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Śledzińska-Bebyn P, Furtak J, Bebyn M, Serafin Z. Beyond conventional imaging: Advancements in MRI for glioma malignancy prediction and molecular profiling. Magn Reson Imaging 2024; 112:63-81. [PMID: 38914147 DOI: 10.1016/j.mri.2024.06.004] [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: 04/04/2024] [Revised: 05/20/2024] [Accepted: 06/20/2024] [Indexed: 06/26/2024]
Abstract
This review examines the advancements in magnetic resonance imaging (MRI) techniques and their pivotal role in diagnosing and managing gliomas, the most prevalent primary brain tumors. The paper underscores the importance of integrating modern MRI modalities, such as diffusion-weighted imaging and perfusion MRI, which are essential for assessing glioma malignancy and predicting tumor behavior. Special attention is given to the 2021 WHO Classification of Tumors of the Central Nervous System, emphasizing the integration of molecular diagnostics in glioma classification, significantly impacting treatment decisions. The review also explores radiogenomics, which correlates imaging features with molecular markers to tailor personalized treatment strategies. Despite technological progress, MRI protocol standardization and result interpretation challenges persist, affecting diagnostic consistency across different settings. Furthermore, the review addresses MRI's capacity to distinguish between tumor recurrence and pseudoprogression, which is vital for patient management. The necessity for greater standardization and collaborative research to harness MRI's full potential in glioma diagnosis and personalized therapy is highlighted, advocating for an enhanced understanding of glioma biology and more effective treatment approaches.
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Affiliation(s)
- Paulina Śledzińska-Bebyn
- Department of Radiology, 10th Military Research Hospital and Polyclinic, 85-681 Bydgoszcz, Poland.
| | - Jacek Furtak
- Department of Clinical Medicine, Faculty of Medicine, University of Science and Technology, Bydgoszcz, Poland; Department of Neurosurgery, 10th Military Research Hospital and Polyclinic, 85-681 Bydgoszcz, Poland
| | - Marek Bebyn
- Department of Internal Diseases, 10th Military Clinical Hospital and Polyclinic, 85-681 Bydgoszcz, Poland
| | - Zbigniew Serafin
- Department of Radiology and Diagnostic Imaging, Nicolaus Copernicus University, Collegium Medicum, Bydgoszcz, Poland
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Sanvito F, Raymond C, Cho NS, Yao J, Hagiwara A, Orpilla J, Liau LM, Everson RG, Nghiemphu PL, Lai A, Prins R, Salamon N, Cloughesy TF, Ellingson BM. Simultaneous quantification of perfusion, permeability, and leakage effects in brain gliomas using dynamic spin-and-gradient-echo echoplanar imaging MRI. Eur Radiol 2024; 34:3087-3101. [PMID: 37882836 PMCID: PMC11045669 DOI: 10.1007/s00330-023-10215-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 07/05/2023] [Accepted: 07/27/2023] [Indexed: 10/27/2023]
Abstract
OBJECTIVE To determine the feasibility and biologic correlations of dynamic susceptibility contrast (DSC), dynamic contrast enhanced (DCE), and quantitative maps derived from contrast leakage effects obtained simultaneously in gliomas using dynamic spin-and-gradient-echo echoplanar imaging (dynamic SAGE-EPI) during a single contrast injection. MATERIALS AND METHODS Thirty-eight patients with enhancing brain gliomas were prospectively imaged with dynamic SAGE-EPI, which was processed to compute traditional DSC metrics (normalized relative cerebral blood flow [nrCBV], percentage of signal recovery [PSR]), DCE metrics (volume transfer constant [Ktrans], extravascular compartment [ve]), and leakage effect metrics: ΔR2,ss* (reflecting T2*-leakage effects), ΔR1,ss (reflecting T1-leakage effects), and the transverse relaxivity at tracer equilibrium (TRATE, reflecting the balance between ΔR2,ss* and ΔR1,ss). These metrics were compared between patient subgroups (treatment-naïve [TN] vs recurrent [R]) and biological features (IDH status, Ki67 expression). RESULTS In IDH wild-type gliomas (IDHwt-i.e., glioblastomas), previous exposure to treatment determined lower TRATE (p = 0.002), as well as higher PSR (p = 0.006), Ktrans (p = 0.17), ΔR1,ss (p = 0.035), ve (p = 0.006), and ADC (p = 0.016). In IDH-mutant gliomas (IDHm), previous treatment determined higher Ktrans and ΔR1,ss (p = 0.026). In TN-gliomas, dynamic SAGE-EPI metrics tended to be influenced by IDH status (p ranging 0.09-0.14). TRATE values above 142 mM-1s-1 were exclusively seen in TN-IDHwt, and, in TN-gliomas, this cutoff had 89% sensitivity and 80% specificity as a predictor of Ki67 > 10%. CONCLUSIONS Dynamic SAGE-EPI enables simultaneous quantification of brain tumor perfusion and permeability, as well as mapping of novel metrics related to cytoarchitecture (TRATE) and blood-brain barrier disruption (ΔR1,ss), with a single contrast injection. CLINICAL RELEVANCE STATEMENT Simultaneous DSC and DCE analysis with dynamic SAGE-EPI reduces scanning time and contrast dose, respectively alleviating concerns about imaging protocol length and gadolinium adverse effects and accumulation, while providing novel leakage effect metrics reflecting blood-brain barrier disruption and tumor tissue cytoarchitecture. KEY POINTS • Traditionally, perfusion and permeability imaging for brain tumors requires two separate contrast injections and acquisitions. • Dynamic spin-and-gradient-echo echoplanar imaging enables simultaneous perfusion and permeability imaging. • Dynamic spin-and-gradient-echo echoplanar imaging provides new image contrasts reflecting blood-brain barrier disruption and cytoarchitecture characteristics.
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Affiliation(s)
- Francesco Sanvito
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, 924 Westwood Blvd, Los Angeles, CA, 90024, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
- Unit of Radiology, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, Viale Camillo Golgi 19, 27100, Pavia, Italy
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, 924 Westwood Blvd, Los Angeles, CA, 90024, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Nicholas S Cho
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, 924 Westwood Blvd, Los Angeles, CA, 90024, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
- Medical Scientist Training Program, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, 7400 Boelter Hall, Los Angeles, CA, 90095, USA
| | - Jingwen Yao
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, 924 Westwood Blvd, Los Angeles, CA, 90024, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Akifumi Hagiwara
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, 924 Westwood Blvd, Los Angeles, CA, 90024, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
- Department of Radiology, Juntendo University School of Medicine, Bunkyo City, 2-Chōme-1-1 Hongō, Tokyo, 113-8421, Japan
| | - Joey Orpilla
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Linda M Liau
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Richard G Everson
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Phioanh L Nghiemphu
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Albert Lai
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Robert Prins
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Noriko Salamon
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Timothy F Cloughesy
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, 924 Westwood Blvd, Los Angeles, CA, 90024, USA.
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA.
- Medical Scientist Training Program, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA.
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, 7400 Boelter Hall, Los Angeles, CA, 90095, USA.
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA.
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA.
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Conte M, Woodall RT, Gutova M, Chen BT, Shiroishi MS, Brown CE, Munson JM, Rockne RC. Structural and practical identifiability of contrast transport models for DCE-MRI. PLoS Comput Biol 2024; 20:e1012106. [PMID: 38748755 PMCID: PMC11132485 DOI: 10.1371/journal.pcbi.1012106] [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: 12/21/2023] [Revised: 05/28/2024] [Accepted: 04/24/2024] [Indexed: 05/28/2024] Open
Abstract
Contrast transport models are widely used to quantify blood flow and transport in dynamic contrast-enhanced magnetic resonance imaging. These models analyze the time course of the contrast agent concentration, providing diagnostic and prognostic value for many biological systems. Thus, ensuring accuracy and repeatability of the model parameter estimation is a fundamental concern. In this work, we analyze the structural and practical identifiability of a class of nested compartment models pervasively used in analysis of MRI data. We combine artificial and real data to study the role of noise in model parameter estimation. We observe that although all the models are structurally identifiable, practical identifiability strongly depends on the data characteristics. We analyze the impact of increasing data noise on parameter identifiability and show how the latter can be recovered with increased data quality. To complete the analysis, we show that the results do not depend on specific tissue characteristics or the type of enhancement patterns of contrast agent signal.
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Affiliation(s)
- Martina Conte
- Department of Mathematical Sciences “G. L. Lagrange”, Politecnico di Torino, Torino, Italy
- Division of Mathematical Oncology and Computational Systems Biology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, California, United States of America
| | - Ryan T. Woodall
- Division of Mathematical Oncology and Computational Systems Biology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, California, United States of America
| | - Margarita Gutova
- Department of Stem Cell Biology and Regenerative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, California, United States of America
| | - Bihong T. Chen
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, California, United States of America
| | - Mark S. Shiroishi
- Department of Radiology, Keck School of Medicine of the University of Southern California, Los Angeles, California, United States of America
| | - Christine E. Brown
- Departments of Hematology & Hematopoietic Cell Transplantation and Immuno-Oncology, Beckman Research Institute, City of Hope National Medical Center Duarte, California, United States of America
| | - Jennifer M. Munson
- Fralin Biomedical Research Institute, Virginia Tech, Roanoke, Virginia, United States of America
| | - Russell C. Rockne
- Division of Mathematical Oncology and Computational Systems Biology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, California, United States of America
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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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5
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Conte M, Woodall RT, Gutova M, Chen BT, Shiroishi MS, Brown CE, Munson JM, Rockne RC. Structural and practical identifiability of contrast transport models for DCE-MRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.19.572294. [PMID: 38187554 PMCID: PMC10769233 DOI: 10.1101/2023.12.19.572294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Compartment models are widely used to quantify blood flow and transport in dynamic contrast-enhanced magnetic resonance imaging. These models analyze the time course of the contrast agent concentration, providing diagnostic and prognostic value for many biological systems. Thus, ensuring accuracy and repeatability of the model parameter estimation is a fundamental concern. In this work, we analyze the structural and practical identifiability of a class of nested compartment models pervasively used in analysis of MRI data. We combine artificial and real data to study the role of noise in model parameter estimation. We observe that although all the models are structurally identifiable, practical identifiability strongly depends on the data characteristics. We analyze the impact of increasing data noise on parameter identifiability and show how the latter can be recovered with increased data quality. To complete the analysis, we show that the results do not depend on specific tissue characteristics or the type of enhancement patterns of contrast agent signal.
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6
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Ahn SH, Ahn SS, Park YW, Park CJ, Lee SK. Association of dynamic susceptibility contrast- and dynamic contrast-enhanced magnetic resonance imaging parameters with molecular marker status in lower-grade gliomas: A retrospective study. Neuroradiol J 2023; 36:49-58. [PMID: 35532193 PMCID: PMC9893160 DOI: 10.1177/19714009221098369] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
PURPOSE Molecular marker status is clinically relevant for treatment planning and predicting the prognosis of gliomas. This study aimed to assess whether quantitative imaging parameters from dynamic susceptibility contrast- (DSC-) and dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) can predict the molecular marker status of lower-grade gliomas (LGGs). MATERIALS AND METHODS Overall, 132 patients with LGGs who underwent DSC- and DCE-MRI were retrospectively enrolled. Statuses of relevant molecular markers including isocitrate dehydrogenase isoenzyme (IDH), 1p19q codeletion, epidermal growth factor receptor (EGFR), O6-methylguanine-DNA methyltransferase (MGMT), and telomerase reverse transcriptase (TERT) were collected. For each molecular marker, age, tumor diameter and location, and DSC- and DCE-MRI parameters, including the normalized cerebral blood volume (nCBV), volume transfer constant (Ktrans), rate transfer coefficient (Kep), extravascular extracellular volume fraction (Ve), and plasma volume fraction (Vp), were compared. Multivariable logistic regression analyses were performed. RESULTS The nCBV was significantly lower in LGGs with IDH mutation (p = .001) and TERT mutation (p = .027) than those without these mutations. Ktrans (p = .034), Ve (p = .023), and Vp (p = .044) values were significantly lower in MGMT methylated LGGs than in MGMT unmethylated LGGs. Perfusion parameters were not significantly associated with EGFR amplification and 1p19q codeletion. Young age (p < .001) and small diameter (p = .001) were significantly associated with IDH mutation. The nCBV was independently associated with IDH status (AUC, 0.817; 95% CI: 0.739-0.894). CONCLUSIONS DSC- and DCE-MRI parameters demonstrated correlations with molecular markers of LGGs. Especially, the nCBV can be helpful in predicting the IDH mutation status.
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Affiliation(s)
- Sung Hee Ahn
- Department of Radiology, Yonsei University College of
Medicine, Seoul, Korea
| | - Sung Soo Ahn
- Department of Radiology, Yonsei University College of
Medicine, Seoul, Korea
| | - Yae Won Park
- Department of Radiology, Yonsei University College of
Medicine, Seoul, Korea
| | - Chae Jung Park
- Department of Radiology, Yonsei University College of
Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology, Yonsei University College of
Medicine, Seoul, Korea
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7
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Sansone G, Vivori N, Vivori C, Di Stefano AL, Picca A. Basic premises: searching for new targets and strategies in diffuse gliomas. Clin Transl Imaging 2022. [DOI: 10.1007/s40336-022-00507-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Kim S, Kim SW, Noh Y, Lee PH, Na DL, Seo SW, Seong JK. Harmonization of Multicenter Cortical Thickness Data by Linear Mixed Effect Model. Front Aging Neurosci 2022; 14:869387. [PMID: 35783130 PMCID: PMC9247505 DOI: 10.3389/fnagi.2022.869387] [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] [Received: 02/04/2022] [Accepted: 05/16/2022] [Indexed: 01/18/2023] Open
Abstract
Objective Analyzing neuroimages being useful method in the field of neuroscience and neurology and solving the incompatibilities across protocols and vendors have become a major problem. We referred to this incompatibility as "center effects," and in this study, we attempted to correct such center effects of cortical feature obtained from multicenter magnetic resonance images (MRIs). Methods For MRI of a total of 4,321 multicenter subjects, the harmonized w-score was calculated by correcting biological covariates such as age, sex, years of education, and intercranial volume (ICV) as fixed effects and center information as a random effect. Afterward, we performed classification tasks using principal component analysis (PCA) and linear discriminant analysis (LDA) to check whether the center effect was successfully corrected from the harmonized w-score. Results First, an experiment was conducted to predict the dataset origin of a random subject sampled from two different datasets, and it was confirmed that the prediction accuracy of linear mixed effect (LME) model-based w-score was significantly closer to the baseline than that of raw cortical thickness. As a second experiment, we classified the data of the normal and patient groups of each dataset, and LME model-based w-score, which is biological-feature-corrected values, showed higher classification accuracy than the raw cortical thickness data. Afterward, to verify the compatibility of the dataset used for LME model training and the dataset that is not, intraobject comparison and w-score RMSE calculation process were performed. Conclusion Through comparison between the LME model-based w-score and existing methods and several classification tasks, we showed that the LME model-based w-score sufficiently corrects the center effects while preserving the disease effects from the dataset. We also showed that the preserved disease effects have a match with well-known disease atrophy patterns such as Alzheimer's disease or Parkinson's disease. Finally, through intrasubject comparison, we found that the difference between centers decreases in the LME model-based w-score compared with the raw cortical thickness and thus showed that our model well-harmonizes the data that are not used for the model training.
