09/04/2023] [Accepted: 09/22/2023] [Indexed: 09/27/2023] Open
Abstract
BACKGROUND Primary central nervous system lymphoma (PCNSL) is a rare malignant tumor originating from the lymphatic hematopoietic system. It exhibits unique imaging manifestations due to its biological characteristics. CASE SUMMARY Magnetic resonance imaging (MRI) with diffusion-weighted imaging (DWI), perfusion-weighted imaging (PWI), and magnetic resonance spectroscopy was performed. The imaging findings showed multiple space-occupying lesions with low signal on T1-weighted imaging, uniform high signal on T2-weighted imaging, and obvious enhancement on contrast-enhanced scans. DWI revealed diffusion restriction, PWI demonstrated hypoperfusion, and spectroscopy showed elevated choline peak and decreased N-acetylaspartic acid. The patient's condition significantly improved after hormone shock therapy. CONCLUSION This case highlights the distinctive imaging features of PCNSL and their importance in accurate diagnosis and management.
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Affiliation(s)
- Li-Hong Liu
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People’s Hospital, Shenzhen 518036, Guangdong Province, China
| | - Han-Wen Zhang
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People’s Hospital, Shenzhen 518036, Guangdong Province, China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510282, Guangdong Province, China
| | - Hong-Bo Zhang
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510282, Guangdong Province, China
- Department of Radiology, Sun Yat-Sen University, Shenzhen 518000, Guangdong Province, China
| | - Xiao-Lei Liu
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People’s Hospital, Shenzhen 518036, Guangdong Province, China
| | - Hua-Zhen Deng
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People’s Hospital, Shenzhen 518036, Guangdong Province, China
| | - Fan Lin
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People’s Hospital, Shenzhen 518036, Guangdong Province, China
| | - Biao Huang
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510282, Guangdong Province, China
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510000, Guangdong Province, China
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Bae J, Li C, Masurkar A, Ge Y, Kim SG. Improving measurement of blood-brain barrier permeability with reduced scan time using deep-learning-derived capillary input function. Neuroimage 2023; 278:120284. [PMID: 37507078 PMCID: PMC10475161 DOI: 10.1016/j.neuroimage.2023.120284] [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: 05/02/2023] [Revised: 07/13/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023] Open
Abstract
PURPOSE In Dynamic contrast-enhanced MRI (DCE-MRI), Arterial Input Function (AIF) has been shown to be a significant contributor to uncertainty in the estimation of kinetic parameters. This study is to assess the feasibility of using a deep learning network to estimate local Capillary Input Function (CIF) to estimate blood-brain barrier (BBB) permeability, while reducing the required scan time. MATERIALS AND METHOD A total of 13 healthy subjects (younger (<40 y/o): 8, older (> 67 y/o): 5) were recruited and underwent 25-min DCE-MRI scans. The 25 min data were retrospectively truncated to 10 min to simulate a reduced scan time of 10 min. A deep learning network was trained to predict the CIF using simulated tissue contrast dynamics with two vascular transport models. The BBB permeability (PS) was measured using 3 methods: (i) Ca-25min, using DCE-MRI data of 25 min with individually sampled AIF (Ca); (ii) Ca-10min, using truncated 10min data with AIF (Ca); and (iii) Cp-10min, using truncated 10 min data with CIF (Cp). The PS estimates from the Ca-25min method were used as reference standard values to assess the accuracy of the Ca-10min and Cp-10min methods in estimating the PS values. RESULTS When compared to the reference method(Ca-25min), the Ca-10min and Cp-10min methods resulted in an overestimation of PS by 217 ± 241 % and 48.0 ± 30.2 %, respectively. The Bland Altman analysis showed that the mean difference from the reference was 8.85 ± 1.78 (x10-4 min-1) with the Ca-10min, while it was reduced to 1.63 ± 2.25 (x10-4 min-1) with the Cp-10min, resulting in an average reduction of 81%. The limits of agreement also reduced by up to 39.2% with the Cp-10min. We found a 75% increase of BBB permeability in the gray matter and a 35% increase in the white matter, when comparing the older group to the younger group. CONCLUSIONS We demonstrated the feasibility of estimating the capillary-level input functions using a deep learning network. We also showed that this method can be used to estimate subtle age-related changes in BBB permeability with reduced scan time, without compromising accuracy. Moreover, the trained deep learning network can automatically select CIF, reducing the potential uncertainty resulting from manual user-intervention.
