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Dawod M, Rush E, Nagib PB, Aduwo J, Bodempudi P, Appiah-Kubi E. The Utility of Prostate-Specific Membrane Antigen-11 PET in Detection and Management of Central Nervous System Neoplasms. Clin Nucl Med 2024; 49:e340-e345. [PMID: 38598534 DOI: 10.1097/rlu.0000000000005157] [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: 04/12/2024]
Abstract
ABSTRACT We present a case series of 5 patients diagnosed with schwannoma and 1 patient diagnosed with astrocytoma who underwent PSMA PET imaging for tumor detection. We retrospectively analyzed the records of 4 male and 2 female patients (mean age, 53.2 ± 13.2) who underwent PSMA PET imaging between March and September 2023. PET interpretation showed increased Ga-PSMA-11 accumulation in all patients with a mean SUV max of 3.11 ± 1.8. This series underscores PSMA PET's potential for CNS neoplasm detection.
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Affiliation(s)
- Mina Dawod
- From the The Ohio State University College of Medicine
| | - Evan Rush
- Department of Radiology, The Ohio State University College of Medicine
| | - Paul B Nagib
- From the The Ohio State University College of Medicine
| | - Jessica Aduwo
- From the The Ohio State University College of Medicine
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2
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Heidari M, Shokrani P. Imaging Role in Diagnosis, Prognosis, and Treatment Response Prediction Associated with High-grade Glioma. JOURNAL OF MEDICAL SIGNALS & SENSORS 2024; 14:7. [PMID: 38993200 PMCID: PMC11111132 DOI: 10.4103/jmss.jmss_30_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 07/31/2022] [Accepted: 03/14/2023] [Indexed: 07/13/2024]
Abstract
Background Glioma is one of the most drug and radiation-resistant tumors. Gliomas suffer from inter- and intratumor heterogeneity which makes the outcome of similar treatment protocols vary from patient to patient. This article is aimed to overview the potential imaging markers for individual diagnosis, prognosis, and treatment response prediction in malignant glioma. Furthermore, the correlation between imaging findings and biological and clinical information of glioma patients is reviewed. Materials and Methods The search strategy in this study is to select related studies from scientific websites such as PubMed, Scopus, Google Scholar, and Web of Science published until 2022. It comprised a combination of keywords such as Biomarkers, Diagnosis, Prognosis, Imaging techniques, and malignant glioma, according to Medical Subject Headings. Results Some imaging parameters that are effective in glioma management include: ADC, FA, Ktrans, regional cerebral blood volume (rCBV), cerebral blood flow (CBF), ve, Cho/NAA and lactate/lipid ratios, intratumoral uptake of 18F-FET (for diagnostic application), RD, ADC, ve, vp, Ktrans, CBFT1, rCBV, tumor blood flow, Cho/NAA, lactate/lipid, MI/Cho, uptakes of 18F-FET, 11C-MET, and 18F-FLT (for prognostic and predictive application). Cerebral blood volume and Ktrans are related to molecular markers such as vascular endothelial growth factor (VEGF). Preoperative ADCmin value of GBM tumors is associated with O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status. 2-hydroxyglutarate metabolite and dynamic 18F-FDOPA positron emission tomography uptake are related to isocitrate dehydrogenase (IDH) mutations. Conclusion Parameters including ADC, RD, FA, rCBV, Ktrans, vp, and uptake of 18F-FET are useful for diagnosis, prognosis, and treatment response prediction in glioma. A significant correlation between molecular markers such as VEGF, MGMT, and IDH mutations with some diffusion and perfusion imaging parameters has been identified.
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Affiliation(s)
- Maryam Heidari
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Parvaneh Shokrani
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
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3
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Urcuyo JC, Curtin L, Langworthy JM, De Leon G, Anderies B, Singleton KW, Hawkins-Daarud A, Jackson PR, Bond KM, Ranjbar S, Lassiter-Morris Y, Clark-Swanson KR, Paulson LE, Sereduk C, Mrugala MM, Porter AB, Baxter L, Salomao M, Donev K, Hudson M, Meyer J, Zeeshan Q, Sattur M, Patra DP, Jones BA, Rahme RJ, Neal MT, Patel N, Kouloumberis P, Turkmani AH, Lyons M, Krishna C, Zimmerman RS, Bendok BR, Tran NL, Hu LS, Swanson KR. Image-localized biopsy mapping of brain tumor heterogeneity: A single-center study protocol. PLoS One 2023; 18:e0287767. [PMID: 38117803 PMCID: PMC10732423 DOI: 10.1371/journal.pone.0287767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 06/13/2023] [Indexed: 12/22/2023] Open
Abstract
Brain cancers pose a novel set of difficulties due to the limited accessibility of human brain tumor tissue. For this reason, clinical decision-making relies heavily on MR imaging interpretation, yet the mapping between MRI features and underlying biology remains ambiguous. Standard (clinical) tissue sampling fails to capture the full heterogeneity of the disease. Biopsies are required to obtain a pathological diagnosis and are predominantly taken from the tumor core, which often has different traits to the surrounding invasive tumor that typically leads to recurrent disease. One approach to solving this issue is to characterize the spatial heterogeneity of molecular, genetic, and cellular features of glioma through the intraoperative collection of multiple image-localized biopsy samples paired with multi-parametric MRIs. We have adopted this approach and are currently actively enrolling patients for our 'Image-Based Mapping of Brain Tumors' study. Patients are eligible for this research study (IRB #16-002424) if they are 18 years or older and undergoing surgical intervention for a brain lesion. Once identified, candidate patients receive dynamic susceptibility contrast (DSC) perfusion MRI and diffusion tensor imaging (DTI), in addition to standard sequences (T1, T1Gd, T2, T2-FLAIR) at their presurgical scan. During surgery, sample anatomical locations are tracked using neuronavigation. The collected specimens from this research study are used to capture the intra-tumoral heterogeneity across brain tumors including quantification of genetic aberrations through whole-exome and RNA sequencing as well as other tissue analysis techniques. To date, these data (made available through a public portal) have been used to generate, test, and validate predictive regional maps of the spatial distribution of tumor cell density and/or treatment-related key genetic marker status to identify biopsy and/or treatment targets based on insight from the entire tumor makeup. This type of methodology, when delivered within clinically feasible time frames, has the potential to further inform medical decision-making by improving surgical intervention, radiation, and targeted drug therapy for patients with glioma.
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Affiliation(s)
- Javier C Urcuyo
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Lee Curtin
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Jazlynn M. Langworthy
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Gustavo De Leon
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Barrett Anderies
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Kyle W. Singleton
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Andrea Hawkins-Daarud
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Pamela R. Jackson
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Kamila M. Bond
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Sara Ranjbar
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Yvette Lassiter-Morris
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Kamala R. Clark-Swanson
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Lisa E. Paulson
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Chris Sereduk
- Department of Cancer Biology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Maciej M. Mrugala
- Department of Neurology, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Oncology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Alyx B. Porter
- Department of Neurology, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Oncology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Leslie Baxter
- Department of Neurophysiology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Marcela Salomao
- Department of Pathology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Kliment Donev
- Department of Pathology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Miles Hudson
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Jenna Meyer
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Qazi Zeeshan
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Mithun Sattur
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Devi P. Patra
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Breck A. Jones
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Rudy J. Rahme
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Matthew T. Neal
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Naresh Patel
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Pelagia Kouloumberis
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Ali H. Turkmani
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Mark Lyons
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Chandan Krishna
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Richard S. Zimmerman
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Bernard R. Bendok
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Nhan L. Tran
- Department of Cancer Biology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Leland S. Hu
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Kristin R. Swanson
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Cancer Biology, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, United States of America
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Patil V, Malik R, Sarawagi R. Comparative study between dynamic susceptibility contrast magnetic resonance imaging and arterial spin labelling perfusion in differentiating low-grade from high-grade brain tumours. Pol J Radiol 2023; 88:e521-e528. [PMID: 38125817 PMCID: PMC10731442 DOI: 10.5114/pjr.2023.132889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 10/06/2023] [Indexed: 12/23/2023] Open
Abstract
Purpose Our aim was to distinguish between low-grade and high-grade brain tumours on the basis of dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) perfusion and arterial spin labelling (ASL) perfusion and to compare DSC and ASL techniques. Material and methods Forty-one patients with brain tumours were evaluated by 3-Tesla MRI. Conventional and perfusion MRI imaging with a 3D pseudo-continuous ASL (PCASL) and DSC perfusion maps were evaluated. Three ROIs were placed to obtain cerebral blood value (CBV) and cerebral blood flow (CBF) in areas of maximum perfusion in brain tumour and normal grey matter. Histopathological diagnosis was considered as the reference. ROC analysis was performed to compare the diagnostic performance and to obtain a feasible cut-off value of perfusion parameters to differentiate low-grade and high-grade brain tumours. Results Normalised perfusion parameters with grey matter (rCBF or rCBV lesion/NGM) of malignant lesions were significantly higher than those of benign lesions in both DSC (normalised rCBF of 2.16 and normalised rCBV of 2.63) and ASL (normalised rCBF of 2.22) perfusion imaging. The normalised cut-off values of DSC (rCBF of 1.1 and rCBV of 1.4) and ASL (rCBF of 1.3) showed similar specificity and near similar sensitivity in distinguishing low-grade and high-grade brain tumours. Conclusions Quantitative analysis of perfusion parameters obtained by both DSC and ASL perfusion techniques can be reliably used to distinguish low-grade and high-grade brain tumours. Normalisation of these values by grey matter gives us more reliable parameters, eliminating the different technical parameters involved in both the techniques.
