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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. [PMID: 38469930 DOI: 10.1002/mrm.30077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [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, Arizona, USA
| | - Dinghui Wang
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Poonam Choudhary
- Department of Neuroradiology, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Sudarshan Ragunathan
- Department of Neuroradiology, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - John P Karis
- Department of Neuroradiology, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - James G Pipe
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Radiology, University of Wisconsin, Madison, Wisconsin, USA
| | - C Chad Quarles
- Department of Neuroradiology, Barrow Neurological Institute, Phoenix, Arizona, USA
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Ashley M Stokes
- Department of Neuroradiology, Barrow Neurological Institute, Phoenix, Arizona, USA
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Chen P, Scarpelli ML, Healey DR, Mehta S, Quarles CC. MRI and amino acid PET detection of whole-brain tumor burden. Front Oncol 2023; 13:1248249. [PMID: 37810983 PMCID: PMC10558180 DOI: 10.3389/fonc.2023.1248249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 08/30/2023] [Indexed: 10/10/2023] Open
Abstract
Background [18F]fluciclovine amino acid PET has shown promise for detecting brain tumor regions undetected on conventional anatomic MRI scans. However, it remains unclear which of these modalities provides a better assessment of the whole brain tumor burden. This study quantifies the performance of [18F]fluciclovine PET and MRI for detecting the whole brain tumor burden. Methods Thirteen rats were orthotopically implanted with fluorescently transduced human glioblastoma cells. Rats underwent MRI (T1- and T2-weighted) and [18F]fluciclovine PET. Next brains were excised, optically cleared, and scanned ex vivo with fluorescence imaging. All images were co-registered using a novel landmark-based registration to enable a spatial comparison. The tumor burden identified on the fluorescent images was considered the ground truth for comparison with the in vivo imaging. Results Across all cases, the PET sensitivity for detecting tumor burden (median 0.67) was not significantly different than MRI (combined T1+T2-weighted) sensitivity (median 0.61; p=0.85). However, the combined PET+MRI sensitivity (median 0.86) was significantly higher than MRI alone (41% higher; p=0.004) or PET alone (28% higher; p=0.0002). The specificity of combined PET+MRI (median=0.91) was significantly lower compared with MRI alone (6% lower; p=0.004) or PET alone (2% lower; p=0.002). Conclusion In these glioblastoma xenografts, [18F]fluciclovine PET did not provide a significant increase in tumor burden detection relative to conventional anatomic MRI. However, a combined PET and MRI assessment did significantly improve detection sensitivity relative to either modality alone, suggesting potential value in a combined assessment for some tumors.
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Affiliation(s)
- Peng Chen
- School of Health Sciences, Purdue University, West Lafayette, IN, United States
| | | | - Debbie R. Healey
- Department of Cancer Systems Imaging, The University of Texas (UT) MD Anderson Cancer Center, Houston, TX, United States
| | - Shwetal Mehta
- Ivy Brain Tumor Center, Barrow Neurological Institute, Phoenix, AZ, United States
| | - C. Chad Quarles
- Department of Cancer Systems Imaging, The University of Texas (UT) MD Anderson Cancer Center, Houston, TX, United States
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4
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Shiroishi MS, Weinert D, Cen SY, Varghese B, Dondlinger T, Prah M, Mendoza J, Nazemi S, Ameli N, Amini N, Shohas S, Chen S, Bigjahan B, Zada G, Chen T, Neman-Ebrahim J, Chang EL, Chow FE, Fan Z, Yang W, Attenello FJ, Ye J, Kim PE, Patel VN, Lerner A, Acharya J, Hu LS, Quarles CC, Boxerman JL, Wu O, Schmainda KM. A cross-sectional study to test equivalence of low- versus intermediate-flip angle dynamic susceptibility contrast MRI measures of relative cerebral blood volume in patients with high-grade gliomas at 1.5 Tesla field strength. Front Oncol 2023; 13:1156843. [PMID: 37799462 PMCID: PMC10548232 DOI: 10.3389/fonc.2023.1156843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 08/21/2023] [Indexed: 10/07/2023] Open
Abstract
Introduction 1.5 Tesla (1.5T) remain a significant field strength for brain imaging worldwide. Recent computer simulations and clinical studies at 3T MRI have suggested that dynamic susceptibility contrast (DSC) MRI using a 30° flip angle ("low-FA") with model-based leakage correction and no gadolinium-based contrast agent (GBCA) preload provides equivalent relative cerebral blood volume (rCBV) measurements to the reference-standard acquisition using a single-dose GBCA preload with a 60° flip angle ("intermediate-FA") and model-based leakage correction. However, it remains unclear whether this holds true at 1.5T. The purpose of this study was to test this at 1.5T in human high-grade glioma (HGG) patients. Methods This was a single-institution cross-sectional study of patients who had undergone 1.5T MRI for HGG. DSC-MRI consisted of gradient-echo echo-planar imaging (GRE-EPI) with a low-FA without preload (30°/P-); this then subsequently served as a preload for the standard intermediate-FA acquisition (60°/P+). Both normalized (nrCBV) and standardized relative cerebral blood volumes (srCBV) were calculated using model-based leakage correction (C+) with IBNeuro™ software. Whole-enhancing lesion mean and median nrCBV and srCBV from the low- and intermediate-FA methods were compared using the Pearson's, Spearman's and intraclass correlation coefficients (ICC). Results Twenty-three HGG patients composing a total of 31 scans were analyzed. The Pearson and Spearman correlations and ICCs between the 30°/P-/C+ and 60°/P+/C+ acquisitions demonstrated high correlations for both mean and median nrCBV and srCBV. Conclusion Our study provides preliminary evidence that for HGG patients at 1.5T MRI, a low FA, no preload DSC-MRI acquisition can be an appealing alternative to the reference standard higher FA acquisition that utilizes a preload.
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Affiliation(s)
- Mark S. Shiroishi
- Department of Radiology, Keck School of Medicine of the University of Southern California (USC), Los Angeles, CA, United States
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Marina del Rey, CA, United States
- Department of Population and Public Health Sciences, Keck School of Medicine of USC, Los Angeles, CA, United States
| | - Dane Weinert
- Department of Radiology, Keck School of Medicine of the University of Southern California (USC), Los Angeles, CA, United States
| | - Steven Y. Cen
- Department of Radiology, Keck School of Medicine of the University of Southern California (USC), Los Angeles, CA, United States
| | - Bino Varghese
- Department of Radiology, Keck School of Medicine of the University of Southern California (USC), Los Angeles, CA, United States
| | | | - Melissa Prah
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Jesse Mendoza
- Department of Radiology, Keck School of Medicine of the University of Southern California (USC), Los Angeles, CA, United States
| | - Sina Nazemi
- Department of Radiology, Keck School of Medicine of the University of Southern California (USC), Los Angeles, CA, United States
| | - Nima Ameli
- Department of Radiology, Keck School of Medicine of the University of Southern California (USC), Los Angeles, CA, United States
| | - Negin Amini
- Department of Radiology, Keck School of Medicine of the University of Southern California (USC), Los Angeles, CA, United States
| | - Salman Shohas
- Department of Radiology, Keck School of Medicine of the University of Southern California (USC), Los Angeles, CA, United States
| | - Shannon Chen
- Department of Radiology, Keck School of Medicine of the University of Southern California (USC), Los Angeles, CA, United States
| | - Bavrina Bigjahan
- Department of Radiology, Keck School of Medicine of the University of Southern California (USC), Los Angeles, CA, United States
| | - Gabriel Zada
- Department of Neurological Surgery, Keck School of Medicine of USC, Los Angeles, CA, United States
| | - Thomas Chen
- Department of Neurological Surgery, Keck School of Medicine of USC, Los Angeles, CA, United States
| | - Josh Neman-Ebrahim
- Department of Neurological Surgery, Keck School of Medicine of USC, Los Angeles, CA, United States
| | - Eric L. Chang
- Department of Radiation Oncology, Keck School of Medicine of USC, Los Angeles, CA, United States
| | - Frances E. Chow
- Department of Neurological Surgery, Keck School of Medicine of USC, Los Angeles, CA, United States
| | - Zhaoyang Fan
- Department of Radiology, Keck School of Medicine of the University of Southern California (USC), Los Angeles, CA, United States
- Department of Radiation Oncology, Keck School of Medicine of USC, Los Angeles, CA, United States
| | - Wensha Yang
- Department of Radiation Oncology, Keck School of Medicine of USC, Los Angeles, CA, United States
| | - Frank J. Attenello
- Department of Neurological Surgery, Keck School of Medicine of USC, Los Angeles, CA, United States
| | - Jason Ye
- Department of Radiation Oncology, Keck School of Medicine of USC, Los Angeles, CA, United States
| | - Paul E. Kim
- Department of Radiology, Keck School of Medicine of the University of Southern California (USC), Los Angeles, CA, United States
| | - Vishal N. Patel
- Department of Radiology, Mayo Clinic, Jacksonville, FL, United States
| | - Alexander Lerner
- Department of Radiology, Keck School of Medicine of the University of Southern California (USC), Los Angeles, CA, United States
| | - Jay Acharya
- Department of Radiology, Keck School of Medicine of the University of Southern California (USC), Los Angeles, CA, United States
| | - Leland S. Hu
- Department of Radiology, Mayo Clinic, Phoenix, AZ, United States
| | - C. Chad Quarles
- Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jerrold L. Boxerman
- Department of Diagnostic Imaging, The Warren Alpert Medical School of Brown University, Providence, RI, United States
| | - Ona Wu
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Kathleen M. Schmainda
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, United States
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Qi D, Li J, Quarles CC, Fonkem E, Wu E. Assessment and prediction of glioblastoma therapy response: challenges and opportunities. Brain 2023; 146:1281-1298. [PMID: 36445396 PMCID: PMC10319779 DOI: 10.1093/brain/awac450] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 11/03/2022] [Accepted: 11/10/2022] [Indexed: 11/30/2022] Open
Abstract
Glioblastoma is the most aggressive type of primary adult brain tumour. The median survival of patients with glioblastoma remains approximately 15 months, and the 5-year survival rate is <10%. Current treatment options are limited, and the standard of care has remained relatively constant since 2011. Over the last decade, a range of different treatment regimens have been investigated with very limited success. Tumour recurrence is almost inevitable with the current treatment strategies, as glioblastoma tumours are highly heterogeneous and invasive. Additionally, another challenging issue facing patients with glioblastoma is how to distinguish between tumour progression and treatment effects, especially when relying on routine diagnostic imaging techniques in the clinic. The specificity of routine imaging for identifying tumour progression early or in a timely manner is poor due to the appearance similarity of post-treatment effects. Here, we concisely describe the current status and challenges in the assessment and early prediction of therapy response and the early detection of tumour progression or recurrence. We also summarize and discuss studies of advanced approaches such as quantitative imaging, liquid biomarker discovery and machine intelligence that hold exceptional potential to aid in the therapy monitoring of this malignancy and early prediction of therapy response, which may decisively transform the conventional detection methods in the era of precision medicine.
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Affiliation(s)
- Dan Qi
- Department of Neurosurgery and Neuroscience Institute, Baylor Scott & White Health, Temple, TX 76502, USA
| | - Jing Li
- School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - C Chad Quarles
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Ekokobe Fonkem
- Department of Neurosurgery and Neuroscience Institute, Baylor Scott & White Health, Temple, TX 76502, USA
- Department of Medical Education, School of Medicine, Texas A&M University, Bryan, TX 77807, USA
| | - Erxi Wu
- Department of Neurosurgery and Neuroscience Institute, Baylor Scott & White Health, Temple, TX 76502, USA
- Department of Medical Education, School of Medicine, Texas A&M University, Bryan, TX 77807, USA
- Department of Pharmaceutical Sciences, Irma Lerma Rangel School of Pharmacy, Texas A&M University, College Station, TX 77843, USA
- Department of Oncology and LIVESTRONG Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA
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6
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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7
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Griffiths DR, Matthew Law L, Young C, Fuentes A, Truran S, Karamanova N, Bell LC, Turner G, Emerson H, Mastroeni D, Gonzales RJ, Reaven PD, Chad Quarles C, Migrino RQ, Lifshitz J. Chronic Cognitive and Cerebrovascular Function after Mild Traumatic Brain Injury in Rats. J Neurotrauma 2022; 39:1429-1441. [PMID: 35593008 PMCID: PMC10870816 DOI: 10.1089/neu.2022.0015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Severe traumatic brain injury (TBI) results in cognitive dysfunction in part due to vascular perturbations. In contrast, the long-term vasculo-cognitive pathophysiology of mild TBI (mTBI) remains unknown. We evaluated mTBI effects on chronic cognitive and cerebrovascular function and assessed their interrelationships. Sprague-Dawley rats received midline fluid percussion injury (n = 20) or sham (n = 21). Cognitive function was assessed (3- and 6-month novel object recognition [NOR], novel object location [NOL], and temporal order object recognition [TOR]). Six-month cerebral blood flow (CBF) and cerebral blood volume (CBV) using contrast magnetic resonance imaging (MRI) and ex vivo circle of Willis artery endothelial and smooth muscle-dependent function were measured. mTBI rats showed significantly impaired NOR, with similar trends (non-significant) in NOL/TOR. Regional CBF and CBV were similar in sham and mTBI. NOR correlated with CBF in lateral hippocampus, medial hippocampus, and primary somatosensory barrel cortex, whereas it inversely correlated with arterial smooth muscle-dependent dilation. Six-month baseline endothelial and smooth muscle-dependent arterial function were similar among mTBI and sham, but post-angiotensin 2 stimulation, mTBI showed no change in smooth muscle-dependent dilation from baseline response, unlike the reduction in sham. mTBI led to chronic cognitive dysfunction and altered angiotensin 2-stimulated smooth muscle-dependent vasoreactivity. The findings of persistent pathophysiological consequences of mTBI in this animal model add to the broader understanding of chronic pathophysiological sequelae in human mild TBI.