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Affiliation(s)
- SeungWook Kim
- Department of Bio-Convergence Engineering, Korea University, Seoul, South Korea
| | - Sung-Woo Kim
- Department of Bio-Convergence Engineering, Korea University, Seoul, South Korea
| | - Young Noh
- Department of Neurology, Gil Medical Center, Gachon University College of Medicine, Incheon, South Korea
| | - Phil Hyu Lee
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
| | - Duk L. Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, South Korea
- Samsung Alzheimer Research Center, Center for Clinical Epidemiology, Samsung Medical Center, Seoul, South Korea
- Department of Health Sciences and Technology, Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, South Korea
| | - Joon-Kyung Seong
- School of Biomedical Engineering, Korea University, Seoul, South Korea
- Department of Artificial Intelligence, Korea University, Seoul, South Korea
- Interdisciplinary Program in Precision Public Health, Korea University, Seoul, South Korea
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Stumpo V, Guida L, Bellomo J, Van Niftrik CHB, Sebök M, Berhouma M, Bink A, Weller M, Kulcsar Z, Regli L, Fierstra J. Hemodynamic Imaging in Cerebral Diffuse Glioma-Part B: Molecular Correlates, Treatment Effect Monitoring, Prognosis, and Future Directions. Cancers (Basel) 2022; 14:1342. [PMID: 35267650 PMCID: PMC8909110 DOI: 10.3390/cancers14051342] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/01/2022] [Accepted: 03/02/2022] [Indexed: 02/05/2023] Open
Abstract
Gliomas, and glioblastoma in particular, exhibit an extensive intra- and inter-tumoral molecular heterogeneity which represents complex biological features correlating to the efficacy of treatment response and survival. From a neuroimaging point of view, these specific molecular and histopathological features may be used to yield imaging biomarkers as surrogates for distinct tumor genotypes and phenotypes. The development of comprehensive glioma imaging markers has potential for improved glioma characterization that would assist in the clinical work-up of preoperative treatment planning and treatment effect monitoring. In particular, the differentiation of tumor recurrence or true progression from pseudoprogression, pseudoresponse, and radiation-induced necrosis can still not reliably be made through standard neuroimaging only. Given the abundant vascular and hemodynamic alterations present in diffuse glioma, advanced hemodynamic imaging approaches constitute an attractive area of clinical imaging development. In this context, the inclusion of objective measurable glioma imaging features may have the potential to enhance the individualized care of diffuse glioma patients, better informing of standard-of-care treatment efficacy and of novel therapies, such as the immunotherapies that are currently increasingly investigated. In Part B of this two-review series, we assess the available evidence pertaining to hemodynamic imaging for molecular feature prediction, in particular focusing on isocitrate dehydrogenase (IDH) mutation status, MGMT promoter methylation, 1p19q codeletion, and EGFR alterations. The results for the differentiation of tumor progression/recurrence from treatment effects have also been the focus of active research and are presented together with the prognostic correlations identified by advanced hemodynamic imaging studies. Finally, the state-of-the-art concepts and advancements of hemodynamic imaging modalities are reviewed together with the advantages derived from the implementation of radiomics and machine learning analyses pipelines.
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Affiliation(s)
- Vittorio Stumpo
- Department of Neurosurgery, University Hospital Zurich, 8091 Zurich, Switzerland; (L.G.); (J.B.); (C.H.B.V.N.); (M.S.); (L.R.); (J.F.)
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, 8057 Zurich, Switzerland; (A.B.); (M.W.); (Z.K.)
| | - Lelio Guida
- Department of Neurosurgery, University Hospital Zurich, 8091 Zurich, Switzerland; (L.G.); (J.B.); (C.H.B.V.N.); (M.S.); (L.R.); (J.F.)
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, 8057 Zurich, Switzerland; (A.B.); (M.W.); (Z.K.)
| | - Jacopo Bellomo
- Department of Neurosurgery, University Hospital Zurich, 8091 Zurich, Switzerland; (L.G.); (J.B.); (C.H.B.V.N.); (M.S.); (L.R.); (J.F.)
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, 8057 Zurich, Switzerland; (A.B.); (M.W.); (Z.K.)
| | - Christiaan Hendrik Bas Van Niftrik
- Department of Neurosurgery, University Hospital Zurich, 8091 Zurich, Switzerland; (L.G.); (J.B.); (C.H.B.V.N.); (M.S.); (L.R.); (J.F.)
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, 8057 Zurich, Switzerland; (A.B.); (M.W.); (Z.K.)
| | - Martina Sebök
- Department of Neurosurgery, University Hospital Zurich, 8091 Zurich, Switzerland; (L.G.); (J.B.); (C.H.B.V.N.); (M.S.); (L.R.); (J.F.)
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, 8057 Zurich, Switzerland; (A.B.); (M.W.); (Z.K.)
| | - Moncef Berhouma
- Department of Neurosurgical Oncology and Vascular Neurosurgery, Pierre Wertheimer Neurological and Neurosurgical Hospital, Hospices Civils de Lyon, 69500 Lyon, France;
| | - Andrea Bink
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, 8057 Zurich, Switzerland; (A.B.); (M.W.); (Z.K.)
- Department of Neuroradiology, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Michael Weller
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, 8057 Zurich, Switzerland; (A.B.); (M.W.); (Z.K.)
- Department of Neurology, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Zsolt Kulcsar
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, 8057 Zurich, Switzerland; (A.B.); (M.W.); (Z.K.)
- Department of Neuroradiology, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Luca Regli
- Department of Neurosurgery, University Hospital Zurich, 8091 Zurich, Switzerland; (L.G.); (J.B.); (C.H.B.V.N.); (M.S.); (L.R.); (J.F.)
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, 8057 Zurich, Switzerland; (A.B.); (M.W.); (Z.K.)
| | - Jorn Fierstra
- Department of Neurosurgery, University Hospital Zurich, 8091 Zurich, Switzerland; (L.G.); (J.B.); (C.H.B.V.N.); (M.S.); (L.R.); (J.F.)
- Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, 8057 Zurich, Switzerland; (A.B.); (M.W.); (Z.K.)
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Corr F, Grimm D, Saß B, Pojskić M, Bartsch JW, Carl B, Nimsky C, Bopp MHA. Radiogenomic Predictors of Recurrence in Glioblastoma—A Systematic Review. J Pers Med 2022; 12:jpm12030402. [PMID: 35330402 PMCID: PMC8952807 DOI: 10.3390/jpm12030402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 02/23/2022] [Accepted: 03/01/2022] [Indexed: 12/10/2022] Open
Abstract
Glioblastoma, as the most aggressive brain tumor, is associated with a poor prognosis and outcome. To optimize prognosis and clinical therapy decisions, there is an urgent need to stratify patients with increased risk for recurrent tumors and low therapeutic success to optimize individual treatment. Radiogenomics establishes a link between radiological and pathological information. This review provides a state-of-the-art picture illustrating the latest developments in the use of radiogenomic markers regarding prognosis and their potential for monitoring recurrence. Databases PubMed, Google Scholar, and Cochrane Library were searched. Inclusion criteria were defined as diagnosis of glioblastoma with histopathological and radiological follow-up. Out of 321 reviewed articles, 43 articles met these inclusion criteria. Included studies were analyzed for the frequency of radiological and molecular tumor markers whereby radiogenomic associations were analyzed. Six main associations were described: radiogenomic prognosis, MGMT status, IDH, EGFR status, molecular subgroups, and tumor location. Prospective studies analyzing prognostic features of glioblastoma together with radiological features are lacking. By reviewing the progress in the development of radiogenomic markers, we provide insights into the potential efficacy of such an approach for clinical routine use eventually enabling early identification of glioblastoma recurrence and therefore supporting a further personalized monitoring and treatment strategy.
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Affiliation(s)
- Felix Corr
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (D.G.); (B.S.); (M.P.); (J.W.B.); (B.C.); (C.N.); (M.H.A.B.)
- EDU Institute of Higher Education, Villa Bighi, Chaplain’s House, KKR 1320 Kalkara, Malta
- Correspondence:
| | - Dustin Grimm
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (D.G.); (B.S.); (M.P.); (J.W.B.); (B.C.); (C.N.); (M.H.A.B.)
- EDU Institute of Higher Education, Villa Bighi, Chaplain’s House, KKR 1320 Kalkara, Malta
| | - Benjamin Saß
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (D.G.); (B.S.); (M.P.); (J.W.B.); (B.C.); (C.N.); (M.H.A.B.)
| | - Mirza Pojskić
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (D.G.); (B.S.); (M.P.); (J.W.B.); (B.C.); (C.N.); (M.H.A.B.)
| | - Jörg W. Bartsch
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (D.G.); (B.S.); (M.P.); (J.W.B.); (B.C.); (C.N.); (M.H.A.B.)
- Center for Mind, Brain and Behavior (CMBB), 35043 Marburg, Germany
| | - Barbara Carl
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (D.G.); (B.S.); (M.P.); (J.W.B.); (B.C.); (C.N.); (M.H.A.B.)
- Department of Neurosurgery, Helios Dr. Horst Schmidt Kliniken, Ludwig-Erhard-Strasse 100, 65199 Wiesbaden, Germany
| | - Christopher Nimsky
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (D.G.); (B.S.); (M.P.); (J.W.B.); (B.C.); (C.N.); (M.H.A.B.)
- Center for Mind, Brain and Behavior (CMBB), 35043 Marburg, Germany
| | - Miriam H. A. Bopp
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (D.G.); (B.S.); (M.P.); (J.W.B.); (B.C.); (C.N.); (M.H.A.B.)
- Center for Mind, Brain and Behavior (CMBB), 35043 Marburg, Germany
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Drug Repurposing for Glioblastoma and Current Advances in Drug Delivery-A Comprehensive Review of the Literature. Biomolecules 2021; 11:biom11121870. [PMID: 34944514 PMCID: PMC8699739 DOI: 10.3390/biom11121870] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 11/19/2021] [Accepted: 12/03/2021] [Indexed: 12/22/2022] Open
Abstract
Glioblastoma (GBM) is the most common primary malignant brain tumor in adults with an extremely poor prognosis. There is a dire need to develop effective therapeutics to overcome the intrinsic and acquired resistance of GBM to current therapies. The process of developing novel anti-neoplastic drugs from bench to bedside can incur significant time and cost implications. Drug repurposing may help overcome that obstacle. A wide range of drugs that are already approved for clinical use for the treatment of other diseases have been found to target GBM-associated signaling pathways and are being repurposed for the treatment of GBM. While many of these drugs are undergoing pre-clinical testing, others are in the clinical trial phase. Since GBM stem cells (GSCs) have been found to be a main source of tumor recurrence after surgery, recent studies have also investigated whether repurposed drugs that target these pathways can be used to counteract tumor recurrence. While several repurposed drugs have shown significant efficacy against GBM cell lines, the blood–brain barrier (BBB) can limit the ability of many of these drugs to reach intratumoral therapeutic concentrations. Localized intracranial delivery may help to achieve therapeutic drug concentration at the site of tumor resection while simultaneously minimizing toxicity and side effects. These strategies can be considered while repurposing drugs for GBM.
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12
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Assessing the reproducibility of high temporal and spatial resolution dynamic contrast-enhanced magnetic resonance imaging in patients with gliomas. Sci Rep 2021; 11:23217. [PMID: 34853347 PMCID: PMC8636480 DOI: 10.1038/s41598-021-02450-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 08/23/2021] [Indexed: 11/11/2022] Open
Abstract
Temporal and spatial resolution of dynamic contrast-enhanced MR imaging (DCE-MRI) is critical to reproducibility, and the reproducibility of high-resolution (HR) DCE-MRI was evaluated. Thirty consecutive patients suspected to have brain tumors were prospectively enrolled with written informed consent. All patients underwent both HR-DCE (voxel size, 1.1 × 1.1 × 1.1 mm3; scan interval, 1.6 s) and conventional DCE (C-DCE; voxel size, 1.25 × 1.25 × 3.0 mm3; scan interval, 4.0 s) MRI. Regions of interests (ROIs) for enhancing lesions were segmented twice in each patient with glioblastoma (n = 7) to calculate DCE parameters (Ktrans, Vp, and Ve). Intraclass correlation coefficients (ICCs) of DCE parameters were obtained. In patients with gliomas (n = 25), arterial input functions (AIFs) and DCE parameters derived from T2 hyperintense lesions were obtained, and DCE parameters were compared according to WHO grades. ICCs of HR-DCE parameters were good to excellent (0.84–0.95), and ICCs of C-DCE parameters were moderate to excellent (0.66–0.96). Maximal signal intensity and wash-in slope of AIFs from HR-DCE MRI were significantly greater than those from C-DCE MRI (31.85 vs. 7.09 and 2.14 vs. 0.63; p < 0.001). Both 95th percentile Ktrans and Ve from HR-DCE and C-DCE MRI could differentiate grade 4 from grade 2 and 3 gliomas (p < 0.05). In conclusion, HR-DCE parameters generally showed better reproducibility than C-DCE parameters, and HR-DCE MRI provided better quality of AIFs.
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13
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Shen N, Zhang S, Cho J, Li S, Zhang J, Xie Y, Wang Y, Zhu W. Application of Cluster Analysis of Time Evolution for Magnetic Resonance Imaging -Derived Oxygen Extraction Fraction Mapping: A Promising Strategy for the Genetic Profile Prediction and Grading of Glioma. Front Neurosci 2021; 15:736891. [PMID: 34671241 PMCID: PMC8520989 DOI: 10.3389/fnins.2021.736891] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 09/06/2021] [Indexed: 11/13/2022] Open
Abstract
Background: The intratumoral heterogeneity of oxygen metabolism and angiogenesis are core hallmarks of glioma, unveiling that genetic aberrations associated with magnetic resonance imaging (MRI) phenotypes may aid in the diagnosis and treatment of glioma. Objective: To explore the predictability of MRI-based oxygen extraction fraction (OEF) mapping using cluster analysis of time evolution (CAT) for genetic profiling and glioma grading. Methods: Ninety-one patients with histopathologically confirmed glioma were examined with CAT for quantitative susceptibility mapping and quantitative blood oxygen level–dependent magnitude-based OEF mapping and dynamic contrast-enhanced (DCE) MRI. Imaging biomarkers, including oxygen metabolism (OEF) and angiogenesis [volume transfer constant, cerebral blood volume (CBV), and cerebral blood flow], were investigated to predict IDH mutation, O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation status, receptor tyrosine kinase (RTK) subgroup, and differentiation of glioblastoma (GBM) vs. lower-grade glioma (LGG). The corresponding DNA sequencing was also obtained. Results were compared with DCE-MRI using receiver operating characteristic (ROC) analysis. Results: IDH1-mutated LGGs exhibited significantly lower OEF and hypoperfusion than IDH wild-type tumors (all p < 0.01). OEF and perfusion metrics showed a tendency toward higher values in MGMT unmethylated GBM, but only OEF retained significance (p = 0.01). Relative prevalence of RTK alterations was associated with increased OEF (p = 0.003) and perfusion values (p < 0.05). ROC analysis suggested OEF achieved best performance for IDH mutation detection [area under the curve (AUC) = 0.828]. None of the investigated parameters enabled prediction of MGMT status except OEF with a moderate AUC of 0.784. Predictive value for RTK subgroup was acceptable by using OEF (AUC = 0.764) and CBV (AUC = 0.754). OEF and perfusion metrics demonstrated excellent performance in glioma grading. Moreover, mutational landscape revealed hypoxia or angiogenesis-relevant gene signatures were associated with specific imaging phenotypes. Conclusion: CAT for MRI-based OEF mapping is a promising technology for oxygen measurement and along with perfusion MRI can predict genetic profiles and tumor grade in a non-invasive and clinically relevant manner. Clinical Impact: Physiological imaging provides an in vivo portrait of genetic alterations in glioma and offers a potential strategy for non-invasively selecting patients for individualized therapies.
