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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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Anil A, Stokes AM, Karis JP, Bell LC, Eschbacher J, Jennings K, Prah MA, Hu LS, Boxerman JL, Schmainda KM, Quarles CC. Identification of a Single-Dose, Low-Flip-Angle-Based CBV Threshold for Fractional Tumor Burden Mapping in Recurrent Glioblastoma. AJNR Am J Neuroradiol 2024:ajnr.A8357. [PMID: 38782593 DOI: 10.3174/ajnr.a8357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 05/21/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND AND PURPOSE DSC-MR imaging can be used to generate fractional tumor burden (FTB) maps via application of relative CBV thresholds to spatially differentiate glioblastoma recurrence from posttreatment radiation effects (PTRE). Image-localized histopathology was previously used to validate FTB maps derived from a reference DSC-MR imaging protocol by using preload, a moderate flip angle (MFA, 60°), and postprocessing leakage correction. Recently, a DSC-MR imaging protocol with a low flip angle (LFA, 30°) with no preload was shown to provide leakage-corrected relative CBV (rCBV) equivalent to the reference protocol. This study aimed to identify the rCBV thresholds for the LFA protocol that generate the most accurate FTB maps, concordant with those obtained from the reference MFA protocol. MATERIALS AND METHODS Fifty-two patients with grade-IV glioblastoma who had prior surgical resection and received chemotherapy and radiation therapy were included in the study. Two sets of DSC-MR imaging data were collected sequentially first by using LFA protocol with no preload, which served as the preload for the subsequent MFA protocol. Standardized relative CBV maps (sRCBV) were obtained for each patient and coregistered with the anatomic postcontrast T1-weighted images. The reference MFA-based FTB maps were computed by using previously published sRCBV thresholds (1.0 and 1.56). A receiver operating characteristics (ROC) analysis was conducted to identify the optimal, voxelwise LFA sRCBV thresholds, and the sensitivity, specificity, and accuracy of the LFA-based FTB maps were computed with respect to the MFA-based reference. RESULTS The mean sRCBV values of tumors across patients exhibited strong agreement (concordance correlation coefficient = 0.99) between the 2 protocols. Using the ROC analysis, the optimal lower LFA threshold that accurately distinguishes PTRE from tumor recurrence was found to be 1.0 (sensitivity: 87.77%; specificity: 90.22%), equivalent to the ground truth. To identify aggressive tumor regions, the ROC analysis identified an upper LFA threshold of 1.37 (sensitivity: 90.87%; specificity: 91.10%) for the reference MFA threshold of 1.56. CONCLUSIONS For LFA-based FTB maps, an sRCBV threshold of 1.0 and 1.37 can differentiate PTRE from recurrent tumors. FTB maps aid in surgical planning, guiding pathologic diagnosis and treatment strategies in the recurrent setting. This study further confirms the reliability of single-dose LFA-based DSC-MR imaging.
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Affiliation(s)
- Aliya Anil
- From the Cancer System Imaging (A.A., C.C.Q.), The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Ashley M Stokes
- Division of Neuroimaging Research and Barrow Neuroimaging Innovation Center (A.M.S.), Barrow Neurological Institute, Phoenix, Arizona
| | - John P Karis
- Department of Neuroradiology (J.P.K.), Barrow Neurological Institute, Phoenix, Arizona
| | - Laura C Bell
- Clinical Imaging Group (L.C.B.), Genentech Inc., San Francisco, California
| | - Jennifer Eschbacher
- Department of Neuropathology (J.E.), Barrow Neurological Institute, Phoenix, Arizona
| | - Kristofer Jennings
- Department of Biostatistics (K.J.), The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Melissa A Prah
- Department of Biophysics (M.A.P., K.M.S.), Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Leland S Hu
- Department of Radiology (L.S.H.), Division of Neuroradiology, Mayo Clinic Arizona, Phoenix, Arizona
| | - Jerrold L Boxerman
- Department of Diagnostic Imaging (J.L.B.), Rhode Island Hospital, Providence, Rhode Island
| | - Kathleen M Schmainda
- Department of Biophysics (M.A.P., K.M.S.), Medical College of Wisconsin, Milwaukee, Wisconsin
| | - C Chad Quarles
- From the Cancer System Imaging (A.A., C.C.Q.), The University of Texas MD Anderson Cancer Center, Houston, Texas
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Li Z, Wang D, Ooi MB, Choudhary P, Ragunathan S, Karis JP, Pipe JG, Quarles CC, Stokes AM. A 3D dual-echo spiral sequence for simultaneous dynamic susceptibility contrast and dynamic contrast-enhanced MRI with single bolus injection. Magn Reson Med 2024; 92:631-644. [PMID: 38469930 PMCID: PMC11207201 DOI: 10.1002/mrm.30077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 02/20/2024] [Accepted: 02/20/2024] [Indexed: 03/13/2024]
Abstract
PURPOSE Perfusion MRI reveals important tumor physiological and pathophysiologic information, making it a critical component in managing brain tumor patients. This study aimed to develop a dual-echo 3D spiral technique with a single-bolus scheme to simultaneously acquire both dynamic susceptibility contrast (DSC) and dynamic contrast-enhanced (DCE) data and overcome the limitations of current EPI-based techniques. METHODS A 3D spiral-based technique with dual-echo acquisition was implemented and optimized on a 3T MRI scanner with a spiral staircase trajectory and through-plane SENSE acceleration for improved speed and image quality, in-plane variable-density undersampling combined with a sliding-window acquisition and reconstruction approach for increased speed, and an advanced iterative deblurring algorithm. Four volunteers were scanned and compared with the standard of care (SOC) single-echo EPI and a dual-echo EPI technique. Two patients were scanned with the spiral technique during a preload bolus and compared with the SOC single-echo EPI collected during the second bolus injection. RESULTS Volunteer data demonstrated that the spiral technique achieved high image quality, reduced geometric artifacts, and high temporal SNR compared with both single-echo and dual-echo EPI. Patient perfusion data showed that the spiral acquisition achieved accurate DSC quantification comparable to SOC single-echo dual-dose EPI, with the additional DCE information. CONCLUSION A 3D dual-echo spiral technique was developed to simultaneously acquire both DSC and DCE data in a single-bolus injection with reduced contrast use. Preliminary volunteer and patient data demonstrated increased temporal SNR, reduced geometric artifacts, and accurate perfusion quantification, suggesting a competitive alternative to SOC-EPI techniques for brain perfusion MRI.
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Affiliation(s)
- Zhiqiang Li
- Department of Neuroradiology, Barrow Neurological Institute, Phoenix, AZ USA
| | - Dinghui Wang
- Department of Radiology, Mayo Clinic, Rochester, MN USA
| | | | - Poonam Choudhary
- Department of Neuroradiology, Barrow Neurological Institute, Phoenix, AZ USA
| | | | - John P Karis
- Department of Neuroradiology, Barrow Neurological Institute, Phoenix, AZ USA
| | - James G Pipe
- Department of Radiology, Mayo Clinic, Rochester, MN USA
- Department of Radiology, University of Wisconsin, Madison, WI USA
| | - C Chad Quarles
- Department of Neuroradiology, Barrow Neurological Institute, Phoenix, AZ USA
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Ashley M Stokes
- Department of Neuroradiology, Barrow Neurological Institute, Phoenix, AZ USA
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Yamin G, Tranvinh E, Lanzman BA, Tong E, Hashmi SS, Patel CB, Iv M. Arterial Spin-Labeling and DSC Perfusion Metrics Improve Agreement in Neuroradiologists' Clinical Interpretations of Posttreatment High-Grade Glioma Surveillance MR Imaging-An Institutional Experience. AJNR Am J Neuroradiol 2024; 45:453-460. [PMID: 38453410 PMCID: PMC11288557 DOI: 10.3174/ajnr.a8190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 11/16/2023] [Indexed: 03/09/2024]
Abstract
BACKGROUND AND PURPOSE MR perfusion has shown value in the evaluation of posttreatment high-grade gliomas, but few studies have shown its impact on the consistency and confidence of neuroradiologists' interpretation in routine clinical practice. We evaluated the impact of adding MR perfusion metrics to conventional contrast-enhanced MR imaging in posttreatment high-grade glioma surveillance imaging. MATERIALS AND METHODS This retrospective study included 45 adults with high-grade gliomas who had posttreatment perfusion MR imaging. Four neuroradiologists assigned Brain Tumor Reporting and Data System scores for each examination on the basis of the interpretation of contrast-enhanced MR imaging and then after the addition of arterial spin-labeling-CBF, DSC-relative CBV, and DSC-fractional tumor burden. Interrater agreement and rater agreement with a multidisciplinary consensus group were assessed with κ statistics. Raters used a 5-point Likert scale to report confidence scores. The frequency of clinically meaningful score changes resulting from the addition of each perfusion metric was determined. RESULTS Interrater agreement was moderate for contrast-enhanced MR imaging alone (κ = 0.63) and higher with perfusion metrics (arterial spin-labeling-CBF, κ = 0.67; DSC-relative CBV, κ = 0.66; DSC-fractional tumor burden, κ = 0.70). Agreement between raters and consensus was highest with DSC-fractional tumor burden (κ = 0.66-0.80). Confidence scores were highest with DSC-fractional tumor burden. Across all raters, the addition of perfusion resulted in clinically meaningful interpretation changes in 2%-20% of patients compared with contrast-enhanced MR imaging alone. CONCLUSIONS Adding perfusion to contrast-enhanced MR imaging improved interrater agreement, rater agreement with consensus, and rater confidence in the interpretation of posttreatment high-grade glioma MR imaging, with the highest agreement and confidence scores seen with DSC-fractional tumor burden. Perfusion MR imaging also resulted in interpretation changes that could change therapeutic management in up to 20% of patients.
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Affiliation(s)
- Ghiam Yamin
- From the Department of Radiology (G.Y., E. Tranvinh, B.A.L., E. Tong, S.S.H., M.I.), Division of Neuroimaging and Neurointervention, Stanford University Medical Center, Stanford, California
| | - Eric Tranvinh
- From the Department of Radiology (G.Y., E. Tranvinh, B.A.L., E. Tong, S.S.H., M.I.), Division of Neuroimaging and Neurointervention, Stanford University Medical Center, Stanford, California
| | - Bryan A Lanzman
- From the Department of Radiology (G.Y., E. Tranvinh, B.A.L., E. Tong, S.S.H., M.I.), Division of Neuroimaging and Neurointervention, Stanford University Medical Center, Stanford, California
| | - Elizabeth Tong
- From the Department of Radiology (G.Y., E. Tranvinh, B.A.L., E. Tong, S.S.H., M.I.), Division of Neuroimaging and Neurointervention, Stanford University Medical Center, Stanford, California
| | - Syed S Hashmi
- From the Department of Radiology (G.Y., E. Tranvinh, B.A.L., E. Tong, S.S.H., M.I.), Division of Neuroimaging and Neurointervention, Stanford University Medical Center, Stanford, California
| | - Chirag B Patel
- Department of Neuro-Oncology (C.B.P.), The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Michael Iv
- From the Department of Radiology (G.Y., E. Tranvinh, B.A.L., E. Tong, S.S.H., M.I.), Division of Neuroimaging and Neurointervention, Stanford University Medical Center, Stanford, California
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Garcia-Ruiz A, Pons-Escoda A, Grussu F, Naval-Baudin P, Monreal-Aguero C, Hermann G, Karunamuni R, Ligero M, Lopez-Rueda A, Oleaga L, Berbís MÁ, Cabrera-Zubizarreta A, Martin-Noguerol T, Luna A, Seibert TM, Majos C, Perez-Lopez R. An accessible deep learning tool for voxel-wise classification of brain malignancies from perfusion MRI. Cell Rep Med 2024; 5:101464. [PMID: 38471504 PMCID: PMC10983037 DOI: 10.1016/j.xcrm.2024.101464] [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: 08/30/2023] [Revised: 11/16/2023] [Accepted: 02/15/2024] [Indexed: 03/14/2024]
Abstract
Noninvasive differential diagnosis of brain tumors is currently based on the assessment of magnetic resonance imaging (MRI) coupled with dynamic susceptibility contrast (DSC). However, a definitive diagnosis often requires neurosurgical interventions that compromise patients' quality of life. We apply deep learning on DSC images from histology-confirmed patients with glioblastoma, metastasis, or lymphoma. The convolutional neural network trained on ∼50,000 voxels from 40 patients provides intratumor probability maps that yield clinical-grade diagnosis. Performance is tested in 400 additional cases and an external validation cohort of 128 patients. The tool reaches a three-way accuracy of 0.78, superior to the conventional MRI metrics cerebral blood volume (0.55) and percentage of signal recovery (0.59), showing high value as a support diagnostic tool. Our open-access software, Diagnosis In Susceptibility Contrast Enhancing Regions for Neuro-oncology (DISCERN), demonstrates its potential in aiding medical decisions for brain tumor diagnosis using standard-of-care MRI.
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Affiliation(s)
- Alonso Garcia-Ruiz
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), 08035 Barcelona, Spain
| | - Albert Pons-Escoda
- Radiology Department, Bellvitge University Hospital, 08907 Barcelona, Spain; Neuro-Oncology Unit, Institut d'Investigacio Biomedica de Bellvitge (IDIBELL), 08907 Barcelona, Spain
| | - Francesco Grussu
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), 08035 Barcelona, Spain
| | - Pablo Naval-Baudin
- Radiology Department, Bellvitge University Hospital, 08907 Barcelona, Spain
| | | | - Gretchen Hermann
- Radiation Medicine Department and Applied Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | - Roshan Karunamuni
- Radiation Medicine Department and Applied Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | - Marta Ligero
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), 08035 Barcelona, Spain
| | | | - Laura Oleaga
- Radiology Department, Hospital Clínic de Barcelona, 08036 Barcelona, Spain
| | - M Álvaro Berbís
- Radiology Department, HT Medica, Hospital San Juan de Dios, 14012 Cordoba, Spain
| | | | | | - Antonio Luna
- Radiology Department, HT Medica, 23008 Jaen, Spain
| | - Tyler M Seibert
- Radiation Medicine Department and Applied Sciences, University of California, San Diego, La Jolla, CA 92093, USA; Radiology Department, University of California, San Diego, La Jolla, CA 92093, USA; Bioengineering Department, University of California, San Diego, La Jolla, CA 92093, USA
| | - Carlos Majos
- Radiology Department, Bellvitge University Hospital, 08907 Barcelona, Spain; Neuro-Oncology Unit, Institut d'Investigacio Biomedica de Bellvitge (IDIBELL), 08907 Barcelona, Spain
| | - Raquel Perez-Lopez
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), 08035 Barcelona, Spain.
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Prah MA, Schmainda KM. Practical guidance to identify and troubleshoot suboptimal DSC-MRI results. FRONTIERS IN RADIOLOGY 2024; 4:1307586. [PMID: 38445104 PMCID: PMC10913595 DOI: 10.3389/fradi.2024.1307586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 02/02/2024] [Indexed: 03/07/2024]
Abstract
Relative cerebral blood volume (rCBV) derived from dynamic susceptibility contrast (DSC) perfusion MR imaging (pMRI) has been shown to be a robust marker of neuroradiological tumor burden. Recent consensus recommendations in pMRI acquisition strategies have provided a pathway for pMRI inclusion in diverse patient care centers, regardless of size or experience. However, even with proper implementation and execution of the DSC-MRI protocol, issues will arise that many centers may not easily recognize or be aware of. Furthermore, missed pMRI issues are not always apparent in the resulting rCBV images, potentiating inaccurate or missed radiological diagnoses. Therefore, we gathered from our database of DSC-MRI datasets, true-to-life examples showcasing the breakdowns in acquisition, postprocessing, and interpretation, along with appropriate mitigation strategies when possible. The pMRI issues addressed include those related to image acquisition and postprocessing with a focus on contrast agent administration, timing, and rate, signal-to-noise quality, and susceptibility artifact. The goal of this work is to provide guidance to minimize and recognize pMRI issues to ensure that only quality data is interpreted.
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Affiliation(s)
- Melissa A. Prah
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Kathleen M. Schmainda
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, United States
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI,United States
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Sabeghi P, Zarand P, Zargham S, Golestany B, Shariat A, Chang M, Yang E, Rajagopalan P, Phung DC, Gholamrezanezhad A. Advances in Neuro-Oncological Imaging: An Update on Diagnostic Approach to Brain Tumors. Cancers (Basel) 2024; 16:576. [PMID: 38339327 PMCID: PMC10854543 DOI: 10.3390/cancers16030576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 01/22/2024] [Accepted: 01/24/2024] [Indexed: 02/12/2024] Open
Abstract
This study delineates the pivotal role of imaging within the field of neurology, emphasizing its significance in the diagnosis, prognostication, and evaluation of treatment responses for central nervous system (CNS) tumors. A comprehensive understanding of both the capabilities and limitations inherent in emerging imaging technologies is imperative for delivering a heightened level of personalized care to individuals with neuro-oncological conditions. Ongoing research in neuro-oncological imaging endeavors to rectify some limitations of radiological modalities, aiming to augment accuracy and efficacy in the management of brain tumors. This review is dedicated to the comparison and critical examination of the latest advancements in diverse imaging modalities employed in neuro-oncology. The objective is to investigate their respective impacts on diagnosis, cancer staging, prognosis, and post-treatment monitoring. By providing a comprehensive analysis of these modalities, this review aims to contribute to the collective knowledge in the field, fostering an informed approach to neuro-oncological care. In conclusion, the outlook for neuro-oncological imaging appears promising, and sustained exploration in this domain is anticipated to yield further breakthroughs, ultimately enhancing outcomes for individuals grappling with CNS tumors.