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Affiliation(s)
- Vaibhav Patil
- All India Institute of Medical Sciences, Bhopal, India
| | - Rajesh Malik
- All India Institute of Medical Sciences, Bhopal, India
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Hasanzadeh A, Moghaddam HS, Shakiba M, Jalali AH, Firouznia K. The Role of Multimodal Imaging in Differentiating Vasogenic from Infiltrative Edema: A Systematic Review. Indian J Radiol Imaging 2023; 33:514-521. [PMID: 37811185 PMCID: PMC10556327 DOI: 10.1055/s-0043-1772466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2023] Open
Abstract
Background High-grade gliomas (HGGs) are the most prevalent primary malignancy of the central nervous system. The tumor results in vasogenic and infiltrative edema . Exact anatomical differentiation of these edemas is so important for surgical planning. Multimodal imaging could be used to differentiate the edema type. Purpose The aim of this study was to investigate the role of multimodal imaging in the differentiation of vasogenic edema from infiltrative edema in patients with HGG (grade III and grade IV). Data Sources A search on PubMed, EMBASE, Scopus, and ISI Web of Science Core Collection up to June 2022 using terms related to (a) multimodal imaging AND (b) HGG AND (c) edema. (PROSPERO registration number: CRD42022336131) Study Selection Two reviewers screened the articles and independently extracted the data. We included original articles assessing the role of multimodal imaging in differentiating vasogenic from infiltrative edema in patients with HGG. Six high-quality articles remained for the narrative synthesis. Data Synthesis Dynamic susceptibility contrast imaging showed that relative cerebral blood volume and relative cerebral blood flow were higher in the infiltrative edema component than in the vasogenic edema component. Diffusion tensor imaging revealed a dispute on fractional anisotropy. The apparent diffusion coefficient was comparable between the two edematous components. Magnetic resonance spectroscopy exhibited an increment in choline/creatinine ratio and choline/N-acetyl aspartate ratio in the infiltrative edema component. Limitations Strict study selection, low sample size of relevant published studies, and heterogeneity in endpoint variables were the major drawbacks. Conclusions Multimodal imaging, including dynamic susceptibility contrast and magnetic resonance spectroscopy, might help differentiate between vasogenic and infiltrative edema.
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Affiliation(s)
- Alireza Hasanzadeh
- Medical School, Tehran University of Medical Sciences, Tehran, Iran
- Advanced Diagnostic and Interventional Radiology Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Hossein Sanjari Moghaddam
- Medical School, Tehran University of Medical Sciences, Tehran, Iran
- Advanced Diagnostic and Interventional Radiology Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Madjid Shakiba
- Advanced Diagnostic and Interventional Radiology Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Amir Hossein Jalali
- Advanced Diagnostic and Interventional Radiology Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Kavous Firouznia
- Advanced Diagnostic and Interventional Radiology Research Center, Tehran University of Medical Sciences, Tehran, Iran
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6
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Goldman J, Hagiwara A, Yao J, Raymond C, Ong C, Bakhti R, Kwon E, Farhat M, Torres C, Erickson LG, Curl BJ, Lee M, Pope WB, Salamon N, Nghiemphu PL, Ji M, Eldred BS, Liau LM, Lai A, Cloughesy TF, Chung C, Ellingson BM. Paradoxical Association Between Relative Cerebral Blood Volume Dynamics Following Chemoradiation and Increased Progression-Free Survival in Newly Diagnosed IDH Wild-Type MGMT Promoter Methylated Glioblastoma With Measurable Disease. Front Oncol 2022; 12:849993. [PMID: 35371980 PMCID: PMC8964348 DOI: 10.3389/fonc.2022.849993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 02/07/2022] [Indexed: 11/15/2022] Open
Abstract
Background and Purpose While relative cerebral blood volume (rCBV) may be diagnostic and prognostic for survival in glioblastoma (GBM), changes in rCBV during chemoradiation in the subset of newly diagnosed GBM with subtotal resection and the impact of MGMT promoter methylation status on survival have not been explored. This study aimed to investigate the association between rCBV response, MGMT methylation status, and progression-free (PFS) and overall survival (OS) in newly diagnosed GBM with measurable enhancing lesions. Methods 1,153 newly diagnosed IDH wild-type GBM patients were screened and 53 patients (4.6%) had measurable post-surgical tumor (>1mL). rCBV was measured before and after patients underwent chemoradiation. Patients with a decrease in rCBV >10% were considered rCBV Responders, while patients with an increase or a decrease in rCBV <10% were considered rCBV Non-Responders. The association between change in enhancing tumor volume, change in rCBV, MGMT promotor methylation status, and PFS or OS were explored. Results A decrease in tumor volume following chemoradiation trended towards longer OS (p=0.12; median OS=26.8 vs. 16.3 months). Paradoxically, rCBV Non-Responders had a significantly improved PFS compared to Responders (p=0.047; median PFS=9.6 vs. 7.2 months). MGMT methylated rCBV Non-Responders exhibited a significantly longer PFS compared to MGMT unmethylated rCBV Non-Responders (p<0.001; median PFS=0.5 vs. 7.1 months), and MGMT methylated rCBV Non-Responders trended towards longer PFS compared to methylated rCBV Responders (p=0.089; median PFS=20.5 vs. 13.8 months). Conclusions This preliminary report demonstrates that in newly diagnosed IDH wild-type GBM with measurable enhancing disease after surgery (5% of patients), an enigmatic non-response in rCBV was associated with longer PFS, particularly in MGMT methylated patients.
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Affiliation(s)
- Jodi Goldman
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Akifumi Hagiwara
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Jingwen Yao
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Christian Ong
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Rojin Bakhti
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Elizabeth Kwon
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Maguy Farhat
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Carlo Torres
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Lily G Erickson
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Brandon J Curl
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Maggie Lee
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Whitney B Pope
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Noriko Salamon
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Phioanh L Nghiemphu
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Matthew Ji
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Blaine S Eldred
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Linda M Liau
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Albert Lai
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Timothy F Cloughesy
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Caroline Chung
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
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7
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Lingl JP, Wunderlich A, Goerke S, Paech D, Ladd ME, Liebig P, Pala A, Kim SY, Braun M, Schmitz BL, Beer M, Rosskopf J. The Value of APTw CEST MRI in Routine Clinical Assessment of Human Brain Tumor Patients at 3T. Diagnostics (Basel) 2022; 12:diagnostics12020490. [PMID: 35204583 PMCID: PMC8871436 DOI: 10.3390/diagnostics12020490] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 02/08/2022] [Accepted: 02/10/2022] [Indexed: 12/10/2022] Open
Abstract
Background. With fast-growing evidence in literature for clinical applications of chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI), this prospective study aimed at applying amide proton transfer-weighted (APTw) CEST imaging in a clinical setting to assess its diagnostic potential in differentiation of intracranial tumors at 3 tesla (T). Methods. Using the asymmetry magnetization transfer ratio (MTRasym) analysis, CEST signals were quantitatively investigated in the tumor areas and in a similar sized region of the normal-appearing white matter (NAWM) on the contralateral hemisphere of 27 patients with intracranial tumors. Area under curve (AUC) analyses were used and results were compared to perfusion-weighted imaging (PWI). Results. Using APTw CEST, contrast-enhancing tumor areas showed significantly higher APTw CEST metrics than contralateral NAWM (AUC = 0.82; p < 0.01). In subgroup analyses of each tumor entity vs. NAWM, statistically significant effects were yielded for glioblastomas (AUC = 0.96; p < 0.01) and for meningiomas (AUC = 1.0; p < 0.01) but not for lymphomas as well as metastases (p > 0.05). PWI showed results comparable to APTw CEST in glioblastoma (p < 0.01). Conclusions. This prospective study confirmed the high diagnostic potential of APTw CEST imaging in a routine clinical setting to differentiate brain tumors.