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Affiliation(s)
- Nanxi Shen
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shun Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Junghun Cho
- Department of Radiology, Weill Cornell Medical College, New York, NY, United States
| | - Shihui Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ju Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yan Xie
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yi Wang
- Department of Radiology, Weill Cornell Medical College, New York, NY, United States.,Department of Biomedical Engineering, Cornell University, Ithaca, NY, United States
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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14
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Jafari SH, Rabiei N, Taghizadieh M, Mirazimi SMA, Kowsari H, Farzin MA, Razaghi Bahabadi Z, Rezaei S, Mohammadi AH, Alirezaei Z, Dashti F, Nejati M. Joint application of biochemical markers and imaging techniques in the accurate and early detection of glioblastoma. Pathol Res Pract 2021; 224:153528. [PMID: 34171601 DOI: 10.1016/j.prp.2021.153528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 06/09/2021] [Accepted: 06/14/2021] [Indexed: 11/28/2022]
Abstract
Glioblastoma is a primary brain tumor with the most metastatic effect in adults. Despite the wide range of multidimensional treatments, tumor heterogeneity is one of the main causes of tumor spread and gives great complexity to diagnostic and therapeutic methods. Therefore, featuring noble noninvasive prognostic methods that are focused on glioblastoma heterogeneity is perceived as an urgent need. Imaging neuro-oncological biomarkers including MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation status, tumor grade along with other tumor characteristics and demographic features (e.g., age) are commonly referred to during diagnostic, therapeutic and prognostic processes. Therefore, the use of new noninvasive prognostic methods focused on glioblastoma heterogeneity is considered an urgent need. Some neuronal biomarkers, including the promoter methylation status of the promoter MGMT, the characteristics and grade of the tumor, along with the patient's demographics (such as age and sex) are involved in diagnosis, treatment, and prognosis. Among the wide array of imaging techniques, magnetic resonance imaging combined with the more physiologically detailed technique of H-magnetic resonance spectroscopy can be useful in diagnosing neurological cancer patients. In addition, intracranial tumor qualitative analysis and sometimes tumor biopsies help in accurate diagnosis. This review summarizes the evidence for biochemical biomarkers being a reliable biomarker in the early detection and disease management in GBM. Moreover, we highlight the correlation between Imaging techniques and biochemical biomarkers and ask whether they can be combined.
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Affiliation(s)
- Seyed Hamed Jafari
- Medical Imaging Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Nikta Rabiei
- School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohammad Taghizadieh
- Department of Pathology, School of Medicine, Center for Women's Health Research Zahra, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Sayad Mohammad Ali Mirazimi
- School of Medicine, Kashan University of Medical Sciences, Kashan, Iran; Student Research Committee, Kashan University of Medical Sciences, Kashan, Iran
| | - Hamed Kowsari
- School of Medicine, Kashan University of Medical Sciences, Kashan, Iran; Student Research Committee, Kashan University of Medical Sciences, Kashan, Iran
| | - Mohammad Amin Farzin
- Department of Laboratory Medicine, School of Allied Medical Sciences, Kashan University of Medical Sciences, Kashan, Iran
| | - Zahra Razaghi Bahabadi
- School of Medicine, Kashan University of Medical Sciences, Kashan, Iran; Student Research Committee, Kashan University of Medical Sciences, Kashan, Iran
| | - Samaneh Rezaei
- Department of Medical Biotechnology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Amir Hossein Mohammadi
- Research Center for Biochemistry and Nutrition in Metabolic Diseases, Institute for Basic Sciences, Kashan University of Medical Sciences, Kashan, Iran
| | - Zahra Alirezaei
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran; Paramedical School, Bushehr University of Medical Sciences, Bushehr, Iran.
| | - Fatemeh Dashti
- School of Medicine, Kashan University of Medical Sciences, Kashan, Iran; Student Research Committee, Kashan University of Medical Sciences, Kashan, Iran.
| | - Majid Nejati
- Anatomical Sciences Research Center, Institute for Basic Sciences, Kashan University of Medical Sciences, Kashan, Iran.
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15
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Park YW, Park JE, Ahn SS, Kim EH, Kang SG, Chang JH, Kim SH, Choi SH, Kim HS, Lee SK. Magnetic Resonance Imaging Parameters for Noninvasive Prediction of Epidermal Growth Factor Receptor Amplification in Isocitrate Dehydrogenase-Wild-Type Lower-Grade Gliomas: A Multicenter Study. Neurosurgery 2021; 89:257-265. [PMID: 33913501 DOI: 10.1093/neuros/nyab136] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 02/21/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The epidermal growth factor receptor (EGFR) amplification status of isocitrate dehydrogenase-wild-type (IDHwt) lower-grade gliomas (LGGs; grade II/III) is one of the key markers for diagnosing molecular glioblastoma. However, the association between EGFR status and imaging parameters is unclear. OBJECTIVE To identify noninvasive imaging parameters from diffusion-weighted and dynamic susceptibility contrast imaging for predicting the EGFR amplification status of IDHwt LGGs. METHODS A total of 86 IDHwt LGG patients with known EGFR amplification status (62 nonamplified and 24 amplified) from 3 tertiary institutions were included. Qualitative and quantitative imaging features, including histogram parameters from apparent diffusion coefficient (ADC), normalized cerebral blood volume (nCBV), and normalized cerebral blood flow (nCBF), were assessed. Univariable and multivariable logistic regression models were constructed. RESULTS On multivariable analysis, multifocal/multicentric distribution (odds ratio [OR] = 11.77, P = .006), mean ADC (OR = 0.01, P = .044), 5th percentile of ADC (OR = 0.01, P = .046), and 95th percentile of nCBF (OR = 1.24, P = .031) were independent predictors of EGFR amplification. The diagnostic performance of the model with qualitative imaging parameters increased significantly when quantitative imaging parameters were added, with areas under the curves of 0.81 and 0.93, respectively (P = .004). CONCLUSION The presence of multifocal/multicentric distribution patterns, lower mean ADC, lower 5th percentile of ADC, and higher 95th percentile of nCBF may be useful imaging biomarkers for EGFR amplification in IDHwt LGGs. Moreover, quantitative imaging biomarkers may add value to qualitative imaging parameters.
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Affiliation(s)
- Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Seoul, Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Eui Hyun Kim
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, South Korea
| | - Seok-Gu Kang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, South Korea
| | - Jong Hee Chang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, South Korea
| | - Se Hoon Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul, South Korea
| | - Seung Hong Choi
- Department of Radiology, Seoul National University Hospital, Seoul, South Korea
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
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Segura-Collar B, Garranzo-Asensio M, Herranz B, Hernández-SanMiguel E, Cejalvo T, Casas BS, Matheu A, Pérez-Núñez Á, Sepúlveda-Sánchez JM, Hernández-Laín A, Palma V, Gargini R, Sánchez-Gómez P. Tumor-Derived Pericytes Driven by EGFR Mutations Govern the Vascular and Immune Microenvironment of Gliomas. Cancer Res 2021; 81:2142-2156. [PMID: 33593822 DOI: 10.1158/0008-5472.can-20-3558] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Revised: 12/28/2020] [Accepted: 02/03/2021] [Indexed: 11/16/2022]
Abstract
The extraordinary plasticity of glioma cells allows them to contribute to different cellular compartments in tumor vessels, reinforcing the vascular architecture. It was recently revealed that targeting glioma-derived pericytes, which represent a big percentage of the mural cell population in aggressive tumors, increases the permeability of the vessels and improves the efficiency of chemotherapy. However, the molecular determinants of this transdifferentiation process have not been elucidated. Here we show that mutations in EGFR stimulate the capacity of glioma cells to function as pericytes in a BMX- (bone marrow and X-linked) and SOX9-dependent manner. Subsequent activation of platelet-derived growth factor receptor beta in the vessel walls of EGFR-mutant gliomas stabilized the vasculature and facilitated the recruitment of immune cells. These changes in the tumor microenvironment conferred a growth advantage to the tumors but also rendered them sensitive to pericyte-targeting molecules such as ibrutinib or sunitinib. In the absence of EGFR mutations, high-grade gliomas were enriched in blood vessels, but showed a highly disrupted blood-brain barrier due to the decreased BMX/SOX9 activation and pericyte coverage, which led to poor oxygenation, necrosis, and hypoxia. Overall, these findings identify EGFR mutations as key regulators of the glioma-to-pericyte transdifferentiation, highlighting the intricate relationship between the tumor cells and their vascular and immune milieu. Our results lay the foundations for a vascular-dependent stratification of gliomas and suggest different therapeutic vulnerabilities determined by the genetic status of EGFR. SIGNIFICANCE: This study identifies the EGFR-related mechanisms that govern the capacity of glioma cells to transdifferentiate into pericytes, regulating the vascular and immune phenotypes of the tumors. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/81/8/2142/F1.large.jpg.
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Affiliation(s)
- Berta Segura-Collar
- Neurooncology Unit, Unidad Funcional de Investigación en Enfermedades Crónicas (UFIEC), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - María Garranzo-Asensio
- Neurooncology Unit, Unidad Funcional de Investigación en Enfermedades Crónicas (UFIEC), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Beatriz Herranz
- Neurooncology Unit, Unidad Funcional de Investigación en Enfermedades Crónicas (UFIEC), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Facultad de Medicina, Universidad Francisco de Vitoria, Madrid, Spain
| | - Esther Hernández-SanMiguel
- Neurooncology Unit, Unidad Funcional de Investigación en Enfermedades Crónicas (UFIEC), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Teresa Cejalvo
- Neurooncology Unit, Unidad Funcional de Investigación en Enfermedades Crónicas (UFIEC), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Bárbara S Casas
- Laboratory of Stem Cells and Developmental Biology, Faculty of Sciences, Universidad de Chile, Santiago, Chile
| | - Ander Matheu
- Cellular Oncology Group, Biodonostia Health Research Institute, San Sebastian, Spain
- IKERBASQUE, Basque Foundation for Science, Bilbao, Spain
- CIBERFES, Instituto de Salud Carlos III, Madrid, Spain
| | - Ángel Pérez-Núñez
- Dto. Neurocirugía, Hospital 12 de Octubre, Universidad Complutense de Madrid, Madrid, Spain
| | | | | | - Verónica Palma
- Laboratory of Stem Cells and Developmental Biology, Faculty of Sciences, Universidad de Chile, Santiago, Chile
| | - Ricardo Gargini
- Neurooncology Unit, Unidad Funcional de Investigación en Enfermedades Crónicas (UFIEC), Instituto de Salud Carlos III (ISCIII), Madrid, Spain.
| | - Pilar Sánchez-Gómez
- Neurooncology Unit, Unidad Funcional de Investigación en Enfermedades Crónicas (UFIEC), Instituto de Salud Carlos III (ISCIII), Madrid, Spain.
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17
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Sanvito F, Castellano A, Falini A. Advancements in Neuroimaging to Unravel Biological and Molecular Features of Brain Tumors. Cancers (Basel) 2021; 13:cancers13030424. [PMID: 33498680 PMCID: PMC7865835 DOI: 10.3390/cancers13030424] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 01/15/2021] [Accepted: 01/19/2021] [Indexed: 12/14/2022] Open
Abstract
Simple Summary Advanced neuroimaging is gaining increasing relevance for the characterization and the molecular profiling of brain tumor tissue. On one hand, for some tumor types, the most widespread advanced techniques, investigating diffusion and perfusion features, have been proven clinically feasible and rather robust for diagnosis and prognosis stratification. In addition, 2-hydroxyglutarate spectroscopy, for the first time, offers the possibility to directly measure a crucial molecular marker. On the other hand, numerous innovative approaches have been explored for a refined evaluation of tumor microenvironments, particularly assessing microstructural and microvascular properties, and the potential applications of these techniques are vast and still to be fully explored. Abstract In recent years, the clinical assessment of primary brain tumors has been increasingly dependent on advanced magnetic resonance imaging (MRI) techniques in order to infer tumor pathophysiological characteristics, such as hemodynamics, metabolism, and microstructure. Quantitative radiomic data extracted from advanced MRI have risen as potential in vivo noninvasive biomarkers for predicting tumor grades and molecular subtypes, opening the era of “molecular imaging” and radiogenomics. This review presents the most relevant advancements in quantitative neuroimaging of advanced MRI techniques, by means of radiomics analysis, applied to primary brain tumors, including lower-grade glioma and glioblastoma, with a special focus on peculiar oncologic entities of current interest. Novel findings from diffusion MRI (dMRI), perfusion-weighted imaging (PWI), and MR spectroscopy (MRS) are hereby sifted in order to evaluate the role of quantitative imaging in neuro-oncology as a tool for predicting molecular profiles, stratifying prognosis, and characterizing tumor tissue microenvironments. Furthermore, innovative technological approaches are briefly addressed, including artificial intelligence contributions and ultra-high-field imaging new techniques. Lastly, after providing an overview of the advancements, we illustrate current clinical applications and future perspectives.
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Affiliation(s)
- Francesco Sanvito
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, 20132 Milan, Italy; (F.S.); (A.F.)
- School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy
- Unit of Radiology, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy
| | - Antonella Castellano
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, 20132 Milan, Italy; (F.S.); (A.F.)
- School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy
- Correspondence: ; Tel.: +39-02-2643-3015
| | - Andrea Falini
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, 20132 Milan, Italy; (F.S.); (A.F.)
- School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy
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18
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Gupta M, Gupta A, Yadav V, Parvaze SP, Singh A, Saini J, Patir R, Vaishya S, Ahlawat S, Gupta RK. Comparative evaluation of intracranial oligodendroglioma and astrocytoma of similar grades using conventional and T1-weighted DCE-MRI. Neuroradiology 2021; 63:1227-1239. [PMID: 33469693 DOI: 10.1007/s00234-021-02636-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 01/05/2021] [Indexed: 11/25/2022]
Abstract
PURPOSE This retrospective study was performed on a 3T MRI to determine the unique conventional MR imaging and T1-weighted DCE-MRI features of oligodendroglioma and astrocytoma and investigate the utility of machine learning algorithms in their differentiation. METHODS Histologically confirmed, 81 treatment-naïve patients were classified into two groups as per WHO 2016 classification: oligodendroglioma (n = 16; grade II, n = 25; grade III) and astrocytoma (n = 10; grade II, n = 30; grade III). The differences in tumor morphology characteristics were evaluated using Z-test. T1-weighted DCE-MRI data were analyzed using an in-house built MATLAB program. The mean 90th percentile of relative cerebral blood flow, relative cerebral blood volume corrected, volume transfer rate from plasma to extracellular extravascular space, and extravascular extracellular space volume values were evaluated using independent Student's t test. Support vector machine (SVM) classifier was constructed to differentiate two groups across grade II, grade III, and grade II+III based on statistically significant features. RESULTS Z-test signified only calcification among conventional MR features to categorize oligodendroglioma and astrocytoma across grade III and grade II+III tumors. No statistical significance was found in the perfusion parameters between two groups and its subtypes. SVM trained on calcification also provided moderate accuracy to differentiate oligodendroglioma from astrocytoma. CONCLUSION We conclude that conventional MR features except calcification and the quantitative T1-weighted DCE-MRI parameters fail to discriminate between oligodendroglioma and astrocytoma. The SVM could not further aid in their differentiation. The study also suggests that the presence of more than 50% T2-FLAIR mismatch may be considered as a more conclusive sign for differentiation of IDH mutant astrocytoma.