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Affiliation(s)
- Daniel R. Griffiths
- Phoenix VA Health Care System, Phoenix, Arizona, USA
- University of Arizona College of Medicine – Phoenix, Phoenix, Arizona, USA
- Barrow Neurological Institute at Phoenix Children’s Hospital, Phoenix, Arizona, USA
| | - L. Matthew Law
- Phoenix VA Health Care System, Phoenix, Arizona, USA
- University of Arizona College of Medicine – Phoenix, Phoenix, Arizona, USA
- Barrow Neurological Institute at Phoenix Children’s Hospital, Phoenix, Arizona, USA
| | - Conor Young
- University of Arizona College of Medicine – Phoenix, Phoenix, Arizona, USA
| | | | - Seth Truran
- Phoenix VA Health Care System, Phoenix, Arizona, USA
| | | | - Laura C. Bell
- Barrow Neurological Institute, Phoenix, Arizona, USA
| | | | | | | | - Rayna J. Gonzales
- University of Arizona College of Medicine – Phoenix, Phoenix, Arizona, USA
| | - Peter D. Reaven
- Phoenix VA Health Care System, Phoenix, Arizona, USA
- University of Arizona College of Medicine – Phoenix, Phoenix, Arizona, USA
| | | | - Raymond Q. Migrino
- Phoenix VA Health Care System, Phoenix, Arizona, USA
- University of Arizona College of Medicine – Phoenix, Phoenix, Arizona, USA
| | - Jonathan Lifshitz
- Phoenix VA Health Care System, Phoenix, Arizona, USA
- University of Arizona College of Medicine – Phoenix, Phoenix, Arizona, USA
- Barrow Neurological Institute at Phoenix Children’s Hospital, Phoenix, Arizona, USA
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8
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Hormuth DA, Farhat M, Christenson C, Curl B, Chad Quarles C, Chung C, Yankeelov TE. Opportunities for improving brain cancer treatment outcomes through imaging-based mathematical modeling of the delivery of radiotherapy and immunotherapy. Adv Drug Deliv Rev 2022; 187:114367. [PMID: 35654212 DOI: 10.1016/j.addr.2022.114367] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/25/2022] [Accepted: 05/25/2022] [Indexed: 11/01/2022]
Abstract
Immunotherapy has become a fourth pillar in the treatment of brain tumors and, when combined with radiation therapy, may improve patient outcomes and reduce the neurotoxicity. As with other combination therapies, the identification of a treatment schedule that maximizes the synergistic effect of radiation- and immune-therapy is a fundamental challenge. Mechanism-based mathematical modeling is one promising approach to systematically investigate therapeutic combinations to maximize positive outcomes within a rigorous framework. However, successful clinical translation of model-generated combinations of treatment requires patient-specific data to allow the models to be meaningfully initialized and parameterized. Quantitative imaging techniques have emerged as a promising source of high quality, spatially and temporally resolved data for the development and validation of mathematical models. In this review, we will present approaches to personalize mechanism-based modeling frameworks with patient data, and then discuss how these techniques could be leveraged to improve brain cancer outcomes through patient-specific modeling and optimization of treatment strategies.
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Affiliation(s)
- David A Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Maguy Farhat
- Departments of Radiation Oncology, MD Anderson Cancer Center, Houston, TX 77230, USA
| | - Chase Christenson
- Departments of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
| | - Brandon Curl
- Departments of Radiation Oncology, MD Anderson Cancer Center, Houston, TX 77230, USA
| | - C Chad Quarles
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Caroline Chung
- Departments of Radiation Oncology, MD Anderson Cancer Center, Houston, TX 77230, USA
| | - Thomas E Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Oncology, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Imaging Physics, MD Anderson Cancer Center, Houston, TX 77230, USA
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9
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McGarry SD, Brehler M, Bukowy JD, Lowman AK, Bobholz SA, Duenweg SR, Banerjee A, Hurrell SL, Malyarenko D, Chenevert TL, Cao Y, Li Y, You D, Fedorov A, Bell LC, Quarles CC, Prah MA, Schmainda KM, Taouli B, LoCastro E, Mazaheri Y, Shukla‐Dave A, Yankeelov TE, Hormuth DA, Madhuranthakam AJ, Hulsey K, Li K, Huang W, Huang W, Muzi M, Jacobs MA, Solaiyappan M, Hectors S, Antic T, Paner GP, Palangmonthip W, Jacobsohn K, Hohenwalter M, Duvnjak P, Griffin M, See W, Nevalainen MT, Iczkowski KA, LaViolette PS. Multi-Site Concordance of Diffusion-Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness. J Magn Reson Imaging 2022; 55:1745-1758. [PMID: 34767682 PMCID: PMC9095769 DOI: 10.1002/jmri.27983] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 10/21/2021] [Accepted: 10/22/2021] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Diffusion-weighted imaging (DWI) is commonly used to detect prostate cancer, and a major clinical challenge is differentiating aggressive from indolent disease. PURPOSE To compare 14 site-specific parametric fitting implementations applied to the same dataset of whole-mount pathologically validated DWI to test the hypothesis that cancer differentiation varies with different fitting algorithms. STUDY TYPE Prospective. POPULATION Thirty-three patients prospectively imaged prior to prostatectomy. FIELD STRENGTH/SEQUENCE 3 T, field-of-view optimized and constrained undistorted single-shot DWI sequence. ASSESSMENT Datasets, including a noise-free digital reference object (DRO), were distributed to the 14 teams, where locally implemented DWI parameter maps were calculated, including mono-exponential apparent diffusion coefficient (MEADC), kurtosis (K), diffusion kurtosis (DK), bi-exponential diffusion (BID), pseudo-diffusion (BID*), and perfusion fraction (F). The resulting parametric maps were centrally analyzed, where differentiation of benign from cancerous tissue was compared between DWI parameters and the fitting algorithms with a receiver operating characteristic area under the curve (ROC AUC). STATISTICAL TEST Levene's test, P < 0.05 corrected for multiple comparisons was considered statistically significant. RESULTS The DRO results indicated minimal discordance between sites. Comparison across sites indicated that K, DK, and MEADC had significantly higher prostate cancer detection capability (AUC range = 0.72-0.76, 0.76-0.81, and 0.76-0.80 respectively) as compared to bi-exponential parameters (BID, BID*, F) which had lower AUC and greater between site variation (AUC range = 0.53-0.80, 0.51-0.81, and 0.52-0.80 respectively). Post-processing parameters also affected the resulting AUC, moving from, for example, 0.75 to 0.87 for MEADC varying cluster size. DATA CONCLUSION We found that conventional diffusion models had consistent performance at differentiating prostate cancer from benign tissue. Our results also indicated that post-processing decisions on DWI data can affect sensitivity and specificity when applied to radiological-pathological studies in prostate cancer. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Sean D. McGarry
- Department of BiophysicsMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Michael Brehler
- Department of RadiologyMedical College of WisconsinMilwaukeeWIUSA
| | - John D. Bukowy
- Department of Electrical Engineering and Computer ScienceMilwaukee School of EngineeringMilwaukeeWIUSA
| | | | - Samuel A. Bobholz
- Department of BiophysicsMedical College of WisconsinMilwaukeeWisconsinUSA
| | | | - Anjishnu Banerjee
- Division of BiostatisticsMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Sarah L. Hurrell
- Department of RadiologyMedical College of WisconsinMilwaukeeWIUSA
| | | | | | - Yue Cao
- Department of RadiologyUniversity of MichiganAnn ArborMichiganUSA,Department of Radiation OncologyUniversity of MichiganAnn ArborMichiganUSA
| | - Yuan Li
- Department of Radiation OncologyUniversity of MichiganAnn ArborMichiganUSA
| | - Daekeun You
- Department of Radiation OncologyUniversity of MichiganAnn ArborMichiganUSA
| | - Andrey Fedorov
- Department of RadiologyBrigham and Women's HospitalBostonMassachusettsUSA
| | - Laura C. Bell
- Division of Neuroimaging ResearchBarrow Neurological InstitutePhoenixArizonaUSA
| | - C. Chad Quarles
- Division of Neuroimaging ResearchBarrow Neurological InstitutePhoenixArizonaUSA
| | - Melissa A. Prah
- Department of BiophysicsMedical College of WisconsinMilwaukeeWisconsinUSA
| | | | - Bachir Taouli
- Department of RadiologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Eve LoCastro
- Department of Medical PhysicsMemorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA
| | - Yousef Mazaheri
- Department of Medical PhysicsMemorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA,Department of RadiologyMemorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA
| | - Amita Shukla‐Dave
- Department of Medical PhysicsMemorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA,Department of RadiologyMemorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA
| | - Thomas E. Yankeelov
- Department of Biomedical Engineering, Diagnostic Medicine, Oncology, Oden Institute for Computational Engineering and Sciences, Livestrong Cancer InstitutesThe University of TexasAustinTexasUSA
| | - David A. Hormuth
- Department of Biomedical Engineering, Diagnostic Medicine, Oncology, Oden Institute for Computational Engineering and Sciences, Livestrong Cancer InstitutesThe University of TexasAustinTexasUSA
| | | | - Keith Hulsey
- Department of RadiologyThe University of Texas Southwestern Medical CenterDallasTexasUSA
| | - Kurt Li
- International School of BeavertonAlohaOregonUSA
| | - Wei Huang
- Advanced Imaging Research CenterOregon Health Sciences UniversityPortlandOregonUSA
| | - Wei Huang
- Department of PathologyOregon Health and Science UniversityMadisonWisconsinUSA
| | - Mark Muzi
- Department of Radiology, Neurology, and Radiation OncologyUniversity of WashingtonSeattleWashingtonUSA
| | - Michael A. Jacobs
- The Russell H. Morgan Department of Radiology and Radiological Science and Sidney Kimmel Comprehensive Cancer CenterJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Meiyappan Solaiyappan
- The Russell H. Morgan Department of Radiology and Radiological Science and Sidney Kimmel Comprehensive Cancer CenterJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Stefanie Hectors
- Department of biomedical engineering and imaging instituteWeill Cornell Medical CollegeNew York CityNew YorkUSA
| | - Tatjana Antic
- Department of PathologyUniversity of ChicagoChicagoIllinoisUSA
| | | | - Watchareepohn Palangmonthip
- Department of PathologyMedical College of WisconsinMilwaukeeWisconsinUSA,Department of PathologyChiang Mai UniversityChiang MaiThailand
| | - Kenneth Jacobsohn
- Department of UrologyMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Mark Hohenwalter
- Department of RadiologyMedical College of WisconsinMilwaukeeWIUSA
| | - Petar Duvnjak
- Department of RadiologyMedical College of WisconsinMilwaukeeWIUSA
| | - Michael Griffin
- Department of RadiologyMedical College of WisconsinMilwaukeeWIUSA
| | - William See
- Department of UrologyMedical College of WisconsinMilwaukeeWisconsinUSA
| | | | | | - Peter S. LaViolette
- Department of RadiologyMedical College of WisconsinMilwaukeeWIUSA,Department of Biomedical EngineeringMedical College of WisconsinMilwaukeeWisconsinUSA
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10
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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. Front Radiol 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] [What about the content of this article? (0)] [Affiliation(s)] [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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11
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Harris DC, Mignucci-Jiménez G, Xu Y, Eikenberry SE, Quarles CC, Preul MC, Kuang Y, Kostelich EJ. Tracking glioblastoma progression after initial resection with minimal reaction-diffusion models. Math Biosci Eng 2022; 19:5446-5481. [PMID: 35603364 DOI: 10.3934/mbe.2022256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
We describe a preliminary effort to model the growth and progression of glioblastoma multiforme, an aggressive form of primary brain cancer, in patients undergoing treatment for recurrence of tumor following initial surgery and chemoradiation. Two reaction-diffusion models are used: the Fisher-Kolmogorov equation and a 2-population model, developed by the authors, that divides the tumor into actively proliferating and quiescent (or necrotic) cells. The models are simulated on 3-dimensional brain geometries derived from magnetic resonance imaging (MRI) scans provided by the Barrow Neurological Institute. The study consists of 17 clinical time intervals across 10 patients that have been followed in detail, each of whom shows significant progression of tumor over a period of 1 to 3 months on sequential follow up scans. A Taguchi sampling design is implemented to estimate the variability of the predicted tumors to using 144 different choices of model parameters. In 9 cases, model parameters can be identified such that the simulated tumor, using both models, contains at least 40 percent of the volume of the observed tumor. We discuss some potential improvements that can be made to the parameterizations of the models and their initialization.