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Affiliation(s)
- Julia P. Lingl
- Department of Radiology, Ulm University, Albert-Einstein-Allee 23, 89081 Ulm, Germany; (J.P.L.); (A.W.); (S.Y.K.); (M.B.); (B.L.S.); (M.B.)
| | - Arthur Wunderlich
- Department of Radiology, Ulm University, Albert-Einstein-Allee 23, 89081 Ulm, Germany; (J.P.L.); (A.W.); (S.Y.K.); (M.B.); (B.L.S.); (M.B.)
| | - Steffen Goerke
- German Cancer Research Center (DKFZ), Department of Medical Physics in Radiology, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; (S.G.); (M.E.L.)
| | - Daniel Paech
- German Cancer Research Center (DKFZ), Division of Radiology, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany;
- Department of Neuroradiology, Venusberg-Campus 1, Bonn University, 53127 Bonn, Germany
| | - Mark E. Ladd
- German Cancer Research Center (DKFZ), Department of Medical Physics in Radiology, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; (S.G.); (M.E.L.)
- Faculty of Medicine, University of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
- Faculty of Physics and Astronomy, University of Heidelberg, Im Neuenheimer Feld 226, 69120 Heidelberg, Germany
| | - Patrick Liebig
- Siemens Healthcare GmbH, Henkestraße 127, 91052 Erlangen, Germany;
| | - Andrej Pala
- Department of Neurosurgery, Bezirkskrankenhaus Guenzburg, Lindenallee 2, 89312 Guenzburg, Germany;
| | - Soung Yung Kim
- Department of Radiology, Ulm University, Albert-Einstein-Allee 23, 89081 Ulm, Germany; (J.P.L.); (A.W.); (S.Y.K.); (M.B.); (B.L.S.); (M.B.)
- Section of Neuroradiology, Bezirkskrankenhaus Guenzburg, Lindenallee 2, 89312 Guenzburg, Germany
| | - Michael Braun
- Department of Radiology, Ulm University, Albert-Einstein-Allee 23, 89081 Ulm, Germany; (J.P.L.); (A.W.); (S.Y.K.); (M.B.); (B.L.S.); (M.B.)
- Section of Neuroradiology, Bezirkskrankenhaus Guenzburg, Lindenallee 2, 89312 Guenzburg, Germany
| | - Bernd L. Schmitz
- Department of Radiology, Ulm University, Albert-Einstein-Allee 23, 89081 Ulm, Germany; (J.P.L.); (A.W.); (S.Y.K.); (M.B.); (B.L.S.); (M.B.)
- Section of Neuroradiology, Bezirkskrankenhaus Guenzburg, Lindenallee 2, 89312 Guenzburg, Germany
| | - Meinrad Beer
- Department of Radiology, Ulm University, Albert-Einstein-Allee 23, 89081 Ulm, Germany; (J.P.L.); (A.W.); (S.Y.K.); (M.B.); (B.L.S.); (M.B.)
| | - Johannes Rosskopf
- Department of Radiology, Ulm University, Albert-Einstein-Allee 23, 89081 Ulm, Germany; (J.P.L.); (A.W.); (S.Y.K.); (M.B.); (B.L.S.); (M.B.)
- Section of Neuroradiology, Bezirkskrankenhaus Guenzburg, Lindenallee 2, 89312 Guenzburg, Germany
- Correspondence:
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Gore S, Chougule T, Jagtap J, Saini J, Ingalhalikar M. A Review of Radiomics and Deep Predictive Modeling in Glioma Characterization. Acad Radiol 2021; 28:1599-1621. [PMID: 32660755 DOI: 10.1016/j.acra.2020.06.016] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 06/11/2020] [Accepted: 06/11/2020] [Indexed: 12/22/2022]
Abstract
Recent developments in glioma categorization based on biological genotypes and application of computational machine learning or deep learning based predictive models using multi-modal MRI biomarkers to assess these genotypes provides potential assurance for optimal and personalized treatment plans and efficacy. Artificial intelligence based quantified assessment of glioma using MRI derived hand-crafted or auto-extracted features have become crucial as genomic alterations can be associated with MRI based phenotypes. This survey integrates all the recent work carried out in state-of-the-art radiomics, and Artificial Intelligence based learning solutions related to molecular diagnosis, prognosis, and treatment monitoring with the aim to create a structured resource on radiogenomic analysis of glioma. Challenges such as inter-scanner variability, requirement of benchmark datasets, prospective validations for clinical applicability are discussed with further scope for designing optimal solutions for glioma stratification with immediate recommendations for further diagnostic decisions and personalized treatment plans for glioma patients.
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MRI and PET of Brain Tumor Neuroinflammation in the Era of Immunotherapy, From the AJR Special Series on Inflammation. AJR Am J Roentgenol 2021; 218:582-596. [PMID: 34259035 DOI: 10.2214/ajr.21.26159] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
With the emergence of immune-modulating therapies, brain tumors present significant diagnostic imaging challenges. These challenges include planning personalized treatment and adjudicating accurate monitoring approaches and therapeutically specific response criteria. This has been due, in part, to the reliance on nonspecific imaging metrics, such as gadolinium-contrast-enhanced MRI or FDG PET, and rapidly evolving biologic understanding of neuroinflammation. The importance of the tumor-immune interaction and ability to therapeutically augment inflammation to improve clinical outcomes necessitates that the radiologist develop a working knowledge of the immune system and its role in clinical neuroimaging. In this article, we review relevant biologic concepts of the tumor microenvironment of primary and metastatic brain tumors, these tumors' interactions with the immune system, and MRI and PET methods for imaging inflammatory elements associated with these malignancies. Recognizing the growing fields of immunotherapeutics and precision oncology, we highlight clinically translatable imaging metrics for the diagnosis and monitoring of brain tumor neuroinflammation. Practical guidance is provided for implementing iron nanoparticle imaging, including imaging indications, protocol, interpretation, and pitfalls. A comprehensive understanding of the inflammatory mechanisms within brain tumors and their imaging features will facilitate the development of innovative non-invasive prognostic and predictive imaging strategies for precision oncology.