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Affiliation(s)
- Paniz Sabeghi
- Department of Radiology, Keck School of Medicine, University of Southern California, 1500 San Pablo St., Los Angeles, CA 90033, USA; (P.S.); (E.Y.); (P.R.); (D.C.P.)
| | - Paniz Zarand
- School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran 1985717411, Iran;
| | - Sina Zargham
- Department of Basic Science, California Northstate University College of Medicine, 9700 West Taron Drive, Elk Grove, CA 95757, USA;
| | - Batis Golestany
- Division of Biomedical Sciences, Riverside School of Medicine, University of California, 900 University Ave., Riverside, CA 92521, USA;
| | - Arya Shariat
- Kaiser Permanente Los Angeles Medical Center, 4867 W Sunset Blvd, Los Angeles, CA 90027, USA;
| | - Myles Chang
- Keck School of Medicine, University of Southern California, 1975 Zonal Avenue, Los Angeles, CA 90089, USA;
| | - Evan Yang
- Department of Radiology, Keck School of Medicine, University of Southern California, 1500 San Pablo St., Los Angeles, CA 90033, USA; (P.S.); (E.Y.); (P.R.); (D.C.P.)
| | - Priya Rajagopalan
- Department of Radiology, Keck School of Medicine, University of Southern California, 1500 San Pablo St., Los Angeles, CA 90033, USA; (P.S.); (E.Y.); (P.R.); (D.C.P.)
| | - Daniel Chang Phung
- Department of Radiology, Keck School of Medicine, University of Southern California, 1500 San Pablo St., Los Angeles, CA 90033, USA; (P.S.); (E.Y.); (P.R.); (D.C.P.)
| | - Ali Gholamrezanezhad
- Department of Radiology, Keck School of Medicine, University of Southern California, 1500 San Pablo St., Los Angeles, CA 90033, USA; (P.S.); (E.Y.); (P.R.); (D.C.P.)
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Nierobisch N, Ludovichetti R, Kadali K, Fierstra J, Hüllner M, Michels L, Achangwa NR, Alcaide-Leon P, Weller M, Kulcsar Z, Hainc N. Comparison of clinically available dynamic susceptibility contrast post processing software to differentiate progression from pseudoprogression in post-treatment high grade glioma. Eur J Radiol 2023; 167:111076. [PMID: 37666072 DOI: 10.1016/j.ejrad.2023.111076] [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: 07/04/2023] [Revised: 08/16/2023] [Accepted: 08/30/2023] [Indexed: 09/06/2023]
Abstract
INTRODUCTION The purpose of this retrospective study was to compare two, widely available software packages for calculation of Dynamic Susceptibility Contrast (DSC) perfusion MRI normalized relative Cerebral Blood Volume (rCBV) values to differentiate tumor progression from pseudoprogression in treated high-grade glioma patients. MATERIAL AND METHODS rCBV maps processed by Siemens Syngo.via (Siemens Healthineers) and Olea Sphere (Olea Medical) software packages were co-registered to contrast-enhanced T1 (T1-CE). Regions of interest based on T1-CE were transferred to the rCBV maps. rCBV was calculated using mean values and normalized using contralateral normal- appearing white matter. The Wilcoxon test was performed to assess for significant differences, and software-specific optimal rCBV cutoff values were determined using the Youden index. Interrater reliability was evaluated for two raters using the intraclass correlation coefficient. RESULTS 41 patients (18 females; median age = 59 years; range 21-77 years) with 49 new or size-increasing post-treatment contrast-enhancing lesions were included (tumor progression = 40 lesions; pseudoprogression = 9 lesions). Optimal rCBV cutoffs of 1.31 (Syngo.via) and 2.40 (Olea) were significantly different, with an AUC of 0.74 and 0.78, respectively. Interrater reliability was 0.85. DISCUSSION We demonstrate that different clinically available MRI DSC-perfusion software packages generate significantly different rCBV cutoff values for the differentiation of tumor progression from pseudoprogression in standard-of-care treated high grade gliomas. Physicians may want to determine the unique value of their perfusion software packages on an institutional level in order to maximize diagnostic accuracy when faced with this clinical challenge. Furthermore, combined with implementation of current DSC-perfusion recommendations, multi-center comparability will be improved.
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Affiliation(s)
- Nathalie Nierobisch
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland
| | - Riccardo Ludovichetti
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland
| | | | - Jorn Fierstra
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland
| | - Martin Hüllner
- Department of Nuclear Medicine, University Hospital of Zurich, University of Zurich, Switzerland
| | - Lars Michels
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich and Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
| | - Ngwe Rawlings Achangwa
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland
| | - Paula Alcaide-Leon
- Department of Medical Imaging, University of Toronto, Toronto, Canada; Joint Department of Medical Imaging, University Health Network, Toronto, Canada
| | - Michael Weller
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland
| | - Zsolt Kulcsar
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland
| | - Nicolin Hainc
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland.
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Hu LS, D'Angelo F, Weiskittel TM, Caruso FP, Fortin Ensign SP, Blomquist MR, Flick MJ, Wang L, Sereduk CP, Meng-Lin K, De Leon G, Nespodzany A, Urcuyo JC, Gonzales AC, Curtin L, Lewis EM, Singleton KW, Dondlinger T, Anil A, Semmineh NB, Noviello T, Patel RA, Wang P, Wang J, Eschbacher JM, Hawkins-Daarud A, Jackson PR, Grunfeld IS, Elrod C, Mazza GL, McGee SC, Paulson L, Clark-Swanson K, Lassiter-Morris Y, Smith KA, Nakaji P, Bendok BR, Zimmerman RS, Krishna C, Patra DP, Patel NP, Lyons M, Neal M, Donev K, Mrugala MM, Porter AB, Beeman SC, Jensen TR, Schmainda KM, Zhou Y, Baxter LC, Plaisier CL, Li J, Li H, Lasorella A, Quarles CC, Swanson KR, Ceccarelli M, Iavarone A, Tran NL. Integrated molecular and multiparametric MRI mapping of high-grade glioma identifies regional biologic signatures. Nat Commun 2023; 14:6066. [PMID: 37770427 PMCID: PMC10539500 DOI: 10.1038/s41467-023-41559-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 09/06/2023] [Indexed: 09/30/2023] Open
Abstract
Sampling restrictions have hindered the comprehensive study of invasive non-enhancing (NE) high-grade glioma (HGG) cell populations driving tumor progression. Here, we present an integrated multi-omic analysis of spatially matched molecular and multi-parametric magnetic resonance imaging (MRI) profiling across 313 multi-regional tumor biopsies, including 111 from the NE, across 68 HGG patients. Whole exome and RNA sequencing uncover unique genomic alterations to unresectable invasive NE tumor, including subclonal events, which inform genomic models predictive of geographic evolution. Infiltrative NE tumor is alternatively enriched with tumor cells exhibiting neuronal or glycolytic/plurimetabolic cellular states, two principal transcriptomic pathway-based glioma subtypes, which respectively demonstrate abundant private mutations or enrichment in immune cell signatures. These NE phenotypes are non-invasively identified through normalized K2 imaging signatures, which discern cell size heterogeneity on dynamic susceptibility contrast (DSC)-MRI. NE tumor populations predicted to display increased cellular proliferation by mean diffusivity (MD) MRI metrics are uniquely associated with EGFR amplification and CDKN2A homozygous deletion. The biophysical mapping of infiltrative HGG potentially enables the clinical recognition of tumor subpopulations with aggressive molecular signatures driving tumor progression, thereby informing precision medicine targeting.
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Affiliation(s)
- Leland S Hu
- Department of Radiology, Mayo Clinic Arizona, Phoenix, AZ, USA.
- Department of Cancer Biology, Mayo Clinic Arizona, Scottsdale, AZ, USA.
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA.
| | - Fulvio D'Angelo
- Department of Neurological Surgery, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, USA.
| | - Taylor M Weiskittel
- Mayo Clinic Alix School of Medicine Minnesota, Rochester, MN, USA
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Francesca P Caruso
- Department of Electrical Engineering and Information Technologies, University of Naples, "Federico II", I-80128, Naples, Italy
- BIOGEM Institute of Molecular Biology and Genetics, I-83031, Ariano Irpino, Italy
| | - Shannon P Fortin Ensign
- Department of Cancer Biology, Mayo Clinic Arizona, Scottsdale, AZ, USA
- Department of Hematology and Oncology, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - Mylan R Blomquist
- Department of Cancer Biology, Mayo Clinic Arizona, Scottsdale, AZ, USA
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
- Mayo Clinic Alix School of Medicine Arizona, Scottsdale, AZ, USA
| | - Matthew J Flick
- Department of Radiology, Mayo Clinic Arizona, Phoenix, AZ, USA
- Department of Cancer Biology, Mayo Clinic Arizona, Scottsdale, AZ, USA
- Mayo Clinic Alix School of Medicine Arizona, Scottsdale, AZ, USA
| | - Lujia Wang
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Christopher P Sereduk
- Department of Cancer Biology, Mayo Clinic Arizona, Scottsdale, AZ, USA
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Kevin Meng-Lin
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Gustavo De Leon
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Ashley Nespodzany
- Department of Neuroimaging Research, Barrow Neurological Institute, Dignity Health, Phoenix, AZ, USA
| | - Javier C Urcuyo
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Ashlyn C Gonzales
- Department of Neuroimaging Research, Barrow Neurological Institute, Dignity Health, Phoenix, AZ, USA
| | - Lee Curtin
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Erika M Lewis
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Kyle W Singleton
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | | | - Aliya Anil
- Department of Neuroimaging Research, Barrow Neurological Institute, Dignity Health, Phoenix, AZ, USA
| | - Natenael B Semmineh
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Teresa Noviello
- Department of Electrical Engineering and Information Technologies, University of Naples, "Federico II", I-80128, Naples, Italy
- BIOGEM Institute of Molecular Biology and Genetics, I-83031, Ariano Irpino, Italy
| | - Reyna A Patel
- Department of Radiology, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - Panwen Wang
- Quantitative Health Sciences, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Junwen Wang
- Division of Applied Oral Sciences & Community Dental Care, The University of Hong Kong, Hong Kong SAR, China
| | - Jennifer M Eschbacher
- Department of Neuropathology, Barrow Neurological Institute, Dignity Health, Phoenix, AZ, USA
| | | | - Pamela R Jackson
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Itamar S Grunfeld
- Department of Psychology, Hunter College, The City University of New York, New York, NY, USA
- Department of Psychology, The Graduate Center, The City University of New York, New York, NY, USA
| | | | - Gina L Mazza
- Quantitative Health Sciences, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Sam C McGee
- Department of Speech and Hearing Science, Arizona State University, Tempe, AZ, USA
| | - Lisa Paulson
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | | | | | - Kris A Smith
- Department of Neurosurgery, Barrow Neurological Institute, Dignity Health, Phoenix, AZ, USA
| | - Peter Nakaji
- Department of Neurosurgery, Banner University Medical Center, University of Arizona, Phoenix, AZ, USA
| | - Bernard R Bendok
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Richard S Zimmerman
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Chandan Krishna
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Devi P Patra
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Naresh P Patel
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Mark Lyons
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Matthew Neal
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Kliment Donev
- Department of Pathology, Mayo Clinic Arizona, Phoenix, AZ, USA
| | | | - Alyx B Porter
- Department of Neurology, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - Scott C Beeman
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, USA
| | | | - Kathleen M Schmainda
- Departments of Biophysics and Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Yuxiang Zhou
- Department of Radiology, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - Leslie C Baxter
- Department of Radiology, Mayo Clinic Arizona, Phoenix, AZ, USA
- Departments of Psychiatry and Psychology, Mayo Clinic, AZ, USA
| | - Christopher L Plaisier
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Jing Li
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Hu Li
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Anna Lasorella
- Department of Biochemistry and Molecular Biology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - C Chad Quarles
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Kristin R Swanson
- Department of Cancer Biology, Mayo Clinic Arizona, Scottsdale, AZ, USA
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Michele Ceccarelli
- Department of Public Health Sciences, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, USA.
| | - Antonio Iavarone
- Department of Neurological Surgery, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, USA.
| | - Nhan L Tran
- Department of Cancer Biology, Mayo Clinic Arizona, Scottsdale, AZ, USA.
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA.
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10
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Cho NS, Hagiwara A, Sanvito F, Ellingson BM. A multi-reader comparison of normal-appearing white matter normalization techniques for perfusion and diffusion MRI in brain tumors. Neuroradiology 2023; 65:559-568. [PMID: 36301349 PMCID: PMC9905164 DOI: 10.1007/s00234-022-03072-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 10/14/2022] [Indexed: 02/08/2023]
Abstract
PURPOSE There remains no consensus normal-appearing white matter (NAWM) normalization method to compute normalized relative cerebral blood volume (nrCBV) and apparent diffusion coefficient (nADC) in brain tumors. This reader study explored nrCBV and nADC differences using different NAWM normalization methods. METHODS Thirty-five newly diagnosed glioma patients were studied. For each patient, two readers created four NAWM regions of interests: (1) a single plane in the centrum semiovale (CSOp), (2) 3 spheres in the centrum semiovale (CSOs), (3) a single plane in the slice of the tumor center (TUMp), and (4) 3 spheres in the slice of the tumor center (TUMs). Readers repeated NAWM segmentations 1 month later. Differences in nrCBV and nADC of the FLAIR hyperintense tumor, inter-/intra-reader variability, and time to segment NAWM were assessed. As a validation step, the diagnostic performance of each method for IDH-status prediction was evaluated. RESULTS Both readers obtained significantly different nrCBV (P < .001), nADC (P < .001), and time to segment NAWM (P < .001) between the four normalization methods. nrCBV and nADC were significantly different between CSO and TUM methods, but not between planar and spherical methods in the same NAWM region. Broadly, CSO methods were quicker than TUM methods, and spherical methods were quicker than planar methods. For all normalization techniques, inter-reader reproducibility and intra-reader repeatability were excellent (intraclass correlation coefficient > 0.9), and the IDH-status predictive performance remained similar. CONCLUSION The selected NAWM region significantly impacts nrCBV and nADC values. CSO methods, particularly CSOs, may be preferred because of time reduction, similar reader variability, and similar diagnostic performance compared to TUM methods.
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Affiliation(s)
- Nicholas S Cho
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, USA
- Medical Scientist Training Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Akifumi Hagiwara
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Francesco Sanvito
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Unit of Radiology, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, Pavia, Italy
| | - Benjamin M Ellingson
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
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11
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Powell SJ, Withey SB, Sun Y, Grist JT, Novak J, MacPherson L, Abernethy L, Pizer B, Grundy R, Morgan PS, Jaspan T, Bailey S, Mitra D, Auer DP, Avula S, Arvanitis TN, Peet A. Applying machine learning classifiers to automate quality assessment of paediatric dynamic susceptibility contrast (DSC-) MRI data. Br J Radiol 2023; 96:20201465. [PMID: 36802769 PMCID: PMC10161906 DOI: 10.1259/bjr.20201465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023] Open
Abstract
OBJECTIVE Investigate the performance of qualitative review (QR) for assessing dynamic susceptibility contrast (DSC-) MRI data quality in paediatric normal brain and develop an automated alternative to QR. METHODS 1027 signal-time courses were assessed by Reviewer 1 using QR. 243 were additionally assessed by Reviewer 2 and % disagreements and Cohen's κ (κ) were calculated. The signal drop-to-noise ratio (SDNR), root mean square error (RMSE), full width half maximum (FWHM) and percentage signal recovery (PSR) were calculated for the 1027 signal-time courses. Data quality thresholds for each measure were determined using QR results. The measures and QR results trained machine learning classifiers. Sensitivity, specificity, precision, classification error and area under the curve from a receiver operating characteristic curve were calculated for each threshold and classifier. RESULTS Comparing reviewers gave 7% disagreements and κ = 0.83. Data quality thresholds of: 7.6 for SDNR; 0.019 for RMSE; 3 s and 19 s for FWHM; and 42.9 and 130.4% for PSR were produced. SDNR gave the best sensitivity, specificity, precision, classification error and area under the curve values of 0.86, 0.86, 0.93, 14.2% and 0.83. Random forest was the best machine learning classifier, giving sensitivity, specificity, precision, classification error and area under the curve of 0.94, 0.83, 0.93, 9.3% and 0.89. CONCLUSION The reviewers showed good agreement. Machine learning classifiers trained on signal-time course measures and QR can assess quality. Combining multiple measures reduces misclassification. ADVANCES IN KNOWLEDGE A new automated quality control method was developed, which trained machine learning classifiers using QR results.