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Affiliation(s)
- Duane C Harris
- School of Mathematical & Statistical Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Giancarlo Mignucci-Jiménez
- Department of Neurosurgery, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, AZ 85013, USA
| | - Yuan Xu
- Department of Neurosurgery, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, AZ 85013, USA
| | - Steffen E Eikenberry
- School of Mathematical & Statistical Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - C Chad Quarles
- Department of Neurosurgery, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, AZ 85013, USA
| | - Mark C Preul
- Department of Neurosurgery, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, AZ 85013, USA
| | - Yang Kuang
- School of Mathematical & Statistical Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Eric J Kostelich
- School of Mathematical & Statistical Sciences, Arizona State University, Tempe, AZ 85281, USA
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12
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Scarpelli ML, Healey DR, Mehta S, Quarles CC. Imaging Glioblastoma With 18F-Fluciclovine Amino Acid Positron Emission Tomography. Front Oncol 2022; 12:829050. [PMID: 35174096 PMCID: PMC8841434 DOI: 10.3389/fonc.2022.829050] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Accepted: 01/12/2022] [Indexed: 11/13/2022] Open
Abstract
IntroductionConventional methods of imaging brain tumors fail to assess metabolically active tumor regions, which limits their capabilities for tumor detection, localization, and response assessment. Positron emission tomography (PET) with 18F-fluciclovine (fluciclovine) provides regional assessment of amino acid uptake in tumors that could overcome some of the limitations of conventional imaging. However, the biological basis of enhanced fluciclovine uptake is insufficiently characterized in brain tumors, which confounds clinical interpretation and application. This study sought to address this gap by correlating multiple biologic quantities with fluciclovine PET uptake across a range of human glioblastoma xenograft models.MethodsThirty-one rats underwent orthotopic implantations with one of five different human glioblastoma cell lines. After tumors were established, fluciclovine PET and magnetic resonance imaging (MRI) scans were performed. The fluciclovine tumor-to-normal-brain (TN) uptake ratio was used to quantify fluciclovine uptake. MRI scans were used to assess tumor volume and gadolinium enhancement status. Histologic assessments quantified tumor cell proliferation, tumor cell density, and tumor cell amino acid transporters (LAT1 and ASCT2). Multivariate linear regression models related fluciclovine uptake with the other measured quantities.ResultsWithin the multivariate regression, the fluciclovine TN uptake ratio (measured 15 to 35 minutes after fluciclovine injection) was most strongly associated with tumor ASCT2 levels (β=0.64; P=0.001). The fluciclovine TN uptake ratio was also significantly associated with tumor volume (β=0.45; P=0.001) and tumor enhancement status (β=0.40; P=0.01). Tumor cell proliferation, tumor cell density, and LAT1 levels were not significantly associated with fluciclovine uptake in any of the multivariate models. In general, both enhancing and non-enhancing tumors could be visualized on fluciclovine PET images, with the median TN uptake ratio across the five tumor lines being 2.4 (range 1.1 to 8.9).ConclusionsIncreased fluciclovine PET uptake was associated with increased levels of the amino acid transporter ASCT2, suggesting fluciclovine PET may be useful for assessing brain tumor amino acid metabolism. Fluciclovine PET uptake was elevated in both enhancing and non-enhancing tumors but the degree of uptake was greater in larger tumors and tumors with enhancement, indicating these variables could confound fluciclovine metabolic measurements if not accounted for.
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Affiliation(s)
| | - Debbie R. Healey
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, AZ, United States
| | - Shwetal Mehta
- Ivy Brain Tumor Center, Barrow Neurological Institute, Phoenix, AZ, United States
| | - C. Chad Quarles
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, AZ, United States
- *Correspondence: C. Chad Quarles,
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13
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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14
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Bell LC, Fuentes AE, Healey DR, Chao R, Bakkar N, Sirianni RW, Medina DX, Bowser RP, Ladha SS, Semmineh NB, Stokes AM, Quarles CC. Longitudinal evaluation of myofiber microstructural changes in a preclinical ALS model using the transverse relaxivity at tracer equilibrium (TRATE): A preliminary study. Magn Reson Imaging 2021; 85:217-221. [PMID: 34715291 DOI: 10.1016/j.mri.2021.10.036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 10/21/2021] [Accepted: 10/23/2021] [Indexed: 12/18/2022]
Abstract
T2⁎ relaxivity contrast imaging may serve as a potential imaging biomarker for amyotrophic lateral sclerosis (ALS) by noninvasively quantifying the tissue microstructure. In this preliminary longitudinal study, we investigated the Transverse Relaxivity at Tracer Equilibrium (TRATE) in three muscle groups between SOD1-G93A (ALS model) rat and a control population at two different timepoints. The control group was time matched to the ALS group such that the second timepoint was the onset of disease. We observed a statistically significant decrease in TRATE over time in the gastrocnemius, tibialis, and digital flexor muscles in the SOD1-G93A model (p-value = 0.003, 0.008, 0.005; respectively), whereas TRATE did not change over time in the control group (p-value = 0.4777, 0.6837, 0.9682; respectively). Immunofluorescent staining revealed a decrease in minimum fiber area and cell density in the SOD1-G93A model when compared to the control group (p-value = 6.043E-10 and 2.265E-10, respectively). These microstructural changes observed from histology align with the theorized biophysical properties of TRATE. We demonstrate that TRATE can longitudinally differentiate disease associated atrophy from healthy muscle and has potential to serve as a biomarker for disease progression and ultimately therapy response in patients with ALS.
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Affiliation(s)
- Laura C Bell
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, United States of America
| | - Alberto E Fuentes
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, United States of America
| | - Deborah R Healey
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, United States of America
| | - Renee Chao
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, United States of America
| | - Nadine Bakkar
- Division of Neurobiology, Barrow Neurological Institute, Phoenix, AZ, United States of America
| | - Rachael W Sirianni
- Vivian L. Smith Department of Neurosurgery, University of Texas Health Science Center, Houston, TX, United States of America
| | - David X Medina
- Division of Neurobiology, Barrow Neurological Institute, Phoenix, AZ, United States of America
| | - Robert P Bowser
- Division of Neurology, Barrow Neurological Institute, Phoenix, AZ, United States of America; Division of Neurobiology, Barrow Neurological Institute, Phoenix, AZ, United States of America
| | - Shafeeq S Ladha
- Division of Neurology, Barrow Neurological Institute, Phoenix, AZ, United States of America
| | - Natenael B Semmineh
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, United States of America
| | - Ashley M Stokes
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, United States of America
| | - C Chad Quarles
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, United States of America.
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15
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Barajas RF, Politi LS, Anzalone N, Schöder H, Fox CP, Boxerman JL, Kaufmann TJ, Quarles CC, Ellingson BM, Auer D, Andronesi OC, Ferreri AJM, Mrugala MM, Grommes C, Neuwelt EA, Ambady P, Rubenstein JL, Illerhaus G, Nagane M, Batchelor TT, Hu LS. Consensus recommendations for MRI and PET imaging of primary central nervous system lymphoma: guideline statement from the International Primary CNS Lymphoma Collaborative Group (IPCG). Neuro Oncol 2021; 23:1056-1071. [PMID: 33560416 PMCID: PMC8248856 DOI: 10.1093/neuonc/noab020] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Advanced molecular and pathophysiologic characterization of primary central nervous system lymphoma (PCNSL) has revealed insights into promising targeted therapeutic approaches. Medical imaging plays a fundamental role in PCNSL diagnosis, staging, and response assessment. Institutional imaging variation and inconsistent clinical trial reporting diminishes the reliability and reproducibility of clinical response assessment. In this context, we aimed to: (1) critically review the use of advanced positron emission tomography (PET) and magnetic resonance imaging (MRI) in the setting of PCNSL; (2) provide results from an international survey of clinical sites describing the current practices for routine and advanced imaging, and (3) provide biologically based recommendations from the International PCNSL Collaborative Group (IPCG) on adaptation of standardized imaging practices. The IPCG provides PET and MRI consensus recommendations built upon previous recommendations for standardized brain tumor imaging protocols (BTIP) in primary and metastatic disease. A biologically integrated approach is provided to addresses the unique challenges associated with the imaging assessment of PCNSL. Detailed imaging parameters facilitate the adoption of these recommendations by researchers and clinicians. To enhance clinical feasibility, we have developed both “ideal” and “minimum standard” protocols at 3T and 1.5T MR systems that will facilitate widespread adoption.
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Affiliation(s)
- Ramon F Barajas
- Department of Radiology, Neuroradiology Section, 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
| | - Letterio S Politi
- Humanitas University and Humanitas Research and Clinical Center - IRCCS, Milan, Italy.,Boston Children's Hospital, Boston, Massachusetts, USA
| | - Nicoletta Anzalone
- Neuroradiology Unit, IRCCS San Raffaele Hospital and Vita-Salute University, Milan, Italy
| | - Heiko Schöder
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Christopher P Fox
- Department of Clinical Haematology, Nottingham University Hospitals NHS Trust, School of Medicine, University of Nottingham, Nottingham, UK
| | - Jerrold L Boxerman
- Department of Diagnostic Imaging, Warren Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | | | - C Chad Quarles
- Department of Neuroimaging Research & Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Departments of Radiological Sciences and Psychiatry, 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
| | - Dorothee Auer
- Versus Arthritis Pain Centre, University of Nottingham, Nottingham, UK.,NIHR Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, UK.,Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
| | - Ovidiu C Andronesi
- A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Andres J M Ferreri
- Lymphoma Unit, Department of Onco-Hematology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Maciej M Mrugala
- Department of Medicine, Division of Hematology and Oncology, Mayo Clinic Cancer Center, Phoenix, Arizona, USA.,Department of Neurology, Mayo Clinic, Phoenix, Arizona, USA
| | - Christian Grommes
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Department of Neurology, Weill Cornell Medical School, New York, New York, USA
| | - Edward A Neuwelt
- Blood-Brain Barrier Program, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurological Surgery, Oregon Health & Science University, Portland, Oregon, USA.,Portland Veterans Affairs Medical Center, Portland, Oregon, USA
| | - Prakash Ambady
- Blood-Brain Barrier Program, Oregon Health & Science University, Portland, Oregon, USA.,Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - James L Rubenstein
- Division of Hematology/Oncology, University of California, San Francisco, California, USA.,Department of Medicine, University of California, San Francisco, California, USA.,Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, California, USA
| | - Gerald Illerhaus
- Clinic of Hematology, Oncology and Palliative Care, Klinikum Stuttgart, Stuttgart, Germany
| | - Motoo Nagane
- Department of Neurosurgery, Kyorin University Faculty of Medicine, Tokyo, Japan
| | - Tracy T Batchelor
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Leland S Hu
- Department of Radiology, Neuroradiology Division, Mayo Clinic, Phoenix, Arizona, USA
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16
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Stokes AM, Ragunathan S, Robison RK, Fuentes A, Bell LC, Karis JP, Pipe JG, Quarles CC. Development of a spiral spin- and gradient-echo (spiral-SAGE) approach for improved multi-parametric dynamic contrast neuroimaging. Magn Reson Med 2021; 86:3082-3095. [PMID: 34288112 DOI: 10.1002/mrm.28933] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 06/29/2021] [Accepted: 06/30/2021] [Indexed: 11/09/2022]
Abstract
PURPOSE The purpose of this study was to develop a spiral-based combined spin- and gradient-echo (spiral-SAGE) method for simultaneous dynamic contrast-enhanced (DCE-MRI) and dynamic susceptibility contrast MRI (DSC-MRI). METHODS Using this sequence, we obtained gradient-echo TEs of 1.69 and 26 ms, a SE TE of 87.72 ms, with a TR of 1663 ms. Using an iterative SENSE reconstruction followed by deblurring, spiral-induced image artifacts were minimized. Healthy volunteer images are shown to demonstrate image quality using the optimized reconstruction, as well as for comparison with EPI-based SAGE. A bioreactor phantom was used to compare dynamic-contrast time courses with both spiral-SAGE and EPI-SAGE. A proof-of-concept cohort of patients with brain tumors shows the range of hemodynamic maps available using spiral-SAGE. RESULTS Comparison of spiral-SAGE images with conventional EPI-SAGE images illustrates substantial reductions of image distortion and artifactual image intensity variations. Bioreactor phantom data show similar dynamic contrast time courses between standard EPI-SAGE and spiral-SAGE for the second and third echoes, whereas first-echo data show improvements in quantifying T1 changes with shorter echo times. In a cohort of patients with brain tumors, spiral-SAGE-based perfusion and permeability maps are shown with comparison with the standard single-echo EPI perfusion map. CONCLUSION Spiral-SAGE provides a substantial improvement for the assessment of perfusion and permeability by mitigating artifacts typically encountered with EPI and by providing a shorter echo time for improved characterization of permeability. Spiral-SAGE enables quantification of perfusion, permeability, and vessel architectural parameters, as demonstrated in brain tumors.