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Affiliation(s)
- Mamta Gupta
- Department of Radiology, Fortis Memorial Research Institute, Sector 44, Gurgaon, Haryana, 122002, India
| | - Abhinav Gupta
- Department of Radiology, Fortis Memorial Research Institute, Sector 44, Gurgaon, Haryana, 122002, India
| | - Virendra Yadav
- Centre for Biomedical Engineering, IIT Delhi, New Delhi, India
| | | | - Anup Singh
- Centre for Biomedical Engineering, IIT Delhi, New Delhi, India
| | - Jitender Saini
- National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India
| | - Rana Patir
- Department of Neurosurgery, Fortis Memorial Research Institute, Sector 44, Gurgaon, Haryana, India
| | - Sandeep Vaishya
- Department of Neurosurgery, Fortis Memorial Research Institute, Sector 44, Gurgaon, Haryana, India
| | - Sunita Ahlawat
- SRL Diagnostics, Fortis Memorial Research Institute, Sector 44, Gurgaon, Haryana, India
| | - Rakesh Kumar Gupta
- Department of Radiology, Fortis Memorial Research Institute, Sector 44, Gurgaon, Haryana, 122002, India.
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19
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Riva M, Lopci E, Gay LG, Nibali MC, Rossi M, Sciortino T, Castellano A, Bello L. Advancing Imaging to Enhance Surgery: From Image to Information Guidance. Neurosurg Clin N Am 2021; 32:31-46. [PMID: 33223024 DOI: 10.1016/j.nec.2020.08.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Conventional magnetic resonance imaging (cMRI) has an established role as a crucial disease parameter in the multidisciplinary management of glioblastoma, guiding diagnosis, treatment planning, assessment, and follow-up. Yet, cMRI cannot provide adequate information regarding tissue heterogeneity and the infiltrative extent beyond the contrast enhancement. Advanced magnetic resonance imaging and PET and newer analytical methods are transforming images into data (radiomics) and providing noninvasive biomarkers of molecular features (radiogenomics), conveying enhanced information for improving decision making in surgery. This review analyzes the shift from image guidance to information guidance that is relevant for the surgical treatment of glioblastoma.
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Affiliation(s)
- Marco Riva
- Department of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, Via Festa del Perdono 7, Milan 20122, Italy; IRCCS Istituto Ortopedico Galeazzi, U.O. Neurochirurgia Oncologica, Milan, Italy.
| | - Egesta Lopci
- Unit of Nuclear Medicine, Humanitas Clinical and Research Center - IRCCS, Via Manzoni 56, Rozzano, Milan 20089, Italy. https://twitter.com/LopciEgesta
| | - Lorenzo G Gay
- IRCCS Istituto Ortopedico Galeazzi, U.O. Neurochirurgia Oncologica, Milan, Italy; Department of Oncology and Hemato-Oncology, Via Festa del Perdono 7, Milan 20122, Italy
| | - Marco Conti Nibali
- IRCCS Istituto Ortopedico Galeazzi, U.O. Neurochirurgia Oncologica, Milan, Italy; Department of Oncology and Hemato-Oncology, Via Festa del Perdono 7, Milan 20122, Italy. https://twitter.com/dr_mcn
| | - Marco Rossi
- IRCCS Istituto Ortopedico Galeazzi, U.O. Neurochirurgia Oncologica, Milan, Italy; Department of Oncology and Hemato-Oncology, Via Festa del Perdono 7, Milan 20122, Italy
| | - Tommaso Sciortino
- IRCCS Istituto Ortopedico Galeazzi, U.O. Neurochirurgia Oncologica, Milan, Italy; Department of Oncology and Hemato-Oncology, Via Festa del Perdono 7, Milan 20122, Italy
| | - Antonella Castellano
- Neuroradiology Unit and CERMAC, Vita-Salute San Raffaele University, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan 20123, Italy. https://twitter.com/antocastella
| | - Lorenzo Bello
- IRCCS Istituto Ortopedico Galeazzi, U.O. Neurochirurgia Oncologica, Milan, Italy; Department of Oncology and Hemato-Oncology, Via Festa del Perdono 7, Milan 20122, Italy
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20
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Blood-Brain Barrier Modulation to Improve Glioma Drug Delivery. Pharmaceutics 2020; 12:pharmaceutics12111085. [PMID: 33198244 PMCID: PMC7697580 DOI: 10.3390/pharmaceutics12111085] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 11/09/2020] [Accepted: 11/10/2020] [Indexed: 02/07/2023] Open
Abstract
The blood-brain barrier (BBB) is formed by brain microvascular endothelial cells that are sealed by tight junctions, making it a significant obstacle for most brain therapeutics. The poor BBB penetration of newly developed therapeutics has therefore played a major role in limiting their clinical success. A particularly challenging therapeutic target is glioma, which is the most frequently occurring malignant brain tumor. Thus, to enhance therapeutic uptake in tumors, researchers have been developing strategies to modulate BBB permeability. However, most conventional BBB opening strategies are difficult to apply in the clinical setting due to their broad, non-specific modulation of the BBB, which can result in damage to normal brain tissue. In this review, we have summarized strategies that could potentially be used to selectively and efficiently modulate the tumor BBB for more effective glioma treatment.
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21
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Diffusion and perfusion MRI may predict EGFR amplification and the TERT promoter mutation status of IDH-wildtype lower-grade gliomas. Eur Radiol 2020; 30:6475-6484. [PMID: 32785770 DOI: 10.1007/s00330-020-07090-3] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 07/02/2020] [Accepted: 07/20/2020] [Indexed: 12/20/2022]
Abstract
OBJECTIVES Epidermal growth factor receptor (EGFR) amplification and telomerase reverse transcriptase promoter (TERTp) mutation status of isocitrate dehydrogenase-wildtype (IDHwt) lower-grade gliomas (LGGs; grade II/III) are crucial for identifying IDHwt LGG with an aggressive clinical course. The purpose of this study was to assess whether parameters from diffusion tensor imaging, dynamic susceptibility contrast (DSC), and diffusion tensor imaging, dynamic contrast-enhanced imaging can predict the EGFR amplification and TERTp mutation status of IDHwt LGGs. METHODS A total of 49 patients with IDHwt LGGs with either known EGFR amplification (39 non-amplified, 10 amplified) or TERTp mutation (19 wildtype, 21 mutant) statuses underwent MRI. The mean ADC, fractional anisotropy (FA), normalized cerebral blood volume (nCBV), normalized cerebral blood flow (nCBF), volume transfer constant (Ktrans), rate transfer coefficient (Kep), extravascular extracellular volume fraction (Ve), and plasma volume fraction (Vp) values were assessed. Univariate and multivariate logistic regression models were constructed. RESULTS EGFR-amplified tumors showed lower mean ADC values than EGFR-non-amplified tumors (p = 0.019). Mean ADC was an independent predictor of EGFR amplification, with an AUC of 0.75. TERTp mutant tumors showed higher mean nCBV (p = 0.020), higher mean nCBF (p = 0.017), and higher mean Vp (p = 0.002) than TERTp wildtype tumors. With multivariate logistic regression, mean Vp was the independent predictor of TERTp mutation status, with an AUC of 0.85. CONCLUSION This exploratory pilot study shows that lower ADC values may be useful for prediction of EGFR amplification, whereas higher Vp values may be useful for prediction of the TERTp mutation status of IDHwt LGGs. KEY POINTS • EGFR amplification and TERTp mutation are key molecular markers that predict an aggressive clinical course of IDHwt LGGs. • EGFR-amplified tumors showed lower ADC values than EGFR-non-amplified tumors, suggesting higher cellularity. • TERTp mutant tumors showed a higher plasma volume fraction than TERTp wildtype tumors, suggesting higher vascular proliferation and tumor angiogenesis.
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22
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Bakas S, Shukla G, Akbari H, Erus G, Sotiras A, Rathore S, Sako C, Min Ha S, Rozycki M, Shinohara RT, Bilello M, Davatzikos C. Overall survival prediction in glioblastoma patients using structural magnetic resonance imaging (MRI): advanced radiomic features may compensate for lack of advanced MRI modalities. J Med Imaging (Bellingham) 2020; 7:031505. [PMID: 32566694 DOI: 10.1117/1.jmi.7.3.031505] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Accepted: 05/20/2020] [Indexed: 12/22/2022] Open
Abstract
Purpose: Glioblastoma, the most common and aggressive adult brain tumor, is considered noncurative at diagnosis, with 14 to 16 months median survival following treatment. There is increasing evidence that noninvasive integrative analysis of radiomic features can predict overall and progression-free survival, using advanced multiparametric magnetic resonance imaging (Adv-mpMRI). If successfully applicable, such noninvasive markers can considerably influence patient management. However, most patients prior to initiation of therapy typically undergo only basic structural mpMRI (Bas-mpMRI, i.e., T1, T1-Gd, T2, and T2-fluid-attenuated inversion recovery) preoperatively, rather than Adv-mpMRI that provides additional vascularization (dynamic susceptibility contrast-MRI) and cell-density (diffusion tensor imaging) related information. Approach: We assess a retrospective cohort of 101 glioblastoma patients with available Adv-mpMRI from a previous study, which has shown that an initial feature panel (IFP, i.e., intensity, volume, location, and growth model parameters) extracted from Adv-mpMRI can yield accurate overall survival stratification. We focus on demonstrating that equally accurate prediction models can be constructed using augmented radiomic feature panels (ARFPs, i.e., integrating morphology and textural descriptors) extracted solely from widely available Bas-mpMRI, obviating the need for using Adv-mpMRI. We extracted 1612 radiomic features from distinct tumor subregions to build multivariate models that stratified patients as long-, intermediate-, or short-survivors. Results: The classification accuracy of the model utilizing Adv-mpMRI protocols and the IFP was 72.77% and degraded to 60.89% when using only Bas-mpMRI. However, utilizing the ARFP on Bas-mpMRI improved the accuracy to 74.26%. Furthermore, Kaplan-Meier analysis demonstrated superior classification of subjects into short-, intermediate-, and long-survivor classes when using ARFP extracted from Bas-mpMRI. Conclusions: This quantitative evaluation indicates that accurate survival prediction in glioblastoma patients is feasible using solely Bas-mpMRI and integrative advanced radiomic features, which can compensate for the lack of Adv-mpMRI. Our finding holds promise for generalization across multiple institutions that may not have access to Adv-mpMRI and to better inform clinical decision-making about aggressive interventions and clinical trials.
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Affiliation(s)
- Spyridon Bakas
- University of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.,University of Pennsylvania, Perelman School of Medicine, Richards Medical Research Laboratories, Department of Radiology, Philadelphia, PA, United States.,University of Pennsylvania, Perelman School of Medicine, Richards Medical Research Laboratories, Department of Pathology and Laboratory Medicine, Philadelphia, PA, United States
| | - Gaurav Shukla
- University of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.,Thomas Jefferson University, Sidney Kimmel Cancer Center, Department of Radiation Oncology, Philadelphia, PA, United States
| | - Hamed Akbari
- University of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.,University of Pennsylvania, Perelman School of Medicine, Richards Medical Research Laboratories, Department of Radiology, Philadelphia, PA, United States
| | - Guray Erus
- University of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.,University of Pennsylvania, Perelman School of Medicine, Richards Medical Research Laboratories, Department of Radiology, Philadelphia, PA, United States
| | - Aristeidis Sotiras
- University of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.,University of Pennsylvania, Perelman School of Medicine, Richards Medical Research Laboratories, Department of Radiology, Philadelphia, PA, United States.,Washington University in St. Louis, School of Medicine, Institute for Informatics, Saint Louis, MO, United States.,Washington University in St. Louis, Department of Radiology, Saint Louis, MO, United States
| | - Saima Rathore
- University of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.,University of Pennsylvania, Perelman School of Medicine, Richards Medical Research Laboratories, Department of Radiology, Philadelphia, PA, United States
| | - Chiharu Sako
- University of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.,University of Pennsylvania, Perelman School of Medicine, Richards Medical Research Laboratories, Department of Radiology, Philadelphia, PA, United States
| | - Sung Min Ha
- University of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.,University of Pennsylvania, Perelman School of Medicine, Richards Medical Research Laboratories, Department of Radiology, Philadelphia, PA, United States
| | - Martin Rozycki
- University of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.,University of Pennsylvania, Perelman School of Medicine, Richards Medical Research Laboratories, Department of Radiology, Philadelphia, PA, United States
| | - Russell T Shinohara
- University of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.,University of Pennsylvania, Perelman School of Medicine, Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Philadelphia, PA, United States
| | - Michel Bilello
- University of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.,University of Pennsylvania, Perelman School of Medicine, Richards Medical Research Laboratories, Department of Radiology, Philadelphia, PA, United States
| | - Christos Davatzikos
- University of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.,University of Pennsylvania, Perelman School of Medicine, Richards Medical Research Laboratories, Department of Radiology, Philadelphia, PA, United States
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23
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Umemura Y, Wang D, Peck KK, Flynn J, Zhang Z, Fatovic R, Anderson ES, Beal K, Shoushtari AN, Kaley T, Young RJ. DCE-MRI perfusion predicts pseudoprogression in metastatic melanoma treated with immunotherapy. J Neurooncol 2019; 146:339-346. [PMID: 31873875 DOI: 10.1007/s11060-019-03379-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Accepted: 12/20/2019] [Indexed: 10/25/2022]
Abstract
PURPOSE It can be challenging to differentiate pseudoprogression from progression. We assessed the ability of dynamic contrast enhanced T1 MRI (DCE-MRI) perfusion to identify pseudoprogression in melanoma brain metastases. METHODS Patients with melanoma brain metastases who underwent immunotherapy and DCE-MRI were identified. Enhancing lesions ≥ 5mm in diameter on DCE-MRI and that were new or increased in size between a week from beginning the treatment, and a month after completing the treatment were included in the analysis. The 90th percentiles of rVp and rKtrans and the presence or absence of hemorrhage were recorded. Histopathology served as the reference standard for pseudoprogression. If not available, pseudoprogression was defined as neurological and radiographic stability or improvement without any new treatment for ≥ 2 months. RESULTS Forty-four patients were identified; 64% received ipilimumab monotherapy for a median duration of 9 weeks (range, 1-138). Sixty-four lesions in 44 patients were included in the study. Of these, nine lesions in eight patients were determined to be pseudoprogression and seven lesions were previously irradiated. Forty-four progression lesions and eight pseudoprogression lesions were hemorrhagic. Median lesion volume for pseudoprogression and progression were not significantly different, at 2.3 cm3 and 3.2 cm3, respectively (p = 0.82). The rVp90 was smaller in pseudoprogression versus progression, at 2.2 and 5.3, respectively (p = 0.02), and remained significant after false discovery rate adjustment (p = 0.04). CONCLUSIONS Pseudoprogression exhibited significantly lower rVp90 on DCE-MRI compared with progression. This knowledge can be useful for managing growing lesions in patients with melanoma brain metastases who are receiving immunotherapy.