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Affiliation(s)
- Stephen J Powell
- Physical Sciences for Health CDT, University of Birmingham, Birmingham, United Kingdom.,Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Stephanie B Withey
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom.,Department of Oncology, Birmingham Children's Hospital, Birmingham, United Kingdom.,RRPPS, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
| | - Yu Sun
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom.,School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China
| | - James T Grist
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Jan Novak
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom.,Department of Oncology, Birmingham Children's Hospital, Birmingham, United Kingdom.,Department of Psychology, Aston Brain Centre, School of Life and Health Sciences, Aston University, Birmingham, United Kingdom
| | - Lesley MacPherson
- Radiology, Birmingham Children's Hospital, Birmingham, United Kingdom
| | - Laurence Abernethy
- Radiology, Alder Hey Children's NHS Foundation Trust, Liverpool, United Kingdom
| | - Barry Pizer
- Oncology, Alder Hey Children's NHS Foundation Trust, Liverpool, United Kingdom
| | - Richard Grundy
- The Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, United Kingdom
| | - Paul S Morgan
- The Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, United Kingdom.,Medical Physics, Nottingham University Hospitals, Nottingham, United Kingdom.,NIHR Nottingham Biomedical Research Centre, Nottingham, United Kingdom
| | - Tim Jaspan
- The Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, United Kingdom.,Radiology, Nottingham University Hospitals, Nottingham, United Kingdom
| | - Simon Bailey
- Sir James Spence Institute of Child Health, Royal Victoria Infirmary, Newcastle upon Tyne, United Kingdom
| | - Dipayan Mitra
- Neuroradiology, The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Dorothee P Auer
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
| | - Shivaram Avula
- Radiology, Alder Hey Children's NHS Foundation Trust, Liverpool, United Kingdom
| | - Theodoros N Arvanitis
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom.,Department of Oncology, Birmingham Children's Hospital, Birmingham, United Kingdom.,Institute of Digital Healthcare, WMG, University of Warwick, Coventry, United Kingdom
| | - Andrew Peet
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom.,Department of Oncology, Birmingham Children's Hospital, Birmingham, United Kingdom
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12
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Anil A, Stokes AM, Chao R, Hu LS, Alhilali L, Karis JP, Bell LC, Quarles CC. Identification of single-dose, dual-echo based CBV threshold for fractional tumor burden mapping in recurrent glioblastoma. Front Oncol 2023; 13:1046629. [PMID: 36733305 PMCID: PMC9887158 DOI: 10.3389/fonc.2023.1046629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 01/03/2023] [Indexed: 01/18/2023] Open
Abstract
Background Relative cerebral blood volume (rCBV) obtained from dynamic susceptibility contrast (DSC) MRI is widely used to distinguish high grade glioma recurrence from post treatment radiation effects (PTRE). Application of rCBV thresholds yield maps to distinguish between regional tumor burden and PTRE, a biomarker termed the fractional tumor burden (FTB). FTB is generally measured using conventional double-dose, single-echo DSC-MRI protocols; recently, a single-dose, dual-echo DSC-MRI protocol was clinically validated by direct comparison to the conventional double-dose, single-echo protocol. As the single-dose, dual-echo acquisition enables reduction in the contrast agent dose and provides greater pulse sequence parameter flexibility, there is a compelling need to establish dual-echo DSC-MRI based FTB mapping. In this study, we determine the optimum standardized rCBV threshold for the single-dose, dual-echo protocol to generate FTB maps that best match those derived from the reference standard, double-dose, single-echo protocol. Methods The study consisted of 23 high grade glioma patients undergoing perfusion scans to confirm suspected tumor recurrence. We sequentially acquired single dose, dual-echo and double dose, single-echo DSC-MRI data. For both protocols, we generated leakage-corrected standardized rCBV maps. Standardized rCBV (sRCBV) thresholds of 1.0 and 1.75 were used to compute single-echo FTB maps as the reference for delineating PTRE (sRCBV < 1.0), tumor with moderate angiogenesis (1.0 < sRCBV < 1.75), and tumor with high angiogenesis (sRCBV > 1.75) regions. To assess the sRCBV agreement between acquisition protocols, the concordance correlation coefficient (CCC) was computed between the mean tumor sRCBV values across the patients. A receiver operating characteristics (ROC) analysis was performed to determine the optimum dual-echo sRCBV threshold. The sensitivity, specificity, and accuracy were compared between the obtained optimized threshold (1.64) and the standard reference threshold (1.75) for the dual-echo sRCBV threshold. Results The mean tumor sRCBV values across the patients showed a strong correlation (CCC = 0.96) between the two protocols. The ROC analysis showed maximum accuracy at thresholds of 1.0 (delineate PTRE from tumor) and 1.64 (differentiate aggressive tumors). The reference threshold (1.75) and the obtained optimized threshold (1.64) yielded similar accuracy, with slight differences in sensitivity and specificity which were not statistically significant (1.75 threshold: Sensitivity = 81.94%; Specificity: 87.23%; Accuracy: 84.58% and 1.64 threshold: Sensitivity = 84.48%; Specificity: 84.97%; Accuracy: 84.73%). Conclusions The optimal sRCBV threshold for single-dose, dual-echo protocol was found to be 1.0 and 1.64 for distinguishing tumor recurrence from PTRE; however, minimal differences were observed when using the standard threshold (1.75) as the upper threshold, suggesting that the standard threshold could be used for both protocols. While the prior study validated the agreement of the mean sRCBV values between the protocols, this study confirmed that their voxel-wise agreement is suitable for reliable FTB mapping. Dual-echo DSC-MRI acquisitions enable robust single-dose sRCBV and FTB mapping, provide pulse sequence parameter flexibility and should improve reproducibility by mitigating variations in preload dose and incubation time.
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Affiliation(s)
- Aliya Anil
- Division of Neuroimaging Research and Barrow Neuroimaging Innovation Center, Barrow Neuroimaging Institute, Phoenix, AZ, United States
| | - Ashley M. Stokes
- Division of Neuroimaging Research and Barrow Neuroimaging Innovation Center, Barrow Neuroimaging Institute, Phoenix, AZ, United States
| | - Renee Chao
- Division of Neuroimaging Research and Barrow Neuroimaging Innovation Center, Barrow Neuroimaging Institute, Phoenix, AZ, United States
| | - Leland S. Hu
- Department of Radiology, Division of Neuroradiology, Mayo Clinic Arizona, Phoenix, AZ, United States
| | - Lea Alhilali
- Neuroradiology, Southwest Neuroimaging at Barrow Neurological Institute, Phoenix, AZ, United States
| | - John P. Karis
- Neuroradiology, Southwest Neuroimaging at Barrow Neurological Institute, Phoenix, AZ, United States
| | - Laura C. Bell
- Early Clinical Development, Genentech, San Francisco, CA, United States
| | - C. Chad Quarles
- Cancer System Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States,*Correspondence: C. Chad Quarles,
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13
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Maiter A, Butteriss D, English P, Lewis J, Hassani A, Bhatnagar P. Assessing the diagnostic accuracy and interobserver agreement of MRI perfusion in differentiating disease progression and pseudoprogression following treatment for glioblastoma in a tertiary UK centre. Clin Radiol 2022; 77:e568-e575. [DOI: 10.1016/j.crad.2022.04.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 04/12/2022] [Indexed: 11/03/2022]
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14
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Withey SB, MacPherson L, Oates A, Powell S, Novak J, Abernethy L, Pizer B, Grundy R, Morgan PS, Bailey S, Mitra D, Arvanitis TN, Auer DP, Avula S, Peet AC. Dynamic susceptibility-contrast magnetic resonance imaging with contrast agent leakage correction aids in predicting grade in pediatric brain tumours: a multicenter study. Pediatr Radiol 2022; 52:1134-1149. [PMID: 35290489 PMCID: PMC9107460 DOI: 10.1007/s00247-021-05266-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 08/31/2021] [Accepted: 12/11/2021] [Indexed: 11/30/2022]
Abstract
BACKGROUND Relative cerebral blood volume (rCBV) measured using dynamic susceptibility-contrast MRI can differentiate between low- and high-grade pediatric brain tumors. Multicenter studies are required for translation into clinical practice. OBJECTIVE We compared leakage-corrected dynamic susceptibility-contrast MRI perfusion parameters acquired at multiple centers in low- and high-grade pediatric brain tumors. MATERIALS AND METHODS Eighty-five pediatric patients underwent pre-treatment dynamic susceptibility-contrast MRI scans at four centers. MRI protocols were variable. We analyzed data using the Boxerman leakage-correction method producing pixel-by-pixel estimates of leakage-uncorrected (rCBVuncorr) and corrected (rCBVcorr) relative cerebral blood volume, and the leakage parameter, K2. Histological diagnoses were obtained. Tumors were classified by high-grade tumor. We compared whole-tumor median perfusion parameters between low- and high-grade tumors and across tumor types. RESULTS Forty tumors were classified as low grade, 45 as high grade. Mean whole-tumor median rCBVuncorr was higher in high-grade tumors than low-grade tumors (mean ± standard deviation [SD] = 2.37±2.61 vs. -0.14±5.55; P<0.01). Average median rCBV increased following leakage correction (2.54±1.63 vs. 1.68±1.36; P=0.010), remaining higher in high-grade tumors than low grade-tumors. Low-grade tumors, particularly pilocytic astrocytomas, showed T1-dominant leakage effects; high-grade tumors showed T2*-dominance (mean K2=0.017±0.049 vs. 0.002±0.017). Parameters varied with tumor type but not center. Median rCBVuncorr was higher (mean = 1.49 vs. 0.49; P=0.015) and K2 lower (mean = 0.005 vs. 0.016; P=0.013) in children who received a pre-bolus of contrast agent compared to those who did not. Leakage correction removed the difference. CONCLUSION Dynamic susceptibility-contrast MRI acquired at multiple centers helped distinguish between children's brain tumors. Relative cerebral blood volume was significantly higher in high-grade compared to low-grade tumors and differed among common tumor types. Vessel leakage correction is required to provide accurate rCBV, particularly in low-grade enhancing tumors.
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Affiliation(s)
- Stephanie B Withey
- RRPPS, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Oncology, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Lesley MacPherson
- Radiology, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK
| | - Adam Oates
- Radiology, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK
| | - Stephen Powell
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Jan Novak
- Oncology, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Department of Psychology, Aston Brain Centre, School of Life and Health Sciences, Aston University, Birmingham, UK
| | | | - Barry Pizer
- Oncology, Alder Hey Children's NHS Foundation Trust, Liverpool, UK
| | - Richard Grundy
- The Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, UK
| | - Paul S Morgan
- The Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, UK
- Medical Physics, Nottingham University Hospitals, Nottingham, UK
- Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, UK
| | - Simon Bailey
- Sir James Spence Institute of Child Health, Royal Victoria Infirmary, Newcastle upon Tyne, UK
| | - Dipayan Mitra
- Neuroradiology, Royal Victoria Infirmary, Newcastle upon Tyne, UK
| | - Theodoros N Arvanitis
- Oncology, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Institute of Digital Healthcare, WMG, University of Warwick, Coventry, UK
| | - Dorothee P Auer
- Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, UK
- Neuroradiology, Nottingham University Hospitals Trust, Nottingham, UK
- NIHR Nottingham Biomedical Research Centre, Nottingham, UK
| | - Shivaram Avula
- Radiology, Alder Hey Children's NHS Foundation Trust, Liverpool, UK
| | - Andrew C Peet
- Oncology, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK.
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK.
- Children's Brain Tumour Research Team, 4th Floor Institute of Child Health, Birmingham Women's and Children's Hospital NHS Foundation Trust, Steelhouse Lane, Birmingham, B4 6NH, UK.
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15
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Malik DG, Rath TJ, Urcuyo Acevedo JC, Canoll PD, Swanson KR, Boxerman JL, Quarles CC, Schmainda KM, Burns TC, Hu LS. Advanced MRI Protocols to Discriminate Glioma From Treatment Effects: State of the Art and Future Directions. FRONTIERS IN RADIOLOGY 2022; 2:809373. [PMID: 37492687 PMCID: PMC10365126 DOI: 10.3389/fradi.2022.809373] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 03/01/2022] [Indexed: 07/27/2023]
Abstract
In the follow-up treatment of high-grade gliomas (HGGs), differentiating true tumor progression from treatment-related effects, such as pseudoprogression and radiation necrosis, presents an ongoing clinical challenge. Conventional MRI with and without intravenous contrast serves as the clinical benchmark for the posttreatment surveillance imaging of HGG. However, many advanced imaging techniques have shown promise in helping better delineate the findings in indeterminate scenarios, as posttreatment effects can often mimic true tumor progression on conventional imaging. These challenges are further confounded by the histologic admixture that can commonly occur between tumor growth and treatment-related effects within the posttreatment bed. This review discusses the current practices in the surveillance imaging of HGG and the role of advanced imaging techniques, including perfusion MRI and metabolic MRI.
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Affiliation(s)
- Dania G. Malik
- Department of Radiology, Mayo Clinic, Phoenix, AZ, United States
| | - Tanya J. Rath
- Department of Radiology, Mayo Clinic, Phoenix, AZ, United States
| | - Javier C. Urcuyo Acevedo
- Mathematical Neurooncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ, United States
| | - Peter D. Canoll
- Departments of Pathology and Cell Biology, Columbia University, New York, NY, United States
| | - Kristin R. Swanson
- Mathematical Neurooncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ, United States
| | - Jerrold L. Boxerman
- Department of Diagnostic Imaging, Brown University, Providence, RI, United States
| | - C. Chad Quarles
- Department of Neuroimaging Research & Barrow Neuroimaging Innovation Center, Barrow Neurologic Institute, Phoenix, AZ, United States
| | - Kathleen M. Schmainda
- Department of Biophysics & Radiology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Terry C. Burns
- Departments of Neurologic Surgery and Neuroscience, Mayo Clinic, Rochester, MN, United States
| | - Leland S. Hu
- Department of Radiology, Mayo Clinic, Phoenix, AZ, United States
- Mathematical Neurooncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ, United States
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16
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Goldman J, Hagiwara A, Yao J, Raymond C, Ong C, Bakhti R, Kwon E, Farhat M, Torres C, Erickson LG, Curl BJ, Lee M, Pope WB, Salamon N, Nghiemphu PL, Ji M, Eldred BS, Liau LM, Lai A, Cloughesy TF, Chung C, Ellingson BM. Paradoxical Association Between Relative Cerebral Blood Volume Dynamics Following Chemoradiation and Increased Progression-Free Survival in Newly Diagnosed IDH Wild-Type MGMT Promoter Methylated Glioblastoma With Measurable Disease. Front Oncol 2022; 12:849993. [PMID: 35371980 PMCID: PMC8964348 DOI: 10.3389/fonc.2022.849993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 02/07/2022] [Indexed: 11/15/2022] Open
Abstract
Background and Purpose While relative cerebral blood volume (rCBV) may be diagnostic and prognostic for survival in glioblastoma (GBM), changes in rCBV during chemoradiation in the subset of newly diagnosed GBM with subtotal resection and the impact of MGMT promoter methylation status on survival have not been explored. This study aimed to investigate the association between rCBV response, MGMT methylation status, and progression-free (PFS) and overall survival (OS) in newly diagnosed GBM with measurable enhancing lesions. Methods 1,153 newly diagnosed IDH wild-type GBM patients were screened and 53 patients (4.6%) had measurable post-surgical tumor (>1mL). rCBV was measured before and after patients underwent chemoradiation. Patients with a decrease in rCBV >10% were considered rCBV Responders, while patients with an increase or a decrease in rCBV <10% were considered rCBV Non-Responders. The association between change in enhancing tumor volume, change in rCBV, MGMT promotor methylation status, and PFS or OS were explored. Results A decrease in tumor volume following chemoradiation trended towards longer OS (p=0.12; median OS=26.8 vs. 16.3 months). Paradoxically, rCBV Non-Responders had a significantly improved PFS compared to Responders (p=0.047; median PFS=9.6 vs. 7.2 months). MGMT methylated rCBV Non-Responders exhibited a significantly longer PFS compared to MGMT unmethylated rCBV Non-Responders (p<0.001; median PFS=0.5 vs. 7.1 months), and MGMT methylated rCBV Non-Responders trended towards longer PFS compared to methylated rCBV Responders (p=0.089; median PFS=20.5 vs. 13.8 months). Conclusions This preliminary report demonstrates that in newly diagnosed IDH wild-type GBM with measurable enhancing disease after surgery (5% of patients), an enigmatic non-response in rCBV was associated with longer PFS, particularly in MGMT methylated patients.
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Affiliation(s)
- Jodi Goldman
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Akifumi Hagiwara
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Jingwen Yao
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Christian Ong
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Rojin Bakhti
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Elizabeth Kwon
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Maguy Farhat
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Carlo Torres
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Lily G Erickson
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Brandon J Curl
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Maggie Lee
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Whitney B Pope
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Noriko Salamon
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Phioanh L Nghiemphu
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Matthew Ji
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Blaine S Eldred
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Linda M Liau
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Albert Lai
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Timothy F Cloughesy
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Caroline Chung
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
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17
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Henriksen OM, del Mar Álvarez-Torres M, Figueiredo P, Hangel G, Keil VC, Nechifor RE, Riemer F, Schmainda KM, Warnert EAH, Wiegers EC, Booth TC. High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 1: Perfusion and Diffusion Techniques. Front Oncol 2022; 12:810263. [PMID: 35359414 PMCID: PMC8961422 DOI: 10.3389/fonc.2022.810263] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 01/05/2022] [Indexed: 01/16/2023] Open
Abstract
Objective Summarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and highlight the latest bench-to-bedside developments. Methods Experts in advanced MRI techniques applied to high-grade glioma treatment response assessment convened through a European framework. Current evidence regarding the potential for monitoring biomarkers in adult high-grade glioma is reviewed, and individual modalities of perfusion, permeability, and microstructure imaging are discussed (in Part 1 of two). In Part 2, we discuss modalities related to metabolism and/or chemical composition, appraise the clinic readiness of the individual modalities, and consider post-processing methodologies involving the combination of MRI approaches (multiparametric imaging) or machine learning (radiomics). Results High-grade glioma vasculature exhibits increased perfusion, blood volume, and permeability compared with normal brain tissue. Measures of cerebral blood volume derived from dynamic susceptibility contrast-enhanced MRI have consistently provided information about brain tumor growth and response to treatment; it is the most clinically validated advanced technique. Clinical studies have proven the potential of dynamic contrast-enhanced MRI for distinguishing post-treatment related effects from recurrence, but the optimal acquisition protocol, mode of analysis, parameter of highest diagnostic value, and optimal cut-off points remain to be established. Arterial spin labeling techniques do not require the injection of a contrast agent, and repeated measurements of cerebral blood flow can be performed. The absence of potential gadolinium deposition effects allows widespread use in pediatric patients and those with impaired renal function. More data are necessary to establish clinical validity as monitoring biomarkers. Diffusion-weighted imaging, apparent diffusion coefficient analysis, diffusion tensor or kurtosis imaging, intravoxel incoherent motion, and other microstructural modeling approaches also allow treatment response assessment; more robust data are required to validate these alone or when applied to post-processing methodologies. Conclusion Considerable progress has been made in the development of these monitoring biomarkers. Many techniques are in their infancy, whereas others have generated a larger body of evidence for clinical application.