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Affiliation(s)
- Ashley M Stokes
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Sudarshan Ragunathan
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Ryan K Robison
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, Arizona, USA.,Philips Healthcare, Nashville, Tennessee, USA.,Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Alberto Fuentes
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Laura C Bell
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - John P Karis
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, Arizona, USA.,Neuroradiology, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - James G Pipe
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, Arizona, USA.,Mayo Clinic, Rochester, Minnesota, USA
| | - C Chad Quarles
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, Arizona, USA
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17
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Ragunathan S, Bell LC, Semmineh N, Stokes AM, Shefner JM, Bowser R, Ladha S, Quarles CC. Evaluation of Amyotrophic Lateral Sclerosis-Induced Muscle Degeneration Using Magnetic Resonance-Based Relaxivity Contrast Imaging (RCI). ACTA ACUST UNITED AC 2021; 7:169-179. [PMID: 34062974 PMCID: PMC8162571 DOI: 10.3390/tomography7020015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 04/23/2021] [Accepted: 04/27/2021] [Indexed: 11/16/2022]
Abstract
(1) Background: This work characterizes the sensitivity of magnetic resonance-based Relaxivity Contrast Imaging (RCI) to Amyotrophic Lateral Sclerosis (ALS)-induced changes in myofiber microstructure. Transverse Relaxivity at Tracer Equilibrium (TRATE), an RCI-based parameter, was evaluated in the lower extremities of ALS patients and healthy subjects. (2) Methods: In this IRB-approved study, 23 subjects (12 ALS patients and 11 healthy controls) were scanned at 3T (Philips, The Netherlands). RCI data were obtained during injection of a gadolinium-based contrast agent. TRATE, fat fraction and T2 measures, were compared in five muscle groups of the calf muscle, between ALS and control populations. TRATE was also evaluated longitudinally (baseline and 6 months) and was compared to clinical measures, namely ALS Functional Rating Scale (ALSFRS-R) and Hand-Held Dynamometry (HHD), in a subset of the ALS population. (3) Results: TRATE was significantly lower (p < 0.001) in ALS-affected muscle than in healthy muscle in all muscle groups. Fat fraction differences between ALS and healthy muscle were statistically significant for the tibialis anterior (p = 0.01), tibialis posterior (p = 0.004), and peroneus longus (p = 0.02) muscle groups but were not statistically significant for the medial (p = 0.07) and lateral gastrocnemius (p = 0.06) muscles. T2 differences between ALS and healthy muscle were statistically significant for the tibialis anterior (p = 0.004), peroneus longus (p = 0.004) and lateral gastrocnemius (p = 0.03) muscle groups but were not statistically significant for the tibialis posterior (p = 0.06) and medial gastrocnemius (p = 0.07) muscles. Longitudinally, TRATE, averaged over all patients, decreased by 28 ± 16% in the tibialis anterior, 47 ± 18% in the peroneus longus, 25 ± 19% in the tibialis posterior, 29 ± 14% in the medial gastrocnemius and 35 ± 18% in the lateral gastrocnemius muscles between two timepoints. ALSFRS-R scores were stable in two of four ALS patients. HHD scores decreased in three of four ALS patients. (4) Conclusion: RCI-based TRATE was shown to consistently differentiate ALS-affected muscle from healthy muscle and also provide a quantitative measure of longitudinal muscle degeneration.
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Affiliation(s)
- Sudarshan Ragunathan
- Barrow Neuroimaging Innovation Center, Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ 85013, USA; (L.C.B.); (N.S.); (A.M.S.); (C.C.Q.)
- Correspondence: ; Tel.: +1-(602)-406-7884
| | - Laura C. Bell
- Barrow Neuroimaging Innovation Center, Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ 85013, USA; (L.C.B.); (N.S.); (A.M.S.); (C.C.Q.)
| | - Natenael Semmineh
- Barrow Neuroimaging Innovation Center, Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ 85013, USA; (L.C.B.); (N.S.); (A.M.S.); (C.C.Q.)
| | - Ashley M. Stokes
- Barrow Neuroimaging Innovation Center, Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ 85013, USA; (L.C.B.); (N.S.); (A.M.S.); (C.C.Q.)
| | - Jeremy M. Shefner
- Department of Neurology, Barrow Neurological Institute, Phoenix, AZ 85013, USA; (J.M.S.); (R.B.)
| | - Robert Bowser
- Department of Neurology, Barrow Neurological Institute, Phoenix, AZ 85013, USA; (J.M.S.); (R.B.)
- Department of Neurobiology, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Shafeeq Ladha
- Gregory W. Fulton ALS and Neuromuscular Disease Center, Barrow Neurological Institute, Phoenix, AZ 85013, USA;
| | - C. Chad Quarles
- Barrow Neuroimaging Innovation Center, Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ 85013, USA; (L.C.B.); (N.S.); (A.M.S.); (C.C.Q.)
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18
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Scarpelli ML, Healey DR, Fuentes A, Kodibagkar VD, Quarles CC. Correlation of Tumor Hypoxia Metrics Derived from 18F-Fluoromisonidazole Positron Emission Tomography and Pimonidazole Fluorescence Images of Optically Cleared Brain Tissue. Tomography 2020; 6:379-388. [PMID: 33364428 PMCID: PMC7744194 DOI: 10.18383/j.tom.2020.00046] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
18F-fluoromisonidazole (FMISO) positron emission tomography (PET) is a widely used noninvasive imaging modality for assessing hypoxia. We describe the first spatial comparison of FMISO PET with an ex vivo reference standard for hypoxia across whole tumor volumes. Eighteen rats were orthotopically implanted with C6 or 9L brain tumors and made to undergo FMISO PET scanning. Whole brains were excised, sliced into 1-mm-thick sections, optically cleared, and fluorescently imaged for pimonidazole using an in vivo imaging system. FMISO maximum tumor uptake, maximum tumor-to-cerebellar uptake (TCmax), and hypoxic fraction (extracted 110 minutes after FMISO injection) were correlated with analogous metrics derived from pimonidazole fluorescence images. FMISO SUVmax was not significantly different between C6 and 9L brain tumors (P = .70), whereas FMISO TCmax and hypoxic fraction were significantly greater for C6 tumors (P < .01). FMISO TCmax was significantly correlated with the maximum tumor pimonidazole intensity (ρ = 0.76, P < .01), whereas FMISO SUVmax was not. FMISO tumor hypoxic fraction was significantly correlated with the pimonidazole-derived hypoxic fraction (ρ = 0.78, P < .01). Given that FMISO TCmax and tumor hypoxic fraction had strong correlations with the pimonidazole reference standard, these metrics may offer more reliable measures of tumor hypoxia than conventional PET uptake metrics (SUVmax). The voxel-wise correlation between FMISO uptake and pimonidazole intensity for a given tumor was strongly dependent on the tumor's TCmax (ρ = 0.81, P < .01) and hypoxic fraction (ρ = 0.85, P < .01), indicating PET measurements within individual voxels showed greater correlation with pimonidazole reference standard in tumors with greater hypoxia.
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Affiliation(s)
- Matthew L. Scarpelli
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, St. Joseph’s Hospital and Medical Center, Phoenix, AZ; and
| | - Debbie R. Healey
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, St. Joseph’s Hospital and Medical Center, Phoenix, AZ; and
| | - Alberto Fuentes
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, St. Joseph’s Hospital and Medical Center, Phoenix, AZ; and
| | - Vikram D. Kodibagkar
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, St. Joseph’s Hospital and Medical Center, Phoenix, AZ; and
| | - C. Chad Quarles
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, St. Joseph’s Hospital and Medical Center, Phoenix, AZ; and
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19
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Sorace AG, Elkassem AA, Galgano SJ, Lapi SE, Larimer BM, Partridge SC, Quarles CC, Reeves K, Napier TS, Song PN, Yankeelov TE, Woodard S, Smith AD. Imaging for Response Assessment in Cancer Clinical Trials. Semin Nucl Med 2020; 50:488-504. [PMID: 33059819 PMCID: PMC7573201 DOI: 10.1053/j.semnuclmed.2020.05.001] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The use of biomarkers is integral to the routine management of cancer patients, including diagnosis of disease, clinical staging and response to therapeutic intervention. Advanced imaging metrics with computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) are used to assess response during new drug development and in cancer research for predictive metrics of response. Key components and challenges to identifying an appropriate imaging biomarker are selection of integral vs integrated biomarkers, choosing an appropriate endpoint and modality, and standardization of the imaging biomarkers for cooperative and multicenter trials. Imaging biomarkers lean on the original proposed quantified metrics derived from imaging such as tumor size or longest dimension, with the most commonly implemented metrics in clinical trials coming from the Response Evaluation Criteria in Solid Tumors (RECIST) criteria, and then adapted versions such as immune-RECIST (iRECIST) and Positron Emission Tomography Response Criteria in Solid Tumors (PERCIST) for immunotherapy response and PET imaging, respectively. There have been many widely adopted biomarkers in clinical trials derived from MRI including metrics that describe cellularity and vascularity from diffusion-weighted (DW)-MRI apparent diffusion coefficient (ADC) and Dynamic Susceptibility Contrast (DSC) or dynamic contrast enhanced (DCE)-MRI (Ktrans, relative cerebral blood volume (rCBV)), respectively. Furthermore, Fluorodexoyglucose (FDG), fluorothymidine (FLT), and fluoromisonidazole (FMISO)-PET imaging, which describe molecular markers of glucose metabolism, proliferation and hypoxia have been implemented into various cancer types to assess therapeutic response to a wide variety of targeted- and chemotherapies. Recently, there have been many functional and molecular novel imaging biomarkers that are being developed that are rapidly being integrated into clinical trials (with anticipation of being implemented into clinical workflow in the future), such as artificial intelligence (AI) and machine learning computational strategies, antibody and peptide specific molecular imaging, and advanced diffusion MRI. These include prostate-specific membrane antigen (PSMA) and trastuzumab-PET, vascular tumor burden extracted from contrast-enhanced CT, diffusion kurtosis imaging, and CD8 or Granzyme B PET imaging. Further excitement surrounds theranostic procedures such as the combination of 68Ga/111In- and 177Lu-DOTATATE to use integral biomarkers to direct care and personalize therapy. However, there are many challenges in the implementation of imaging biomarkers that remains, including understand the accuracy, repeatability and reproducibility of both acquisition and analysis of these imaging biomarkers. Despite the challenges associated with the biological and technical validation of novel imaging biomarkers, a distinct roadmap has been created that is being implemented into many clinical trials to advance the development and implementation to create specific and sensitive novel imaging biomarkers of therapeutic response to continue to transform medical oncology.