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Sequential implementation of DSC-MR perfusion and dynamic [ 18F]FET PET allows efficient differentiation of glioma progression from treatment-related changes. Eur J Nucl Med Mol Imaging 2020; 48:1956-1965. [PMID: 33241456 PMCID: PMC8113145 DOI: 10.1007/s00259-020-05114-0] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 11/08/2020] [Indexed: 11/21/2022]
Abstract
Purpose Perfusion-weighted MRI (PWI) and O-(2-[18F]fluoroethyl-)-l-tyrosine ([18F]FET) PET are both applied to discriminate tumor progression (TP) from treatment-related changes (TRC) in patients with suspected recurrent glioma. While the combination of both methods has been reported to improve the diagnostic accuracy, the performance of a sequential implementation has not been further investigated. Therefore, we retrospectively analyzed the diagnostic value of consecutive PWI and [18F]FET PET. Methods We evaluated 104 patients with WHO grade II–IV glioma and suspected TP on conventional MRI using PWI and dynamic [18F]FET PET. Leakage corrected maximum relative cerebral blood volumes (rCBVmax) were obtained from dynamic susceptibility contrast PWI. Furthermore, we calculated static (i.e., maximum tumor to brain ratios; TBRmax) and dynamic [18F]FET PET parameters (i.e., Slope). Definitive diagnoses were based on histopathology (n = 42) or clinico-radiological follow-up (n = 62). The diagnostic performance of PWI and [18F]FET PET parameters to differentiate TP from TRC was evaluated by analyzing receiver operating characteristic and area under the curve (AUC). Results Across all patients, the differentiation of TP from TRC using rCBVmax or [18F]FET PET parameters was moderate (AUC = 0.69–0.75; p < 0.01). A rCBVmax cutoff > 2.85 had a positive predictive value for TP of 100%, enabling a correct TP diagnosis in 44 patients. In the remaining 60 patients, combined static and dynamic [18F]FET PET parameters (TBRmax, Slope) correctly discriminated TP and TRC in a significant 78% of patients, increasing the overall accuracy to 87%. A subgroup analysis of isocitrate dehydrogenase (IDH) mutant tumors indicated a superior performance of PWI to [18F]FET PET (AUC = 0.8/< 0.62, p < 0.01/≥ 0.3). Conclusion While marked hyperperfusion on PWI indicated TP, [18F]FET PET proved beneficial to discriminate TP from TRC when PWI remained inconclusive. Thus, our results highlight the clinical value of sequential use of PWI and [18F]FET PET, allowing an economical use of diagnostic methods. The impact of an IDH mutation needs further investigation. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-020-05114-0.
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Alkanhal H, Das K, Rathi N, Syed K, Poptani H. Differentiating Nonenhancing Grade II Gliomas from Grade III Gliomas Using Diffusion Tensor Imaging and Dynamic Susceptibility Contrast MRI. World Neurosurg 2020; 146:e555-e564. [PMID: 33152494 DOI: 10.1016/j.wneu.2020.10.144] [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: 06/22/2020] [Revised: 10/25/2020] [Accepted: 10/26/2020] [Indexed: 11/27/2022]
Abstract
BACKGROUND Contrast enhancement in a brain tumor on magnetic resonance imaging is typically indicative of a high-grade glioma. However, a significant proportion of nonenhancing gliomas can be either grade II or III. While gross total resection remains the primary goal, imaging biomarkers may guide management when surgery is not possible, especially for nonenhancing gliomas. The utility of diffusion tensor imaging and dynamic susceptibility contrast magnetic resonance imaging was evaluated in differentiating nonenhancing gliomas. METHODS Retrospective analysis was performed on imaging data from 72 nonenhancing gliomas, including grade II (n = 49) and III (n = 23) gliomas. Diffusion tensor imaging and dynamic susceptibility contrast data were used to generate fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity as well as cerebral blood volume, cerebral blood flow, and mean transit time maps. Univariate and multivariate logistic regression and area under the curve analyses were used to measure sensitivity and specificity of imaging parameters. A subanalysis was performed to evaluate the utility of imaging parameters in differentiating between different histologic groups. RESULTS Logistic regression analysis indicated that tumor volume and relative mean transit time could differentiate between grade II and III nonenhancing gliomas. At a cutoff value of 0.33, this combination provided an area under the curve of 0.71, 70.6% sensitivity, and 64.3% specificity. Logistic regression analyses demonstrated much higher sensitivity and specificity in the differentiation of astrocytomas from oligodendrogliomas or identification of grades within these histologic subtypes. CONCLUSIONS Diffusion tensor imaging and dynamic susceptibility contrast imaging can aid in differentiation of nonenhancing grade II and III gliomas and between histologic subtypes.
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Affiliation(s)
- Hatham Alkanhal
- Centre for Preclinical Imaging, University of Liverpool, Liverpool, United Kingdom
| | - Kumar Das
- Department of Neuroradiology, Walton Centre NHS Trust, Liverpool, United Kingdom
| | - Nitika Rathi
- Department of Pathology, Walton Centre NHS Trust, Liverpool, United Kingdom
| | - Khaja Syed
- Department of Pathology, Walton Centre NHS Trust, Liverpool, United Kingdom
| | - Harish Poptani
- Centre for Preclinical Imaging, University of Liverpool, Liverpool, United Kingdom.
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Surendra KL, Patwari S, Agrawal S, Chadaga H, Nagadi A. Percentage signal intensity recovery: A step ahead of rCBV in DSC MR perfusion imaging for the differentiation of common neoplasms of brain. Indian J Cancer 2020; 57:36-43. [PMID: 31898591 DOI: 10.4103/ijc.ijc_421_18] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Context Relative cerebral blood volume (rCBV) and percentage signal recovery (PSR) obtained from T2* dynamic susceptibility contrast magnetic resonance imaging are important parameters for brain tumor assessment. Aim To study the accuracy of PSR in the differentiation of low-grade glioma, high-grade glioma, lymphoma, and metastases particularly in comparison to rCBV. Settings and Design Retrospective observational study. Subjects and Methods Study included pathologically confirmed cases of 10 low-grade glioma, 22 high-grade glioma, 6 lymphoma, and 12 metastases (Total 50). PSR, relative PSR (rPSR), and rCBV were calculated. Statistical Analysis Used Accuracy of these parameters studied statistically using analysis of variance and ROC (Receiver operating characteristic) curves. Results rCBV was higher in metastases (3.45 ± 2.82) and high-grade glioma (3.47 ± 1.62), whereas was low in lymphoma (1.03 ± 0.74) and low-grade glioma (1.43 ± 0.47) with P value of 0.030. PSR was low in metastases (48 ± 16.18), intermediate in glioma (73.24 ± 6.39 and 88.26 ± 6.05, high and low grade), and high in lymphoma (112.16 ± 10.57) with P value < 0.000. rPSR was higher for lymphoma (1.73 ± 0.57) than high-grade glioma (0.85 ± 0.11) and metastasis (0.69 ± 0.19) with P value <.000. Area under ROC for PSR was greater than rCBV in differentiating metastases from lymphoma (1.00 vs 0.13), high-grade glioma from lymphoma (1.00 vs 0.38), high-grade glioma from metastases (0.89 vs 0.58), and high-grade glioma from low-grade glioma (0.96 vs 0.03) with excellent curve characteristics. F values for PSR and rPSR from ANOVA analysis were 71.47 and 36.77, was better than rCBV (3.84) in differentiating these groups. Conclusions Percentage of signal recovery shows low recovery values in metastases, intermediate recovery values in glioma, and overshoot in lymphoma. PSR values show lower overlap than rCBV between lymphoma and metastases; and between high grade glioma and metastases. PSR difference is also higher than rCBV between low- and high-grade gliomas. Hence, PSR can potentially help as an additional perfusion parameter in the preoperative differentiation of these tumors.