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Affiliation(s)
- Yoshie Umemura
- Department of Neurology, University of Michigan, 1914 Taubman Center, 1500 E. Medical Center Dr., SPC 5316, Ann Arbor, MI, 48109 5316, USA.
| | - Diane Wang
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Kyung K Peck
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jessica Flynn
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Zhigang Zhang
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Robin Fatovic
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Erik S Anderson
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Kathryn Beal
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Thomas Kaley
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Robert J Young
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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24
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Davatzikos C, Sotiras A, Fan Y, Habes M, Erus G, Rathore S, Bakas S, Chitalia R, Gastounioti A, Kontos D. Precision diagnostics based on machine learning-derived imaging signatures. Magn Reson Imaging 2019; 64:49-61. [PMID: 31071473 PMCID: PMC6832825 DOI: 10.1016/j.mri.2019.04.012] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Revised: 04/24/2019] [Accepted: 04/29/2019] [Indexed: 01/08/2023]
Abstract
The complexity of modern multi-parametric MRI has increasingly challenged conventional interpretations of such images. Machine learning has emerged as a powerful approach to integrating diverse and complex imaging data into signatures of diagnostic and predictive value. It has also allowed us to progress from group comparisons to imaging biomarkers that offer value on an individual basis. We review several directions of research around this topic, emphasizing the use of machine learning in personalized predictions of clinical outcome, in breaking down broad umbrella diagnostic categories into more detailed and precise subtypes, and in non-invasively estimating cancer molecular characteristics. These methods and studies contribute to the field of precision medicine, by introducing more specific diagnostic and predictive biomarkers of clinical outcome, therefore pointing to better matching of treatments to patients.
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Affiliation(s)
- Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, United States of America.
| | - Aristeidis Sotiras
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, United States of America
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, United States of America
| | - Mohamad Habes
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, United States of America
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, United States of America
| | - Saima Rathore
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, United States of America
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, United States of America
| | - Rhea Chitalia
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, United States of America
| | - Aimilia Gastounioti
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, United States of America
| | - Despina Kontos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, United States of America
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25
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Fathi Kazerooni A, Bakas S, Saligheh Rad H, Davatzikos C. Imaging signatures of glioblastoma molecular characteristics: A radiogenomics review. J Magn Reson Imaging 2019; 52:54-69. [PMID: 31456318 DOI: 10.1002/jmri.26907] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Accepted: 08/09/2019] [Indexed: 02/06/2023] Open
Abstract
Over the past few decades, the advent and development of genomic assessment methods and computational approaches have raised the hopes for identifying therapeutic targets that may aid in the treatment of glioblastoma. However, the targeted therapies have barely been successful in their effort to cure glioblastoma patients, leaving them with a grim prognosis. Glioblastoma exhibits high heterogeneity, both spatially and temporally. The existence of different genetic subpopulations in glioblastoma allows this tumor to adapt itself to environmental forces. Therefore, patients with glioblastoma respond poorly to the prescribed therapies, as treatments are directed towards the whole tumor and not to the specific genetic subregions. Genomic alterations within the tumor develop distinct radiographic phenotypes. In this regard, MRI plays a key role in characterizing molecular signatures of glioblastoma, based on regional variations and phenotypic presentation of the tumor. Radiogenomics has emerged as a (relatively) new field of research to explore the connections between genetic alterations and imaging features. Radiogenomics offers numerous advantages, including noninvasive and global assessment of the tumor and its response to therapies. In this review, we summarize the potential role of radiogenomic techniques to stratify patients according to their specific tumor characteristics with the goal of designing patient-specific therapies. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;52:54-69.
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Affiliation(s)
- Anahita Fathi Kazerooni
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Hamidreza Saligheh Rad
- Quantitative MR Imaging and Spectroscopy Group (QMISG), Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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26
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Ozturk K, Soylu E, Tolunay S, Narter S, Hakyemez B. Dynamic Contrast-Enhanced T1-Weighted Perfusion Magnetic Resonance Imaging Identifies Glioblastoma Immunohistochemical Biomarkers via Tumoral and Peritumoral Approach: A Pilot Study. World Neurosurg 2019; 128:e195-e208. [PMID: 31003026 DOI: 10.1016/j.wneu.2019.04.089] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2019] [Revised: 04/08/2019] [Accepted: 04/09/2019] [Indexed: 12/13/2022]
Abstract
OBJECTIVE We aimed to evaluate the usefulness of dynamic contrast-enhanced T1-weighted perfusion magnetic resonance imaging (DCE-pMRI) to predict certain immunohistochemical (IHC) biomarkers of glioblastoma (GB) in this pilot study. METHODS We retrospectively reviewed 36 patients (male/female, 25:11; mean age, 53 years; age range, 29-85 years) who had pretreatment DCE-pMRI with IHC analysis of their excised GBs. Regions of interest of the enhancing tumor (ER) and nonenhancing peritumoral region (NER) were used to calculate DCE-pMRI parameters of volume transfer constant, back flux constant, volume of the extravascular extracellular space, initial area under enhancement curve, and maximum slope. IHC biomarkers including Ki-67 labeling index, epidermal growth factor receptor (EGFR), oligodendrocyte transcription factor 2 (OLIG2), isocitrate dehydrogenase 1 (IDH1), and p53 mutation status were determined. The imaging metrics of GB with IHC markers were compared using the Kruskal-Wallis test and Spearman correlation analysis. RESULTS Among 30 patients with available IDH1 status, 14 patients (46.6%) had IDH1 mutation. EGFR amplification was present in 24/36 (66.6%) patients. Mean Ki-67 labeling index was 29% (range, 1.5%-80%). p53 mutation was present in 20/36 GBs (55%), whereas OLIG2 expression was positive in 29/36 GBs (80.5%). Various DCE-pMRI parameters gathered from the ER and NER were significantly correlated with IDH1 mutation, EGFR amplification, and OLIG2 expression (P < 0.05). Ki-67 labeling index showed a strong positive correlation with initial area under enhancement curve (r = 0.619; P < 0.001). CONCLUSIONS DCE-pMRI could determine surrogate IHC biomarkers in GB via tumoral and peritumoral approach, potential targets for individualized treatment protocols.
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Affiliation(s)
- Kerem Ozturk
- Department of Radiology, Uludag University Faculty of Medicine, Bursa, Turkey
| | - Esra Soylu
- Department of Radiology, Uludag University Faculty of Medicine, Bursa, Turkey
| | - Sahsine Tolunay
- Department of Pathology, Uludag University Faculty of Medicine, Bursa, Turkey
| | - Selin Narter
- Department of Pathology, Uludag University Faculty of Medicine, Bursa, Turkey
| | - Bahattin Hakyemez
- Department of Radiology, Uludag University Faculty of Medicine, Bursa, Turkey.
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27
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Sarkaria JN, Hu LS, Parney IF, Pafundi DH, Brinkmann DH, Laack NN, Giannini C, Burns TC, Kizilbash SH, Laramy JK, Swanson KR, Kaufmann TJ, Brown PD, Agar NYR, Galanis E, Buckner JC, Elmquist WF. Is the blood-brain barrier really disrupted in all glioblastomas? A critical assessment of existing clinical data. Neuro Oncol 2019; 20:184-191. [PMID: 29016900 DOI: 10.1093/neuonc/nox175] [Citation(s) in RCA: 408] [Impact Index Per Article: 81.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
The blood-brain barrier (BBB) excludes the vast majority of cancer therapeutics from normal brain. However, the importance of the BBB in limiting drug delivery and efficacy is controversial in high-grade brain tumors, such as glioblastoma (GBM). The accumulation of normally brain impenetrant radiographic contrast material in essentially all GBM has popularized a belief that the BBB is uniformly disrupted in all GBM patients so that consideration of drug distribution across the BBB is not relevant in designing therapies for GBM. However, contrary to this view, overwhelming clinical evidence demonstrates that there is also a clinically significant tumor burden with an intact BBB in all GBM, and there is little doubt that drugs with poor BBB permeability do not provide therapeutically effective drug exposures to this fraction of tumor cells. This review provides an overview of the clinical literature to support a central hypothesis: that all GBM patients have tumor regions with an intact BBB, and cure for GBM will only be possible if these regions of tumor are adequately treated.
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Affiliation(s)
- Jann N Sarkaria
- Mayo Clinic, Rochester, Minnesota (J.N.S., I.F.P., D.H.P., D.H.B., N.N.L., C.G., T.C.B., S.H.K., T.J.K., P.D.B., E.G., J.C.B.)
| | - Leland S Hu
- Mayo Clinic, Scottsdale, Arizona (L.S.H., K.R.S.)
| | - Ian F Parney
- Mayo Clinic, Rochester, Minnesota (J.N.S., I.F.P., D.H.P., D.H.B., N.N.L., C.G., T.C.B., S.H.K., T.J.K., P.D.B., E.G., J.C.B.)
| | - Deanna H Pafundi
- Mayo Clinic, Rochester, Minnesota (J.N.S., I.F.P., D.H.P., D.H.B., N.N.L., C.G., T.C.B., S.H.K., T.J.K., P.D.B., E.G., J.C.B.)
| | - Debra H Brinkmann
- Mayo Clinic, Rochester, Minnesota (J.N.S., I.F.P., D.H.P., D.H.B., N.N.L., C.G., T.C.B., S.H.K., T.J.K., P.D.B., E.G., J.C.B.)
| | - Nadia N Laack
- Mayo Clinic, Rochester, Minnesota (J.N.S., I.F.P., D.H.P., D.H.B., N.N.L., C.G., T.C.B., S.H.K., T.J.K., P.D.B., E.G., J.C.B.)
| | - Caterina Giannini
- Mayo Clinic, Rochester, Minnesota (J.N.S., I.F.P., D.H.P., D.H.B., N.N.L., C.G., T.C.B., S.H.K., T.J.K., P.D.B., E.G., J.C.B.)
| | - Terence C Burns
- Mayo Clinic, Rochester, Minnesota (J.N.S., I.F.P., D.H.P., D.H.B., N.N.L., C.G., T.C.B., S.H.K., T.J.K., P.D.B., E.G., J.C.B.)
| | - Sani H Kizilbash
- Mayo Clinic, Rochester, Minnesota (J.N.S., I.F.P., D.H.P., D.H.B., N.N.L., C.G., T.C.B., S.H.K., T.J.K., P.D.B., E.G., J.C.B.)
| | - Janice K Laramy
- University of Minnesota, Minneapolis, Minnesota (J.K.L., W.F.E.)
| | | | - Timothy J Kaufmann
- Mayo Clinic, Rochester, Minnesota (J.N.S., I.F.P., D.H.P., D.H.B., N.N.L., C.G., T.C.B., S.H.K., T.J.K., P.D.B., E.G., J.C.B.)
| | - Paul D Brown
- Mayo Clinic, Rochester, Minnesota (J.N.S., I.F.P., D.H.P., D.H.B., N.N.L., C.G., T.C.B., S.H.K., T.J.K., P.D.B., E.G., J.C.B.)
| | | | - Evanthia Galanis
- Mayo Clinic, Rochester, Minnesota (J.N.S., I.F.P., D.H.P., D.H.B., N.N.L., C.G., T.C.B., S.H.K., T.J.K., P.D.B., E.G., J.C.B.)
| | - Jan C Buckner
- Mayo Clinic, Rochester, Minnesota (J.N.S., I.F.P., D.H.P., D.H.B., N.N.L., C.G., T.C.B., S.H.K., T.J.K., P.D.B., E.G., J.C.B.)
| | - William F Elmquist
- Mayo Clinic, Rochester, Minnesota (J.N.S., I.F.P., D.H.P., D.H.B., N.N.L., C.G., T.C.B., S.H.K., T.J.K., P.D.B., E.G., J.C.B.)
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Seow P, Wong JHD, Ahmad-Annuar A, Mahajan A, Abdullah NA, Ramli N. Quantitative magnetic resonance imaging and radiogenomic biomarkers for glioma characterisation: a systematic review. Br J Radiol 2018; 91:20170930. [PMID: 29902076 PMCID: PMC6319852 DOI: 10.1259/bjr.20170930] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Revised: 05/25/2018] [Accepted: 06/07/2018] [Indexed: 02/05/2023] Open
Abstract
OBJECTIVE: The diversity of tumour characteristics among glioma patients, even within same tumour grade, is a big challenge for disease outcome prediction. A possible approach for improved radiological imaging could come from combining information obtained at the molecular level. This review assembles recent evidence highlighting the value of using radiogenomic biomarkers to infer the underlying biology of gliomas and its correlation with imaging features. METHODS: A literature search was done for articles published between 2002 and 2017 on Medline electronic databases. Of 249 titles identified, 38 fulfilled the inclusion criteria, with 14 articles related to quantifiable imaging parameters (heterogeneity, vascularity, diffusion, cell density, infiltrations, perfusion, and metabolite changes) and 24 articles relevant to molecular biomarkers linked to imaging. RESULTS: Genes found to correlate with various imaging phenotypes were EGFR, MGMT, IDH1, VEGF, PDGF, TP53, and Ki-67. EGFR is the most studied gene related to imaging characteristics in the studies reviewed (41.7%), followed by MGMT (20.8%) and IDH1 (16.7%). A summary of the relationship amongst glioma morphology, gene expressions, imaging characteristics, prognosis and therapeutic response are presented. CONCLUSION: The use of radiogenomics can provide insights to understanding tumour biology and the underlying molecular pathways. Certain MRI characteristics that show strong correlations with EGFR, MGMT and IDH1 could be used as imaging biomarkers. Knowing the pathways involved in tumour progression and their associated imaging patterns may assist in diagnosis, prognosis and treatment management, while facilitating personalised medicine. ADVANCES IN KNOWLEDGE: Radiogenomics can offer clinicians better insight into diagnosis, prognosis, and prediction of therapeutic responses of glioma.
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Affiliation(s)
| | | | - Azlina Ahmad-Annuar
- Department of Biomedical Science, University of Malaya, Kuala Lumpur, Malaysia
| | - Abhishek Mahajan
- Department of Radiodiagnosis and Imaging, Tata Memorial Hospital, Mumbai, India
| | - Nor Aniza Abdullah
- Department of Computer System and Technology, University of Malaya, Kuala Lumpur, Malaysia
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Akbari H, Bakas S, Pisapia JM, Nasrallah MP, Rozycki M, Martinez-Lage M, Morrissette JJD, Dahmane N, O’Rourke DM, Davatzikos C. In vivo evaluation of EGFRvIII mutation in primary glioblastoma patients via complex multiparametric MRI signature. Neuro Oncol 2018; 20:1068-1079. [PMID: 29617843 PMCID: PMC6280148 DOI: 10.1093/neuonc/noy033] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Background Epidermal growth factor receptor variant III (EGFRvIII) is a driver mutation and potential therapeutic target in glioblastoma. Non-invasive in vivo EGFRvIII determination, using clinically acquired multiparametric MRI sequences, could assist in assessing spatial heterogeneity related to EGFRvIII, currently not captured via single-specimen analyses. We hypothesize that integration of subtle, yet distinctive, quantitative imaging/radiomic patterns using machine learning may lead to non-invasively determining molecular characteristics, and particularly the EGFRvIII mutation. Methods We integrated diverse imaging features, including the tumor's spatial distribution pattern, via support vector machines, to construct an imaging signature of EGFRvIII. This signature was evaluated in independent discovery (n = 75) and replication (n = 54) cohorts of de novo glioblastoma, and compared with the EGFRvIII status obtained through an assay based on next-generation sequencing. Results The cross-validated accuracy of the EGFRvIII signature in classifying the mutation status in individual patients of the independent discovery and replication cohorts was 85.3% (specificity = 86.3%, sensitivity = 83.3%, area under the curve [AUC] = 0.85) and 87% (specificity = 90%, sensitivity = 78.6%, AUC = 0.86), respectively. The signature was consistent with EGFRvIII+ tumors having increased neovascularization and cell density, as well as a distinctive spatial pattern involving relatively more frontal and parietal regions compared with EGFRvIII- tumors. Conclusions An imaging signature of EGFRvIII was found, revealing a complex, yet distinct macroscopic glioblastoma phenotype. By non-invasively capturing the tumor in its entirety, the proposed methodology can assist in evaluating the tumor's spatial heterogeneity, hence overcoming common spatial sampling limitations of tissue-based analyses. This signature can preoperatively stratify patients for EGFRvIII-targeted therapies, and potentially monitor dynamic mutational changes during treatment.