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Affiliation(s)
- Otto M. Henriksen
- Department of Clinical Physiology, Nuclear Medicine and PET, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | | | - Patricia Figueiredo
- Department of Bioengineering and Institute for Systems and Robotics-Lisboa, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Gilbert Hangel
- Department of Neurosurgery, Medical University, Vienna, Austria
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University, Vienna, Austria
| | - Vera C. Keil
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, Netherlands
| | - Ruben E. Nechifor
- International Institute for the Advanced Studies of Psychotherapy and Applied Mental Health, Department of Clinical Psychology and Psychotherapy, Babes-Bolyai University, Cluj-Napoca, Romania
| | - Frank Riemer
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Kathleen M. Schmainda
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, United States
| | | | - Evita C. Wiegers
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Thomas C. Booth
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School of Biomedical Engineering and Imaging Sciences, St. Thomas’ Hospital, King’s College London, London, United Kingdom
- Department of Neuroradiology, King’s College Hospital NHS Foundation Trust, London, United Kingdom
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18
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Cindil E, Sendur HN, Cerit MN, Erdogan N, Celebi F, Dag N, Celtikci E, Inan A, Oner Y, Tali T. Prediction of IDH Mutation Status in High-grade Gliomas Using DWI and High T1-weight DSC-MRI. Acad Radiol 2022; 29 Suppl 3:S52-S62. [PMID: 33685792 DOI: 10.1016/j.acra.2021.02.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 01/24/2021] [Accepted: 02/03/2021] [Indexed: 01/09/2023]
Abstract
RATIONALE AND OBJECTIVES We aimed to evaluate the diagnostic performance of diffusion-weighted imaging (DWI) and dynamic susceptibility contrast-enhanced (DSC) magnetic resonance imaging (MRI) parameters in the noninvasive prediction of the isocitrate dehydrogenase (IDH) mutation status in high-grade gliomas (HGGs). MATERIALS AND METHODS A total of 58 patients with histopathologically proved HGGs were included in this retrospective study. All patients underwent multiparametric MRI on 3-T, including DSC-MRI and DWI before surgery. The mean apparent diffusion coefficient (ADC), relative maximum cerebral blood volume (rCBV), and percentage signal recovery (PSR) of the tumor core were measured and compared depending on the IDH mutation status and tumor grade. The Mann-Whitney U test was used to detect statistically significant differences in parameters between IDH-mutant-type (IDH-m-type) and IDH-wild-type (IDH-w-type) HGGs. Receiver operating characteristic curve (ROC) analysis was performed to evaluate the diagnostic performance. RESULTS The rCBV was significantly higher, and the PSR value was significantly lower in IDH-w-type tumors than in the IDH-m group (p = 0.002 and <0.001, respectively).The ADC value in IDH-w-type tumors was significantly lower compared with the one in IDH-m types (p = 0.023), but remarkable overlaps were found between the groups. The PSR showed the best diagnostic performance with an AUC of 0.938 and with an accuracy rate of 0.87 at the optimal cutoff value of 86.85. The combination of the PSR and the rCBV for the identification of the IDH mutation status increased the discrimination ability at the AUC level of 0.955. In terms of each tumor grade, the PSR and rCBV showed significant differences between the IDH-m and IDH-w groups (p ≤0.001). CONCLUSION The rCBV and PSR from DSC-MRI may be feasible noninvasive imaging parameters for predicting the IDH mutation status in HGGs. The standardization of the imaging protocol is indispensable to the utility of DSC perfusion MRI in wider clinical usage.
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19
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Luo X, Zhu Y, Zhang Y, Zhang Q, Wang X, Deng X. Parameters of MR perfusion-weighted imaging predict the response and prognosis to high-dose methotrexate-based chemotherapy in immunocompetent patients with primary central nervous system lymphoma. J Clin Neurosci 2022; 95:151-158. [DOI: 10.1016/j.jocn.2021.12.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Revised: 11/26/2021] [Accepted: 12/07/2021] [Indexed: 01/04/2023]
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20
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Fu R, Szidonya L, Barajas RF, Ambady P, Varallyay C, Neuwelt EA. Diagnostic performance of DSC perfusion MRI to distinguish tumor progression and treatment-related changes: a systematic review and meta-analysis. Neurooncol Adv 2022; 4:vdac027. [PMID: 35386567 PMCID: PMC8982196 DOI: 10.1093/noajnl/vdac027] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Background In patients with high-grade glioma (HGG), true disease progression and treatment-related changes often appear similar on magnetic resonance imaging (MRI), making it challenging to evaluate therapeutic response. Dynamic susceptibility contrast (DSC) MRI has been extensively studied to differentiate between disease progression and treatment-related changes. This systematic review evaluated and synthesized the evidence for using DSC MRI to distinguish true progression from treatment-related changes. Methods We searched Ovid MEDLINE and the Ovid MEDLINE in-process file (January 2005-October 2019) and the reference lists. Studies on test performance of DSC MRI using relative cerebral blood volume in HGG patients were included. One investigator abstracted data, and a second investigator confirmed them; two investigators independently assessed study quality. Meta-analyses were conducted to quantitatively synthesize area under the receiver operating curve (AUROC), sensitivity, and specificity. Results We screened 1177 citations and included 28 studies with 638 patients with true tumor progression, and 430 patients with treatment-related changes. Nineteen studies reported AUROC and the combined AUROC is 0.85 (95% CI, 0.81-0.90). All studies contributed data for sensitivity and specificity, and the pooled sensitivity and specificity are 0.84 (95% CI, 0.80-0.88), and 0.78 (95% CI, 0.72-0.83). Extensive subgroup analyses based on study, treatment, and imaging characteristics generally showed similar results. Conclusions There is moderate strength of evidence that relative cerebral blood volume obtained from DSC imaging demonstrated "excellent" ability to discriminate true tumor progression from treatment-related changes, with robust sensitivity and specificity.
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Affiliation(s)
- Rongwei Fu
- Oregon Health & Science University-Portland State University, School of Public Health, Portland, Oregon, USA.,Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
| | - Laszlo Szidonya
- Department of Radiology, Oregon Health & Science University, Portland, Oregon, USA.,Neuro-Oncology Program, Oregon Health & Science University, Portland, Oregon, USA.,Heart and Vascular Center, Diagnostic Radiology, Semmelweis University, Budapest, Hungary
| | - Ramon F Barajas
- Department of Radiology, Oregon Health & Science University, Portland, Oregon, USA.,Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA.,Knight Cancer Institute Translational Oncology Program, Oregon Health & Science University, Portland, Oregon, USA
| | - Prakash Ambady
- Neuro-Oncology Program, Oregon Health & Science University, Portland, Oregon, USA
| | | | - Edward A Neuwelt
- Neuro-Oncology Program, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurosurgery, Oregon Health and Sciences University, Portland, Oregon, USA.,Office of Research and Development, Department of Veterans Affairs Medical Center, Portland, Oregon, USA
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21
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Stokes AM, Bergamino M, Alhilali L, Hu LS, Karis JP, Baxter LC, Bell LC, Quarles CC. Evaluation of single bolus, dual-echo dynamic susceptibility contrast MRI protocols in brain tumor patients. J Cereb Blood Flow Metab 2021; 41:3378-3390. [PMID: 34415211 PMCID: PMC8669280 DOI: 10.1177/0271678x211039597] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Relative cerebral blood volume (rCBV) obtained from dynamic susceptibility contrast (DSC) MRI is adversely impacted by contrast agent leakage in brain tumors. Using simulations, we previously demonstrated that multi-echo DSC-MRI protocols provide improvements in contrast agent dosing, pulse sequence flexibility, and rCBV accuracy. The purpose of this study is to assess the in-vivo performance of dual-echo acquisitions in patients with brain tumors (n = 59). To verify pulse sequence flexibility, four single-dose dual-echo acquisitions were tested with variations in contrast agent dose, flip angle, and repetition time, and the resulting dual-echo rCBV was compared to standard single-echo rCBV obtained with preload (double-dose). Dual-echo rCBV was comparable to standard double-dose single-echo protocols (mean (standard deviation) tumor rCBV 2.17 (1.28) vs. 2.06 (1.20), respectively). High rCBV similarity was observed (CCC = 0.96), which was maintained across both flip angle (CCC = 0.98) and repetition time (CCC = 0.96) permutations, demonstrating that dual-echo acquisitions provide flexibility in acquisition parameters. Furthermore, a single dual-echo acquisition was shown to enable quantification of both perfusion and permeability metrics. In conclusion, single-dose dual-echo acquisitions provide similar rCBV to standard double-dose single-echo acquisitions, suggesting contrast agent dose can be reduced while providing significant pulse sequence flexibility and complementary tumor perfusion and permeability metrics.
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Affiliation(s)
- Ashley M Stokes
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Maurizio Bergamino
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Lea Alhilali
- Neuroradiology, Southwest Neuroimaging at Barrow Neurological Institute, Phoenix, AZ, USA
| | - Leland S Hu
- Department of Radiology, Division of Neuroradiology, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - John P Karis
- Neuroradiology, Southwest Neuroimaging at Barrow Neurological Institute, Phoenix, AZ, USA
| | - Leslie C Baxter
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, USA.,Department of Radiology, Division of Neuroradiology, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - Laura C Bell
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, USA
| | - C Chad Quarles
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, USA
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22
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Arzanforoosh F, Croal PL, van Garderen KA, Smits M, Chappell MA, Warnert EAH. Effect of Applying Leakage Correction on rCBV Measurement Derived From DSC-MRI in Enhancing and Nonenhancing Glioma. Front Oncol 2021; 11:648528. [PMID: 33869047 PMCID: PMC8044812 DOI: 10.3389/fonc.2021.648528] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Accepted: 02/25/2021] [Indexed: 01/06/2023] Open
Abstract
Purpose Relative cerebral blood volume (rCBV) is the most widely used parameter derived from DSC perfusion MR imaging for predicting brain tumor aggressiveness. However, accurate rCBV estimation is challenging in enhancing glioma, because of contrast agent extravasation through a disrupted blood-brain barrier (BBB), and even for nonenhancing glioma with an intact BBB, due to an elevated steady-state contrast agent concentration in the vasculature after first passage. In this study a thorough investigation of the effects of two different leakage correction algorithms on rCBV estimation for enhancing and nonenhancing tumors was conducted. Methods Two datasets were used retrospectively in this study: 1. A publicly available TCIA dataset (49 patients with 35 enhancing and 14 nonenhancing glioma); 2. A dataset acquired clinically at Erasmus MC (EMC, Rotterdam, NL) (47 patients with 20 enhancing and 27 nonenhancing glial brain lesions). The leakage correction algorithms investigated in this study were: a unidirectional model-based algorithm with flux of contrast agent from the intra- to the extravascular extracellular space (EES); and a bidirectional model-based algorithm additionally including flow from EES to the intravascular space. Results In enhancing glioma, the estimated average contrast-enhanced tumor rCBV significantly (Bonferroni corrected Wilcoxon Signed Rank Test, p < 0.05) decreased across the patients when applying unidirectional and bidirectional correction: 4.00 ± 2.11 (uncorrected), 3.19 ± 1.65 (unidirectional), and 2.91 ± 1.55 (bidirectional) in TCIA dataset and 2.51 ± 1.3 (uncorrected), 1.72 ± 0.84 (unidirectional), and 1.59 ± 0.9 (bidirectional) in EMC dataset. In nonenhancing glioma, a significant but smaller difference in observed rCBV was found after application of both correction methods used in this study: 1.42 ± 0.60 (uncorrected), 1.28 ± 0.46 (unidirectional), and 1.24 ± 0.37 (bidirectional) in TCIA dataset and 0.91 ± 0.49 (uncorrected), 0.77 ± 0.37 (unidirectional), and 0.67 ± 0.34 (bidirectional) in EMC dataset. Conclusion Both leakage correction algorithms were found to change rCBV estimation with BBB disruption in enhancing glioma, and to a lesser degree in nonenhancing glioma. Stronger effects were found for bidirectional leakage correction than for unidirectional leakage correction.
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Affiliation(s)
- Fatemeh Arzanforoosh
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands
| | - Paula L Croal
- Radiological Sciences, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom.,Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Karin A van Garderen
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands
| | - Michael A Chappell
- Radiological Sciences, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom.,Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom.,NIHR Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom
| | - Esther A H Warnert
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands
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23
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Strauss SB, Meng A, Ebani EJ, Chiang GC. Imaging Glioblastoma Posttreatment: Progression, Pseudoprogression, Pseudoresponse, Radiation Necrosis. Neuroimaging Clin N Am 2021; 31:103-120. [PMID: 33220823 DOI: 10.1016/j.nic.2020.09.010] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Radiographic monitoring of posttreatment glioblastoma is important for clinical trials and determining next steps in management. Evaluation for tumor progression is confounded by the presence of treatment-related radiographic changes, making a definitive determination less straight-forward. The purpose of this article was to describe imaging tools available for assessing treatment response in glioblastoma, as well as to highlight the definitions, pathophysiology, and imaging features typical of true progression, pseudoprogression, pseudoresponse, and radiation necrosis.
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Affiliation(s)
- Sara B Strauss
- Department of Radiology, Weill Cornell Medical Center, 525 East 68th Street, Box 141, New York, NY 10065, USA
| | - Alicia Meng
- Department of Radiology, Weill Cornell Medical Center, 525 East 68th Street, Box 141, New York, NY 10065, USA
| | - Edward J Ebani
- Department of Radiology, Weill Cornell Medical Center, 525 East 68th Street, Box 141, New York, NY 10065, USA
| | - Gloria C Chiang
- Department of Radiology, Weill Cornell Medical Center, 525 East 68th Street, Box 141, New York, NY 10065, USA.
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24
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Guo L, Li X, Cao H, Hua J, Mei Y, Pillai JJ, Wu Y. Inflow-based vascular-space-occupancy (iVASO) might potentially predict IDH mutation status and tumor grade in diffuse cerebral gliomas. J Neuroradiol 2021; 49:267-274. [PMID: 33482231 DOI: 10.1016/j.neurad.2021.01.002] [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: 09/15/2020] [Revised: 12/13/2020] [Accepted: 01/11/2021] [Indexed: 02/08/2023]
Abstract
PURPOSE The aim of the study is to assess the diagnostic performance of inflow-based vascular-space-occupancy (iVASO) MR imaging for differentiating glioblastomas (grade IV, GBM) and lower-grade diffuse gliomas (grade II and III, LGG) and its potential to predict IDH mutation status. METHODS One hundred and two patients with diffuse cerebral glioma (56 males; median age, 43.5 years) underwent iVASO and dynamic susceptibility contrast (DSC) MR imaging. The iVASO-derived arteriolar cerebral blood volume (CBVa), relative CBVa (rCBVa), and the DSC-derived relative cerebral blood volume (rCBV) were obtained, and these measurements were compared between the GBM group (n = 43) and the LGG group (n = 59) and between the IDH-mutation group (n = 54) and the IDH-wild group (n = 48). RESULTS Significant correlation was observed between rCBV and CBVa (P < 0.001) or rCBVa (P < 0.001). Both CBVa (P < 0.001) and rCBVa (P < 0.001) were higher in the GBM group. Both CBVa (P < 0.001) and rCBVa (P < 0.001) were lower in the IDH-mutation group compared to the IDH-wild group. Receiver operating characteristic analyses showed the area under curve (AUC) of 0.95 with CBVa and 0.97 with rCBVa in differentiating GBM from LGG. The AUCs were 0.82 and 0.85 for CBVa and rCBVa in predicting IDH gene status, respectively, which were lower than that of rCBV (AUC = 0.90). Combined rCBV and rCBVa significantly improved the diagnostic performance (AUC = 0.95). CONCLUSIONS iVASO MR imaging has the potential to predict IDH mutation and grade in glioma.
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Affiliation(s)
- Liuji Guo
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, PR China
| | - Xiaodan Li
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, PR China
| | - Haimei Cao
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, PR China
| | - Jun Hua
- Neurosection, Division of MRI Research, Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Yingjie Mei
- China International Center, Philips Healthcare, Guangzhou, PR China
| | - Jay J Pillai
- Division of Neuroradiology, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Yuankui Wu
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, PR China.