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Affiliation(s)
- K L Surendra
- Department of Radiology, Columbia Asia Referral Hospital, Bangalore, Karnataka, India
| | - Sriram Patwari
- Department of Radiology, Columbia Asia Referral Hospital, Bangalore, Karnataka, India
| | - Shishir Agrawal
- Department of Radiology, Columbia Asia Referral Hospital, Bangalore, Karnataka, India
| | - Harsha Chadaga
- Department of Radiology, Columbia Asia Referral Hospital, Bangalore, Karnataka, India
| | - Anita Nagadi
- Department of Radiology, Columbia Asia Referral Hospital, Bangalore, Karnataka, India
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Schmidt MA, Knott M, Hoelter P, Engelhorn T, Larsson EM, Nguyen T, Essig M, Doerfler A. Standardized acquisition and post-processing of dynamic susceptibility contrast perfusion in patients with brain tumors, cerebrovascular disease and dementia: comparability of post-processing software. Br J Radiol 2020; 93:20190543. [PMID: 31617743 PMCID: PMC6948086 DOI: 10.1259/bjr.20190543] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 10/07/2019] [Accepted: 10/10/2019] [Indexed: 12/18/2022] Open
Abstract
OBJECTIVE MR-perfusion post-processing still lacks standardization. This study evaluates the results of perfusion analysis with two established software solutions in a large series of patients with different diseases when a highly standardized processing workflow is ensured. METHODS Multicenter data of 260 patients (80 with brain tumors, 124 with cerebrovascular disease and 56 with dementia examined with the same MR protocol) were analyzed. Raw data sets were processed with two software suites: Olea sphere and NordicICE. Group differences were analyzed with paired t-tests and one-way ANOVA. RESULTS Perfusion metrics were significantly different for all examined diseases in the unaffected brain for both software suites [ratio cortex/white matter left hemisphere: mean transit time (MTT) 0.991 vs 0.847, p < 0.05; relative cerebral bloodflow (rBF) 3.23 vs 4.418, p < 0.001; relative cerebral bloodvolume (rBVc) 2.813 vs 3.884, p < 0.001; right hemisphere: MTT 1.079 vs 0.854, p < 0.05; rBF 3.262 vs 4.378, p < 0.001; rBVc 2.762 vs 3.935, p < 0.001)]. Perfusion results were also significantly different in patients with stroke (ratio cortex/white matter affected hemisphere: MTT 1.058 vs 0.784; p < 0.001), dementia (ratio cortex/white matter left hemisphere: rBVc 1.152 vs 1.795, p < 0.001; right hemisphere: rBVc 1.396 vs 1.662, p < 0.05) and brain tumors (ratio cortex/whole tumor rBVc: 0.778 vs 0.919, p < 0.001 and ratio cortex/tumor hotspot rBVc: 0.529 vs 0.512, p < 0.05). CONCLUSION Despite a highly standardized workflow, parametric perfusion maps are depended on the chosen software. Radiologists should consider software related variances when using dynamic susceptibility contrast perfusion for clinical imaging and research. ADVANCES IN KNOWLEDGE This multicenter study compared perfusion parameters calculated by two commercial dynamic susceptibility contrast perfusion post-processing software solutions in different central nervous system disorders with a large sample size and a highly standardized processing workflow. Despite, parametric perfusion maps are depended on the chosen software which impacts clinical imaging and research.
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Affiliation(s)
- Manuel Alexander Schmidt
- Department of Neuroradiology, Friedrich-Alexander-University Erlangen-Nuremberg, Schwabachanlage 6, 91054 Erlangen, Germany
| | - Michael Knott
- Department of Neuroradiology, Friedrich-Alexander-University Erlangen-Nuremberg, Schwabachanlage 6, 91054 Erlangen, Germany
| | - Philip Hoelter
- Department of Neuroradiology, Friedrich-Alexander-University Erlangen-Nuremberg, Schwabachanlage 6, 91054 Erlangen, Germany
| | - Tobias Engelhorn
- Department of Neuroradiology, Friedrich-Alexander-University Erlangen-Nuremberg, Schwabachanlage 6, 91054 Erlangen, Germany
| | - Elna Marie Larsson
- Department of Surgical Sciences, Uppsala University, SE-75185 Uppsala, Radiology, Sweden
| | - Than Nguyen
- Department of Radiology, University of Ottawa Faculty of Medicine, 501 Smyth Road, Ottawa, Canada
| | | | - Arnd Doerfler
- Department of Neuroradiology, Friedrich-Alexander-University Erlangen-Nuremberg, Schwabachanlage 6, 91054 Erlangen, Germany
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Daboudi M, Papadaki E, Vakis A, Chlouverakis G, Makrakis D, Karageorgou D, Simos P, Koukouraki S. Brain SPECT and perfusion MRI: do they provide complementary information about the tumour lesion and its grading? Clin Radiol 2019; 74:652.e1-652.e9. [PMID: 31164195 DOI: 10.1016/j.crad.2019.03.025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Accepted: 03/22/2019] [Indexed: 10/26/2022]
Abstract
AIM To evaluate the relative and combined utility of 99mTc-tetrofosmin (99mTc-TF) brain single-photon-emission computed tomography (SPECT) and dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) in grading brain gliomas. MATERIALS AND METHODS Thirty-six patients with clinically suspected brain tumours were assessed by 99mTc-TF SPECT and DSC-MRI. Brain tumour malignancy was confirmed in all patients at histopathology. On both techniques brain lesions were evaluated via visual and semi-quantitative analysis methods (deriving tetrofosmin index [T-index] and relative cerebral blood volume [rCBV] ratios, respectively). RESULTS 99mTc-TF SPECT showed abnormally elevated tracer uptake in 31/36 patients whereas MRI detected the brain tumour in all patients. Optimal cut-off values of each index for discriminating between low- and high-grade gliomas were obtained through receiver operating characteristic (ROC) analyses. A T-index cut-off of 6.35 ensured 82% sensitivity and 71% specificity for discriminating between high- and low-grade gliomas, whereas a relative rCBV ratio cut-off of 1.80 achieved 91% sensitivity and 100% specificity. Requiring a positive result on either technique to characterise a high-grade glioma was associated with similar specificity and slightly increased sensitivity. CONCLUSION Both imaging techniques, 99mTF SPECT and DSC MRI, may provide complementary indices of tumour grade and have an independent diagnostic value for high-risk tumours.
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Affiliation(s)
- M Daboudi
- Department of Nuclear Medicine, School of Medicine, University of Crete, Heraklion, Crete, Greece.
| | - E Papadaki
- Department of Radiology, School of Medicine, University of Crete, Heraklion, Crete, Greece; Institute of Computer Science, Foundation of Research and Technology, Heraklion, Crete, Greece
| | - A Vakis
- Department of Neurosurgery, School of Medicine, University of Crete, Heraklion, Crete, Greece
| | - G Chlouverakis
- Biostatistics Lab., Department of Social and Family Medicine, School of Medicine, University of Crete, Heraklion, Crete, Greece
| | - D Makrakis
- Department of Radiology, School of Medicine, University of Crete, Heraklion, Crete, Greece
| | - D Karageorgou
- Department of Radiology, School of Medicine, University of Crete, Heraklion, Crete, Greece
| | - P Simos
- Institute of Computer Science, Foundation of Research and Technology, Heraklion, Crete, Greece; Department of Psychiatry, School of Medicine, University of Crete, Heraklion, Crete, Greece
| | - S Koukouraki
- Department of Nuclear Medicine, School of Medicine, University of Crete, Heraklion, Crete, Greece
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Kasenene A, Baidya A, Shams S, Xu HB. Evaluation of tumor response to antiangiogenic therapy in patients with recurrent gliomas using contrast-enhanced perfusion-weighted magnetic resonance imaging techniques: A meta-analysis. World J Meta-Anal 2019; 7:51-65. [DOI: 10.13105/wjma.v7.i2.51] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 02/07/2019] [Accepted: 02/14/2019] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND It is of vital importance to find radiologic biomarkers that can accurately predict treatment response. Usually, the initiation of antiangiogenic therapy causes a rapid decrease in the contrast enhancing tumor. However, the treatment response is observed only in a fraction of patients due to the partial radiological response secondary to stabilization of abnormal vessels which does not essentially indicate a true antitumor effect. Perfusion-weighted magnetic resonance imaging (PW-MRI) techniques have shown implicitness as a strong imaging biomarker for gliomas since they give hemodynamic information of blood vessels. Hence, there is a rapid expansion of PW-MRI related studies and clinical applications.
AIM To determine the diagnostic performance of PW-MRI techniques including: (A) dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI); and (B) dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) for evaluating response to antiangiogenic therapy in patients with recurrent gliomas.
METHODS Databases such as PubMed (MEDLINE included), EMBASE, and Google Scholar were searched for relevant original articles. The included studies were assessed for methodological quality with the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Medical imaging follow-up or histopathological analysis was used as the reference standard. The data were extracted by two reviewers independently, and then the sensitivity, specificity, summary receiver operating characteristic curve, area under the curve (AUC), and heterogeneity were calculated using Meta-Disc 1.4 software.