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Affiliation(s)
- Hamed Akbari
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadephia, Pennsylvania
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadephia, Pennsylvania
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadephia, Pennsylvania
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadephia, Pennsylvania
| | - Jared M Pisapia
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadephia, Pennsylvania
| | - MacLean P Nasrallah
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadephia, Pennsylvania
| | - Martin Rozycki
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadephia, Pennsylvania
| | - Maria Martinez-Lage
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadephia, Pennsylvania
| | - Jennifer J D Morrissette
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadephia, Pennsylvania
| | - Nadia Dahmane
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadephia, Pennsylvania
| | - Donald M O’Rourke
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadephia, Pennsylvania
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadephia, Pennsylvania
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadephia, Pennsylvania
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30
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Jansen RW, van Amstel P, Martens RM, Kooi IE, Wesseling P, de Langen AJ, Menke-Van der Houven van Oordt CW, Jansen BHE, Moll AC, Dorsman JC, Castelijns JA, de Graaf P, de Jong MC. Non-invasive tumor genotyping using radiogenomic biomarkers, a systematic review and oncology-wide pathway analysis. Oncotarget 2018; 9:20134-20155. [PMID: 29732009 PMCID: PMC5929452 DOI: 10.18632/oncotarget.24893] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2017] [Accepted: 02/26/2018] [Indexed: 12/12/2022] Open
Abstract
With targeted treatments playing an increasing role in oncology, the need arises for fast non-invasive genotyping in clinical practice. Radiogenomics is a rapidly evolving field of research aimed at identifying imaging biomarkers useful for non-invasive genotyping. Radiogenomic genotyping has the advantage that it can capture tumor heterogeneity, can be performed repeatedly for treatment monitoring, and can be performed in malignancies for which biopsy is not available. In this systematic review of 187 included articles, we compiled a database of radiogenomic associations and unraveled networks of imaging groups and gene pathways oncology-wide. Results indicated that ill-defined tumor margins and tumor heterogeneity can potentially be used as imaging biomarkers for 1p/19q codeletion in glioma, relevant for prognosis and disease profiling. In non-small cell lung cancer, FDG-PET uptake and CT-ground-glass-opacity features were associated with treatment-informing traits including EGFR-mutations and ALK-rearrangements. Oncology-wide gene pathway analysis revealed an association between contrast enhancement (imaging) and the targetable VEGF-signalling pathway. Although the need of independent validation remains a concern, radiogenomic biomarkers showed potential for prognosis prediction and targeted treatment selection. Quantitative imaging enhanced the potential of multiparametric radiogenomic models. A wealth of data has been compiled for guiding future research towards robust non-invasive genomic profiling.
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Affiliation(s)
- Robin W Jansen
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Paul van Amstel
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Roland M Martens
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Irsan E Kooi
- Department of Clinical Genetics, VU University Medical Center, Amsterdam, The Netherlands
| | - Pieter Wesseling
- Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands.,Department of Pathology, Princess Máxima Center for Pediatric Oncology and University Medical Center Utrecht, Utrecht, The Netherlands
| | - Adrianus J de Langen
- Department of Respiratory Diseases, VU University Medical Center, Amsterdam, The Netherlands
| | | | - Bernard H E Jansen
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Annette C Moll
- Department of Ophthalmology, VU University Medical Center, Amsterdam, The Netherlands
| | - Josephine C Dorsman
- Department of Clinical Genetics, VU University Medical Center, Amsterdam, The Netherlands
| | - Jonas A Castelijns
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Pim de Graaf
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Marcus C de Jong
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
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31
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Iv M, Yoon BC, Heit JJ, Fischbein N, Wintermark M. Current Clinical State of Advanced Magnetic Resonance Imaging for Brain Tumor Diagnosis and Follow Up. Semin Roentgenol 2018; 53:45-61. [DOI: 10.1053/j.ro.2017.11.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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32
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Mullen KM, Huang RY. An Update on the Approach to the Imaging of Brain Tumors. Curr Neurol Neurosci Rep 2017; 17:53. [PMID: 28516376 DOI: 10.1007/s11910-017-0760-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
PURPOSE OF REVIEW Neuroimaging plays a critical role in diagnosis of brain tumors and in assessment of response to therapy. However, challenges remain, including accurately and reproducibly assessing response to therapy, defining endpoints for neuro-oncology trials, providing prognostic information, and differentiating progressive disease from post-therapeutic changes particularly in the setting of antiangiogenic and other novel therapies. RECENT FINDINGS Recent advances in the imaging of brain tumors include application of advanced MRI imaging techniques to assess tumor response to therapy and analysis of imaging features correlating to molecular markers, grade, and prognosis. This review aims to summarize recent advances in imaging as applied to current diagnostic and therapeutic neuro-oncologic challenges.
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Affiliation(s)
- Katherine M Mullen
- Department of Radiology, Brigham and Women's Hospital, 75 Francis St, Boston, MA, 02115, USA
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, 75 Francis St, Boston, MA, 02115, USA.
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Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci Data 2017; 4:170117. [PMID: 28872634 PMCID: PMC5685212 DOI: 10.1038/sdata.2017.117] [Citation(s) in RCA: 719] [Impact Index Per Article: 102.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Accepted: 07/14/2017] [Indexed: 01/04/2023] Open
Abstract
Gliomas belong to a group of central nervous system tumors, and consist of various sub-regions. Gold standard labeling of these sub-regions in radiographic imaging is essential for both clinical and computational studies, including radiomic and radiogenomic analyses. Towards this end, we release segmentation labels and radiomic features for all pre-operative multimodal magnetic resonance imaging (MRI) (n=243) of the multi-institutional glioma collections of The Cancer Genome Atlas (TCGA), publicly available in The Cancer Imaging Archive (TCIA). Pre-operative scans were identified in both glioblastoma (TCGA-GBM, n=135) and low-grade-glioma (TCGA-LGG, n=108) collections via radiological assessment. The glioma sub-region labels were produced by an automated state-of-the-art method and manually revised by an expert board-certified neuroradiologist. An extensive panel of radiomic features was extracted based on the manually-revised labels. This set of labels and features should enable i) direct utilization of the TCGA/TCIA glioma collections towards repeatable, reproducible and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments, as well as ii) performance evaluation of computer-aided segmentation methods, and comparison to our state-of-the-art method.
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Abstract
PURPOSE OF REVIEW Magnetic resonance imaging (MRI) is routinely employed in the diagnosis and clinical management of brain tumors. This review provides an overview of the advancements in the field of MRI, with a particular focus on the quantitative assessment by advanced physiological magnetic resonance techniques in light of the new molecular classification of brain tumor. RECENT FINDINGS Understanding how molecular phenotypes of brain tumors are reflected in noninvasive imaging is the goal of radiogenomics, which aims at determining the association between imaging features and molecular markers in neuro-oncology. Advanced MRI techniques such as diffusion magnetic resonance imaging and perfusion-weighted imaging add important structural, hemodynamic, and physiological information for tumor diagnosis and classification, as well as to stratify tumor response. Magnetic resonance spectroscopy is able to depict with unprecedented accuracy metabolic biomarkers, which are relevant for molecular subtyping. Ultra-high-field imaging enhances anatomical detail and enables to explore new horizon in tumor imaging. SUMMARY The noninvasive MRI-based assessment of tumor malignancy and molecular status may offer the opportunity to predict prognosis and to select patients who may be candidates for individualized targeted therapies, providing more sensitive tools for their follow-up.
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Bakas S, Akbari H, Pisapia J, Martinez-Lage M, Rozycki M, Rathore S, Dahmane N, O'Rourke DM, Davatzikos C. In Vivo Detection of EGFRvIII in Glioblastoma via Perfusion Magnetic Resonance Imaging Signature Consistent with Deep Peritumoral Infiltration: The φ-Index. Clin Cancer Res 2017; 23:4724-4734. [PMID: 28428190 DOI: 10.1158/1078-0432.ccr-16-1871] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2016] [Revised: 11/30/2016] [Accepted: 04/17/2017] [Indexed: 12/24/2022]
Abstract
Purpose: The epidermal growth factor receptor variant III (EGFRvIII) mutation has been considered a driver mutation and therapeutic target in glioblastoma, the most common and aggressive brain cancer. Currently, detecting EGFRvIII requires postoperative tissue analyses, which are ex vivo and unable to capture the tumor's spatial heterogeneity. Considering the increasing evidence of in vivo imaging signatures capturing molecular characteristics of cancer, this study aims to detect EGFRvIII in primary glioblastoma noninvasively, using routine clinically acquired imaging.Experimental Design: We found peritumoral infiltration and vascularization patterns being related to EGFRvIII status. We therefore constructed a quantitative within-patient peritumoral heterogeneity index (PHI/φ-index), by contrasting perfusion patterns of immediate and distant peritumoral edema. Application of φ-index in preoperative perfusion scans of independent discovery (n = 64) and validation (n = 78) cohorts, revealed the generalizability of this EGFRvIII imaging signature.Results: Analysis in both cohorts demonstrated that the obtained signature is highly accurate (89.92%), specific (92.35%), and sensitive (83.77%), with significantly distinctive ability (P = 4.0033 × 10-10, AUC = 0.8869). Findings indicated a highly infiltrative-migratory phenotype for EGFRvIII+ tumors, which displayed similar perfusion patterns throughout peritumoral edema. Contrarily, EGFRvIII- tumors displayed perfusion dynamics consistent with peritumorally confined vascularization, suggesting potential benefit from extensive peritumoral resection/radiation.Conclusions: This EGFRvIII signature is potentially suitable for clinical translation, since obtained from analysis of clinically acquired images. Use of within-patient heterogeneity measures, rather than population-based associations, renders φ-index potentially resistant to inter-scanner variations. Overall, our findings enable noninvasive evaluation of EGFRvIII for patient selection for targeted therapy, stratification into clinical trials, personalized treatment planning, and potentially treatment-response evaluation. Clin Cancer Res; 23(16); 4724-34. ©2017 AACR.
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Affiliation(s)
- Spyridon Bakas
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Hamed Akbari
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Jared Pisapia
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.,Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Maria Martinez-Lage
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Martin Rozycki
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Saima Rathore
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Nadia Dahmane
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Donald M O'Rourke
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania. .,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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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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Lin X, Lee M, Buck O, Woo KM, Zhang Z, Hatzoglou V, Omuro A, Arevalo-Perez J, Thomas AA, Huse J, Peck K, Holodny AI, Young RJ. Diagnostic Accuracy of T1-Weighted Dynamic Contrast-Enhanced-MRI and DWI-ADC for Differentiation of Glioblastoma and Primary CNS Lymphoma. AJNR Am J Neuroradiol 2016; 38:485-491. [PMID: 27932505 DOI: 10.3174/ajnr.a5023] [Citation(s) in RCA: 56] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2016] [Accepted: 10/07/2016] [Indexed: 01/20/2023]
Abstract
BACKGROUND AND PURPOSE Glioblastoma and primary CNS lymphoma dictate different neurosurgical strategies; it is critical to distinguish them preoperatively. However, current imaging modalities do not effectively differentiate them. We aimed to examine the use of DWI and T1-weighted dynamic contrast-enhanced-MR imaging as potential discriminative tools. MATERIALS AND METHODS We retrospectively reviewed 18 patients with primary CNS lymphoma and 36 matched patients with glioblastoma with pretreatment DWI and dynamic contrast-enhanced-MR imaging. VOIs were drawn around the tumor on contrast-enhanced T1WI and FLAIR images; these images were transferred onto coregistered ADC maps to obtain the ADC and onto dynamic contrast-enhanced perfusion maps to obtain the plasma volume and permeability transfer constant. Histogram analysis was performed to determine the mean and relative ADCmean and relative 90th percentile values for plasma volume and the permeability transfer constant. Nonparametric tests were used to assess differences, and receiver operating characteristic analysis was performed for optimal threshold calculations. RESULTS The enhancing component of primary CNS lymphoma was found to have significantly lower ADCmean (1.1 × 10-3 versus 1.4 × 10-3; P < .001) and relative ADCmean (1.5 versus 1.9; P < .001) and relative 90th percentile values for plasma volume (3.7 versus 5.0; P < .05) than the enhancing component of glioblastoma, but not significantly different relative 90th percentile values for the permeability transfer constant (5.4 versus 4.4; P = .83). The nonenhancing portions of glioblastoma and primary CNS lymphoma did not differ in these parameters. On the basis of receiver operating characteristic analysis, mean ADC provided the best threshold (area under the curve = 0.83) to distinguish primary CNS lymphoma from glioblastoma, which was not improved with normalized ADC or the addition of perfusion parameters. CONCLUSIONS ADC was superior to dynamic contrast-enhanced-MR imaging perfusion, alone or in combination, in differentiating primary CNS lymphoma from glioblastoma.
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Affiliation(s)
- X Lin
- From the Departments of Neurology (X.L., A.O., A.A.T.).,Department of Neurology (X.L.), National Neuroscience Institute, Singapore
| | - M Lee
- Radiology (M.L., O.B., V.H., J.A.-P., A.I.H., R.J.Y.)
| | - O Buck
- Radiology (M.L., O.B., V.H., J.A.-P., A.I.H., R.J.Y.)
| | - K M Woo
- Epidemiology and Biostatistics (K.M.W., Z.Z.)
| | - Z Zhang
- Epidemiology and Biostatistics (K.M.W., Z.Z.)
| | - V Hatzoglou
- Radiology (M.L., O.B., V.H., J.A.-P., A.I.H., R.J.Y.).,The Brain Tumor Center (V.H., A.O., A.I.H., R.J.Y.), Memorial Sloan Kettering Cancer Center, New York, New York
| | - A Omuro
- From the Departments of Neurology (X.L., A.O., A.A.T.).,The Brain Tumor Center (V.H., A.O., A.I.H., R.J.Y.), Memorial Sloan Kettering Cancer Center, New York, New York
| | | | - A A Thomas
- From the Departments of Neurology (X.L., A.O., A.A.T.)
| | | | | | - A I Holodny
- Radiology (M.L., O.B., V.H., J.A.-P., A.I.H., R.J.Y.).,The Brain Tumor Center (V.H., A.O., A.I.H., R.J.Y.), Memorial Sloan Kettering Cancer Center, New York, New York
| | - R J Young
- Radiology (M.L., O.B., V.H., J.A.-P., A.I.H., R.J.Y.) .,The Brain Tumor Center (V.H., A.O., A.I.H., R.J.Y.), Memorial Sloan Kettering Cancer Center, New York, New York
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Kickingereder P, Bonekamp D, Nowosielski M, Kratz A, Sill M, Burth S, Wick A, Eidel O, Schlemmer HP, Radbruch A, Debus J, Herold-Mende C, Unterberg A, Jones D, Pfister S, Wick W, von Deimling A, Bendszus M, Capper D. Radiogenomics of Glioblastoma: Machine Learning-based Classification of Molecular Characteristics by Using Multiparametric and Multiregional MR Imaging Features. Radiology 2016; 281:907-918. [PMID: 27636026 DOI: 10.1148/radiol.2016161382] [Citation(s) in RCA: 204] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Purpose To evaluate the association of multiparametric and multiregional magnetic resonance (MR) imaging features with key molecular characteristics in patients with newly diagnosed glioblastoma. Materials and Methods Retrospective data evaluation was approved by the local ethics committee, and the requirement to obtain informed consent was waived. Preoperative MR imaging features were correlated with key molecular characteristics within a single-institution cohort of 152 patients with newly diagnosed glioblastoma. Preoperative MR imaging features (n = 31) included multiparametric (anatomic and diffusion-, perfusion-, and susceptibility-weighted images) and multiregional (contrast-enhancing regions and hyperintense regions at nonenhanced fluid-attenuated inversion recovery imaging) information with histogram quantification of tumor volumes, volume ratios, apparent diffusion coefficients, cerebral blood flow, cerebral blood volume, and intratumoral susceptibility signals. Molecular characteristics determined included global DNA methylation subgroups (eg, mesenchymal, RTK I "PGFRA," RTK II "classic"), MGMT promoter methylation status, and hallmark copy number variations (EGFR, PDGFRA, MDM4, and CDK4 amplification; PTEN, CDKN2A, NF1, and RB1 loss). Univariate analyses (voxel-lesion symptom mapping for tumor location, Wilcoxon test for all other MR imaging features) and machine learning models were applied to study the strength of association and discriminative value of MR imaging features for predicting underlying molecular characteristics. Results There was no tumor location predilection for any of the assessed molecular parameters (permutation-adjusted P > .05). Univariate imaging parameter associations were noted for EGFR amplification and CDKN2A loss, with both demonstrating increased Gaussian-normalized relative cerebral blood volume and Gaussian-normalized relative cerebral blood flow values (area under the receiver operating characteristics curve: 63%-69%, false discovery rate-adjusted P < .05). Subjecting all MR imaging features to machine learning-based classification enabled prediction of EGFR amplification status and the RTK II glioblastoma subgroup with a moderate, yet significantly greater, accuracy (63% for EGFR [P < .01], 61% for RTK II [P = .01]) than prediction by chance; prediction accuracy for all other molecular parameters was not significant. Conclusion The authors found associations between established MR imaging features and molecular characteristics, although not of sufficient strength to enable generation of machine learning classification models for reliable and clinically meaningful prediction of molecular characteristics in patients with glioblastoma. © RSNA, 2016 Online supplemental material is available for this article.