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Affiliation(s)
- Jonghyun Bae
- Vilcek Institute of Graduate Biomedical Science, New York University School of Medicine; Center for Biomedical Imaging, Radiology, New York University School of Medicine; Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine; Department of Radiology, Weill Cornell Medical College.
| | - Chenyang Li
- Vilcek Institute of Graduate Biomedical Science, New York University School of Medicine; Center for Biomedical Imaging, Radiology, New York University School of Medicine; Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine.
| | - Arjun Masurkar
- Center for Cognitive Neurology, Department of Neurology, New York University School of Medicine; Department of Neuroscience & Physiology, New York University School of Medicine; Neuroscience Institute, New York University School of Medicine.
| | - Yulin Ge
- Center for Biomedical Imaging, Radiology, New York University School of Medicine; Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine.
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9
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Yu X, Hong W, Ye M, Lai M, Shi C, Li L, Ye K, Xu J, Ai R, Shan C, Cai L, Luo L. Atypical primary central nervous system lymphoma and glioblastoma: multiparametric differentiation based on non-enhancing volume, apparent diffusion coefficient, and arterial spin labeling. Eur Radiol 2023; 33:5357-5367. [PMID: 37171492 PMCID: PMC10326108 DOI: 10.1007/s00330-023-09681-2] [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: 06/11/2022] [Revised: 01/02/2023] [Accepted: 02/24/2023] [Indexed: 05/13/2023]
Abstract
OBJECTIVES To evaluate the multiparametric diagnostic performance with non-enhancing tumor volume, apparent diffusion coefficient (ADC), and arterial spin labeling (ASL) to differentiate between atypical primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM). METHODS One hundred and fifty-eight patients with pathologically confirmed typical PCNSL (n = 59), atypical PCNSL (hemorrhage, necrosis, or heterogeneous contrast enhancement, n = 29), and GBM (n = 70) were selected. Relative minimum ADC (rADCmin), mean (rADCmean), maximum (rADCmax), and rADCmax-min (rADCdif) were obtained by standardization of the contralateral white matter. Maximum cerebral blood flow (CBFmax) was obtained according to the ASL-CBF map. The regions of interests (ROIs) were manually delineated on the inner side of the tumor to further generate a 3D-ROI and obtain the non-enhancing tumor (nET) volume. The area under the curve (AUC) was used to evaluate the diagnostic performance. RESULTS Atypical PCNSLs showed significantly lower rADCmax, rADCmean, and rADCdif than that of GBMs. GBMs showed significantly higher CBFmax and nET volume ratios than that of atypical PCNSLs. Combined three-variable models with rADCmean, CBFmax, and nET volume ratio were superior to one- and two-variable models. The AUC of the three-variable model was 0.96, and the sensitivity and specificity were 90% and 96.55%, respectively. CONCLUSION The combined evaluation of rADCmean, CBFmax, and nET volume allowed for reliable differentiation between atypical PCNSL and GBM. KEY POINTS • Atypical PCNSL is easily misdiagnosed as glioblastoma, which leads to unnecessary surgical resection. • The nET volume, ADC, and ASL-derived parameter (CBF) were lower for atypical PCNSL than that for glioblastoma. • The combination of multiple parameters performed well (AUC = 0.96) in the discrimination between atypical PCNSL and glioblastoma.