RESULTS This study analyzed a total of six articles. The overall sensitivity for DCE-MRI and DSC-MRI was 0.69 [95% confidence interval (CI): 0.53-0.82], and the specificity was 0.99 (95%CI: 0.93-1) by a random effects model (DerSimonianee-Laird model). The likelihood ratio (LR) +, LR-, and diagnostic odds ratio (DOR) were 12.84 (4.54-36.28), 0.35 (0.22-0.53), and 24.44 (7.19-83.06), respectively. The AUC (± SE) was 0.9921 (± 0.0120), and the Q* index (± SE) was 0.9640 (± 0.0323). For DSC-MRI, the sensitivity was 0.73, the specificity was 0.98, the LR+ was 7.82, the LR- was 0.32, the DOR was 31.65, the AUC (± SE) was 0.9925 (± 0.0132), and the Q* index was 0.9649 (± 0.0363). For DCE-MRI, the sensitivity was 0.41, the specificity was 0.97, the LR+ was 5.34, the LR- was 0.71, the DOR was 8.76, the AUC (± SE) was 0.9922 (± 0.2218), and the Q* index was 0.8935 (± 0.3037).
CONCLUSION This meta-analysis demonstrated a beneficial value of PW-MRI (DSC-MRI and DCE-MRI) in monitoring the response of recurrent gliomas to antiangiogenic therapy, with reasonable sensitivity, specificity, +LR, and -LR.
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Affiliation(s)
- Akanganyira Kasenene
- Department of Radiology and Nuclear Medicine, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan 430071, Hubei Province, China
| | - Aju Baidya
- Department of Radiology and Nuclear Medicine, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan 430071, Hubei Province, China
| | - Salman Shams
- Department of Radiology and Nuclear Medicine, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan 430071, Hubei Province, China
| | - Hai-Bo Xu
- Department of Radiology and Nuclear Medicine, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan 430071, Hubei Province, China
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Hedderich D, Kluge A, Pyka T, Zimmer C, Kirschke JS, Wiestler B, Preibisch C. Consistency of normalized cerebral blood volume values in glioblastoma using different leakage correction algorithms on dynamic susceptibility contrast magnetic resonance imaging data without and with preload. J Neuroradiol 2018; 46:44-51. [PMID: 29753641 DOI: 10.1016/j.neurad.2018.04.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Revised: 02/27/2018] [Accepted: 04/21/2018] [Indexed: 10/16/2022]
Abstract
BACKGROUND AND PURPOSE Several leakage correction algorithms for dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI)-based cerebral blood volume (CBV) measurement have been proposed, and combination with a preload of contrast agent is generally recommended. A single bolus application scheme would largely simplify and facilitate standardized clinical applications, while reducing contrast agent (CA) dose. The aim of this study was, therefore, to investigate whether appropriate leakage correction redundantizes prebolus application by comparing normalized DSC-based CBV (nCBV) measures of two consecutive CA boli. MATERIALS AND METHODS Twenty-seven patients with suspected glioblastoma (WHO-grade-IV) underwent DSC-MRI during two consecutive boli of Gd-based CA. Four variants of two post-processing leakage correction techniques were compared with respect to nCBV in contrast enhancing tumor tissue. First, a reference curve approach with first pass and full integration of corrected ΔR2*(t), and second, a deconvolution-based approach using singular value decomposition (SVD) with a standard noise-dependent cutoff or Tikhonov regularization. RESULTS Compared to respective uncorrected values, all leakage correction techniques increased nCBV for data acquired without prebolus, while there was no consistent trend for data acquired with prebolus. The best agreement between corrected nCBV values in contrast enhancing tumor, obtained in the same patients without and with prebolus, respectively, was obtained for the reference curve-based correction approach with either first pass or full integration. CONCLUSION The reference curve-based leakage correction approach with integration-based nCBV calculation yielded a high accordance between nCBV values without and with prebolus, respectively. Thus, it appears possible to obtain valid nCBV in glioblastoma with a single CA injection.
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Affiliation(s)
- Dennis Hedderich
- Department of Diagnostic and Interventional Neuroradiology, Technische Universität München, Ismaningerst. 22, 81675 Munich, Germany
| | - Anne Kluge
- Department of Diagnostic and Interventional Neuroradiology, Technische Universität München, Ismaningerst. 22, 81675 Munich, Germany
| | - Thomas Pyka
- Clinic for Nuclear Medicine, Technische Universität München, Ismaningerst. 22, 81675 Munich, Germany
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, Technische Universität München, Ismaningerst. 22, 81675 Munich, Germany
| | - Jan S Kirschke
- Department of Diagnostic and Interventional Neuroradiology, Technische Universität München, Ismaningerst. 22, 81675 Munich, Germany
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, Technische Universität München, Ismaningerst. 22, 81675 Munich, Germany
| | - Christine Preibisch
- Department of Diagnostic and Interventional Neuroradiology, Technische Universität München, Ismaningerst. 22, 81675 Munich, Germany; Clinic for Neurology, Technische Universität München, Ismaningerst. 22, 81675 Munich, Germany.
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Liu TT, Achrol AS, Mitchell LA, Rodriguez SA, Feroze A, Iv M, Kim C, Chaudhary N, Gevaert O, Stuart JM, Harsh GR, Chang SD, Rubin DL. Magnetic resonance perfusion image features uncover an angiogenic subgroup of glioblastoma patients with poor survival and better response to antiangiogenic treatment. Neuro Oncol 2018; 19:997-1007. [PMID: 28007759 DOI: 10.1093/neuonc/now270] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Background In previous clinical trials, antiangiogenic therapies such as bevacizumab did not show efficacy in patients with newly diagnosed glioblastoma (GBM). This may be a result of the heterogeneity of GBM, which has a variety of imaging-based phenotypes and gene expression patterns. In this study, we sought to identify a phenotypic subtype of GBM patients who have distinct tumor-image features and molecular activities and who may benefit from antiangiogenic therapies. Methods Quantitative image features characterizing subregions of tumors and the whole tumor were extracted from preoperative and pretherapy perfusion magnetic resonance (MR) images of 117 GBM patients in 2 independent cohorts. Unsupervised consensus clustering was performed to identify robust clusters of GBM in each cohort. Cox survival and gene set enrichment analyses were conducted to characterize the clinical significance and molecular pathway activities of the clusters. The differential treatment efficacy of antiangiogenic therapy between the clusters was evaluated. Results A subgroup of patients with elevated perfusion features was identified and was significantly associated with poor patient survival after accounting for other clinical covariates (P values <.01; hazard ratios > 3) consistently found in both cohorts. Angiogenesis and hypoxia pathways were enriched in this subgroup of patients, suggesting the potential efficacy of antiangiogenic therapy. Patients of the angiogenic subgroups pooled from both cohorts, who had chemotherapy information available, had significantly longer survival when treated with antiangiogenic therapy (log-rank P=.022). Conclusions Our findings suggest that an angiogenic subtype of GBM patients may benefit from antiangiogenic therapy with improved overall survival.