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Boxerman JL, Quarles CC, Hu LS, Erickson BJ, Gerstner ER, Smits M, Kaufmann TJ, Barboriak DP, Huang RH, Wick W, Weller M, Galanis E, Kalpathy-Cramer J, Shankar L, Jacobs P, Chung C, van den Bent MJ, Chang S, Al Yung WK, Cloughesy TF, Wen PY, Gilbert MR, Rosen BR, Ellingson BM, Schmainda KM. Consensus recommendations for a dynamic susceptibility contrast MRI protocol for use in high-grade gliomas. Neuro Oncol 2020; 22:1262-1275. [PMID: 32516388 PMCID: PMC7523451 DOI: 10.1093/neuonc/noaa141] [Citation(s) in RCA: 112] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Despite the widespread clinical use of dynamic susceptibility contrast (DSC) MRI, DSC-MRI methodology has not been standardized, hindering its utilization for response assessment in multicenter trials. Recently, the DSC-MRI Standardization Subcommittee of the Jumpstarting Brain Tumor Drug Development Coalition issued an updated consensus DSC-MRI protocol compatible with the standardized brain tumor imaging protocol (BTIP) for high-grade gliomas that is increasingly used in the clinical setting and is the default MRI protocol for the National Clinical Trials Network. After reviewing the basis for controversy over DSC-MRI protocols, this paper provides evidence-based best practices for clinical DSC-MRI as determined by the Committee, including pulse sequence (gradient echo vs spin echo), BTIP-compliant contrast agent dosing (preload and bolus), flip angle (FA), echo time (TE), and post-processing leakage correction. In summary, full-dose preload, full-dose bolus dosing using intermediate (60°) FA and field strength-dependent TE (40-50 ms at 1.5 T, 20-35 ms at 3 T) provides overall best accuracy and precision for cerebral blood volume estimates. When single-dose contrast agent usage is desired, no-preload, full-dose bolus dosing using low FA (30°) and field strength-dependent TE provides excellent performance, with reduced contrast agent usage and elimination of potential systematic errors introduced by variations in preload dose and incubation time.
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Affiliation(s)
- Jerrold L Boxerman
- Department of Diagnostic Imaging, Warren Alpert Medical School, Brown University, Providence, Rhode Island, USA
- Representative of the Eastern Cooperative Oncology Group–American College of Radiology Imaging Network (ECOG-ACRIN) Cancer Research Group
- Representative of the American Society of Neuroradiology (ASNR)
- Representative of the American Society of Functional Neuroradiology (ASFNR)
| | - Chad C Quarles
- Department of Neuroimaging Research and Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Leland S Hu
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, USA
- Representative of the Alliance for Clinical Trials in Oncology
- Representative of the American Society of Neuroradiology (ASNR)
| | - Bradley J Erickson
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
- Representative of the Alliance for Clinical Trials in Oncology
- Representative of the RSNA Quantitative Imaging Biomarker Alliance (QIBA)
- Representative of the American Society of Neuroradiology (ASNR)
| | - Elizabeth R Gerstner
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Representative of the Adult Brain Tumor Consortium (ABTC)
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC–University Medical Center Rotterdam, Rotterdam, Netherlands
- Representative of the European Organisation for Research and Treatment of Cancer (EORTC)
| | - Timothy J Kaufmann
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
- Representative of the Alliance for Clinical Trials in Oncology
| | - Daniel P Barboriak
- Department of Radiology, Duke University School of Medicine, Durham, North Carolina, USA
- Representative of the Eastern Cooperative Oncology Group–American College of Radiology Imaging Network (ECOG-ACRIN) Cancer Research Group
- Representative of the RSNA Quantitative Imaging Biomarker Alliance (QIBA)
- Representative of the American Society of Neuroradiology (ASNR)
| | - Raymond H Huang
- Department of Radiology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
- Center for Neuro-Oncology, Dana-Farber/Brigham and Women’s Cancer Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Wolfgang Wick
- Department of Neurooncology, National Center of Tumor Disease, University Clinic Heidelberg, Heidelberg, Germany
- Representative of the European Organisation for Research and Treatment of Cancer (EORTC)
| | - Michael Weller
- Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland
- Representative of the European Organisation for Research and Treatment of Cancer (EORTC)
| | - Evanthia Galanis
- Division of Medical Oncology, Department of Oncology, Mayo Clinic, Rochester, Minnesota, USA
- Representative of the Alliance for Clinical Trials in Oncology
| | - Jayashree Kalpathy-Cramer
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Lalitha Shankar
- Division of Cancer Treatment and Diagnosis, National Cancer Institute (NCI), Bethesda, Maryland, USA
| | - Paula Jacobs
- Division of Cancer Treatment and Diagnosis, National Cancer Institute (NCI), Bethesda, Maryland, USA
| | - Caroline Chung
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- Representative of the Alliance for Clinical Trials in Oncology
| | - Martin J van den Bent
- Department of Neuro-Oncology, Erasmus MC Cancer Institute, Rotterdam, Netherlands
- Representative of the European Organisation for Research and Treatment of Cancer (EORTC)
| | - Susan Chang
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA
| | - W K Al Yung
- Department of Neuro-Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program and UCLA Brain Tumor Imaging Laboratory (BTIL), David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Patrick Y Wen
- Center for Neuro-Oncology, Dana-Farber/Brigham and Women’s Cancer Center, Harvard Medical School, Boston, Massachusetts, USA
- Representative of the Adult Brain Tumor Consortium (ABTC)
| | - Mark R Gilbert
- Neuro-Oncology Branch, National Cancer Institute (NCI), Bethesda, Maryland, USA
- Representative of the Radiation Therapy Oncology Group (RTOG)
| | - Bruce R Rosen
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Benjamin M Ellingson
- UCLA Neuro-Oncology Program and UCLA Brain Tumor Imaging Laboratory (BTIL), David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
- Departments of Radiological Sciences, Psychiatry, and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
- Representative of the Adult Brain Tumor Consortium (ABTC)
- Representative of the Ivy Consortium for Early Phase Clinical Trials
- Representative of the Eastern Cooperative Oncology Group–American College of Radiology Imaging Network (ECOG-ACRIN) Cancer Research Group
- Representative of the RSNA Quantitative Imaging Biomarker Alliance (QIBA)
- Representative of the American Society of Neuroradiology (ASNR)
| | - Kathleen M Schmainda
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
- Representative of the Eastern Cooperative Oncology Group–American College of Radiology Imaging Network (ECOG-ACRIN) Cancer Research Group
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Pons-Escoda A, Garcia-Ruiz A, Naval-Baudin P, Cos M, Vidal N, Plans G, Bruna J, Perez-Lopez R, Majos C. Presurgical Identification of Primary Central Nervous System Lymphoma with Normalized Time-Intensity Curve: A Pilot Study of a New Method to Analyze DSC-PWI. AJNR Am J Neuroradiol 2020; 41:1816-1824. [PMID: 32943424 DOI: 10.3174/ajnr.a6761] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 07/03/2020] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE DSC-PWI has demonstrated promising results in the presurgical diagnosis of brain tumors. While most studies analyze specific parameters derived from time-intensity curves, very few have directly analyzed the whole curves. The aims of this study were the following: 1) to design a new method of postprocessing time-intensity curves, which renders normalized curves, and 2) to test its feasibility and performance on the diagnosis of primary central nervous system lymphoma. MATERIALS AND METHODS Diagnostic MR imaging of patients with histologically confirmed primary central nervous system lymphoma were retrospectively reviewed. Correlative cases of glioblastoma, anaplastic astrocytoma, metastasis, and meningioma, matched by date and number, were retrieved for comparison. Time-intensity curves of enhancing tumor and normal-appearing white matter were obtained for each case. Enhancing tumor curves were normalized relative to normal-appearing white matter. We performed pair-wise comparisons for primary central nervous system lymphoma against the other tumor type. The best discriminatory time points of the curves were obtained through a stepwise selection. Logistic binary regression was applied to obtain prediction models. The generated algorithms were applied in a test subset. RESULTS A total of 233 patients were included in the study: 47 primary central nervous system lymphomas, 48 glioblastomas, 39 anaplastic astrocytomas, 49 metastases, and 50 meningiomas. The classifiers satisfactorily performed all bilateral comparisons in the test subset (primary central nervous system lymphoma versus glioblastoma, area under the curve = 0.96 and accuracy = 93%; versus anaplastic astrocytoma, 0.83 and 71%; versus metastases, 0.95 and 93%; versus meningioma, 0.93 and 96%). CONCLUSIONS The proposed method for DSC-PWI time-intensity curve normalization renders comparable curves beyond technical and patient variability. Normalized time-intensity curves performed satisfactorily for the presurgical identification of primary central nervous system lymphoma.
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Affiliation(s)
- A Pons-Escoda
- Radiology Department (A.P.-E., P.N.-B., M.C., C.M.), Institut de Diagnòstic per la Imatge, Hospital Universitari de Bellvitge. L'Hospitalet de Llobregat, Barcelona, Spain .,Neurooncology Unit (A.P.-E., N.V., G.P., J.B., C.M.), Insitut Català d'Oncologia, Institut d'Investigació Biomèdica de Bellvitge, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain
| | - A Garcia-Ruiz
- Radiomics Group (A.G.-R., R.P.-L.), Vall d'Hebron Institut d'Oncologia, Barcelona, Spain
| | - P Naval-Baudin
- Radiology Department (A.P.-E., P.N.-B., M.C., C.M.), Institut de Diagnòstic per la Imatge, Hospital Universitari de Bellvitge. L'Hospitalet de Llobregat, Barcelona, Spain
| | - M Cos
- Radiology Department (A.P.-E., P.N.-B., M.C., C.M.), Institut de Diagnòstic per la Imatge, Hospital Universitari de Bellvitge. L'Hospitalet de Llobregat, Barcelona, Spain
| | - N Vidal
- Pathology Department (N.V.), Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain.,Neurooncology Unit (A.P.-E., N.V., G.P., J.B., C.M.), Insitut Català d'Oncologia, Institut d'Investigació Biomèdica de Bellvitge, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain
| | - G Plans
- Neurosurgery Department (G.P.), Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain.,Neurooncology Unit (A.P.-E., N.V., G.P., J.B., C.M.), Insitut Català d'Oncologia, Institut d'Investigació Biomèdica de Bellvitge, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain
| | - J Bruna
- Neurology Department (J.B.), Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain.,Neurooncology Unit (A.P.-E., N.V., G.P., J.B., C.M.), Insitut Català d'Oncologia, Institut d'Investigació Biomèdica de Bellvitge, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain
| | - R Perez-Lopez
- Radiomics Group (A.G.-R., R.P.-L.), Vall d'Hebron Institut d'Oncologia, Barcelona, Spain
| | - C Majos
- Radiology Department (A.P.-E., P.N.-B., M.C., C.M.), Institut de Diagnòstic per la Imatge, Hospital Universitari de Bellvitge. L'Hospitalet de Llobregat, Barcelona, Spain.,Neurooncology Unit (A.P.-E., N.V., G.P., J.B., C.M.), Insitut Català d'Oncologia, Institut d'Investigació Biomèdica de Bellvitge, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain
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Abdalla G, Hammam A, Anjari M, D'Arco DF, Bisdas DS. Glioma surveillance imaging: current strategies, shortcomings, challenges and outlook. BJR Open 2020; 2:20200009. [PMID: 33178973 PMCID: PMC7594888 DOI: 10.1259/bjro.20200009] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 05/29/2020] [Accepted: 06/01/2020] [Indexed: 01/11/2023] Open
Abstract
Inaccurate assessment of surveillance imaging to assess response to glioma therapy may have life-changing consequences. Varied management plans including chemotherapy, radiotherapy or immunotherapy may all contribute to heterogeneous post-treatment appearances and the overlap between the morphological features of pseudoprogression, pseudoresponse and radiation necrosis can make their discrimination very challenging. Therefore, there has been a drive to develop objective strategies for post-treatment assessment of brain gliomas. This review discusses the most important of these approaches such as the RANO "Response Assessment in Neuro-Oncology", iRANO "Immunotherapy Response Assessment in Neuro-Oncology" and RAPNO "Response Assessment in Paediatric Neuro-Oncology" models. In addition to these systematic approaches for glioma surveillance, the relatively limited information provided by conventional imaging modalities alone has motivated the development of novel advanced magnetic resonance (MR) and metabolic imaging methods for further discrimination between viable tumour and treatment induced changes. Multiple clinical trials and meta-analyses have investigated the diagnostic performance of these novel techniques in the follow up of brain gliomas, including both single modality descriptive studies and comparative imaging assessment. In this manuscript, we review the literature and discuss the promises and pitfalls of frequently studied modalities in glioma surveillance imaging, including MR perfusion, MR diffusion and MR spectroscopy. In addition, we evaluate other promising MR techniques such as chemical exchange saturation transfer as well as fludeoxyglucose and non-FDG positron emission tomography techniques.
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Affiliation(s)
- Gehad Abdalla
- Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Trust, London, UK
| | - Ahmed Hammam
- Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Trust, London, UK
| | - Mustafa Anjari
- Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Trust, London, UK
| | - Dr. Felice D'Arco
- Department of Neuroradiology, Great Ormond Street Hospital for Children, London, UK
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28
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B VA, S M, R V, F M, M YP. Exploring Two Methods of CBV Estimation in Two Groups of Grade III Gliomas with Different Appearance on Post-Contrast T1 Images. J Biomed Phys Eng 2020; 10:283-290. [PMID: 32637372 PMCID: PMC7321391 DOI: 10.31661/jbpe.v0i0.1176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Accepted: 05/20/2019] [Indexed: 06/11/2023]
Abstract
BACKGROUND Many studies have used Cerebral Blood Volume (CBV) for gliomas grading and there has been in good agreement between CBV and tumor grade. Almost all of those studies have emphasized the importance of leakage correction due to the underestimation/overestimation of CBV caused by T1/T2* leakage effect in enhanced cases of tumors, especially high grade ones. OBJECTIVES The aim of this study is to investigate two methods of CBV estimation in two groups of gliomas with the same grade and different appearance on post contrast T1 images (Enhanced vs. Non-enhanced ones). MATERIAL AND METHODS In this retrospective study, eight glioma patients with histopatologically confirmed grade III were equally divided into two groups (with enhancement (group 1) and without enhancement (group 2)), and retrospectively studied. Imaging was performed on a 3 tesla MR Scanner and included gradient-echo DSC, 3D T1-weighted dataset and FLAIR images. The conventional method of CBV measurement (Integration over the whole curve of CTC- method 1) and the GVF fitting (method 2) was done using Matlab. RESULTS The observed mean rCBV in the tumor ROI was 2.85 and 2.12 for group 1 with method 1 and 2, respectively. Mean rCBV in the tumor ROI for group 2 was 1.24 and 1.11 with method 1 and 2, respectively. CONCLUSION In conclusion, this pilot study demonstrated that with combined use of pre-bolus and accounting for T2* effect, CBV could be considered as a criterion for the categorization of glioma tumors.
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Affiliation(s)
- Vejdani Afkham B
- MSc, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Masjoodi S
- PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Vosoughi R
- MSc, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Mosayebian F
- MSc, Department of radiology technology, School of Applied Sciences, Shahid Beheshti University of Medical Sciences
| | - Yousef Pour M
- PhD, School of Medicine, Aja university of Medical Science, Tehran, Iran
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29
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Park I, Lupo JM, Nelson SJ. Correlation of Tumor Perfusion Between Carbon-13 Imaging with Hyperpolarized Pyruvate and Dynamic Susceptibility Contrast MRI in Pre-Clinical Model of Glioblastoma. Mol Imaging Biol 2020; 21:626-632. [PMID: 30225760 DOI: 10.1007/s11307-018-1275-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
PURPOSE The purpose of this study was to compare C-13 imaging parameters with hyperpolarized [1-13C]pyruvate with conventional gadolinium (Gd)-based perfusion weighted imaging using an orthotopic xenograft model of human glioblastoma multiforme (GBM). PROCEDURES C-13 3D magnetic resonance spectroscopic imaging (MRSI) data were obtained from 14 tumor-bearing rats after the injection of hyperpolarized [1-13C]pyruvate at a 3T scanner. Dynamic susceptibility contrast (DSC) perfusion-weighted MR images were obtained following intravenous administration of Gd-DTPA. Normalized lactate, pyruvate, total carbon, and lactate to pyruvate ratio from C-13 MRSI data were compared with normalized peak height and percent recovery of ΔR2* curve from the DSC images in the voxels containing tumor using a Pearson's linear correlation. RESULTS Normalized peak height from DSC imaging showed substantial correlations with normalized lactate (r = 0.6, p = 0.02) and total carbon (r = 0.6, p = 0.02) from hyperpolarized C-13 MRSI data. CONCLUSIONS Since the peak height in the ΔR2* curve from DSC data is related to the extent of blood volume, these hyperpolarized C-13 imaging parameters may be used to assess blood volume in rodent intracranial xenograft models of GBM.