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Affiliation(s)
- Xiaojun Yu
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, No. 613, Huangpu Road West, Tianhe District, Guangdong Province, Guangzhou, 510630, China
| | - Weiping Hong
- Department of Oncology, Guangdong Sanjiu Brain Hospital, Guangzhou, 510510, China
| | - Minting Ye
- Department of Oncology, Guangdong Sanjiu Brain Hospital, Guangzhou, 510510, China
| | - Mingyao Lai
- Department of Oncology, Guangdong Sanjiu Brain Hospital, Guangzhou, 510510, China
| | - Changzheng Shi
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, No. 613, Huangpu Road West, Tianhe District, Guangdong Province, Guangzhou, 510630, China
| | - Linzhen Li
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, No. 613, Huangpu Road West, Tianhe District, Guangdong Province, Guangzhou, 510630, China
| | - Kunlin Ye
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, No. 613, Huangpu Road West, Tianhe District, Guangdong Province, Guangzhou, 510630, China
| | - Jiali Xu
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, No. 613, Huangpu Road West, Tianhe District, Guangdong Province, Guangzhou, 510630, China
| | - Ruyu Ai
- Department of Oncology, Guangdong Sanjiu Brain Hospital, Guangzhou, 510510, China
| | - Changguo Shan
- Department of Oncology, Guangdong Sanjiu Brain Hospital, Guangzhou, 510510, China
| | - Linbo Cai
- Department of Oncology, Guangdong Sanjiu Brain Hospital, Guangzhou, 510510, China.
| | - Liangping Luo
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, No. 613, Huangpu Road West, Tianhe District, Guangdong Province, Guangzhou, 510630, China.
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10
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Starck L, Skeie BS, Bartsch H, Grüner R. Arterial input functions in dynamic susceptibility contrast MRI (DSC-MRI) in longitudinal evaluation of brain metastases. Acta Radiol 2023; 64:1166-1174. [PMID: 35786055 DOI: 10.1177/02841851221109702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) could be helpful to separate true disease progression from pseudo-progression in brain metastases when assessing the need for retreatment. However, the selection of arterial input functions (AIFs) is not standardized for analysis, limiting its use for this application. PURPOSE To compare population-based AIFs, AIFs specific to each patient, and AIFs specific to every visit in the longitudinal follow-up of brain metastases. MATERIAL AND METHODS Longitudinal data were collected from eight patients before treatment (6 of 8 patients) and after treatment (6-17 visits). Imaging was performed using a 1.5-T MRI system. Lesions were segmented by subtracting precontrast images from postcontrast images. Cerebral blood volume (rCBV) and cerebral blood flow (rCBF) were computed, and Pearson's product moment correlation coefficients were calculated to evaluate similarity of DSC parameters dependent on various AIF choices across time. AIF shape characteristics were compared. Parameter differences between white matter (WM) and gray matter (GM) were obtained to determine which AIF choice maximizes tissue differentiation. RESULTS Although DSC parameters follow similar patterns in time, the various AIF selections cause large parameter variations with relative standard deviations of up to ±60%. AIFs sampled in one patient across sessions more similar in shape than AIFs sampled across patients. Estimates of rCBV based on scan-specific AIFs differentiated better between perfusion in WM and GM than patient-specific or population-based AIFs (P ≤ 0.02). CONCLUSION Results indicate that scan-specific AIFs are the best choice for DSC-MRI parameter estimations in the longitudinal follow-up of brain metastases.