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Affiliation(s)
- Anna G Sorace
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL; Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL; O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL.
| | - Asser A Elkassem
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL
| | - Samuel J Galgano
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL; O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL
| | - Suzanne E Lapi
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL; O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL; Department of Chemistry, University of Alabama at Birmingham, Birmingham, AL
| | - Benjamin M Larimer
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL; O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL
| | | | - C Chad Quarles
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ
| | - Kirsten Reeves
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL; Cancer Biology, University of Alabama at Birmingham, Birmingham, AL
| | - Tiara S Napier
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL; Cancer Biology, University of Alabama at Birmingham, Birmingham, AL
| | - Patrick N Song
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL
| | - Thomas E Yankeelov
- Department of Biomedical Engineering, University of Texas at Austin, Austin, TX; Department of Diagnostic Medicine, University of Texas at Austin, Austin, TX; Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX
| | - Stefanie Woodard
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL
| | - Andrew D Smith
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL; O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL
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20
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Bell LC, Semmineh N, An H, Eldeniz C, Wahl R, Schmainda KM, Prah MA, Erickson BJ, Korfiatis P, Wu C, Sorace AG, Yankeelov TE, Rutledge N, Chenevert TL, Malyarenko D, Liu Y, Brenner A, Hu LS, Zhou Y, Boxerman JL, Yen YF, Kalpathy-Cramer J, Beers AL, Muzi M, Madhuranthakam AJ, Pinho M, Johnson B, Quarles CC. Evaluating the Use of rCBV as a Tumor Grade and Treatment Response Classifier Across NCI Quantitative Imaging Network Sites: Part II of the DSC-MRI Digital Reference Object (DRO) Challenge. Tomography 2020; 6:203-208. [PMID: 32548297 PMCID: PMC7289259 DOI: 10.18383/j.tom.2020.00012] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
We have previously characterized the reproducibility of brain tumor relative cerebral blood volume (rCBV) using a dynamic susceptibility contrast magnetic resonance imaging digital reference object across 12 sites using a range of imaging protocols and software platforms. As expected, reproducibility was highest when imaging protocols and software were consistent, but decreased when they were variable. Our goal in this study was to determine the impact of rCBV reproducibility for tumor grade and treatment response classification. We found that varying imaging protocols and software platforms produced a range of optimal thresholds for both tumor grading and treatment response, but the performance of these thresholds was similar. These findings further underscore the importance of standardizing acquisition and analysis protocols across sites and software benchmarking.
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Affiliation(s)
- Laura C. Bell
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ
| | - Natenael Semmineh
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ
| | - Hongyu An
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO
| | - Cihat Eldeniz
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO
| | - Richard Wahl
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO
| | - Kathleen M. Schmainda
- Departments of Radiology; and
- Biophysics, Medical College of Wisconsin, Milwaukee, WI
| | | | | | | | - Chengyue Wu
- Oden Institute for Computational Engineering and Sciences, Departments of Biomedical Engineering, Diagnostic Medicine, and Oncology, Livestrong Cancer Institutes, University of Texas at Austin, Austin, TX
| | - Anna G. Sorace
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL
| | - Thomas E. Yankeelov
- Oden Institute for Computational Engineering and Sciences, Departments of Biomedical Engineering, Diagnostic Medicine, and Oncology, Livestrong Cancer Institutes, University of Texas at Austin, Austin, TX
| | - Neal Rutledge
- Oden Institute for Computational Engineering and Sciences, Departments of Biomedical Engineering, Diagnostic Medicine, and Oncology, Livestrong Cancer Institutes, University of Texas at Austin, Austin, TX
| | | | | | - Yichu Liu
- UT Health San Antonio, San Antonio, TX
| | | | - Leland S. Hu
- Department of Radiology, Mayo Clinic, Scottsdale, AZ
| | - Yuxiang Zhou
- Department of Radiology, Mayo Clinic, Scottsdale, AZ
| | - Jerrold L. Boxerman
- Department of Diagnostic Imaging, Rhode Island Hospital and Alpert Medical School of Brown University, Providence, RI
| | - Yi-Fen Yen
- Department of Radiology, MGH—Martinos Center for Biomedical Imaging, Boston, MA
| | | | - Andrew L. Beers
- Department of Radiology, MGH—Martinos Center for Biomedical Imaging, Boston, MA
| | - Mark Muzi
- Radiology, University of Washington, Seattle, WA
| | | | - Marco Pinho
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX; and
| | - Brian Johnson
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX; and
- Philips Healthcare, Gainesville, FL
| | - C. Chad Quarles
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ
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21
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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22
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Hormuth DA, Sorace AG, Virostko J, Abramson RG, Bhujwalla ZM, Enriquez-Navas P, Gillies R, Hazle JD, Mason RP, Quarles CC, Weis JA, Whisenant JG, Xu J, Yankeelov TE. Translating preclinical MRI methods to clinical oncology. J Magn Reson Imaging 2019; 50:1377-1392. [PMID: 30925001 PMCID: PMC6766430 DOI: 10.1002/jmri.26731] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 03/14/2019] [Accepted: 03/14/2019] [Indexed: 02/05/2023] Open
Abstract
The complexity of modern in vivo magnetic resonance imaging (MRI) methods in oncology has dramatically changed in the last 10 years. The field has long since moved passed its (unparalleled) ability to form images with exquisite soft-tissue contrast and morphology, allowing for the enhanced identification of primary tumors and metastatic disease. Currently, it is not uncommon to acquire images related to blood flow, cellularity, and macromolecular content in the clinical setting. The acquisition of images related to metabolism, hypoxia, pH, and tissue stiffness are also becoming common. All of these techniques have had some component of their invention, development, refinement, validation, and initial applications in the preclinical setting using in vivo animal models of cancer. In this review, we discuss the genesis of quantitative MRI methods that have been successfully translated from preclinical research and developed into clinical applications. These include methods that interrogate perfusion, diffusion, pH, hypoxia, macromolecular content, and tissue mechanical properties for improving detection, staging, and response monitoring of cancer. For each of these techniques, we summarize the 1) underlying biological mechanism(s); 2) preclinical applications; 3) available repeatability and reproducibility data; 4) clinical applications; and 5) limitations of the technique. We conclude with a discussion of lessons learned from translating MRI methods from the preclinical to clinical setting, and a presentation of four fundamental problems in cancer imaging that, if solved, would result in a profound improvement in the lives of oncology patients. Level of Evidence: 5 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1377-1392.
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Affiliation(s)
- David A. Hormuth
- Institute for Computational Engineering and Sciences,Livestrong Cancer Institutes, The University of Texas at Austin
| | - Anna G. Sorace
- Department of Biomedical Engineering, The University of Texas at Austin,Department of Diagnostic Medicine, The University of Texas at Austin,Department of Oncology, The University of Texas at Austin,Livestrong Cancer Institutes, The University of Texas at Austin
| | - John Virostko
- Department of Diagnostic Medicine, The University of Texas at Austin,Department of Oncology, The University of Texas at Austin,Livestrong Cancer Institutes, The University of Texas at Austin
| | - Richard G. Abramson
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center
| | | | - Pedro Enriquez-Navas
- Departments of Cancer Imaging and Metabolism, Cancer Physiology, The Moffitt Cancer Center
| | - Robert Gillies
- Departments of Cancer Imaging and Metabolism, Cancer Physiology, The Moffitt Cancer Center
| | - John D. Hazle
- Imaging Physics, The University of Texas M.D. Anderson Cancer Center
| | - Ralph P. Mason
- Department of Radiology, The University of Texas Southwestern Medical Center
| | - C. Chad Quarles
- Department of NeuroImaging Research, The Barrow Neurological Institute
| | - Jared A. Weis
- Department of Biomedical Engineering Wake Forest School of Medicine
| | | | - Junzhong Xu
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center,Institute of Imaging Science, Vanderbilt University Medical Center
| | - Thomas E. Yankeelov
- Institute for Computational Engineering and Sciences,Department of Biomedical Engineering, The University of Texas at Austin,Department of Diagnostic Medicine, The University of Texas at Austin,Department of Oncology, The University of Texas at Austin,Livestrong Cancer Institutes, The University of Texas at Austin
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23
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Lee MD, Baird GL, Bell LC, Quarles CC, Boxerman JL. Utility of Percentage Signal Recovery and Baseline Signal in DSC-MRI Optimized for Relative CBV Measurement for Differentiating Glioblastoma, Lymphoma, Metastasis, and Meningioma. AJNR Am J Neuroradiol 2019; 40:1445-1450. [PMID: 31371360 DOI: 10.3174/ajnr.a6153] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 06/21/2019] [Indexed: 12/14/2022]
Abstract
BACKGROUND AND PURPOSE The percentage signal recovery in non-leakage-corrected (no preload, high flip angle, intermediate TE) DSC-MR imaging is known to differ significantly for glioblastoma, metastasis, and primary CNS lymphoma. Because the percentage signal recovery is influenced by preload and pulse sequence parameters, we investigated whether the percentage signal recovery can still differentiate these common contrast-enhancing neoplasms using a DSC-MR imaging protocol designed for relative CBV accuracy (preload, intermediate flip angle, low TE). MATERIALS AND METHODS We retrospectively analyzed DSC-MR imaging of treatment-naïve, pathology-proved glioblastomas (n = 14), primary central nervous system lymphomas (n = 7), metastases (n = 20), and meningiomas (n = 13) using a protocol designed for relative CBV accuracy (a one-quarter-dose preload and single-dose bolus of gadobutrol, TR/TE = 1290/40 ms, flip angle = 60° at 1.5T). Mean percentage signal recovery, relative CBV, and normalized baseline signal intensity were compared within contrast-enhancing lesion volumes. Classification accuracy was determined by receiver operating characteristic analysis. RESULTS Relative CBV best differentiated meningioma from glioblastoma and from metastasis with areas under the curve of 0.84 and 0.82, respectively. The percentage signal recovery best differentiated primary central nervous system lymphoma from metastasis with an area under the curve of 0.81. Relative CBV and percentage signal recovery were similar in differentiating primary central nervous system lymphoma from glioblastoma and from meningioma. Although neither relative CBV nor percentage signal recovery differentiated glioblastoma from metastasis, mean normalized baseline signal intensity achieved 86% sensitivity and 50% specificity. CONCLUSIONS Similar to results for non-preload-based DSC-MR imaging, percentage signal recovery for one-quarter-dose preload-based, intermediate flip angle DSC-MR imaging differentiates most pair-wise comparisons of glioblastoma, metastasis, primary central nervous system lymphoma, and meningioma, except for glioblastoma versus metastasis. Differences in normalized post-preload baseline signal for glioblastoma and metastasis, reflecting a snapshot of dynamic contrast enhancement, may motivate the use of single-dose multiecho protocols permitting simultaneous quantification of DSC-MR imaging and dynamic contrast-enhanced MR imaging parameters.
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Affiliation(s)
- M D Lee
- From the Warren Alpert Medical School of Brown University (M.D.L., J.L.B.), Providence, Rhode Island
| | - G L Baird
- Department of Diagnostic Imaging (G.L.B., J.L.B.), Rhode Island Hospital, Providence, Rhode Island
| | - L C Bell
- Division of Neuroimaging Research (L.C.B., C.C.Q.), Barrow Neurological Institute, Phoenix, Arizona
| | - C C Quarles
- Division of Neuroimaging Research (L.C.B., C.C.Q.), Barrow Neurological Institute, Phoenix, Arizona
| | - J L Boxerman
- From the Warren Alpert Medical School of Brown University (M.D.L., J.L.B.), Providence, Rhode Island
- Department of Diagnostic Imaging (G.L.B., J.L.B.), Rhode Island Hospital, Providence, Rhode Island
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24
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Stokes AM, Semmineh NB, Nespodzany A, Bell LC, Quarles CC. Systematic assessment of multi-echo dynamic susceptibility contrast MRI using a digital reference object. Magn Reson Med 2019; 83:109-123. [PMID: 31400035 DOI: 10.1002/mrm.27914] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 06/14/2019] [Accepted: 07/02/2019] [Indexed: 11/07/2022]
Abstract
PURPOSE Brain tumor dynamic susceptibility contrast (DSC) MRI is adversely impacted by T1 and T 2 ∗ contrast agent leakage effects that result in inaccurate hemodynamic metrics. While multi-echo acquisitions remove T1 leakage effects, there is no consensus on the optimal set of acquisition parameters. Using a computational approach, we systematically evaluated a wide range of acquisition strategies to determine the optimal multi-echo DSC-MRI perfusion protocol. METHODS Using a population-based DSC-MRI digital reference object (DRO), we assessed the influence of preload dosing (no preload and full dose preload), field strength (1.5 and 3T), pulse sequence parameters (echo time, repetition time, and flip angle), and leakage correction on relative cerebral blood volume (rCBV) and flow (rCBF) accuracy. We also compared multi-echo DSC-MRI protocols with standard single-echo protocols. RESULTS Multi-echo DSC-MRI is highly consistent across all protocols, and multi-echo rCBV (with or without use of a preload dose) had higher accuracy than single-echo rCBV. Regression analysis showed that choice of repetition time and flip angle had minimal impact on multi-echo rCBV and rCBV, indicating the potential for significant flexibility in acquisition parameters. The echo time combination had minimal impact on rCBV, though longer echo times should be avoided, particularly at higher field strengths. Leakage correction improved rCBV accuracy in all cases. Multi-echo rCBF was less biased than single-echo rCBF, although rCBF accuracy was reduced overall relative to rCBV. CONCLUSIONS Multi-echo acquisitions were more robust than single-echo, essentially decoupling both repetition time and flip angle from rCBV accuracy. Multi-echo acquisitions obviate the need for preload dosing, although leakage correction to remove residual T 2 ∗ leakage effects remains compulsory for high rCBV accuracy.