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Affiliation(s)
- Ilwoo Park
- Department of Radiology, Chonnam National University Medical School, Jeabongro 42, Donggu, Gwangju, 61469, South Korea. .,Department of Radiology, Chonnam National University Hospital, Gwangju, South Korea.
| | - Janine M Lupo
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Sarah J Nelson
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
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30
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Hoxworth JM, Eschbacher JM, Gonzales AC, Singleton KW, Leon GD, Smith KA, Stokes AM, Zhou Y, Mazza GL, Porter AB, Mrugala MM, Zimmerman RS, Bendok BR, Patra DP, Krishna C, Boxerman JL, Baxter LC, Swanson KR, Quarles CC, Schmainda KM, Hu LS. Performance of Standardized Relative CBV for Quantifying Regional Histologic Tumor Burden in Recurrent High-Grade Glioma: Comparison against Normalized Relative CBV Using Image-Localized Stereotactic Biopsies. AJNR Am J Neuroradiol 2020; 41:408-415. [PMID: 32165359 DOI: 10.3174/ajnr.a6486] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 12/23/2019] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Perfusion MR imaging measures of relative CBV can distinguish recurrent tumor from posttreatment radiation effects in high-grade gliomas. Currently, relative CBV measurement requires normalization based on user-defined reference tissues. A recently proposed method of relative CBV standardization eliminates the need for user input. This study compares the predictive performance of relative CBV standardization against relative CBV normalization for quantifying recurrent tumor burden in high-grade gliomas relative to posttreatment radiation effects. MATERIALS AND METHODS We recruited 38 previously treated patients with high-grade gliomas (World Health Organization grades III or IV) undergoing surgical re-resection for new contrast-enhancing lesions concerning for recurrent tumor versus posttreatment radiation effects. We recovered 112 image-localized biopsies and quantified the percentage of histologic tumor content versus posttreatment radiation effects for each sample. We measured spatially matched normalized and standardized relative CBV metrics (mean, median) and fractional tumor burden for each biopsy. We compared relative CBV performance to predict tumor content, including the Pearson correlation (r), against histologic tumor content (0%-100%) and the receiver operating characteristic area under the curve for predicting high-versus-low tumor content using binary histologic cutoffs (≥50%; ≥80% tumor). RESULTS Across relative CBV metrics, fractional tumor burden showed the highest correlations with tumor content (0%-100%) for normalized (r = 0.63, P < .001) and standardized (r = 0.66, P < .001) values. With binary cutoffs (ie, ≥50%; ≥80% tumor), predictive accuracies were similar for both standardized and normalized metrics and across relative CBV metrics. Median relative CBV achieved the highest area under the curve (normalized = 0.87, standardized = 0.86) for predicting ≥50% tumor, while fractional tumor burden achieved the highest area under the curve (normalized = 0.77, standardized = 0.80) for predicting ≥80% tumor. CONCLUSIONS Standardization of relative CBV achieves similar performance compared with normalized relative CBV and offers an important step toward workflow optimization and consensus methodology.
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Affiliation(s)
- J M Hoxworth
- From the Departments of Radiology (J.M.H., Y.Z., L.S.H.)
| | | | | | - K W Singleton
- Precision Neurotherapeutics Lab (K.W.S., G.D.L., B.R.B., K.R.S.), Mayo Clinic in Arizona, Phoenix, Arizona
| | - G D Leon
- Precision Neurotherapeutics Lab (K.W.S., G.D.L., B.R.B., K.R.S.), Mayo Clinic in Arizona, Phoenix, Arizona
| | - K A Smith
- Keller Center for Imaging Innovation (A.M.S.), Barrow Neurological Institute, Phoenix, Arizona
| | - A M Stokes
- Keller Center for Imaging Innovation (A.M.S.), Barrow Neurological Institute, Phoenix, Arizona
| | - Y Zhou
- From the Departments of Radiology (J.M.H., Y.Z., L.S.H.)
| | - G L Mazza
- Department of Health Sciences Research (G.L.M.), Division of Biomedical Statistics and Informatics, Mayo Clinic Scottsdale, Scottsdale, Arizona
| | | | | | | | - B R Bendok
- Precision Neurotherapeutics Lab (K.W.S., G.D.L., B.R.B., K.R.S.), Mayo Clinic in Arizona, Phoenix, Arizona
| | - D P Patra
- Departments of Neurosurgery (D.P.P.)
| | | | - J L Boxerman
- Department of Diagnostic Imaging (J.L.B.), Rhode Island Hospital, Providence, Rhode Island
| | - L C Baxter
- Neuropsychology (L.C.B.), Mayo Clinic Hospital, Phoenix, Arizona
| | - K R Swanson
- Precision Neurotherapeutics Lab (K.W.S., G.D.L., B.R.B., K.R.S.), Mayo Clinic in Arizona, Phoenix, Arizona
| | | | - K M Schmainda
- Department of Radiology (K.M.S.), Medical College of Wisconsin, Milwaukee, Wisconsin
| | - L S Hu
- From the Departments of Radiology (J.M.H., Y.Z., L.S.H.)
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31
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Hu LS, Hawkins-Daarud A, Wang L, Li J, Swanson KR. Imaging of intratumoral heterogeneity in high-grade glioma. Cancer Lett 2020; 477:97-106. [PMID: 32112907 DOI: 10.1016/j.canlet.2020.02.025] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 02/17/2020] [Accepted: 02/19/2020] [Indexed: 12/19/2022]
Abstract
High-grade glioma (HGG), and particularly Glioblastoma (GBM), can exhibit pronounced intratumoral heterogeneity that confounds clinical diagnosis and management. While conventional contrast-enhanced MRI lacks the capability to resolve this heterogeneity, advanced MRI techniques and PET imaging offer a spectrum of physiologic and biophysical image features to improve the specificity of imaging diagnoses. Published studies have shown how integrating these advanced techniques can help better define histologically distinct targets for surgical and radiation treatment planning, and help evaluate the regional heterogeneity of tumor recurrence and response assessment following standard adjuvant therapy. Application of texture analysis and machine learning (ML) algorithms has also enabled the emerging field of radiogenomics, which can spatially resolve the regional and genetically distinct subpopulations that coexist within a single GBM tumor. This review focuses on the latest advances in neuro-oncologic imaging and their clinical applications for the assessment of intratumoral heterogeneity.
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Affiliation(s)
- Leland S Hu
- Department of Radiology, Mayo Clinic Arizona, 5777 E Mayo Blvd, Phoenix, AZ, 85054, USA.
| | - Andrea Hawkins-Daarud
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd, Support, Services Building Suite 2-700, Phoenix, AZ, 85054, USA.
| | - Lujia Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281, USA.
| | - Jing Li
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281, USA.
| | - Kristin R Swanson
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd, Support, Services Building Suite 2-700, Phoenix, AZ, 85054, USA.
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32
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Yu XR, Cao BL, Li W, Tian Y, Du ZL. Accuracy of Tumor Perfusion Assessment in Rat C6 Gliomas Model with USPIO. Open Med (Wars) 2019; 14:778-784. [PMID: 31737781 PMCID: PMC6843489 DOI: 10.1515/med-2019-0091] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Accepted: 08/17/2019] [Indexed: 11/17/2022] Open
Abstract
Detailed characterization of the permeability and vascular volume of brain tumor vasculature can provide essential insights into tumor physiology. In this study, we evaluated the consistency of measurements in tumor blood volume and examined the feasibility of using ultrasmall superparamagnetic iron oxide (USPIO) versus gadolinium-diethylene triamine pentaacetic acid (Gd-DTPA) as contrast agents for MR perfusion imaging of brain gliomas in C6 Rats. Eighteen rats were intracerebrally implanted with C6 glioma cells, randomly divided into two groups and examined by 3.0T perfusion MR imaging with Gd-DTPA and USPIO. Tumor relative cerebral blood volume (rCBV) and relative maximum signal reduction ratio (rSRRmax) were created based on analysis of MR perfusion images. The mean values for rCBV were 2.09 and 1.57 in the USPIO and the Gd-DTPA groups, respectively, and rSRRmax values were 1.92 and 1.02 in the USPIO and the Gd-DTPA groups, respectively, showing signifi cant differences in both rCBV and rSRRmax between the USPIO and the Gd-DTPA groups (P < 0.05). The results showed that early vascular leakage occurred with gadolinium rather than USPIO in perfusion assessment, revealing that USPIO was useful in perfusion MR imaging for the assessment of tumor vasculature.
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Affiliation(s)
- Xiang-Rong Yu
- Department of Radiology, Zhuhai Hospital of Jinan University, Zhuhai People's Hospital, Zhuhai, China
| | - Bo-Ling Cao
- Department of Radiology, Zhuhai Hospital of Jinan University, Zhuhai People's Hospital, Zhuhai, China
| | - Wei Li
- Department of Radiology, Zhuhai Hospital of Jinan University, Zhuhai People's Hospital, Zhuhai, China
| | - Ye Tian
- Department of Radiology, Zhuhai Hospital of Jinan University, Zhuhai People's Hospital, Zhuhai, China
| | - Zhong-Li Du
- Zhuhai Hospital of Jinan University, Zhuhai People's Hospital, Zhuhai, 519000, China
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Iv M, Liu X, Lavezo J, Gentles AJ, Ghanem R, Lummus S, Born DE, Soltys SG, Nagpal S, Thomas R, Recht L, Fischbein N. Perfusion MRI-Based Fractional Tumor Burden Differentiates between Tumor and Treatment Effect in Recurrent Glioblastomas and Informs Clinical Decision-Making. AJNR Am J Neuroradiol 2019; 40:1649-1657. [PMID: 31515215 DOI: 10.3174/ajnr.a6211] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Accepted: 08/01/2019] [Indexed: 12/15/2022]
Abstract
BACKGROUND AND PURPOSE Fractional tumor burden better correlates with histologic tumor volume fraction in treated glioblastoma than other perfusion metrics such as relative CBV. We defined fractional tumor burden classes with low and high blood volume to distinguish tumor from treatment effect and to determine whether fractional tumor burden can inform treatment-related decision-making. MATERIALS AND METHODS Forty-seven patients with high-grade gliomas (primarily glioblastoma) with recurrent contrast-enhancing lesions on DSC-MR imaging were retrospectively evaluated after surgical sampling. Histopathologic examination defined treatment effect versus tumor. Normalized relative CBV thresholds of 1.0 and 1.75 were used to define low, intermediate, and high fractional tumor burden classes in each histopathologically defined group. Performance was assessed with an area under the receiver operating characteristic curve. Consensus agreement among physician raters reporting hypothetic changes in treatment-related decisions based on fractional tumor burden was compared with actual real-time treatment decisions. RESULTS Mean lower fractional tumor burden, high fractional tumor burden, and relative CBV of the contrast-enhancing volume were significantly different between treatment effect and tumor (P = .002, P < .001, and P < .001), with tumor having significantly higher fractional tumor burden and relative CBV and lower fractional tumor burden. No significance was found with intermediate fractional tumor burden. Performance of the area under the receiver operating characteristic curve was the following: high fractional tumor burden, 0.85; low fractional tumor burden, 0.7; and relative CBV, 0.81. In comparing treatment decisions, there were disagreements in 7% of tumor and 44% of treatment effect cases; in the latter, all disagreements were in cases with scattered atypical cells. CONCLUSIONS High fractional tumor burden and low fractional tumor burden define fractions of the contrast-enhancing lesion volume with high and low blood volume, respectively, and can differentiate treatment effect from tumor in recurrent glioblastomas. Fractional tumor burden maps can also help to inform clinical decision-making.
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Affiliation(s)
- M Iv
- From the Departments of Neuroimaging and Neurointervention (M.I., N.F.)
| | - X Liu
- Department of Neurosurgery (X.L.), Shengjing Hospital of China Medical University, Shenyang, China
| | - J Lavezo
- Pathology (J.L., R.G., S.L., D.E.B.)
| | - A J Gentles
- Medicine (Biomedical Informatics Research) (A.J.G.)
| | - R Ghanem
- Pathology (J.L., R.G., S.L., D.E.B.)
| | - S Lummus
- Pathology (J.L., R.G., S.L., D.E.B.)
| | - D E Born
- Pathology (J.L., R.G., S.L., D.E.B.)
| | | | - S Nagpal
- Neurology (Neuro-Oncology) (S.N., R.T., L.R.), Stanford University, Stanford, California
| | - R Thomas
- Neurology (Neuro-Oncology) (S.N., R.T., L.R.), Stanford University, Stanford, California
| | - L Recht
- Neurology (Neuro-Oncology) (S.N., R.T., L.R.), Stanford University, Stanford, California
| | - N Fischbein
- From the Departments of Neuroimaging and Neurointervention (M.I., N.F.)
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Strauss SB, Meng A, Ebani EJ, Chiang GC. Imaging Glioblastoma Posttreatment: Progression, Pseudoprogression, Pseudoresponse, Radiation Necrosis. Radiol Clin North Am 2019; 57:1199-1216. [PMID: 31582045 DOI: 10.1016/j.rcl.2019.07.003] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Radiographic monitoring of posttreatment glioblastoma is important for clinical trials and determining next steps in management. Evaluation for tumor progression is confounded by the presence of treatment-related radiographic changes, making a definitive determination less straight-forward. The purpose of this article was to describe imaging tools available for assessing treatment response in glioblastoma, as well as to highlight the definitions, pathophysiology, and imaging features typical of true progression, pseudoprogression, pseudoresponse, and radiation necrosis.
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Affiliation(s)
- Sara B Strauss
- Department of Radiology, Weill Cornell Medical Center, 525 East 68th Street, Box 141, New York, NY 10065, USA
| | - Alicia Meng
- Department of Radiology, Weill Cornell Medical Center, 525 East 68th Street, Box 141, New York, NY 10065, USA
| | - Edward J Ebani
- Department of Radiology, Weill Cornell Medical Center, 525 East 68th Street, Box 141, New York, NY 10065, USA
| | - Gloria C Chiang
- Department of Radiology, Weill Cornell Medical Center, 525 East 68th Street, Box 141, New York, NY 10065, USA.
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Saini J, Gupta RK, Kumar M, Singh A, Saha I, Santosh V, Beniwal M, Kandavel T, Cauteren MV. Comparative evaluation of cerebral gliomas using rCBV measurements during sequential acquisition of T1-perfusion and T2*-perfusion MRI. PLoS One 2019; 14:e0215400. [PMID: 31017934 PMCID: PMC6481809 DOI: 10.1371/journal.pone.0215400] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 04/01/2019] [Indexed: 12/31/2022] Open
Abstract
Objective To assess the inter-technique agreement of relative cerebral blood volume (rCBV) measurements obtained using T1- and T2*-perfusion MRI on 3T scanner in glioma patients. Methods A total of 49 adult patients with gliomas underwent both on T1- and T2*-perfusion in the same scanning session, and rCBV maps were estimated using both methods. For the quantitative analysis; Two independent observers recorded the rCBV values from the tumor as well as contralateral brain tissue from both T1- and T2*-perfusion. Inter-observer and inter-technique rCBV measurement agreement were determined by using 95% Bland-Altman limits of agreement and intra-class correlation coefficient (ICC) statistics. Results Qualitative analysis of the conventional and perfusion images showed that 16/49 (32.65%) tumors showed high susceptibility, and in these patients T2*-perfusion maps were suboptimal. Bland-Altman plots revealed an agreement between two independent observers recorded rCBV values for both T1- and T2*-perfusion. The ICC demonstrated strong agreement between rCBV values recorded by two observers for both T2* (ICC = 0.96, p = 0.040) and T1 (ICC = 0.97, p = 0.026) perfusion and similarly, good agreement was noted between rCBV estimated using two methods (ICC = 0.74, P<0.001). ROC analysis showed that rCBV estimated using T1- and T2*-perfusion methods were able to discriminate between grade-III and grade-IV tumors with AUC of 0.723 and 0.767 respectively. Comparison of AUC values of two ROC curves did not show any significant difference. Conclusions In the current study, T1- and T2*-perfusion showed similar diagnostic performance for discrimination of grade III and grade IV gliomas; however, T1-perfusion was found to be better for the evaluation of tumors with intratumoral hemorrhage, postoperative recurrent tumors, and lesions near skull base. We conclude that T1-perfusion MRI with a single dose of contrast could be used as an alternative to T2*-perfusion to overcome the issues associated with this technique in brain tumors for reliable perfusion quantification.