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Affiliation(s)
- Lea Starck
- Department of Physics and Technology, 1658University of Bergen, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre, Bergen, Norway
| | - Bente Sandvei Skeie
- Department of Neurosurgery, 60498Haukeland University Hospital, Bergen, Norway
| | - Hauke Bartsch
- Mohn Medical Imaging and Visualization Centre, Bergen, Norway
- Department of Radiology, 60498Haukeland University Hospital, Bergen, Norway
| | - Renate Grüner
- Department of Physics and Technology, 1658University of Bergen, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre, Bergen, Norway
- Department of Radiology, 60498Haukeland University Hospital, Bergen, Norway
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Nenning KH, Gesperger J, Furtner J, Nemc A, Roetzer-Pejrimovsky T, Choi SW, Mitter C, Leber SL, Hofmanninger J, Klughammer J, Ergüner B, Bauer M, Brada M, Chong K, Brandner-Kokalj T, Freyschlag CF, Grams A, Haybaeck J, Hoenigschnabl S, Hoffermann M, Iglseder S, Kiesel B, Kitzwoegerer M, Kleindienst W, Marhold F, Moser P, Oberndorfer S, Pinggera D, Scheichel F, Sherif C, Stockhammer G, Stultschnig M, Thomé C, Trenkler J, Urbanic-Purkart T, Weis S, Widhalm G, Wuertz F, Preusser M, Baumann B, Simonitsch-Klupp I, Nam DH, Bock C, Langs G, Woehrer A. Radiomic features define risk and are linked to DNA methylation attributes in primary CNS lymphoma. Neurooncol Adv 2023; 5:vdad136. [PMID: 38024240 PMCID: PMC10676053 DOI: 10.1093/noajnl/vdad136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2023] Open
Abstract
Background The prognostic roles of clinical and laboratory markers have been exploited to model risk in patients with primary CNS lymphoma, but these approaches do not fully explain the observed variation in outcome. To date, neuroimaging or molecular information is not used. The aim of this study was to determine the utility of radiomic features to capture clinically relevant phenotypes, and to link those to molecular profiles for enhanced risk stratification. Methods In this retrospective study, we investigated 133 patients across 9 sites in Austria (2005-2018) and an external validation site in South Korea (44 patients, 2013-2016). We used T1-weighted contrast-enhanced MRI and an L1-norm regularized Cox proportional hazard model to derive a radiomic risk score. We integrated radiomic features with DNA methylation profiles using machine learning-based prediction, and validated the most relevant biological associations in tissues and cell lines. Results The radiomic risk score, consisting of 20 mostly textural features, was a strong and independent predictor of survival (multivariate hazard ratio = 6.56 [3.64-11.81]) that remained valid in the external validation cohort. Radiomic features captured gene regulatory differences such as in BCL6 binding activity, which was put forth as testable treatment target for a subset of patients. Conclusions The radiomic risk score was a robust and complementary predictor of survival and reflected characteristics in underlying DNA methylation patterns. Leveraging imaging phenotypes to assess risk and inform epigenetic treatment targets provides a concept on which to advance prognostic modeling and precision therapy for this aggressive cancer.
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Affiliation(s)
- Karl-Heinz Nenning
- Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Laboratory, Medical University of Vienna, Vienna, Austria
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, New York, USA
| | - Johanna Gesperger
- Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Julia Furtner
- Division of Neuroradiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
- Research Center for Medical Image Analysis and Artificial Intelligence (MIAAI), Faculty of Medicine and Dentistry, Danube Private University, Krems, Austria
| | - Amelie Nemc
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Thomas Roetzer-Pejrimovsky
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria
| | - Seung-Won Choi
- Department of Neurosurgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Christian Mitter
- Division of Neuroradiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Stefan L Leber
- Division of Neuroradiology, Vascular, and Interventional Radiology, Department of Radiology, Medical University of Graz, Graz, Austria
| | - Johannes Hofmanninger
- Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Laboratory, Medical University of Vienna, Vienna, Austria
| | - Johanna Klughammer
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
- Department of Biochemistry, Gene Center, Ludwig-Maximilians-University, München, Germany
| | - Bekir Ergüner
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Marlies Bauer
- Department of Neurosurgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Martina Brada
- Department of Pathology, Klinik Landstraße, Vienna, Austria
| | - Kyuha Chong
- Department of Neurosurgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | | | | | - Astrid Grams