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Affiliation(s)
- Ashley M Stokes
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, Arizona
| | - Natenael B Semmineh
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, Arizona
| | - Ashley Nespodzany
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, Arizona
| | - Laura C Bell
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, Arizona
| | - C Chad Quarles
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, Arizona
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25
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Bell LC, Stokes AM, Quarles CC. Analysis of postprocessing steps for residue function dependent dynamic susceptibility contrast (DSC)-MRI biomarkers and their clinical impact on glioma grading for both 1.5 and 3T. J Magn Reson Imaging 2019; 51:547-553. [PMID: 31206948 DOI: 10.1002/jmri.26837] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 05/30/2019] [Accepted: 05/31/2019] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Dynamic susceptibility contrast (DSC)-MRI analysis pipelines differ across studies and sites, potentially confounding the clinical value and use of the derived biomarkers. PURPOSE/HYPOTHESIS To investigate how postprocessing steps for computation of cerebral blood volume (CBV) and residue function dependent parameters (cerebral blood flow [CBF], mean transit time [MTT], capillary transit heterogeneity [CTH]) impact glioma grading. STUDY TYPE Retrospective study from The Cancer Imaging Archive (TCIA). POPULATION Forty-nine subjects with low- and high-grade gliomas. FIELD STRENGTH/SEQUENCE 1.5 and 3.0T clinical systems using a single-echo echo planar imaging (EPI) acquisition. ASSESSMENT Manual regions of interest (ROIs) were provided by TCIA and automatically segmented ROIs were generated by k-means clustering. CBV was calculated based on conventional equations. Residue function dependent biomarkers (CBF, MTT, CTH) were found by two deconvolution methods: circular discretization followed by a signal-to-noise ratio (SNR)-adapted eigenvalue thresholding (Method 1) and Volterra discretization with L-curve-based Tikhonov regularization (Method 2). STATISTICAL TESTS Analysis of variance, receiver operating characteristics (ROC), and logistic regression tests. RESULTS MTT alone was unable to statistically differentiate glioma grade (P > 0.139). When normalized, tumor CBF, CTH, and CBV did not differ across field strengths (P > 0.141). Biomarkers normalized to automatically segmented regions performed equally (rCTH AUROC is 0.73 compared with 0.74) or better (rCBF AUROC increases from 0.74-0.84; rCBV AUROC increases 0.78-0.86) than manually drawn ROIs. By updating the current deconvolution steps (Method 2), rCTH can act as a classifier for glioma grade (P < 0.007), but not if processed by current conventional DSC methods (Method 1) (P > 0.577). Lastly, higher-order biomarkers (eg, rCBF and rCTH) along with rCBV increases AUROC to 0.92 for differentiating tumor grade as compared with 0.78 and 0.86 (manual and automatic reference regions, respectively) for rCBV alone. DATA CONCLUSION With optimized analysis pipelines, higher-order perfusion biomarkers (rCBF and rCTH) improve glioma grading as compared with CBV alone. Additionally, postprocessing steps impact thresholds needed for glioma grading. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:547-553.
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Affiliation(s)
- Laura C Bell
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Ashley M Stokes
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - C Chad Quarles
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, Arizona, USA
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26
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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27
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Bell LC, Semmineh N, An H, Eldeniz C, Wahl R, Schmainda KM, Prah MA, Erickson BJ, Korfiatis P, Wu C, Sorace AG, Yankeelov TE, Rutledge N, Chenevert TL, Malyarenko D, Liu Y, Brenner A, Hu LS, Zhou Y, Boxerman JL, Yen YF, Kalpathy-Cramer J, Beers AL, Muzi M, Madhuranthakam AJ, Pinho M, Johnson B, Quarles CC. Evaluating Multisite rCBV Consistency from DSC-MRI Imaging Protocols and Postprocessing Software Across the NCI Quantitative Imaging Network Sites Using a Digital Reference Object (DRO). Tomography 2019; 5:110-117. [PMID: 30854448 PMCID: PMC6403027 DOI: 10.18383/j.tom.2018.00041] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Relative cerebral blood volume (rCBV) cannot be used as a response metric in clinical trials, in part, because of variations in biomarker consistency and associated interpretation across sites, stemming from differences in image acquisition and postprocessing methods (PMs). This study leveraged a dynamic susceptibility contrast magnetic resonance imaging digital reference object to characterize rCBV consistency across 12 sites participating in the Quantitative Imaging Network (QIN), specifically focusing on differences in site-specific imaging protocols (IPs; n = 17), and PMs (n = 19) and differences due to site-specific IPs and PMs (n = 25). Thus, high agreement across sites occurs when 1 managing center processes rCBV despite slight variations in the IP. This result is most likely supported by current initiatives to standardize IPs. However, marked intersite disagreement was observed when site-specific software was applied for rCBV measurements. This study's results have important implications for comparing rCBV values across sites and trials, where variability in PMs could confound the comparison of therapeutic effectiveness and/or any attempts to establish thresholds for categorical response to therapy. To overcome these challenges and ensure the successful use of rCBV as a clinical trial biomarker, we recommend the establishment of qualifying and validating site- and trial-specific criteria for scanners and acquisition methods (eg, using a validated phantom) and the software tools used for dynamic susceptibility contrast magnetic resonance imaging analysis (eg, using a digital reference object where the ground truth is known).
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Affiliation(s)
- Laura C. Bell
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ
| | - Natenael Semmineh
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ
| | - Hongyu An
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO
| | - Cihat Eldeniz
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO
| | - Richard Wahl
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO
| | - Kathleen M. Schmainda
- Departments of Radiology and Biophysics, Medical College of Wisconsin, Wauwatosa, WI
| | - Melissa A. Prah
- Departments of Radiology and Biophysics, Medical College of Wisconsin, Wauwatosa, WI
| | | | | | - Chengyue Wu
- Department of Diagnostic Medicine, University of Texas at Austin, Austin, TX
| | - Anna G. Sorace
- Department of Diagnostic Medicine, University of Texas at Austin, Austin, TX
| | - Thomas E. Yankeelov
- Department of Diagnostic Medicine, University of Texas at Austin, Austin, TX
| | - Neal Rutledge
- Department of Diagnostic Medicine, University of Texas at Austin, Austin, TX
| | | | | | - Yichu Liu
- UT Health San Antonio, San Antonio, TX
| | | | - Leland S. Hu
- Department of Radiology, Mayo Clinic, Scottsdale, AZ
| | - Yuxiang Zhou
- Department of Radiology, Mayo Clinic, Scottsdale, AZ
| | - Jerrold L. Boxerman
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI;,Alpert Medical School of Brown University, Providence, RI
| | - Yi-Fen Yen
- Department of Radiology, Massachusetts General Hospital, Boston, MA
| | | | - Andrew L. Beers
- Department of Radiology, Massachusetts General Hospital, Boston, MA
| | - Mark Muzi
- Department of Radiology, University of Washington, Seattle, Washington
| | | | - Marco Pinho
- UT Southwestern Medical Center, Dallas, TX; and
| | - Brian Johnson
- UT Southwestern Medical Center, Dallas, TX; and,Philips Healthcare, Gainesville, FL
| | - C. Chad Quarles
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ
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28
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Tantawy MN, Charles Manning H, Peterson TE, Colvin DC, Gore JC, Lu W, Chen Z, Chad Quarles C. Translocator Protein PET Imaging in a Preclinical Prostate Cancer Model. Mol Imaging Biol 2019; 20:200-204. [PMID: 28822038 DOI: 10.1007/s11307-017-1113-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
PURPOSE The identification and targeting of biomarkers specific to prostate cancer (PCa) could improve its detection. Given the high expression of translocator protein (TSPO) in PCa, we investigated the use of [18F]VUIIS1008 (a novel TSPO-targeting radioligand) coupled with positron emission tomography (PET) to identify PCa in mice and to characterize their TSPO uptake. PROCEDURES Ptenpc-/-, Trp53pc-/- prostate cancer-bearing mice (n = 9, 4-6 months old) were imaged in a 7T MRI scanner for lesion localization. Within 24 h, the mice were imaged using a microPET scanner for 60 min in dynamic mode following a retro-orbital injection of ~ 18 MBq [18F]VUIIS1008. Following imaging, tumors were harvested and stained with a TSPO antibody. Regions of interest (ROIs) were drawn around the tumor and muscle (hind limb) in the PET images. Time-activity curves (TACs) were recorded over the duration of the scan for each ROI. The mean activity concentrations between 40 and 60 min post radiotracer administration between tumor and muscle were compared. RESULTS Tumor presence was confirmed by visual inspection of the MR images. The uptake of [18F]VUIIS1008 in the tumors was significantly higher (p < 0.05) than that in the muscle, where the percent injected dose per unit volume for tumor was 7.1 ± 1.6 % ID/ml and that of muscle was < 1 % ID/ml. In addition, positive TSPO expression was observed in tumor tissue analysis. CONCLUSIONS The foregoing preliminary data suggest that TSPO may be a useful biomarker of PCa. Therefore, using TSPO-targeting PET ligands, such as [18F]VUIIS1008, may improve PCa detectability and characterization.
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Affiliation(s)
- Mohammed N Tantawy
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, 1161 21st Ave. S., AA 1105 MCN, Nashville, TN, 37232, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - H Charles Manning
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, 1161 21st Ave. S., AA 1105 MCN, Nashville, TN, 37232, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.,Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.,Program in Chemical and Physical Biology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Todd E Peterson
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, 1161 21st Ave. S., AA 1105 MCN, Nashville, TN, 37232, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Daniel C Colvin
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, 1161 21st Ave. S., AA 1105 MCN, Nashville, TN, 37232, USA
| | - John C Gore
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, 1161 21st Ave. S., AA 1105 MCN, Nashville, TN, 37232, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Wenfu Lu
- Department of Biochemistry and Cancer Biology, Meharry Medical College, Nashville, TN, 37208, USA
| | - Zhenbang Chen
- Department of Biochemistry and Cancer Biology, Meharry Medical College, Nashville, TN, 37208, USA
| | - C Chad Quarles
- Imaging Research, Barrow Neurological Institute, 350 W Thomas Rd, Phoenix, AZ, 85013, USA.
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29
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Wang F, Katagiri D, Li K, Takahashi K, Wang S, Nagasaka S, Li H, Quarles CC, Zhang MZ, Shimizu A, Gore JC, Harris RC, Takahashi T. Assessment of renal fibrosis in murine diabetic nephropathy using quantitative magnetization transfer MRI. Magn Reson Med 2018; 80:2655-2669. [PMID: 29845659 PMCID: PMC6269231 DOI: 10.1002/mrm.27231] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2017] [Revised: 03/19/2018] [Accepted: 04/03/2018] [Indexed: 12/26/2022]
Abstract
PURPOSE Renal fibrosis is a hallmark of progressive renal disease; however, current clinical tests are insufficient for assessing renal fibrosis. Here we evaluated the utility of quantitative magnetization transfer MRI in detecting renal fibrosis in a murine model of progressive diabetic nephropathy (DN). METHODS The db/db eNOS-/- mice, a well-recognized model of progressive DN, and normal wild-type mice were imaged at 7T. The quantitative magnetization transfer data were collected in coronal plane using a 2D magnetization transfer prepared spoiled gradient echo sequence with a Gaussian-shaped presaturation pulse. Parameters were derived using a two-pool fitting model. A normal range of cortical pool size ratio (PSR) was defined as Mean±2SD of wild-type kidneys (N = 20). The cortical regions whose PSR values exceeded this threshold (threshold PSR) were assessed. The correlations between the PSR-based and histological (collagen IV or picrosirius red stain) fibrosis measurements were evaluated. RESULTS Compared with wild-type mice, moderate increases in mean PSR values and scattered clusters of high PSR region were observed in cortex of DN mouse kidneys. Abnormally high PSR regions (% area) that were detected by the threshold PSR were significantly increased in renal cortexes of DN mice. These regions progressively increased on aging and highly correlated with histological fibrosis measures, while the mean PSR values correlated much less. CONCLUSION Renal fibrosis in DN can be assessed by the quantitative magnetization transfer MRI and threshold analysis. This technique may be used as a novel imaging biomarker for DN and other renal diseases.