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Affiliation(s)
- Philipp Kickingereder
- From the Department of Neuroradiology, University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., S.B., O.E., M.B.); German Cancer Research Center (DKFZ), Department of Radiology, Heidelberg, Germany (D.B., H.P.S., A.R.); Department of Neurology, Medical University Innsbruck, Austria (M.N.); Department of Neuropathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.K., A.v.D., D.C.); German Cancer Consortium (DKTK), Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (A.K., A.v.D., D.C.); Division of Biostatistics, DKFZ, Heidelberg, Germany (M.S.); Neurology Clinic, University of Heidelberg Medical Center, Heidelberg, Germany (M.N., S.B., A.W., W.W.); Department of Radiation Oncology, University of Heidelberg Medical Center, Heidelberg, Germany (J.D.); Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (J.D.); Division of Neurosurgical Research, Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (C.H.M.); Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (A.U.); Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (D.J., S.P.); German Cancer Consortium (DKTK) Core Center Heidelberg, Heidelberg, Germany (D.J., S.P.); Department of Pediatric Oncology, Hematology and Immunology, Heidelberg University Hospital, Heidelberg, Germany (S.P.); and German Cancer Consortium (DKTK), Clinical Cooperation Unit Neurooncology, DKFZ, Heidelberg, Germany (W.W.)
| | - David Bonekamp
- From the Department of Neuroradiology, University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., S.B., O.E., M.B.); German Cancer Research Center (DKFZ), Department of Radiology, Heidelberg, Germany (D.B., H.P.S., A.R.); Department of Neurology, Medical University Innsbruck, Austria (M.N.); Department of Neuropathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.K., A.v.D., D.C.); German Cancer Consortium (DKTK), Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (A.K., A.v.D., D.C.); Division of Biostatistics, DKFZ, Heidelberg, Germany (M.S.); Neurology Clinic, University of Heidelberg Medical Center, Heidelberg, Germany (M.N., S.B., A.W., W.W.); Department of Radiation Oncology, University of Heidelberg Medical Center, Heidelberg, Germany (J.D.); Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (J.D.); Division of Neurosurgical Research, Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (C.H.M.); Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (A.U.); Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (D.J., S.P.); German Cancer Consortium (DKTK) Core Center Heidelberg, Heidelberg, Germany (D.J., S.P.); Department of Pediatric Oncology, Hematology and Immunology, Heidelberg University Hospital, Heidelberg, Germany (S.P.); and German Cancer Consortium (DKTK), Clinical Cooperation Unit Neurooncology, DKFZ, Heidelberg, Germany (W.W.)
| | - Martha Nowosielski
- From the Department of Neuroradiology, University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., S.B., O.E., M.B.); German Cancer Research Center (DKFZ), Department of Radiology, Heidelberg, Germany (D.B., H.P.S., A.R.); Department of Neurology, Medical University Innsbruck, Austria (M.N.); Department of Neuropathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.K., A.v.D., D.C.); German Cancer Consortium (DKTK), Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (A.K., A.v.D., D.C.); Division of Biostatistics, DKFZ, Heidelberg, Germany (M.S.); Neurology Clinic, University of Heidelberg Medical Center, Heidelberg, Germany (M.N., S.B., A.W., W.W.); Department of Radiation Oncology, University of Heidelberg Medical Center, Heidelberg, Germany (J.D.); Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (J.D.); Division of Neurosurgical Research, Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (C.H.M.); Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (A.U.); Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (D.J., S.P.); German Cancer Consortium (DKTK) Core Center Heidelberg, Heidelberg, Germany (D.J., S.P.); Department of Pediatric Oncology, Hematology and Immunology, Heidelberg University Hospital, Heidelberg, Germany (S.P.); and German Cancer Consortium (DKTK), Clinical Cooperation Unit Neurooncology, DKFZ, Heidelberg, Germany (W.W.)
| | - Annekathrin Kratz
- From the Department of Neuroradiology, University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., S.B., O.E., M.B.); German Cancer Research Center (DKFZ), Department of Radiology, Heidelberg, Germany (D.B., H.P.S., A.R.); Department of Neurology, Medical University Innsbruck, Austria (M.N.); Department of Neuropathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.K., A.v.D., D.C.); German Cancer Consortium (DKTK), Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (A.K., A.v.D., D.C.); Division of Biostatistics, DKFZ, Heidelberg, Germany (M.S.); Neurology Clinic, University of Heidelberg Medical Center, Heidelberg, Germany (M.N., S.B., A.W., W.W.); Department of Radiation Oncology, University of Heidelberg Medical Center, Heidelberg, Germany (J.D.); Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (J.D.); Division of Neurosurgical Research, Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (C.H.M.); Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (A.U.); Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (D.J., S.P.); German Cancer Consortium (DKTK) Core Center Heidelberg, Heidelberg, Germany (D.J., S.P.); Department of Pediatric Oncology, Hematology and Immunology, Heidelberg University Hospital, Heidelberg, Germany (S.P.); and German Cancer Consortium (DKTK), Clinical Cooperation Unit Neurooncology, DKFZ, Heidelberg, Germany (W.W.)
| | - Martin Sill
- From the Department of Neuroradiology, University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., S.B., O.E., M.B.); German Cancer Research Center (DKFZ), Department of Radiology, Heidelberg, Germany (D.B., H.P.S., A.R.); Department of Neurology, Medical University Innsbruck, Austria (M.N.); Department of Neuropathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.K., A.v.D., D.C.); German Cancer Consortium (DKTK), Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (A.K., A.v.D., D.C.); Division of Biostatistics, DKFZ, Heidelberg, Germany (M.S.); Neurology Clinic, University of Heidelberg Medical Center, Heidelberg, Germany (M.N., S.B., A.W., W.W.); Department of Radiation Oncology, University of Heidelberg Medical Center, Heidelberg, Germany (J.D.); Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (J.D.); Division of Neurosurgical Research, Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (C.H.M.); Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (A.U.); Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (D.J., S.P.); German Cancer Consortium (DKTK) Core Center Heidelberg, Heidelberg, Germany (D.J., S.P.); Department of Pediatric Oncology, Hematology and Immunology, Heidelberg University Hospital, Heidelberg, Germany (S.P.); and German Cancer Consortium (DKTK), Clinical Cooperation Unit Neurooncology, DKFZ, Heidelberg, Germany (W.W.)
| | - Sina Burth
- From the Department of Neuroradiology, University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., S.B., O.E., M.B.); German Cancer Research Center (DKFZ), Department of Radiology, Heidelberg, Germany (D.B., H.P.S., A.R.); Department of Neurology, Medical University Innsbruck, Austria (M.N.); Department of Neuropathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.K., A.v.D., D.C.); German Cancer Consortium (DKTK), Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (A.K., A.v.D., D.C.); Division of Biostatistics, DKFZ, Heidelberg, Germany (M.S.); Neurology Clinic, University of Heidelberg Medical Center, Heidelberg, Germany (M.N., S.B., A.W., W.W.); Department of Radiation Oncology, University of Heidelberg Medical Center, Heidelberg, Germany (J.D.); Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (J.D.); Division of Neurosurgical Research, Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (C.H.M.); Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (A.U.); Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (D.J., S.P.); German Cancer Consortium (DKTK) Core Center Heidelberg, Heidelberg, Germany (D.J., S.P.); Department of Pediatric Oncology, Hematology and Immunology, Heidelberg University Hospital, Heidelberg, Germany (S.P.); and German Cancer Consortium (DKTK), Clinical Cooperation Unit Neurooncology, DKFZ, Heidelberg, Germany (W.W.)
| | - Antje Wick
- From the Department of Neuroradiology, University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., S.B., O.E., M.B.); German Cancer Research Center (DKFZ), Department of Radiology, Heidelberg, Germany (D.B., H.P.S., A.R.); Department of Neurology, Medical University Innsbruck, Austria (M.N.); Department of Neuropathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.K., A.v.D., D.C.); German Cancer Consortium (DKTK), Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (A.K., A.v.D., D.C.); Division of Biostatistics, DKFZ, Heidelberg, Germany (M.S.); Neurology Clinic, University of Heidelberg Medical Center, Heidelberg, Germany (M.N., S.B., A.W., W.W.); Department of Radiation Oncology, University of Heidelberg Medical Center, Heidelberg, Germany (J.D.); Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (J.D.); Division of Neurosurgical Research, Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (C.H.M.); Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (A.U.); Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (D.J., S.P.); German Cancer Consortium (DKTK) Core Center Heidelberg, Heidelberg, Germany (D.J., S.P.); Department of Pediatric Oncology, Hematology and Immunology, Heidelberg University Hospital, Heidelberg, Germany (S.P.); and German Cancer Consortium (DKTK), Clinical Cooperation Unit Neurooncology, DKFZ, Heidelberg, Germany (W.W.)
| | - Oliver Eidel
- From the Department of Neuroradiology, University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., S.B., O.E., M.B.); German Cancer Research Center (DKFZ), Department of Radiology, Heidelberg, Germany (D.B., H.P.S., A.R.); Department of Neurology, Medical University Innsbruck, Austria (M.N.); Department of Neuropathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.K., A.v.D., D.C.); German Cancer Consortium (DKTK), Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (A.K., A.v.D., D.C.); Division of Biostatistics, DKFZ, Heidelberg, Germany (M.S.); Neurology Clinic, University of Heidelberg Medical Center, Heidelberg, Germany (M.N., S.B., A.W., W.W.); Department of Radiation Oncology, University of Heidelberg Medical Center, Heidelberg, Germany (J.D.); Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (J.D.); Division of Neurosurgical Research, Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (C.H.M.); Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (A.U.); Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (D.J., S.P.); German Cancer Consortium (DKTK) Core Center Heidelberg, Heidelberg, Germany (D.J., S.P.); Department of Pediatric Oncology, Hematology and Immunology, Heidelberg University Hospital, Heidelberg, Germany (S.P.); and German Cancer Consortium (DKTK), Clinical Cooperation Unit Neurooncology, DKFZ, Heidelberg, Germany (W.W.)
| | - Heinz-Peter Schlemmer
- From the Department of Neuroradiology, University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., S.B., O.E., M.B.); German Cancer Research Center (DKFZ), Department of Radiology, Heidelberg, Germany (D.B., H.P.S., A.R.); Department of Neurology, Medical University Innsbruck, Austria (M.N.); Department of Neuropathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.K., A.v.D., D.C.); German Cancer Consortium (DKTK), Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (A.K., A.v.D., D.C.); Division of Biostatistics, DKFZ, Heidelberg, Germany (M.S.); Neurology Clinic, University of Heidelberg Medical Center, Heidelberg, Germany (M.N., S.B., A.W., W.W.); Department of Radiation Oncology, University of Heidelberg Medical Center, Heidelberg, Germany (J.D.); Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (J.D.); Division of Neurosurgical Research, Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (C.H.M.); Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (A.U.); Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (D.J., S.P.); German Cancer Consortium (DKTK) Core Center Heidelberg, Heidelberg, Germany (D.J., S.P.); Department of Pediatric Oncology, Hematology and Immunology, Heidelberg University Hospital, Heidelberg, Germany (S.P.); and German Cancer Consortium (DKTK), Clinical Cooperation Unit Neurooncology, DKFZ, Heidelberg, Germany (W.W.)
| | - Alexander Radbruch
- From the Department of Neuroradiology, University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., S.B., O.E., M.B.); German Cancer Research Center (DKFZ), Department of Radiology, Heidelberg, Germany (D.B., H.P.S., A.R.); Department of Neurology, Medical University Innsbruck, Austria (M.N.); Department of Neuropathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.K., A.v.D., D.C.); German Cancer Consortium (DKTK), Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (A.K., A.v.D., D.C.); Division of Biostatistics, DKFZ, Heidelberg, Germany (M.S.); Neurology Clinic, University of Heidelberg Medical Center, Heidelberg, Germany (M.N., S.B., A.W., W.W.); Department of Radiation Oncology, University of Heidelberg Medical Center, Heidelberg, Germany (J.D.); Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (J.D.); Division of Neurosurgical Research, Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (C.H.M.); Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (A.U.); Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (D.J., S.P.); German Cancer Consortium (DKTK) Core Center Heidelberg, Heidelberg, Germany (D.J., S.P.); Department of Pediatric Oncology, Hematology and Immunology, Heidelberg University Hospital, Heidelberg, Germany (S.P.); and German Cancer Consortium (DKTK), Clinical Cooperation Unit Neurooncology, DKFZ, Heidelberg, Germany (W.W.)
| | - Jürgen Debus
- From the Department of Neuroradiology, University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., S.B., O.E., M.B.); German Cancer Research Center (DKFZ), Department of Radiology, Heidelberg, Germany (D.B., H.P.S., A.R.); Department of Neurology, Medical University Innsbruck, Austria (M.N.); Department of Neuropathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.K., A.v.D., D.C.); German Cancer Consortium (DKTK), Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (A.K., A.v.D., D.C.); Division of Biostatistics, DKFZ, Heidelberg, Germany (M.S.); Neurology Clinic, University of Heidelberg Medical Center, Heidelberg, Germany (M.N., S.B., A.W., W.W.); Department of Radiation Oncology, University of Heidelberg Medical Center, Heidelberg, Germany (J.D.); Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (J.D.); Division of Neurosurgical Research, Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (C.H.M.); Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (A.U.); Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (D.J., S.P.); German Cancer Consortium (DKTK) Core Center Heidelberg, Heidelberg, Germany (D.J., S.P.); Department of Pediatric Oncology, Hematology and Immunology, Heidelberg University Hospital, Heidelberg, Germany (S.P.); and German Cancer Consortium (DKTK), Clinical Cooperation Unit Neurooncology, DKFZ, Heidelberg, Germany (W.W.)