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Affiliation(s)
- Jitender Saini
- Department of Neuroimaging & Interventional Radiology, National Institute of Mental, Health and Neurosciences, Bangalore, Karnataka, India
- * E-mail:
| | - Rakesh Kumar Gupta
- Department of Radiology and Imaging, Fortis Memorial Hospital and Research Institute, Gurgaon, Haryana, India
| | - Manoj Kumar
- Department of Neuroimaging & Interventional Radiology, National Institute of Mental, Health and Neurosciences, Bangalore, Karnataka, India
| | - Anup Singh
- Center for Biomedical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India
| | - Indrajit Saha
- Philips Health Systems, Philips India Limited, Gurgaon, Haryana, India
| | - Vani Santosh
- Department of Neuropathology, National Institute of Mental, Health and Neurosciences, Bangalore, Karnataka, India
| | - Manish Beniwal
- Department of Neurosurgery, National Institute of Mental, Health and Neurosciences, Bangalore, Karnataka, India
| | - Thennarasu Kandavel
- Department of Biostatistics, National Institute of Mental, Health and Neurosciences, Bangalore, Karnataka, India
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Schmainda KM, Prah MA, Hu LS, Quarles CC, Semmineh N, Rand SD, Connelly JM, Anderies B, Zhou Y, Liu Y, Logan B, Stokes A, Baird G, Boxerman JL. Moving Toward a Consensus DSC-MRI Protocol: Validation of a Low-Flip Angle Single-Dose Option as a Reference Standard for Brain Tumors. AJNR Am J Neuroradiol 2019; 40:626-633. [PMID: 30923088 DOI: 10.3174/ajnr.a6015] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Accepted: 01/18/2019] [Indexed: 12/21/2022]
Abstract
BACKGROUND AND PURPOSE DSC-MR imaging using preload, intermediate (60°) flip angle and postprocessing leakage correction has gained traction as a standard methodology. Simulations suggest that DSC-MR imaging with flip angle = 30° and no preload yields relative CBV practically equivalent to the reference standard. This study tested this hypothesis in vivo. MATERIALS AND METHODS Eighty-four patients with brain lesions were enrolled in this 3-institution study. Forty-three patients satisfied the inclusion criteria. DSC-MR imaging (3T, single-dose gadobutrol, gradient recalled-echo-EPI, TE = 20-35 ms, TR = 1.2-1.63 seconds) was performed twice for each patient, with flip angle = 30°-35° and no preload (P-), which provided preload (P+) for the subsequent intermediate flip angle = 60°. Normalized relative CBV and standardized relative CBV maps were generated, including postprocessing with contrast agent leakage correction (C+) and without (C-) contrast agent leakage correction. Contrast-enhancing lesion volume, mean relative CBV, and contrast-to-noise ratio obtained with 30°/P-/C-, 30°/P-/C+, and 60°/P+/C- were compared with 60°/P+/C+ using the Lin concordance correlation coefficient and Bland-Altman analysis. Equivalence between the 30°/P-/C+ and 60°/P+/C+ protocols and the temporal SNR for the 30°/P- and 60°/P+ DSC-MR imaging data was also determined. RESULTS Compared with 60°/P+/C+, 30°/P-/C+ had closest mean standardized relative CBV (P = .61), highest Lin concordance correlation coefficient (0.96), and lowest Bland-Altman bias (μ = 1.89), compared with 30°/P-/C- (P = .02, Lin concordance correlation coefficient = 0.59, μ = 14.6) and 60°/P+/C- (P = .03, Lin concordance correlation coefficient = 0.88, μ = -10.1) with no statistical difference in contrast-to-noise ratios across protocols. The normalized relative CBV and standardized relative CBV were statistically equivalent at the 10% level using either the 30°/P-/C+ or 60°/P+/C+ protocols. Temporal SNR was not significantly different for 30°/P- and 60°/P+ (P = .06). CONCLUSIONS Tumor relative CBV derived from low-flip angle, no-preload DSC-MR imaging with leakage correction is an attractive single-dose alternative to the higher dose reference standard.
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Affiliation(s)
- K M Schmainda
- From the Departments of Biophysics (K.M.S., M.A.P.) .,Radiology (K.M.S., S.D.R.)
| | - M A Prah
- From the Departments of Biophysics (K.M.S., M.A.P.)
| | - L S Hu
- Departments of Radiology (L.S.H., Y.Z.)
| | - C C Quarles
- Division of Imaging Research (C.C.Q., N.S., A.S.), Barrow Neurological Institute, Phoenix, Arizona
| | - N Semmineh
- Division of Imaging Research (C.C.Q., N.S., A.S.), Barrow Neurological Institute, Phoenix, Arizona
| | | | | | - B Anderies
- Neurosurgery (B.A.), Mayo Clinic, Scottsdale, Arizona
| | - Y Zhou
- Departments of Radiology (L.S.H., Y.Z.)
| | - Y Liu
- Division of Biostatistics, Institute for Health and Society (Y.L., B.L.), Medical College of Wisconsin, Milwaukee, Wisconsin
| | - B Logan
- Division of Biostatistics, Institute for Health and Society (Y.L., B.L.), Medical College of Wisconsin, Milwaukee, Wisconsin
| | - A Stokes
- Division of Imaging Research (C.C.Q., N.S., A.S.), Barrow Neurological Institute, Phoenix, Arizona
| | - G Baird
- Department of Diagnostic Imaging (J.L.B., G.B.), Rhode Island Hospital and Warren Alpert Medical School of Brown University, Providence, Rhode Island
| | - J L Boxerman
- Department of Diagnostic Imaging (J.L.B., G.B.), Rhode Island Hospital and Warren Alpert Medical School of Brown University, Providence, Rhode Island
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Johnson DR, Guerin JB, Ruff MW, Fang S, Hunt CH, Morris JM, Pearse Morris P, Kaufmann TJ. Glioma response assessment: Classic pitfalls, novel confounders, and emerging imaging tools. Br J Radiol 2018; 92:20180730. [PMID: 30412421 DOI: 10.1259/bjr.20180730] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Neuroimaging plays a pivotal role in the care of patients with infiltrating gliomas, in whom imaging changes are often the first indications of tumor response or progression. Unfortunately, evaluation of glioma response is often not straightforward, even for experienced radiologists. Post-surgical or radiation-related changes may mimic the appearance of disease progression, while medications such as corticosteroids and antiangiogenic agents may mimic tumor response without truly arresting tumor growth or improving patient survival. Immunotherapy response can result in inflammatory changes which manifest as progressively increasing tumor enhancement and edema over months. Many of these pitfalls can be minimized or avoided altogether by the use of modern brain tumor response criteria, while others will require new imaging tools before they can be fully addressed. Advanced MRI methods and novel positron emission tomography (PET) agents are proving important for this purpose, and their role will undoubtedly continue to grow in the future.
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Affiliation(s)
| | | | - Michael W Ruff
- 2 Department of Neurology, Mayo Clinic , Rochester, MN , US
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Semmineh NB, Bell LC, Stokes AM, Hu LS, Boxerman JL, Quarles CC. Optimization of Acquisition and Analysis Methods for Clinical Dynamic Susceptibility Contrast MRI Using a Population-Based Digital Reference Object. AJNR Am J Neuroradiol 2018; 39:1981-1988. [PMID: 30309842 PMCID: PMC6239921 DOI: 10.3174/ajnr.a5827] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Accepted: 06/08/2018] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE The accuracy of DSC-MR imaging CBV maps in glioblastoma depends on acquisition and analysis protocols. Multisite protocol heterogeneity has challenged standardization initiatives due to the difficulties of in vivo validation. This study sought to compare the accuracy of routinely used protocols using a digital reference object. MATERIALS AND METHODS The digital reference object consisted of approximately 10,000 simulated voxels recapitulating typical signal heterogeneity encountered in vivo. The influence of acquisition and postprocessing methods on CBV reliability was evaluated across 6912 parameter combinations, including contrast agent dosing schemes, pulse sequence parameters, field strengths, and postprocessing methods. Accuracy and precision were assessed using the concordance correlation coefficient and coefficient of variation. RESULTS Across all parameter space, the optimal protocol included full-dose contrast agent preload and bolus, intermediate (60°) flip angle, 30-ms TE, and postprocessing with a leakage-correction algorithm (concordance correlation coefficient = 0.97, coefficient of variation = 6.6%). Protocols with no preload or fractional dose preload and bolus using these acquisition parameters were generally less robust. However, a protocol with no preload, full-dose bolus, and low (30°) flip angle performed very well (concordance correlation coefficient = 0.93, coefficient of variation = 8.7% at 1.5T and concordance correlation coefficient = 0.92, coefficient of variation = 8.2% at 3T). CONCLUSIONS Schemes with full-dose preload and bolus maximize CBV accuracy and reduce variability, which could enable smaller sample sizes and more reliable detection of CBV changes in clinical trials. When a lower total contrast agent dose is desired, use of a low flip angle, no preload, and full-dose bolus protocol may provide an attractive alternative.
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Affiliation(s)
- N B Semmineh
- From the Department of Imaging Research (N.B.S., L.C.B., A.M.S., C.C.Q.), Barrow Neurological Institute, Phoenix, Arizona
| | - L C Bell
- From the Department of Imaging Research (N.B.S., L.C.B., A.M.S., C.C.Q.), Barrow Neurological Institute, Phoenix, Arizona
| | - A M Stokes
- From the Department of Imaging Research (N.B.S., L.C.B., A.M.S., C.C.Q.), Barrow Neurological Institute, Phoenix, Arizona
| | - L S Hu
- Department of Radiology (L.S.H.), Mayo Clinic Arizona, Phoenix, Arizona
| | - J L Boxerman
- Department of Diagnostic Imaging (J.L.B.), Rhode Island Hospital, Providence, Rhode Island
| | - C C Quarles
- From the Department of Imaging Research (N.B.S., L.C.B., A.M.S., C.C.Q.), Barrow Neurological Institute, Phoenix, Arizona
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Qin L, Li X, Li A, Cheng S, Qu J, Reinshagen K, Hu J, Himes N, Lu G, Xu X, Young GS. Clinical Validation of Automatable Gaussian Normalized CBV in Brain Tumor Analysis: Superior Reproducibility and Slightly Better Association with Survival than Current Standard Manual Normal Appearing White Matter Normalization. Transl Oncol 2018; 11:1398-1405. [PMID: 30216765 PMCID: PMC6138997 DOI: 10.1016/j.tranon.2018.07.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Revised: 07/23/2018] [Accepted: 07/30/2018] [Indexed: 10/28/2022] Open
Abstract
PURPOSE To validate Gaussian normalized cerebral blood volume (GN-nCBV) by association with overall survival (OS) in newly diagnosed glioblastoma patients and compare this association with current standard white matter normalized cerebral blood volume (WN-nCBV). METHODS We retrieved spin-echo echo-planar dynamic susceptibility contrast MRI acquired after maximal resection and prior to radiation therapy between 2006 and 2011 in 51 adult patients (28 male, 23 female; age 23-87 years) with newly diagnosed glioblastoma. Software code was developed in house to perform Gaussian normalization of CBV to the standard deviation of the whole brain CBV. Three expert readers manually selected regions of interest in tumor and normal-appearing white matter on CBV maps. Receiver operating characteristics (ROC) curves associating nCBV with 15-month OS were calculated for both GN-nCBV and WN-nCBV. Reproducibility and interoperator variability were compared using within-subject coefficient of variation (wCV) and intraclass correlation coefficients (ICCs). RESULTS GN-nCBV ICC (≥0.82) and wCV (≤21%) were superior to WN-nCBV ICC (0.54-0.55) and wCV (≥46%). The area under the ROC curve analysis demonstrated both GN-nCBV and WN-nCBV to be good predictors of OS, but GN-nCBV was consistently superior, although the difference was not statistically significant. CONCLUSION GN-nCBV has a slightly better association with clinical gold standard OS than conventional WM-nCBV in our glioblastoma patient cohort. This equivalent or superior validity, combined with the advantages of higher reproducibility, lower interoperator variability, and easier automation, makes GN-nCBV superior to WM-nCBV for clinical and research use in glioma patients. We recommend widespread adoption and incorporation of GN-nCBV into commercial dynamic susceptibility contrast processing software.
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Affiliation(s)
- Lei Qin
- Dana-Farber Cancer Institute, Department of Imaging, Boston, MA, USA; Harvard Medical School, Department of Radiology, Boston, MA, USA
| | - Xiang Li
- Brigham and Women's Hospital, Department of Radiology, Boston, MA, USA; Affiliated Cancer Hospital of Zhengzhou University, Department of Radiology, Zhengzhou, Henan, China
| | - Angie Li
- Brigham and Women's Hospital, Department of Radiology, Boston, MA, USA; The Robert Larner, M.D. College of Medicine at the University of Vermont, Burlington, VT, USA
| | - Suchun Cheng
- Dana-Farber Cancer Institute, Department of Biostatistics and Computational Biology, Boston, MA, USA
| | - Jinrong Qu
- Brigham and Women's Hospital, Department of Radiology, Boston, MA, USA; Affiliated Cancer Hospital of Zhengzhou University, Department of Radiology, Zhengzhou, Henan, China
| | - Katherine Reinshagen
- Harvard Medical School, Department of Radiology, Boston, MA, USA; Brigham and Women's Hospital, Department of Radiology, Boston, MA, USA; Massachusetts Eye and Ear Infirmary, Department of Radiology, Boston, MA, USA
| | - Jiani Hu
- Dana-Farber Cancer Institute, Department of Biostatistics and Computational Biology, Boston, MA, USA
| | - Nathan Himes
- Brigham and Women's Hospital, Department of Radiology, Boston, MA, USA; Medical Imaging of Lehigh Valley, Lehigh Valley Hospital, Allentown, PA, USA
| | - Gao Lu
- Brigham and Women's Hospital, Department of Radiology, Boston, MA, USA; Peking Union Medical College Hospital, Department of Neurosurgery, Beijing, China
| | - Xiaoyin Xu
- Peking Union Medical College Hospital, Department of Neurosurgery, Beijing, China; Peking Union Medical College Hospital, Department of Neurosurgery, Beijing, China
| | - Geoffrey S Young
- Dana-Farber Cancer Institute, Department of Imaging, Boston, MA, USA; Harvard Medical School, Department of Radiology, Boston, MA, USA; Brigham and Women's Hospital, Department of Radiology, Boston, MA, USA.
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Hedderich D, Kluge A, Pyka T, Zimmer C, Kirschke JS, Wiestler B, Preibisch C. Consistency of normalized cerebral blood volume values in glioblastoma using different leakage correction algorithms on dynamic susceptibility contrast magnetic resonance imaging data without and with preload. J Neuroradiol 2018; 46:44-51. [PMID: 29753641 DOI: 10.1016/j.neurad.2018.04.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Revised: 02/27/2018] [Accepted: 04/21/2018] [Indexed: 10/16/2022]
Abstract
BACKGROUND AND PURPOSE Several leakage correction algorithms for dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI)-based cerebral blood volume (CBV) measurement have been proposed, and combination with a preload of contrast agent is generally recommended. A single bolus application scheme would largely simplify and facilitate standardized clinical applications, while reducing contrast agent (CA) dose. The aim of this study was, therefore, to investigate whether appropriate leakage correction redundantizes prebolus application by comparing normalized DSC-based CBV (nCBV) measures of two consecutive CA boli. MATERIALS AND METHODS Twenty-seven patients with suspected glioblastoma (WHO-grade-IV) underwent DSC-MRI during two consecutive boli of Gd-based CA. Four variants of two post-processing leakage correction techniques were compared with respect to nCBV in contrast enhancing tumor tissue. First, a reference curve approach with first pass and full integration of corrected ΔR2*(t), and second, a deconvolution-based approach using singular value decomposition (SVD) with a standard noise-dependent cutoff or Tikhonov regularization. RESULTS Compared to respective uncorrected values, all leakage correction techniques increased nCBV for data acquired without prebolus, while there was no consistent trend for data acquired with prebolus. The best agreement between corrected nCBV values in contrast enhancing tumor, obtained in the same patients without and with prebolus, respectively, was obtained for the reference curve-based correction approach with either first pass or full integration. CONCLUSION The reference curve-based leakage correction approach with integration-based nCBV calculation yielded a high accordance between nCBV values without and with prebolus, respectively. Thus, it appears possible to obtain valid nCBV in glioblastoma with a single CA injection.
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Affiliation(s)
- Dennis Hedderich
- Department of Diagnostic and Interventional Neuroradiology, Technische Universität München, Ismaningerst. 22, 81675 Munich, Germany
| | - Anne Kluge
- Department of Diagnostic and Interventional Neuroradiology, Technische Universität München, Ismaningerst. 22, 81675 Munich, Germany
| | - Thomas Pyka
- Clinic for Nuclear Medicine, Technische Universität München, Ismaningerst. 22, 81675 Munich, Germany
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, Technische Universität München, Ismaningerst. 22, 81675 Munich, Germany
| | - Jan S Kirschke
- Department of Diagnostic and Interventional Neuroradiology, Technische Universität München, Ismaningerst. 22, 81675 Munich, Germany
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, Technische Universität München, Ismaningerst. 22, 81675 Munich, Germany
| | - Christine Preibisch
- Department of Diagnostic and Interventional Neuroradiology, Technische Universität München, Ismaningerst. 22, 81675 Munich, Germany; Clinic for Neurology, Technische Universität München, Ismaningerst. 22, 81675 Munich, Germany.
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Thust SC, van den Bent MJ, Smits M. Pseudoprogression of brain tumors. J Magn Reson Imaging 2018; 48:571-589. [PMID: 29734497 PMCID: PMC6175399 DOI: 10.1002/jmri.26171] [Citation(s) in RCA: 178] [Impact Index Per Article: 29.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2018] [Accepted: 04/07/2018] [Indexed: 12/11/2022] Open
Abstract
This review describes the definition, incidence, clinical implications, and magnetic resonance imaging (MRI) findings of pseudoprogression of brain tumors, in particular, but not limited to, high-grade glioma. Pseudoprogression is an important clinical problem after brain tumor treatment, interfering not only with day-to-day patient care but also the execution and interpretation of clinical trials. Radiologically, pseudoprogression is defined as a new or enlarging area(s) of contrast agent enhancement, in the absence of true tumor growth, which subsides or stabilizes without a change in therapy. The clinical definitions of pseudoprogression have been quite variable, which may explain some of the differences in reported incidences, which range from 9-30%. Conventional structural MRI is insufficient for distinguishing pseudoprogression from true progressive disease, and advanced imaging is needed to obtain higher levels of diagnostic certainty. Perfusion MRI is the most widely used imaging technique to diagnose pseudoprogression and has high reported diagnostic accuracy. Diagnostic performance of MR spectroscopy (MRS) appears to be somewhat higher, but MRS is less suitable for the routine and universal application in brain tumor follow-up. The combination of MRS and diffusion-weighted imaging and/or perfusion MRI seems to be particularly powerful, with diagnostic accuracy reaching up to or even greater than 90%. While diagnostic performance can be high with appropriate implementation and interpretation, even a combination of techniques, however, does not provide 100% accuracy. It should also be noted that most studies to date are small, heterogeneous, and retrospective in nature. Future improvements in diagnostic accuracy can be expected with harmonization of acquisition and postprocessing, quantitative MRI and computer-aided diagnostic technology, and meticulous evaluation with clinical and pathological data. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.