- Department of Neuroradiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Johannes Haybaeck
- Institute of Pathology, Neuropathology and Molecular Pathology, Medical University of Innsbruck, Innsbruck, Austria
- Center for Molecular Biomedicine, Institute of Pathology, Medical University of Graz, Diagnostic and Research, Graz, Austria
| | | | - Markus Hoffermann
- Department of Neurosurgery, State Hospital Feldkirch, Feldkirch, Austria
| | - Sarah Iglseder
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Barbara Kiesel
- Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Melitta Kitzwoegerer
- Department of Pathology, University Hospital St. Poelten, Karl Landsteiner University of Health Sciences, St. Poelten, Austria
| | - Waltraud Kleindienst
- Department of Neurology, Paracelsus Medical University Salzburg, Salzburg, Austria
| | - Franz Marhold
- Department of Neurosurgery, University Hospital St. Poelten, Karl Landsteiner University of Health Sciences, St. Poelten, Austria
| | - Patrizia Moser
- Department of Neurosurgery, Medical University of Innsbruck, Innsbruck, Austria
- Department of Pathology, Innpath, Tirolkliniken, Innsbruck, Austria
| | - Stefan Oberndorfer
- Department of Neurology, University Hospital St. Poelten, Karl Landsteiner University of Health Sciences, St. Poelten, Austria
| | - Daniel Pinggera
- Department of Neurosurgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Florian Scheichel
- Department of Neurosurgery, University Hospital St. Poelten, Karl Landsteiner University of Health Sciences, St. Poelten, Austria
| | - Camillo Sherif
- Department of Neurosurgery, University Hospital St. Poelten, Karl Landsteiner University of Health Sciences, St. Poelten, Austria
| | | | | | - Claudius Thomé
- Department of Neurosurgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Johannes Trenkler
- Institute of Neuroradiology, Kepler University Hospital, NeuromedCampus, Johannes Kepler University of Linz, Linz, Austria
| | - Tadeja Urbanic-Purkart
- Department of Neurology, Medical University of Graz, Graz, Austria
- Division of Neuroradiology, Vascular and Interventional Radiology, Medical University of Graz, Graz, Austria
| | - Serge Weis
- Division of Neuropathology, Kepler University Hospital, NeuromedCampus, Johannes Kepler University, Linz, Austria
| | - Georg Widhalm
- Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Franz Wuertz
- Institute of Pathology, State Hospital Klagenfurt, Klagenfurt, Austria
| | - Matthias Preusser
- Division of Oncology, Department of Internal Medicine 1, Medical University of Vienna, Vienna, Austria
| | - Bernhard Baumann
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | | | - Do-Hyun Nam
- Department of Neurosurgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Christoph Bock
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
- Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria
| | - Georg Langs
- Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Laboratory, Medical University of Vienna, Vienna, Austria
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Adelheid Woehrer
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria
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Abstract
In 2001, the concept of the neurovascular unit was introduced at the Stroke Progress Review Group meeting. The neurovascular unit is an important element of the health and disease status of blood vessels and nerves in the central nervous system. Since then, the neurovascular unit has attracted increasing interest from research teams, who have contributed greatly to the prevention, treatment, and prognosis of stroke and neurodegenerative diseases. However, additional research is needed to establish an efficient, low-cost, and low-energy in vitro model of the neurovascular unit, as well as enable noninvasive observation of neurovascular units in vivo and in vitro. In this review, we first summarize the composition of neurovascular units, then investigate the efficacy of different types of stem cells and cell culture methods in the construction of neurovascular unit models, and finally assess the progress of imaging methods used to observe neurovascular units in recent years and their positive role in the monitoring and investigation of the mechanisms of a variety of central nervous system diseases.
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Affiliation(s)
- Taiwei Dong
- College of Pharmacy, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi Province, China
| | - Min Li
- College of Pharmacy, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi Province, China
| | - Feng Gao
- College of Pharmacy, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi Province, China
| | - Peifeng Wei
- College of Pharmacy, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi Province, China
| | - Jian Wang
- College of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Provinve, China
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