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Affiliation(s)
- Feng Wang
- Vanderbilt University Institute of Imaging Science, TN, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University School of Medicine, TN, USA
| | - Daisuke Katagiri
- Division of Nephrology and Hypertension, Vanderbilt University School of Medicine, TN, USA
| | - Ke Li
- Vanderbilt University Institute of Imaging Science, TN, USA
| | - Keiko Takahashi
- Division of Nephrology and Hypertension, Vanderbilt University School of Medicine, TN, USA
| | - Suwan Wang
- Division of Nephrology and Hypertension, Vanderbilt University School of Medicine, TN, USA
| | - Shinya Nagasaka
- Division of Nephrology and Hypertension, Vanderbilt University School of Medicine, TN, USA
- Department of Analytic Human Pathology, Nippon Medical School, Tokyo, Japan
| | - Hua Li
- Vanderbilt University Institute of Imaging Science, TN, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University School of Medicine, TN, USA
| | - C. Chad Quarles
- Vanderbilt University Institute of Imaging Science, TN, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University School of Medicine, TN, USA
| | - Ming-Zhi Zhang
- Division of Nephrology and Hypertension, Vanderbilt University School of Medicine, TN, USA
| | - Akira Shimizu
- Department of Analytic Human Pathology, Nippon Medical School, Tokyo, Japan
| | - John C. Gore
- Vanderbilt University Institute of Imaging Science, TN, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University School of Medicine, TN, USA
| | - Raymond C. Harris
- Division of Nephrology and Hypertension, Vanderbilt University School of Medicine, TN, USA
| | - Takamune Takahashi
- Division of Nephrology and Hypertension, Vanderbilt University School of Medicine, TN, USA
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Woodall RT, Barnes SL, Hormuth DA, Sorace AG, Quarles CC, Yankeelov TE. The effects of intravoxel contrast agent diffusion on the analysis of DCE-MRI data in realistic tissue domains. Magn Reson Med 2018; 80:330-340. [PMID: 29115690 PMCID: PMC5876107 DOI: 10.1002/mrm.26995] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2017] [Revised: 09/17/2017] [Accepted: 10/16/2017] [Indexed: 02/05/2023]
Abstract
PURPOSE Quantitative evaluation of dynamic contrast enhanced MRI (DCE-MRI) allows for estimating perfusion, vessel permeability, and tissue volume fractions by fitting signal intensity curves to pharmacokinetic models. These compart mental models assume rapid equilibration of contrast agent within each voxel. However, there is increasing evidence that this assumption is violated for small molecular weight gadolinium chelates. To evaluate the error introduced by this invalid assumption, we simulated DCE-MRI experiments with volume fractions computed from entire histological tumor cross-sections obtained from murine studies. METHODS A 2D finite element model of a diffusion-compensated Tofts-Kety model was developed to simulate dynamic T1 signal intensity data. Digitized histology slices were segmented into vascular (vp ), cellular and extravascular extracellular (ve ) volume fractions. Within this domain, Ktrans (the volume transfer constant) was assigned values from 0 to 0.5 min-1 . A representative signal enhancement curve was then calculated for each imaging voxel and the resulting simulated DCE-MRI data analyzed by the extended Tofts-Kety model. RESULTS Results indicated parameterization errors of -19.1% ± 10.6% in Ktrans , -4.92% ± 3.86% in ve , and 79.5% ± 16.8% in vp for use of Gd-DTPA over 4 tumor domains. CONCLUSION These results indicate a need for revising the standard model of DCE-MRI to incorporate a correction for slow diffusion of contrast agent. Magn Reson Med 80:330-340, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Ryan T. Woodall
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas 78732,Center for Computational Oncology, Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, Texas 78732
| | - Stephanie L. Barnes
- Center for Computational Oncology, Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, Texas 78732
| | - David A. Hormuth
- Center for Computational Oncology, Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, Texas 78732
| | - Anna G. Sorace
- Department of Internal Medicine, The University of Texas at Austin, Austin, Texas 78732
| | | | - Thomas E. Yankeelov
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas 78732,Department of Internal Medicine, The University of Texas at Austin, Austin, Texas 78732,Center for Computational Oncology, Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, Texas 78732,Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Texas 78732
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Schmainda KM, Prah MA, Rand SD, Liu Y, Logan B, Muzi M, Rane SD, Da X, Yen YF, Kalpathy-Cramer J, Chenevert TL, Hoff B, Ross B, Cao Y, Aryal MP, Erickson B, Korfiatis P, Dondlinger T, Bell L, Hu L, Kinahan PE, Quarles CC. Multisite Concordance of DSC-MRI Analysis for Brain Tumors: Results of a National Cancer Institute Quantitative Imaging Network Collaborative Project. AJNR Am J Neuroradiol 2018; 39:1008-1016. [PMID: 29794239 DOI: 10.3174/ajnr.a5675] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Accepted: 02/07/2018] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Standard assessment criteria for brain tumors that only include anatomic imaging continue to be insufficient. While numerous studies have demonstrated the value of DSC-MR imaging perfusion metrics for this purpose, they have not been incorporated due to a lack of confidence in the consistency of DSC-MR imaging metrics across sites and platforms. This study addresses this limitation with a comparison of multisite/multiplatform analyses of shared DSC-MR imaging datasets of patients with brain tumors. MATERIALS AND METHODS DSC-MR imaging data were collected after a preload and during a bolus injection of gadolinium contrast agent using a gradient recalled-echo-EPI sequence (TE/TR = 30/1200 ms; flip angle = 72°). Forty-nine low-grade (n = 13) and high-grade (n = 36) glioma datasets were uploaded to The Cancer Imaging Archive. Datasets included a predetermined arterial input function, enhancing tumor ROIs, and ROIs necessary to create normalized relative CBV and CBF maps. Seven sites computed 20 different perfusion metrics. Pair-wise agreement among sites was assessed with the Lin concordance correlation coefficient. Distinction of low- from high-grade tumors was evaluated with the Wilcoxon rank sum test followed by receiver operating characteristic analysis to identify the optimal thresholds based on sensitivity and specificity. RESULTS For normalized relative CBV and normalized CBF, 93% and 94% of entries showed good or excellent cross-site agreement (0.8 ≤ Lin concordance correlation coefficient ≤ 1.0). All metrics could distinguish low- from high-grade tumors. Optimum thresholds were determined for pooled data (normalized relative CBV = 1.4, sensitivity/specificity = 90%:77%; normalized CBF = 1.58, sensitivity/specificity = 86%:77%). CONCLUSIONS By means of DSC-MR imaging data obtained after a preload of contrast agent, substantial consistency resulted across sites for brain tumor perfusion metrics with a common threshold discoverable for distinguishing low- from high-grade tumors.
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Affiliation(s)
- K M Schmainda
- From the Department of Radiology (K.M.S., M.A.P., S.D.R.)
| | - M A Prah
- From the Department of Radiology (K.M.S., M.A.P., S.D.R.)
| | - S D Rand
- From the Department of Radiology (K.M.S., M.A.P., S.D.R.).,Department of Radiology (M.M., S.D.R., P.E.K.), University of Washington, Seattle, Washington
| | - Y Liu
- Division of Biostatistics (Y.L., B.L.), Institute for Health and Society, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - B Logan
- Division of Biostatistics (Y.L., B.L.), Institute for Health and Society, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - M Muzi
- Department of Radiology (M.M., S.D.R., P.E.K.), University of Washington, Seattle, Washington
| | - S D Rane
- From the Department of Radiology (K.M.S., M.A.P., S.D.R.)
| | - X Da
- Department of Radiology (X.D.), Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts
| | - Y-F Yen
- Athinoula A. Martinos Center for Biomedical Imaging (Y.-F.Y., J.K.-C.), Department of Radiology, Harvard Medical School/Massachusetts General Hospital, Charlestown, Massachusetts
| | - J Kalpathy-Cramer
- Athinoula A. Martinos Center for Biomedical Imaging (Y.-F.Y., J.K.-C.), Department of Radiology, Harvard Medical School/Massachusetts General Hospital, Charlestown, Massachusetts
| | | | - B Hoff
- Department of Radiology (T.L.C., B.H., B.R.)
| | - B Ross
- Department of Radiology (T.L.C., B.H., B.R.)
| | - Y Cao
- Departments of Radiation Oncology, Radiology, and Biomedical Engineering (Y.C., M.P.A.), University of Michigan, Ann Arbor, Michigan
| | - M P Aryal
- Departments of Radiation Oncology, Radiology, and Biomedical Engineering (Y.C., M.P.A.), University of Michigan, Ann Arbor, Michigan
| | - B Erickson
- Department of Radiology (B.E., P.K.), Mayo Clinic, Rochester, Minnesota
| | - P Korfiatis
- Department of Radiology (B.E., P.K.), Mayo Clinic, Rochester, Minnesota
| | - T Dondlinger
- Imaging Biometrics LLC (T.D.), Elm Grove, Wisconsin
| | - L Bell
- Division of Imaging Research (L.B., C.C.Q.), Barrow Neurological Institute, Phoenix, Arizona
| | - L Hu
- Department of Radiology (L.H.), Mayo Clinic, Scottsdale, Arizona
| | - P E Kinahan
- Department of Radiology (M.M., S.D.R., P.E.K.), University of Washington, Seattle, Washington
| | - C C Quarles
- Division of Imaging Research (L.B., C.C.Q.), Barrow Neurological Institute, Phoenix, Arizona
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Newitt DC, Malyarenko D, Chenevert TL, Quarles CC, Bell L, Fedorov A, Fennessy F, Jacobs MA, Solaiyappan M, Hectors S, Taouli B, Muzi M, Kinahan PE, Schmainda KM, Prah MA, Taber EN, Kroenke C, Huang W, Arlinghaus LR, Yankeelov TE, Cao Y, Aryal M, Yen YF, Kalpathy-Cramer J, Shukla-Dave A, Fung M, Liang J, Boss M, Hylton N. Multisite concordance of apparent diffusion coefficient measurements across the NCI Quantitative Imaging Network. J Med Imaging (Bellingham) 2018; 5:011003. [PMID: 29021993 PMCID: PMC5633866 DOI: 10.1117/1.jmi.5.1.011003] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Accepted: 09/12/2017] [Indexed: 12/26/2022] Open
Abstract
Diffusion weighted MRI has become ubiquitous in many areas of medicine, including cancer diagnosis and treatment response monitoring. Reproducibility of diffusion metrics is essential for their acceptance as quantitative biomarkers in these areas. We examined the variability in the apparent diffusion coefficient (ADC) obtained from both postprocessing software implementations utilized by the NCI Quantitative Imaging Network and online scan time-generated ADC maps. Phantom and in vivo breast studies were evaluated for two ([Formula: see text]) and four ([Formula: see text]) [Formula: see text]-value diffusion metrics. Concordance of the majority of implementations was excellent for both phantom ADC measures and in vivo [Formula: see text], with relative biases [Formula: see text] ([Formula: see text]) and [Formula: see text] (phantom [Formula: see text]) but with higher deviations in ADC at the lowest phantom ADC values. In vivo [Formula: see text] concordance was good, with typical biases of [Formula: see text] to 3% but higher for online maps. Multiple b-value ADC implementations were separated into two groups determined by the fitting algorithm. Intergroup mean ADC differences ranged from negligible for phantom data to 2.8% for [Formula: see text] in vivo data. Some higher deviations were found for individual implementations and online parametric maps. Despite generally good concordance, implementation biases in ADC measures are sometimes significant and may be large enough to be of concern in multisite studies.