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Affiliation(s)
- Tiffany T Liu
- Department of Neurosurgery, Stanford University, Stanford, California; Department of Radiology, Stanford University, Stanford, California; Stanford Center for Biomedical Informatics Research and Biomedical Informatics Training Program, Stanford, California; School of Medicine, Stanford University, Stanford, California; Department of Biomolecular Engineering, University of California, Santa Cruz, California
| | - Achal S Achrol
- Department of Neurosurgery, Stanford University, Stanford, California; Department of Radiology, Stanford University, Stanford, California; Stanford Center for Biomedical Informatics Research and Biomedical Informatics Training Program, Stanford, California; School of Medicine, Stanford University, Stanford, California; Department of Biomolecular Engineering, University of California, Santa Cruz, California
| | - Lex A Mitchell
- Department of Neurosurgery, Stanford University, Stanford, California; Department of Radiology, Stanford University, Stanford, California; Stanford Center for Biomedical Informatics Research and Biomedical Informatics Training Program, Stanford, California; School of Medicine, Stanford University, Stanford, California; Department of Biomolecular Engineering, University of California, Santa Cruz, California
| | - Scott A Rodriguez
- Department of Neurosurgery, Stanford University, Stanford, California; Department of Radiology, Stanford University, Stanford, California; Stanford Center for Biomedical Informatics Research and Biomedical Informatics Training Program, Stanford, California; School of Medicine, Stanford University, Stanford, California; Department of Biomolecular Engineering, University of California, Santa Cruz, California
| | - Abdullah Feroze
- Department of Neurosurgery, Stanford University, Stanford, California; Department of Radiology, Stanford University, Stanford, California; Stanford Center for Biomedical Informatics Research and Biomedical Informatics Training Program, Stanford, California; School of Medicine, Stanford University, Stanford, California; Department of Biomolecular Engineering, University of California, Santa Cruz, California
| | - Michael Iv
- Department of Neurosurgery, Stanford University, Stanford, California; Department of Radiology, Stanford University, Stanford, California; Stanford Center for Biomedical Informatics Research and Biomedical Informatics Training Program, Stanford, California; School of Medicine, Stanford University, Stanford, California; Department of Biomolecular Engineering, University of California, Santa Cruz, California
| | - Christine Kim
- Department of Neurosurgery, Stanford University, Stanford, California; Department of Radiology, Stanford University, Stanford, California; Stanford Center for Biomedical Informatics Research and Biomedical Informatics Training Program, Stanford, California; School of Medicine, Stanford University, Stanford, California; Department of Biomolecular Engineering, University of California, Santa Cruz, California
| | - Navjot Chaudhary
- Department of Neurosurgery, Stanford University, Stanford, California; Department of Radiology, Stanford University, Stanford, California; Stanford Center for Biomedical Informatics Research and Biomedical Informatics Training Program, Stanford, California; School of Medicine, Stanford University, Stanford, California; Department of Biomolecular Engineering, University of California, Santa Cruz, California
| | - Olivier Gevaert
- Department of Neurosurgery, Stanford University, Stanford, California; Department of Radiology, Stanford University, Stanford, California; Stanford Center for Biomedical Informatics Research and Biomedical Informatics Training Program, Stanford, California; School of Medicine, Stanford University, Stanford, California; Department of Biomolecular Engineering, University of California, Santa Cruz, California
| | - Josh M Stuart
- Department of Neurosurgery, Stanford University, Stanford, California; Department of Radiology, Stanford University, Stanford, California; Stanford Center for Biomedical Informatics Research and Biomedical Informatics Training Program, Stanford, California; School of Medicine, Stanford University, Stanford, California; Department of Biomolecular Engineering, University of California, Santa Cruz, California
| | - Griffith R Harsh
- Department of Neurosurgery, Stanford University, Stanford, California; Department of Radiology, Stanford University, Stanford, California; Stanford Center for Biomedical Informatics Research and Biomedical Informatics Training Program, Stanford, California; School of Medicine, Stanford University, Stanford, California; Department of Biomolecular Engineering, University of California, Santa Cruz, California
| | - Steven D Chang
- Department of Neurosurgery, Stanford University, Stanford, California; Department of Radiology, Stanford University, Stanford, California; Stanford Center for Biomedical Informatics Research and Biomedical Informatics Training Program, Stanford, California; School of Medicine, Stanford University, Stanford, California; Department of Biomolecular Engineering, University of California, Santa Cruz, California
| | - Daniel L Rubin
- Department of Neurosurgery, Stanford University, Stanford, California; Department of Radiology, Stanford University, Stanford, California; Stanford Center for Biomedical Informatics Research and Biomedical Informatics Training Program, Stanford, California; School of Medicine, Stanford University, Stanford, California; Department of Biomolecular Engineering, University of California, Santa Cruz, California
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18
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Diagnostic Efficacy of Perfusion Magnetic Resonance Imaging in Supratentorial Glioma Grading. IRANIAN JOURNAL OF RADIOLOGY 2018. [DOI: 10.5812/iranjradiol.13696] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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19
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Valentini MC, Mellai M, Annovazzi L, Melcarne A, Denysenko T, Cassoni P, Casalone C, Maurella C, Grifoni S, Fania P, Cistaro A, Schiffer D. Comparison among conventional and advanced MRI, 18F-FDG PET/CT, phenotype and genotype in glioblastoma. Oncotarget 2017; 8:91636-91653. [PMID: 29207673 PMCID: PMC5710953 DOI: 10.18632/oncotarget.21482] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2016] [Accepted: 07/30/2017] [Indexed: 12/20/2022] Open
Abstract
Glioblastoma (GB) is a highly heterogeneous tumor. In order to identify in vivo the most malignant tumor areas, the extent of tumor infiltration and the sites giving origin to GB stem cells (GSCs), we combined positron emission tomography/computed tomography (PET/CT) and conventional and advanced magnetic resonance imaging (MRI) with histology, immunohistochemistry and molecular genetics. Prior to dura opening and tumor resection, forty-eight biopsy specimens [23 of contrast-enhancing (CE) and 25 of non-contrast enhancing (NE) regions] from 12 GB patients were obtained by a frameless image-guided stereotactic biopsy technique. The highest values of 2-[18F]-fluoro-2-deoxy-D-glucose maximum standardized uptake value (18F-FDG SUVmax), relative cerebral blood volume (rCBV), Choline/Creatine (Cho/Cr), Choline/N-acetylaspartate (Cho/NAA) and Lipids/Lactate (LL) ratio have been observed in the CE region. They corresponded to the most malignant tumor phenotype, to the greatest molecular spectrum and stem cell potential. On the contrary, apparent diffusion coefficient (ADC) and fractional anisotropy (FA) in the CE region were very variable. 18F-FDG SUVmax, Cho/Cr and Cho/NAA ratio resulted the most suitable parameters to detect tumor infiltration. In edematous areas, reactive astrocytes and microglia/macrophages were influencing variables. Combined MRI and 18F-FDG PET/CT allowed to recognize the specific biological significance of the different identified areas of GB.
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Affiliation(s)
| | - Marta Mellai
- Research Center/Policlinico di Monza Foundation, 13100 Vercelli, Italy
| | - Laura Annovazzi
- Research Center/Policlinico di Monza Foundation, 13100 Vercelli, Italy
| | - Antonio Melcarne
- Department of Neurosurgery, A.O.U. Città della Salute e della Scienza, 10126 Turin, Italy
| | - Tetyana Denysenko
- Research Center/Policlinico di Monza Foundation, 13100 Vercelli, Italy
| | - Paola Cassoni
- Department of Medical Sciences, University of Turin, 10126 Turin, Italy
| | - Cristina Casalone
- Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d’Aosta, 10154 Turin, Italy
| | - Cristiana Maurella
- Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d’Aosta, 10154 Turin, Italy
| | - Silvia Grifoni
- Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d’Aosta, 10154 Turin, Italy
| | - Piercarlo Fania
- Positron Emission Tomography Center IRMET S.p.A, Euromedic Inc., 10136 Turin, Italy
| | - Angelina Cistaro
- Positron Emission Tomography Center IRMET S.p.A, Euromedic Inc., 10136 Turin, Italy
- Institute of Cognitive Sciences and Technologies, National Research Council, 00185 Rome, Italy
| | - Davide Schiffer
- Research Center/Policlinico di Monza Foundation, 13100 Vercelli, Italy
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20
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Ziegler J, Bastian A, Lerner M, Bailey-Downs L, Saunders D, Smith N, Sutton J, Battiste JD, Ihnat MA, Gangjee A, Towner RA. AG488 as a therapy against gliomas. Oncotarget 2017; 8:71833-71844. [PMID: 29069750 PMCID: PMC5641093 DOI: 10.18632/oncotarget.18284] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Accepted: 05/05/2017] [Indexed: 11/25/2022] Open
Abstract
High-grade gliomas such as glioblastomas (GBM) present a deadly prognosis following diagnosis and very few effective treatment options. Here, we investigate if the small molecule AG488 can be an effective therapy against GBM with both anti-angiogenic as well as an anti-microtubule inhibiting modalities, using a human G55 glioma xenograft model in nude mice. From in vitro studies, we report that AG488 incubation reduced cell viability in G55 and HMEC-1 cells more so than TMZ treatment, and AG488 treatment also decreased cell viability in normal astrocytes, but not as much as for G55 cells (p<0.0001). In vivo investigations indicated that AG488 therapy helped reduce tumor volumes (p<0.0001), prolong survival (p<0.01), increase tumor perfusion (p<0.01), and decrease microvessel density (MVD) (p<0.05), compared to untreated mice or mice treated with non-specific IgG, in the G55 xenograft model. Additionally, AG488 did not induce apoptosis in normal mouse brain tissue. Animal survival and tumor volume changes for AG488 were comparable to TMZ or anti-VEGF therapies, however AG488 was found to be more effective in decreasing tumor-related vascularity (perfusion and MVD). AG488 is a potential novel therapy against high-grade gliomas.