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Affiliation(s)
- Stefanie C. Thust
- Lysholm Neuroradiology DepartmentNational Hospital for Neurology and NeurosurgeryLondonUK
- Department of Brain Rehabilitation and RepairUCL Institute of NeurologyLondonUK
- Imaging DepartmentUniversity College London HospitalLondonUK
| | - Martin J. van den Bent
- Department of NeurologyThe Brain Tumor Centre at Erasmus MC Cancer InstituteRotterdamThe Netherlands
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MCUniversity Medical Centre RotterdamRotterdamThe Netherlands
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Quarles CC, Bell LC, Stokes AM. Imaging vascular and hemodynamic features of the brain using dynamic susceptibility contrast and dynamic contrast enhanced MRI. Neuroimage 2018; 187:32-55. [PMID: 29729392 DOI: 10.1016/j.neuroimage.2018.04.069] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2017] [Revised: 04/27/2018] [Accepted: 04/29/2018] [Indexed: 12/22/2022] Open
Abstract
In the context of neurologic disorders, dynamic susceptibility contrast (DSC) and dynamic contrast enhanced (DCE) MRI provide valuable insights into cerebral vascular function, integrity, and architecture. Even after two decades of use, these modalities continue to evolve as their biophysical and kinetic basis is better understood, with improvements in pulse sequences and accelerated imaging techniques and through application of more robust and automated data analysis strategies. Here, we systematically review each of these elements, with a focus on how their integration improves kinetic parameter accuracy and the development of new hemodynamic biomarkers that provide sub-voxel sensitivity (e.g., capillary transit time and flow heterogeneity). Regarding contrast mechanisms, we discuss the dipole-dipole interactions and susceptibility effects that give rise to simultaneous T1, T2 and T2∗ relaxation effects, including their quantification, influence on pulse sequence parameter optimization, and use in methods such as vessel size and vessel architectural imaging. The application of technologic advancements, such as parallel imaging, simultaneous multi-slice, undersampled k-space acquisitions, and sliding window strategies, enables improved spatial and/or temporal resolution of DSC and DCE acquisitions. Such acceleration techniques have also enabled the implementation of, clinically feasible, simultaneous multi-echo spin- and gradient echo acquisitions, providing more comprehensive and quantitative interrogation of T1, T2 and T2∗ changes. Characterizing these relaxation rate changes through different post-processing options allows for the quantification of hemodynamics and vascular permeability. The application of different biophysical models provides insight into traditional hemodynamic parameters (e.g., cerebral blood volume) and more advanced parameters (e.g., capillary transit time heterogeneity). We provide insight into the appropriate selection of biophysical models and the necessary post-processing steps to ensure reliable measurements while minimizing potential sources of error. We show representative examples of advanced DSC- and DCE-MRI methods applied to pathologic conditions affecting the cerebral microcirculation, including brain tumors, stroke, aging, and multiple sclerosis. The maturation and standardization of conventional DSC- and DCE-MRI techniques has enabled their increased integration into clinical practice and use in clinical trials, which has, in turn, spurred renewed interest in their technological and biophysical development, paving the way towards a more comprehensive assessment of cerebral hemodynamics.
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Affiliation(s)
- C Chad Quarles
- Division of Neuro imaging Research, Barrow Neurological Institute, 350 W. Thomas Rd, Phoenix, AZ, USA.
| | - Laura C Bell
- Division of Neuro imaging Research, Barrow Neurological Institute, 350 W. Thomas Rd, Phoenix, AZ, USA
| | - Ashley M Stokes
- Division of Neuro imaging Research, Barrow Neurological Institute, 350 W. Thomas Rd, Phoenix, AZ, USA
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Soliman RK, Gamal SA, Essa AHA, Othman MH. Preoperative Grading of Glioma Using Dynamic Susceptibility Contrast MRI: Relative Cerebral Blood Volume Analysis of Intra-tumoural and Peri-tumoural Tissue. Clin Neurol Neurosurg 2018; 167:86-92. [DOI: 10.1016/j.clineuro.2018.01.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2017] [Revised: 12/27/2017] [Accepted: 01/07/2018] [Indexed: 11/28/2022]
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Thust SC, Heiland S, Falini A, Jäger HR, Waldman AD, Sundgren PC, Godi C, Katsaros VK, Ramos A, Bargallo N, Vernooij MW, Yousry T, Bendszus M, Smits M. Glioma imaging in Europe: A survey of 220 centres and recommendations for best clinical practice. Eur Radiol 2018. [PMID: 29536240 PMCID: PMC6028837 DOI: 10.1007/s00330-018-5314-5] [Citation(s) in RCA: 135] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Objectives At a European Society of Neuroradiology (ESNR) Annual Meeting 2015 workshop, commonalities in practice, current controversies and technical hurdles in glioma MRI were discussed. We aimed to formulate guidance on MRI of glioma and determine its feasibility, by seeking information on glioma imaging practices from the European Neuroradiology community. Methods Invitations to a structured survey were emailed to ESNR members (n=1,662) and associates (n=6,400), European national radiologists’ societies and distributed via social media. Results Responses were received from 220 institutions (59% academic). Conventional imaging protocols generally include T2w, T2-FLAIR, DWI, and pre- and post-contrast T1w. Perfusion MRI is used widely (85.5%), while spectroscopy seems reserved for specific indications. Reasons for omitting advanced imaging modalities include lack of facility/software, time constraints and no requests. Early postoperative MRI is routinely carried out by 74% within 24–72 h, but only 17% report a percent measure of resection. For follow-up, most sites (60%) issue qualitative reports, while 27% report an assessment according to the RANO criteria. A minority of sites use a reporting template (23%). Conclusion Clinical best practice recommendations for glioma imaging assessment are proposed and the current role of advanced MRI modalities in routine use is addressed. Key Points • We recommend the EORTC-NBTS protocol as the clinical standard glioma protocol. • Perfusion MRI is recommended for diagnosis and follow-up of glioma. • Use of advanced imaging could be promoted with increased education activities. • Most response assessment is currently performed qualitatively. • Reporting templates are not widely used, and could facilitate standardisation. Electronic supplementary material The online version of this article (10.1007/s00330-018-5314-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- S C Thust
- Lysholm Neuroradiology Department, National Hospital for Neurology and Neurosurgery, London, UK
- Department of Brain Rehabilitation and Repair, UCL Institute of Neurology, London, UK
- Imaging Department, University College London Hospital, London, UK
| | - S Heiland
- Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany
| | - A Falini
- Department of Neuroradiology, San Raffaele Scientific Institute, Milan, Italy
| | - H R Jäger
- Lysholm Neuroradiology Department, National Hospital for Neurology and Neurosurgery, London, UK
- Department of Brain Rehabilitation and Repair, UCL Institute of Neurology, London, UK
- Imaging Department, University College London Hospital, London, UK
| | - A D Waldman
- Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK
| | - P C Sundgren
- Institution for Clinical Sciences/Radiology, Lund University, Lund, Sweden
- Centre for Imaging and Physiology, Skåne University hospital, Lund, Sweden
| | - C Godi
- Department of Neuroradiology, San Raffaele Scientific Institute, Milan, Italy
| | - V K Katsaros
- General Anti-Cancer and Oncological Hospital "Agios Savvas", Athens, Greece
- Central Clinic of Athens, Athens, Greece
- University of Athens, Athens, Greece
| | - A Ramos
- Hospital 12 de Octubre, Madrid, Spain
| | - N Bargallo
- Image Diagnostic Centre, Hospital Clinic de Barcelona, Barcelona, Spain
- Magnetic Resonance Core Facility, Institut per la Recerca Biomedica August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - M W Vernooij
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - T Yousry
- Lysholm Neuroradiology Department, National Hospital for Neurology and Neurosurgery, London, UK
| | - M Bendszus
- Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany
| | - M Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.
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Dongas J, Asahina AT, Bacchi S, Patel S. Magnetic Resonance Perfusion Imaging in the Diagnosis of High-Grade Glioma Progression and Treatment-Related Changes: A Systematic Review. ACTA ACUST UNITED AC 2018. [DOI: 10.4236/ojmn.2018.83024] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Bell LC, Hu LS, Stokes AM, McGee SC, Baxter LC, Quarles CC. Characterizing the Influence of Preload Dosing on Percent Signal Recovery (PSR) and Cerebral Blood Volume (CBV) Measurements in a Patient Population With High-Grade Glioma Using Dynamic Susceptibility Contrast MRI. ACTA ACUST UNITED AC 2017; 3:89-95. [PMID: 28825039 PMCID: PMC5557059 DOI: 10.18383/j.tom.2017.00004] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
With DSC-MRI, contrast agent leakage effects in brain tumors can either be leveraged for percent signal recovery (PSR) measurements or be adequately resolved for accurate relative cerebral blood volume (rCBV) measurements. Leakage effects can be dimished by administration of a preload dose before imaging and/or specific postprocessing steps. This study compares the consistency of both PSR and rCBV measurements as a function of varying preload doses in a retrospective analysis of 14 subjects with high-grade gliomas. The scans consisted of 6 DSC-MRI scans during 6 sequential bolus injections (0.05 mmol/kg). Mean PSR was calculated for tumor and normal-appearing white matter regions of interest. DSC-MRI data were corrected for leakage effects before computing mean tumor rCBV. Statistical differences were seen across varying preloads for tumor PSR (P value = 4.57E-24). Tumor rCBV values did not exhibit statistically significant differences across preloads (P value = .14) and were found to be highly consistent for clinically relevant preloads (intraclass correlation coefficient = 0.93). For a 0.05 mmol/kg injection bolus and pulse sequence parameters used, the highest PSR contrast between normal-appearing white matter and tumor occurs when no preload is used. This suggests that studies using PSR as a biomarker should acquire DSC-MRI data without preload. The finding that leakage-corrected rCBV values do not depend on the presence or dose of preload contradicts that of previous studies with dissimilar acquisition protocols. This further confirms the sensitivity of rCBV to preload dosing schemes and pulse sequence parameters and highlights the importance of standardization efforts for achieving multisite rCBV consistency.
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Affiliation(s)
- Laura C Bell
- Division of Imaging Research, Barrow Neurological Institute, Phoenix, Arizona
| | - Leland S Hu
- Department of Radiology, Mayo Clinic Arizona, Scottsdale, Arizona
| | - Ashley M Stokes
- Division of Imaging Research, Barrow Neurological Institute, Phoenix, Arizona
| | - Samuel C McGee
- Division of Imaging Research, Barrow Neurological Institute, Phoenix, Arizona
| | - Leslie C Baxter
- Division of Imaging Research, Barrow Neurological Institute, Phoenix, Arizona
| | - C Chad Quarles
- Division of Imaging Research, Barrow Neurological Institute, Phoenix, Arizona
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Longitudinal DSC-MRI for Distinguishing Tumor Recurrence From Pseudoprogression in Patients With a High-grade Glioma. Am J Clin Oncol 2017; 40:228-234. [PMID: 25436828 DOI: 10.1097/coc.0000000000000156] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
OBJECTIVE For patients with high-grade glioma on clinical trials it is important to accurately assess time of disease progression. However, differentiation between pseudoprogression (PsP) and progressive disease (PD) is unreliable with standard magnetic resonance imaging (MRI) techniques. Dynamic susceptibility contrast perfusion MRI (DSC-MRI) can measure relative cerebral blood volume (rCBV) and may help distinguish PsP from PD. METHODS A subset of patients with high-grade glioma on a phase II clinical trial with temozolomide, paclitaxel poliglumex, and concurrent radiation were assessed. Nine patients (3 grade III, 6 grade IV), with a total of 19 enhancing lesions demonstrating progressive enhancement (≥25% increase from nadir) on postchemoradiation conventional contrast-enhanced MRI, had serial DSC-MRI. Mean leakage-corrected rCBV within enhancing lesions was computed for all postchemoradiation time points. RESULTS Of the 19 progressively enhancing lesions, 10 were classified as PsP and 9 as PD by biopsy/surgery or serial enhancement patterns during interval follow-up MRI. Mean rCBV at initial progressive enhancement did not differ significantly between PsP and PD (2.35 vs. 2.17; P=0.67). However, change in rCBV at first subsequent follow-up (-0.84 vs. 0.84; P=0.001) and the overall linear trend in rCBV after initial progressive enhancement (negative vs. positive slope; P=0.04) differed significantly between PsP and PD. CONCLUSIONS Longitudinal trends in rCBV may be more useful than absolute rCBV in distinguishing PsP from PD in chemoradiation-treated high-grade gliomas with DSC-MRI. Further studies of DSC-MRI in high-grade glioma as a potential technique for distinguishing PsP from PD are indicated.
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Murayama K, Nishiyama Y, Hirose Y, Abe M, Ohyu S, Ninomiya A, Fukuba T, Katada K, Toyama H. Differentiating between Central Nervous System Lymphoma and High-grade Glioma Using Dynamic Susceptibility Contrast and Dynamic Contrast-enhanced MR Imaging with Histogram Analysis. Magn Reson Med Sci 2017; 17:42-49. [PMID: 28515410 PMCID: PMC5760232 DOI: 10.2463/mrms.mp.2016-0113] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
Purpose: We evaluated the diagnostic performance of histogram analysis of data from a combination of dynamic susceptibility contrast (DSC)-MRI and dynamic contrast-enhanced (DCE)-MRI for quantitative differentiation between central nervous system lymphoma (CNSL) and high-grade glioma (HGG), with the aim of identifying useful perfusion parameters as objective radiological markers for differentiating between them. Methods: Eight lesions with CNSLs and 15 with HGGs who underwent MRI examination, including DCE and DSC-MRI, were enrolled in our retrospective study. DSC-MRI provides a corrected cerebral blood volume (cCBV), and DCE-MRI provides a volume transfer coefficient (Ktrans) for transfer from plasma to the extravascular extracellular space. Ktrans and cCBV were measured from a round region-of-interest in the slice of maximum size on the contrast-enhanced lesion. The differences in t values between CNSL and HGG for determining the most appropriate percentile of Ktrans and cCBV were investigated. The differences in Ktrans, cCBV, and Ktrans/cCBV between CNSL and HGG were investigated using histogram analysis. Receiver operating characteristic (ROC) analysis of Ktrans, cCBV, and Ktrans/cCBV ratio was performed. Results: The 30th percentile (C30) in Ktrans and 80th percentile (C80) in cCBV were the most appropriate percentiles for distinguishing between CNSL and HGG from the differences in t values. CNSL showed significantly lower C80 cCBV, significantly higher C30 Ktrans, and significantly higher C30 Ktrans/C80 cCBV than those of HGG. In ROC analysis, C30 Ktrans/C80 cCBV had the best discriminative value for differentiating between CNSL and HGG as compared to C30 Ktrans or C80 cCBV. Conclusion: The combination of Ktrans by DCE-MRI and cCBV by DSC-MRI was found to reveal the characteristics of vascularity and permeability of a lesion more precisely than either Ktrans or cCBV alone. Histogram analysis of these vascular microenvironments enabled quantitative differentiation between CNSL and HGG.
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Affiliation(s)
| | | | - Yuichi Hirose
- Department of Neurosurgery, Fujita Health University
| | - Masato Abe
- Department of Pathology, School of Health Sciences, Fujita Health University
| | | | | | - Takashi Fukuba
- Department of Radiology, Fujita Health University Hospital
| | - Kazuhiro Katada
- Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University
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Incidence of Tumour Progression and Pseudoprogression in High-Grade Gliomas: a Systematic Review and Meta-Analysis. Clin Neuroradiol 2017; 28:401-411. [PMID: 28466127 PMCID: PMC6105173 DOI: 10.1007/s00062-017-0584-x] [Citation(s) in RCA: 97] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2016] [Accepted: 04/04/2017] [Indexed: 12/29/2022]
Abstract
Background High-grade gliomas are the most common primary brain tumours. Pseudoprogression describes the false appearance of radiation-induced progression on MRI. A distinction should be made from true tumour progression to correctly plan treatment. However, there is wide variation of reported pseudoprogression. We thus aimed to establish the incidence of pseudoprogression and tumour progression in high-grade glioma patients with a systematic review and meta-analysis. Methods We searched PubMed, Embase and Web of Science on the incidence of pseudoprogression and tumour progression in adult high-grade glioma patients from 2005, the latest on 8 October 2014. Histology or imaging follow-up was used as reference standard. Extracted data included number of patients with worsening of imaging findings on T1 postcontrast or T2/FLAIR, pseudoprogression and tumour progression. Study quality was assessed. Heterogeneity was tested with I2. Pooling of the results was done with random models using Metaprop in STATA (StataCorp. Stata Statistical Software. College Station, TX: StataCorp LP). Results We identified 73 studies. MRI progression occurred in 2603 patients. Of these, 36% (95% confidence interval [CI] 33–40%) demonstrated pseudoprogression, 60% (95%CI 56–64%) tumour progression and unknown outcome was present in the remaining 4% of the patients (range 1–37%). Conclusion This meta-analysis demonstrated for the first time a notably high pooled incidence of pseudoprogression in patients with a form of progression across the available literature. This highlighted the full extent of the problem of the currently conventional MRI-based Response Assessment in Neuro-Oncology (RANO) criteria for treatment evaluation in high-grade gliomas. This underscores the need for more accurate treatment evaluation using advanced imaging to improve diagnostic accuracy and therapeutic approach. Electronic supplementary material The online version of this article (doi: 10.1007/s00062-017-0584-x) contains supplementary material, which is available to authorized users. It contains the characteristics of the included studies (supplementary table 1) and a full search strategy (see supplementary search strategy).
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