| | - Christel Herold-Mende
- From the Department of Neuroradiology, University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., S.B., O.E., M.B.); German Cancer Research Center (DKFZ), Department of Radiology, Heidelberg, Germany (D.B., H.P.S., A.R.); Department of Neurology, Medical University Innsbruck, Austria (M.N.); Department of Neuropathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.K., A.v.D., D.C.); German Cancer Consortium (DKTK), Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (A.K., A.v.D., D.C.); Division of Biostatistics, DKFZ, Heidelberg, Germany (M.S.); Neurology Clinic, University of Heidelberg Medical Center, Heidelberg, Germany (M.N., S.B., A.W., W.W.); Department of Radiation Oncology, University of Heidelberg Medical Center, Heidelberg, Germany (J.D.); Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (J.D.); Division of Neurosurgical Research, Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (C.H.M.); Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (A.U.); Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (D.J., S.P.); German Cancer Consortium (DKTK) Core Center Heidelberg, Heidelberg, Germany (D.J., S.P.); Department of Pediatric Oncology, Hematology and Immunology, Heidelberg University Hospital, Heidelberg, Germany (S.P.); and German Cancer Consortium (DKTK), Clinical Cooperation Unit Neurooncology, DKFZ, Heidelberg, Germany (W.W.)
| | - Andreas Unterberg
- From the Department of Neuroradiology, University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., S.B., O.E., M.B.); German Cancer Research Center (DKFZ), Department of Radiology, Heidelberg, Germany (D.B., H.P.S., A.R.); Department of Neurology, Medical University Innsbruck, Austria (M.N.); Department of Neuropathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.K., A.v.D., D.C.); German Cancer Consortium (DKTK), Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (A.K., A.v.D., D.C.); Division of Biostatistics, DKFZ, Heidelberg, Germany (M.S.); Neurology Clinic, University of Heidelberg Medical Center, Heidelberg, Germany (M.N., S.B., A.W., W.W.); Department of Radiation Oncology, University of Heidelberg Medical Center, Heidelberg, Germany (J.D.); Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (J.D.); Division of Neurosurgical Research, Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (C.H.M.); Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (A.U.); Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (D.J., S.P.); German Cancer Consortium (DKTK) Core Center Heidelberg, Heidelberg, Germany (D.J., S.P.); Department of Pediatric Oncology, Hematology and Immunology, Heidelberg University Hospital, Heidelberg, Germany (S.P.); and German Cancer Consortium (DKTK), Clinical Cooperation Unit Neurooncology, DKFZ, Heidelberg, Germany (W.W.)
| | - David Jones
- From the Department of Neuroradiology, University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., S.B., O.E., M.B.); German Cancer Research Center (DKFZ), Department of Radiology, Heidelberg, Germany (D.B., H.P.S., A.R.); Department of Neurology, Medical University Innsbruck, Austria (M.N.); Department of Neuropathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.K., A.v.D., D.C.); German Cancer Consortium (DKTK), Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (A.K., A.v.D., D.C.); Division of Biostatistics, DKFZ, Heidelberg, Germany (M.S.); Neurology Clinic, University of Heidelberg Medical Center, Heidelberg, Germany (M.N., S.B., A.W., W.W.); Department of Radiation Oncology, University of Heidelberg Medical Center, Heidelberg, Germany (J.D.); Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (J.D.); Division of Neurosurgical Research, Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (C.H.M.); Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (A.U.); Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (D.J., S.P.); German Cancer Consortium (DKTK) Core Center Heidelberg, Heidelberg, Germany (D.J., S.P.); Department of Pediatric Oncology, Hematology and Immunology, Heidelberg University Hospital, Heidelberg, Germany (S.P.); and German Cancer Consortium (DKTK), Clinical Cooperation Unit Neurooncology, DKFZ, Heidelberg, Germany (W.W.)
| | - Stefan Pfister
- From the Department of Neuroradiology, University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., S.B., O.E., M.B.); German Cancer Research Center (DKFZ), Department of Radiology, Heidelberg, Germany (D.B., H.P.S., A.R.); Department of Neurology, Medical University Innsbruck, Austria (M.N.); Department of Neuropathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.K., A.v.D., D.C.); German Cancer Consortium (DKTK), Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (A.K., A.v.D., D.C.); Division of Biostatistics, DKFZ, Heidelberg, Germany (M.S.); Neurology Clinic, University of Heidelberg Medical Center, Heidelberg, Germany (M.N., S.B., A.W., W.W.); Department of Radiation Oncology, University of Heidelberg Medical Center, Heidelberg, Germany (J.D.); Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (J.D.); Division of Neurosurgical Research, Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (C.H.M.); Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (A.U.); Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (D.J., S.P.); German Cancer Consortium (DKTK) Core Center Heidelberg, Heidelberg, Germany (D.J., S.P.); Department of Pediatric Oncology, Hematology and Immunology, Heidelberg University Hospital, Heidelberg, Germany (S.P.); and German Cancer Consortium (DKTK), Clinical Cooperation Unit Neurooncology, DKFZ, Heidelberg, Germany (W.W.)
| | - Wolfgang Wick
- From the Department of Neuroradiology, University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., S.B., O.E., M.B.); German Cancer Research Center (DKFZ), Department of Radiology, Heidelberg, Germany (D.B., H.P.S., A.R.); Department of Neurology, Medical University Innsbruck, Austria (M.N.); Department of Neuropathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.K., A.v.D., D.C.); German Cancer Consortium (DKTK), Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (A.K., A.v.D., D.C.); Division of Biostatistics, DKFZ, Heidelberg, Germany (M.S.); Neurology Clinic, University of Heidelberg Medical Center, Heidelberg, Germany (M.N., S.B., A.W., W.W.); Department of Radiation Oncology, University of Heidelberg Medical Center, Heidelberg, Germany (J.D.); Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (J.D.); Division of Neurosurgical Research, Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (C.H.M.); Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (A.U.); Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (D.J., S.P.); German Cancer Consortium (DKTK) Core Center Heidelberg, Heidelberg, Germany (D.J., S.P.); Department of Pediatric Oncology, Hematology and Immunology, Heidelberg University Hospital, Heidelberg, Germany (S.P.); and German Cancer Consortium (DKTK), Clinical Cooperation Unit Neurooncology, DKFZ, Heidelberg, Germany (W.W.)
| | - Andreas von Deimling
- From the Department of Neuroradiology, University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., S.B., O.E., M.B.); German Cancer Research Center (DKFZ), Department of Radiology, Heidelberg, Germany (D.B., H.P.S., A.R.); Department of Neurology, Medical University Innsbruck, Austria (M.N.); Department of Neuropathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.K., A.v.D., D.C.); German Cancer Consortium (DKTK), Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (A.K., A.v.D., D.C.); Division of Biostatistics, DKFZ, Heidelberg, Germany (M.S.); Neurology Clinic, University of Heidelberg Medical Center, Heidelberg, Germany (M.N., S.B., A.W., W.W.); Department of Radiation Oncology, University of Heidelberg Medical Center, Heidelberg, Germany (J.D.); Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (J.D.); Division of Neurosurgical Research, Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (C.H.M.); Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (A.U.); Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (D.J., S.P.); German Cancer Consortium (DKTK) Core Center Heidelberg, Heidelberg, Germany (D.J., S.P.); Department of Pediatric Oncology, Hematology and Immunology, Heidelberg University Hospital, Heidelberg, Germany (S.P.); and German Cancer Consortium (DKTK), Clinical Cooperation Unit Neurooncology, DKFZ, Heidelberg, Germany (W.W.)
| | - Martin Bendszus
- From the Department of Neuroradiology, University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., S.B., O.E., M.B.); German Cancer Research Center (DKFZ), Department of Radiology, Heidelberg, Germany (D.B., H.P.S., A.R.); Department of Neurology, Medical University Innsbruck, Austria (M.N.); Department of Neuropathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.K., A.v.D., D.C.); German Cancer Consortium (DKTK), Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (A.K., A.v.D., D.C.); Division of Biostatistics, DKFZ, Heidelberg, Germany (M.S.); Neurology Clinic, University of Heidelberg Medical Center, Heidelberg, Germany (M.N., S.B., A.W., W.W.); Department of Radiation Oncology, University of Heidelberg Medical Center, Heidelberg, Germany (J.D.); Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (J.D.); Division of Neurosurgical Research, Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (C.H.M.); Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (A.U.); Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (D.J., S.P.); German Cancer Consortium (DKTK) Core Center Heidelberg, Heidelberg, Germany (D.J., S.P.); Department of Pediatric Oncology, Hematology and Immunology, Heidelberg University Hospital, Heidelberg, Germany (S.P.); and German Cancer Consortium (DKTK), Clinical Cooperation Unit Neurooncology, DKFZ, Heidelberg, Germany (W.W.)
| | - David Capper
- From the Department of Neuroradiology, University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., S.B., O.E., M.B.); German Cancer Research Center (DKFZ), Department of Radiology, Heidelberg, Germany (D.B., H.P.S., A.R.); Department of Neurology, Medical University Innsbruck, Austria (M.N.); Department of Neuropathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.K., A.v.D., D.C.); German Cancer Consortium (DKTK), Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (A.K., A.v.D., D.C.); Division of Biostatistics, DKFZ, Heidelberg, Germany (M.S.); Neurology Clinic, University of Heidelberg Medical Center, Heidelberg, Germany (M.N., S.B., A.W., W.W.); Department of Radiation Oncology, University of Heidelberg Medical Center, Heidelberg, Germany (J.D.); Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (J.D.); Division of Neurosurgical Research, Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (C.H.M.); Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany (A.U.); Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (D.J., S.P.); German Cancer Consortium (DKTK) Core Center Heidelberg, Heidelberg, Germany (D.J., S.P.); Department of Pediatric Oncology, Hematology and Immunology, Heidelberg University Hospital, Heidelberg, Germany (S.P.); and German Cancer Consortium (DKTK), Clinical Cooperation Unit Neurooncology, DKFZ, Heidelberg, Germany (W.W.)
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39
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Vajapeyam S, Stamoulis C, Ricci K, Kieran M, Poussaint TY. Automated Processing of Dynamic Contrast-Enhanced MRI: Correlation of Advanced Pharmacokinetic Metrics with Tumor Grade in Pediatric Brain Tumors. AJNR Am J Neuroradiol 2016; 38:170-175. [PMID: 27633807 DOI: 10.3174/ajnr.a4949] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Accepted: 08/01/2016] [Indexed: 12/29/2022]
Abstract
BACKGROUND AND PURPOSE Pharmacokinetic parameters from dynamic contrast-enhanced MR imaging have proved useful for differentiating brain tumor grades in adults. In this study, we retrospectively reviewed dynamic contrast-enhanced perfusion data from children with newly diagnosed brain tumors and analyzed the pharmacokinetic parameters correlating with tumor grade. MATERIALS AND METHODS Dynamic contrast-enhanced MR imaging data from 38 patients were analyzed by using commercially available software. Subjects were categorized into 2 groups based on pathologic analyses consisting of low-grade (World Health Organization I and II) and high-grade (World Health Organization III and IV) tumors. Pharmacokinetic parameters were compared between the 2 groups by using linear regression models. For parameters that were statistically distinct between the 2 groups, sensitivity and specificity were also estimated. RESULTS Eighteen tumors were classified as low-grade, and 20, as high-grade. Transfer constant from the blood plasma into the extracellular extravascular space (Ktrans), rate constant from extracellular extravascular space back into blood plasma (Kep), and extracellular extravascular volume fraction (Ve) were all significantly correlated with tumor grade; high-grade tumors showed higher Ktrans, higher Kep, and lower Ve. Although all 3 parameters had high specificity (range, 82%-100%), Kep had the highest specificity for both grades. Optimal sensitivity was achieved for Ve, with a combined sensitivity of 76% (compared with 71% for Ktrans and Kep). CONCLUSIONS Pharmacokinetic parameters derived from dynamic contrast-enhanced MR imaging can effectively discriminate low- and high-grade pediatric brain tumors.
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Affiliation(s)
- S Vajapeyam
- From the Departments of Radiology (S.V., C.S., T.Y.P.) .,Harvard Medical School (S.V., C.S., M.K., T.Y.P.), Boston, Massachusetts
| | - C Stamoulis
- From the Departments of Radiology (S.V., C.S., T.Y.P.).,Neurology (C.S.), Boston Children's Hospital, Boston, Massachusetts.,Harvard Medical School (S.V., C.S., M.K., T.Y.P.), Boston, Massachusetts
| | - K Ricci
- Cancer Center (K.R.), Massachusetts General Hospital, Boston, Massachusetts
| | - M Kieran
- Department of Pediatric Oncology (M.K.), Dana-Farber Cancer Center, Boston, Massachusetts.,Harvard Medical School (S.V., C.S., M.K., T.Y.P.), Boston, Massachusetts
| | - T Young Poussaint
- From the Departments of Radiology (S.V., C.S., T.Y.P.).,Harvard Medical School (S.V., C.S., M.K., T.Y.P.), Boston, Massachusetts
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40
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Politi LS, Brugnara G, Castellano A, Cadioli M, Altabella L, Peviani M, Mazzoleni S, Falini A, Galli R. T1-Weighted Dynamic Contrast-Enhanced MRI Is a Noninvasive Marker of Epidermal Growth Factor Receptor vIII Status in Cancer Stem Cell-Derived Experimental Glioblastomas. AJNR Am J Neuroradiol 2016; 37:E49-51. [PMID: 26988813 DOI: 10.3174/ajnr.a4774] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- L S Politi
- Neuroimaging Research, Hematology/Oncology Division Boston Children's Hospital/Dana Farber Cancer Institute Boston, Massachusetts Radiology Department University of Massachusetts Medical School Worcester, Massachusetts Neuroradiology Unit and CERMAC Vita-Salute San Raffaele University and IRCCS San Raffaele Scientific Institute Milan, Italy
| | - G Brugnara
- Neuroradiology Unit and CERMAC Vita-Salute San Raffaele University and IRCCS San Raffaele Scientific Institute Milan, Italy
| | - A Castellano
- Neuroradiology Unit and CERMAC Vita-Salute San Raffaele University and IRCCS San Raffaele Scientific Institute Milan, Italy
| | - M Cadioli
- Neuroradiology Unit and CERMAC Vita-Salute San Raffaele University and IRCCS San Raffaele Scientific Institute Milan, Italy
| | - L Altabella
- Neuroradiology Unit and CERMAC Vita-Salute San Raffaele University and IRCCS San Raffaele Scientific Institute Milan, Italy
| | - M Peviani
- Neuroimaging Research, Hematology/Oncology Division Boston Children's Hospital/Dana Farber Cancer Institute Boston, Massachusetts Neuroradiology Unit and CERMAC Vita-Salute San Raffaele University and IRCCS San Raffaele Scientific Institute Milan, Italy
| | - S Mazzoleni
- Neural Stem Cell Biology Unit, Division of Regenerative Medicine, Stem Cells and Gene Therapy IRCCS San Raffaele Scientific Institute Milan, Italy
| | - A Falini
- Neuroradiology Unit and CERMAC Vita-Salute San Raffaele University and IRCCS San Raffaele Scientific Institute Milan, Italy
| | - R Galli
- Neural Stem Cell Biology Unit, Division of Regenerative Medicine, Stem Cells and Gene Therapy IRCCS San Raffaele Scientific Institute, Milan, Italy
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