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Affiliation(s)
- Jadith Ziegler
- Advanced Magnetic Resonance Center, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA.,Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Anja Bastian
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Megan Lerner
- Department of Surgery Research Laboratory, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Lora Bailey-Downs
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Debra Saunders
- Advanced Magnetic Resonance Center, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA
| | - Nataliya Smith
- Advanced Magnetic Resonance Center, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA
| | - Jake Sutton
- Advanced Magnetic Resonance Center, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA
| | - James D Battiste
- Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Michael A Ihnat
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Aleem Gangjee
- Graduate School of Pharmaceutical Sciences, Duquesne University, Pittsburgh, PA, USA
| | - Rheal A Towner
- Advanced Magnetic Resonance Center, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA.,Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.,Department of Pharmaceutical Sciences, College of Pharmacy, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.,Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
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21
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Clinical Applications of Contrast-Enhanced Perfusion MRI Techniques in Gliomas: Recent Advances and Current Challenges. CONTRAST MEDIA & MOLECULAR IMAGING 2017; 2017:7064120. [PMID: 29097933 PMCID: PMC5612612 DOI: 10.1155/2017/7064120] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2016] [Accepted: 02/23/2017] [Indexed: 01/12/2023]
Abstract
Gliomas possess complex and heterogeneous vasculatures with abnormal hemodynamics. Despite considerable advances in diagnostic and therapeutic techniques for improving tumor management and patient care in recent years, the prognosis of malignant gliomas remains dismal. Perfusion-weighted magnetic resonance imaging techniques that could noninvasively provide superior information on vascular functionality have attracted much attention for evaluating brain tumors. However, nonconsensus imaging protocols and postprocessing analysis among different institutions impede their integration into standard-of-care imaging in clinic. And there have been very few studies providing a comprehensive evidence-based and systematic summary. This review first outlines the status of glioma theranostics and tumor-associated vascular pathology and then presents an overview of the principles of dynamic contrast-enhanced MRI (DCE-MRI) and dynamic susceptibility contrast-MRI (DSC-MRI), with emphasis on their recent clinical applications in gliomas including tumor grading, identification of molecular characteristics, differentiation of glioma from other brain tumors, treatment response assessment, and predicting prognosis. Current challenges and future perspectives are also highlighted.
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Zabala-Travers S, Choi M, Cheng WC, Badano A. Effect of color visualization and display hardware on the visual assessment of pseudocolor medical images. Med Phys 2016; 42:2942-54. [PMID: 26127048 DOI: 10.1118/1.4921125] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Even though the use of color in the interpretation of medical images has increased significantly in recent years, the ad hoc manner in which color is handled and the lack of standard approaches have been associated with suboptimal and inconsistent diagnostic decisions with a negative impact on patient treatment and prognosis. The purpose of this study is to determine if the choice of color scale and display device hardware affects the visual assessment of patterns that have the characteristics of functional medical images. METHODS Perfusion magnetic resonance imaging (MRI) was the basis for designing and performing experiments. Synthetic images resembling brain dynamic-contrast enhanced MRI consisting of scaled mixtures of white, lumpy, and clustered backgrounds were used to assess the performance of a rainbow ("jet"), a heated black-body ("hot"), and a gray ("gray") color scale with display devices of different quality on the detection of small changes in color intensity. The authors used a two-alternative, forced-choice design where readers were presented with 600 pairs of images. Each pair consisted of two images of the same pattern flipped along the vertical axis with a small difference in intensity. Readers were asked to select the image with the highest intensity. Three differences in intensity were tested on four display devices: a medical-grade three-million-pixel display, a consumer-grade monitor, a tablet device, and a phone. RESULTS The estimates of percent correct show that jet outperformed hot and gray in the high and low range of the color scales for all devices with a maximum difference in performance of 18% (confidence intervals: 6%, 30%). Performance with hot was different for high and low intensity, comparable to jet for the high range, and worse than gray for lower intensity values. Similar performance was seen between devices using jet and hot, while gray performance was better for handheld devices. Time of performance was shorter with jet. CONCLUSIONS Our findings demonstrate that the choice of color scale and display hardware affects the visual comparative analysis of pseudocolor images. Follow-up studies in clinical settings are being considered to confirm the results with patient images.
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Affiliation(s)
- Silvina Zabala-Travers
- Division of Imaging, Diagnostics, and Software Reliability, OSEL/CDRH/FDA, Silver Spring, Maryland 20993
| | - Mina Choi
- Division of Imaging, Diagnostics, and Software Reliability, OSEL/CDRH/FDA, Silver Spring, Maryland 20993
| | - Wei-Chung Cheng
- Division of Imaging, Diagnostics, and Software Reliability, OSEL/CDRH/FDA, Silver Spring, Maryland 20993
| | - Aldo Badano
- Division of Imaging, Diagnostics, and Software Reliability, OSEL/CDRH/FDA, Silver Spring, Maryland 20993
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Sun C, Yu Y, Wang L, Wu B, Xia L, Feng F, Ling Z, Wang S. Additive antiangiogenesis effect of ginsenoside Rg3 with low-dose metronomic temozolomide on rat glioma cells both in vivo and in vitro. JOURNAL OF EXPERIMENTAL & CLINICAL CANCER RESEARCH : CR 2016; 35:32. [PMID: 26872471 PMCID: PMC4752767 DOI: 10.1186/s13046-015-0274-y] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Accepted: 12/17/2015] [Indexed: 11/23/2022]
Abstract
Background Glioblastoma is the most common and deadly primary brain tumor in adults. Low-dose,metronomic (LDM) temozolomide (TMZ) displays improved efficacy in the treatment of glioblastoma by targeting angiogenesis, but has a limited effect on recurrence. The antiangiogenesis drug ginsenoside Rg3 (RG3) is the main active ingredient of ginseng, a popular herbal medicine. Methods Using an in vitro and a rat model of an orthotopic glioma allograft, this study was to determine whether RG3 enhanced the antiangiogenesis activity of LDM TMZ in the treatment of glioblastoma. Results Our results showed that combined use of TMZ with RG3 displayed additive inhibition on proliferation of both human umbilical vein endothelial cells (HUVEC) and rat C6 glioma cells in vitro. They additively arrested cell cycle, increased apoptosis, and decreased VEGF-A and BCL-2 expression in HUVEC. Antiangiogenesis effect was also evaluated in the rat model of orthotopic glioma allograft, based upon markers including relative cerebral blood volume (rCBV) by magnetic resonance imaging (MRI), VEGF levels and microvessel density (MVD)/CD34 staining. LDM TMZ alone was potent in suppressing angiogenesis and tumor growth, whereas RG3 alone only had modest antiangiogenesis effects. Combined treatment significantly and additively suppressed angiogenesis, without additive inhibitory effects on allografted tumor growth. Conclusions These data provide evidence showing the efficacy of LDM TMZ on glioma treatment. The combined additive antiangiogenesis effect suggests that RG3 has the potential to further increase the efficacy of LDM TMZ in the treatment of glioblastoma. Electronic supplementary material The online version of this article (doi:10.1186/s13046-015-0274-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Caixing Sun
- Department of Neurosurgery, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China.
| | - Yang Yu
- Department of Neurosurgery, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China.
| | - Lizhen Wang
- Department of Neurosurgery, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China.
| | - Bin Wu
- Department of Neurosurgery, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China.
| | - Liang Xia
- Department of Neurosurgery, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China.
| | - Fang Feng
- Department of Neurosurgery, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China.
| | - Zhiqiang Ling
- Zhejiang Cancer Research Institute, Zhejiang Cancer Hospital, 38 Guangji Road, Hangzhou, Zhejiang, 310022, China.
| | - Shihua Wang
- Department of Cancer Biology, Wake Forest School of Medicine, Medical Center Boulevard, NC, 27157, USA.
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