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Affiliation(s)
- David C. Newitt
- University of California San Francisco, Department of Radiology and Biomedical Imaging, San Francisco, California, United States
| | - Dariya Malyarenko
- University of Michigan, Department of Radiology, Ann Arbor, Michigan, United States
| | - Thomas L. Chenevert
- University of Michigan, Department of Radiology, Ann Arbor, Michigan, United States
| | - C. Chad Quarles
- Barrow Neurological Institute, Division of Imaging Research, Phoenix, Arizona, United States
| | - Laura Bell
- Barrow Neurological Institute, Division of Imaging Research, Phoenix, Arizona, United States
| | - Andriy Fedorov
- Harvard Medical School, Brigham and Women’s Hospital, Department of Radiology, Boston, Massachusetts, United States
| | - Fiona Fennessy
- Harvard Medical School, Brigham and Women’s Hospital, Department of Radiology, Boston, Massachusetts, United States
| | - Michael A. Jacobs
- The Johns Hopkins School of Medicine, Russell H. Morgan Department of Radiology and Radiological Science and Sidney Kimmel Comprehensive Cancer Center, Baltimore, Maryland, United States
| | - Meiyappan Solaiyappan
- The Johns Hopkins School of Medicine, Russell H. Morgan Department of Radiology and Radiological Science and Sidney Kimmel Comprehensive Cancer Center, Baltimore, Maryland, United States
| | - Stefanie Hectors
- Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Bachir Taouli
- Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Mark Muzi
- University of Washington, Department of Radiology, Neurology, and Radiation Oncology, Seattle, Washington, United States
| | - Paul E. Kinahan
- University of Washington, Department of Radiology, Neurology, and Radiation Oncology, Seattle, Washington, United States
| | - Kathleen M. Schmainda
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - Melissa A. Prah
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - Erin N. Taber
- Oregon Health and Science University, Advanced Imaging Research Center, Portland, Oregon, United States
| | - Christopher Kroenke
- Oregon Health and Science University, Advanced Imaging Research Center, Portland, Oregon, United States
| | - Wei Huang
- Oregon Health and Science University, Advanced Imaging Research Center, Portland, Oregon, United States
| | - Lori R. Arlinghaus
- Vanderbilt University Medical Center, Vanderbilt University Institute of Imaging Science, Nashville, Tennessee, United States
| | - Thomas E. Yankeelov
- The University of Texas at Austin, Institute for Computational and Engineering Sciences, Department of Biomedical Engineering and Diagnostic Medicine, Austin, Texas, United States
| | - Yue Cao
- University of Michigan, Radiation Oncology, Radiology, and Biomedical Engineering, Ann Arbor, Michigan, United States
| | - Madhava Aryal
- University of Michigan, Radiation Oncology, Radiology, and Biomedical Engineering, Ann Arbor, Michigan, United States
| | - Yi-Fen Yen
- Harvard Medical School, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Charlestown, Massachusetts, United States
| | - Jayashree Kalpathy-Cramer
- Harvard Medical School, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Charlestown, Massachusetts, United States
| | - Amita Shukla-Dave
- Memorial Sloan-Kettering Cancer Center, Department of Medical Physics and Radiology, New York, New York, United States
| | - Maggie Fung
- Memorial Sloan-Kettering Cancer Center, GE Healthcare, New York, New York, United States
| | | | - Michael Boss
- National Institute of Standards and Technology, Applied Physics Division, Boulder, Colorado, United States
- University of Colorado Boulder, Department of Physics, Boulder, Colorado, United States
| | - Nola Hylton
- University of California San Francisco, Department of Radiology and Biomedical Imaging, San Francisco, California, United States
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Bell LC, Does MD, Stokes AM, Baxter LC, Schmainda KM, Dueck AC, Quarles CC. Optimization of DSC MRI Echo Times for CBV Measurements Using Error Analysis in a Pilot Study of High-Grade Gliomas. AJNR Am J Neuroradiol 2017; 38:1710-1715. [PMID: 28684456 PMCID: PMC5591773 DOI: 10.3174/ajnr.a5295] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2016] [Accepted: 05/07/2017] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE The optimal TE must be calculated to minimize the variance in CBV measurements made with DSC MR imaging. Simulations can be used to determine the influence of the TE on CBV, but they may not adequately recapitulate the in vivo heterogeneity of precontrast T2*, contrast agent kinetics, and the biophysical basis of contrast agent-induced T2* changes. The purpose of this study was to combine quantitative multiecho DSC MRI T2* time curves with error analysis in order to compute the optimal TE for a traditional single-echo acquisition. MATERIALS AND METHODS Eleven subjects with high-grade gliomas were scanned at 3T with a dual-echo DSC MR imaging sequence to quantify contrast agent-induced T2* changes in this retrospective study. Optimized TEs were calculated with propagation of error analysis for high-grade glial tumors, normal-appearing white matter, and arterial input function estimation. RESULTS The optimal TE is a weighted average of the T2* values that occur as a contrast agent bolus transverses a voxel. The mean optimal TEs were 30.0 ± 7.4 ms for high-grade glial tumors, 36.3 ± 4.6 ms for normal-appearing white matter, and 11.8 ± 1.4 ms for arterial input function estimation (repeated-measures ANOVA, P < .001). CONCLUSIONS Greater heterogeneity was observed in the optimal TE values for high-grade gliomas, and mean values of all 3 ROIs were statistically significant. The optimal TE for the arterial input function estimation is much shorter; this finding implies that quantitative DSC MR imaging acquisitions would benefit from multiecho acquisitions. In the case of a single-echo acquisition, the optimal TE prescribed should be 30-35 ms (without a preload) and 20-30 ms (with a standard full-dose preload).
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Affiliation(s)
- L C Bell
- From the Division of Imaging Research (L.C. Bell, A.M.S., L.C. Baxter, C.C.Q.), Barrow Neurological Institute, Phoenix, Arizona
| | - M D Does
- Department of Biomedical Engineering (M.D.D.), Vanderbilt University Institute of Imaging Science, Nashville, Tennessee
| | - A M Stokes
- From the Division of Imaging Research (L.C. Bell, A.M.S., L.C. Baxter, C.C.Q.), Barrow Neurological Institute, Phoenix, Arizona
| | - L C Baxter
- From the Division of Imaging Research (L.C. Bell, A.M.S., L.C. Baxter, C.C.Q.), Barrow Neurological Institute, Phoenix, Arizona
| | - K M Schmainda
- Departments of Biophysics and Radiology (K.M.S.), Medical College of Wisconsin, Milwaukee, Wisconsin
| | - A C Dueck
- Division of Health Sciences Research (A.C.D.), Section of Biostatistics, Mayo Clinic, Scottsdale, Arizona
| | - C C Quarles
- From the Division of Imaging Research (L.C. Bell, A.M.S., L.C. Baxter, C.C.Q.), Barrow Neurological Institute, Phoenix, Arizona
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Semmineh NB, Stokes AM, Bell LC, Boxerman JL, Quarles CC. A Population-Based Digital Reference Object (DRO) for Optimizing Dynamic Susceptibility Contrast (DSC)-MRI Methods for Clinical Trials. ACTA ACUST UNITED AC 2017; 3:41-49. [PMID: 28584878 PMCID: PMC5454781 DOI: 10.18383/j.tom.2016.00286] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
The standardization and broad-scale integration of dynamic susceptibility contrast (DSC)-magnetic resonance imaging (MRI) have been confounded by a lack of consensus on DSC-MRI methodology for preventing potential relative cerebral blood volume inaccuracies, including the choice of acquisition protocols and postprocessing algorithms. Therefore, we developed a digital reference object (DRO), using physiological and kinetic parameters derived from in vivo data, unique voxel-wise 3-dimensional tissue structures, and a validated MRI signal computational approach, aimed at validating image acquisition and analysis methods for accurately measuring relative cerebral blood volume in glioblastomas. To achieve DSC-MRI signals representative of the temporal characteristics, magnitude, and distribution of contrast agent-induced T1 and T2* changes observed across multiple glioblastomas, the DRO's input parameters were trained using DSC-MRI data from 23 glioblastomas (>40 000 voxels). The DRO's ability to produce reliable signals for combinations of pulse sequence parameters and contrast agent dosing schemes unlike those in the training data set was validated by comparison with in vivo dual-echo DSC-MRI data acquired in a separate cohort of patients with glioblastomas. Representative applications of the DRO are presented, including the selection of DSC-MRI acquisition and postprocessing methods that optimize CBV accuracy, determination of the impact of DSC-MRI methodology choices on sample size requirements, and the assessment of treatment response in clinical glioblastoma trials.
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Affiliation(s)
- Natenael B Semmineh
- Department of Imaging Research, Barrow Neurological Institute, Phoenix, Arizona
| | - Ashley M Stokes
- Department of Imaging Research, Barrow Neurological Institute, Phoenix, Arizona
| | - Laura C Bell
- Department of Imaging Research, Barrow Neurological Institute, Phoenix, Arizona
| | - Jerrold L Boxerman
- Department of Diagnostic Imaging, RI Hospital and Alpert Medical School of Brown University, Providence, Rhode Island
| | - C Chad Quarles
- Department of Imaging Research, Barrow Neurological Institute, Phoenix, Arizona
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Sorace AG, Barnes SL, Quarles CC, McIntyre JO, Yankeelov TE. Abstract P4-02-02: Increased tumor perfusion following treatment with trastuzumab as measured by contrast-enhanced ultrasound. Cancer Res 2017. [DOI: 10.1158/1538-7445.sabcs16-p4-02-02] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Introduction: The primary purpose of this study is to determine if the order of dosing in standard-of-care (SOC) combination therapy for HER2+ breast cancer has an effect on the perfusion characteristics within the tumor. Improving the intratumoral delivery of cytotoxic systemic therapy is a significant challenge in advancing cancer treatment. Knowledge of the vascular changes following SOC treatments could enable optimizing their order and timing, potentially leading to significantly improved response. Currently, one SOC regimen for HER2+ breast cancer treatment is doxorubicin administered for 3-4 cycles prior to trastuzumab. Our goal is to quantitatively map the changes in perfusion in response to different combinations of trastuzumab plus doxorubicin treatment through imaging in a murine model of HER2+ breast cancer.
Experimental Design: BT474 breast cancer cells (1×107) were subcutaneously implanted into mice (n = 12) and randomly assigned into three treatment groups: two doses of trastuzumab (10 mg/kg) followed by doxorubicin (1.5 mg/kg), doxorubicin prior to trastuzumab (same total drug dosage as group 1), and saline. After tumors reached ~225 mm3, animals were imaged with contrast-enhanced ultrasound (CEUS) (VisualSonics Vevo 770, Definity microbubbles) before treatment (day 0), and on days 1, 3, 4, and 7. Treatment occurred on days 0, 3 and 4. Percent change (from baseline, day 0 scans) of the CEUS signal intensity quantified from the functional vasculature (surrogate for vessel perfusion) following contrast injection were measured for each animal for each day. Tumors were extracted on day 7, and sectioned, paraffin-embedded, and stained with CD31, alpha-SMA and H&E.
Results: Tumors treated with trastuzumab initially exhibited a significant increase in CEUS signal intensity (from the functional vasculature) on day 1 compared to tumors initially treated with doxorubicin (p < 0.01). Additionally, compared to the control tumors, tumors treated with trastuzumab prior to doxorubicin revealed a significant increase in perfusion (change in signal intensity of functional vasculature) of contrast agent on days 3 (p = 0.01), 4 (p = 0.001) and day 7 (p < 0.01). There were no significant differences in the doxorubicin treated first group and the controls on any of the days (p > 0.25). Qualitative differences were noted between control and treated groups for alpha-SMA, no apparent differences were noted in microvessel density.
Conclusion: Trastuzumab significantly improves a tumor's vascular perfusion in this HER2+ breast cancer model. Doxorubicin dosing prior to treatment with trastuzumab may potentially be hindering the intratumoral delivery of the subsequently delivered, targeted therapy. Improving the tumor's functional vasculature by altering the order of dosing of these combination therapies by giving trastuzumab prior to cytotoxic therapy has potential to enhance both the delivery and the effectiveness of these combination therapies. These data indicate a potential pathway to optimize therapeutic efficacy for individual HER2+ breast cancer patients.
Citation Format: Sorace AG, Barnes SL, Quarles CC, McIntyre JO, Yankeelov TE. Increased tumor perfusion following treatment with trastuzumab as measured by contrast-enhanced ultrasound [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P4-02-02.
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Affiliation(s)
- AG Sorace
- The University of Texas at Austin, Austin, TX; St. Joseph's Hospital and Medical Center, Barrow Neurological Institute, Phoenix, AZ; Vanderbilt University Medical Center, Nashville, TN
| | - SL Barnes
- The University of Texas at Austin, Austin, TX; St. Joseph's Hospital and Medical Center, Barrow Neurological Institute, Phoenix, AZ; Vanderbilt University Medical Center, Nashville, TN
| | - CC Quarles
- The University of Texas at Austin, Austin, TX; St. Joseph's Hospital and Medical Center, Barrow Neurological Institute, Phoenix, AZ; Vanderbilt University Medical Center, Nashville, TN
| | - JO McIntyre
- The University of Texas at Austin, Austin, TX; St. Joseph's Hospital and Medical Center, Barrow Neurological Institute, Phoenix, AZ; Vanderbilt University Medical Center, Nashville, TN
| | - TE Yankeelov
- The University of Texas at Austin, Austin, TX; St. Joseph's Hospital and Medical Center, Barrow Neurological Institute, Phoenix, AZ; Vanderbilt University Medical Center, Nashville, TN
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Abramson RG, Arlinghaus LR, Dula AN, Quarles CC, Stokes AM, Weis JA, Whisenant JG, Chekmenev EY, Zhukov I, Williams JM, Yankeelov TE. MR Imaging Biomarkers in Oncology Clinical Trials. Magn Reson Imaging Clin N Am 2016; 24:11-29. [PMID: 26613873 DOI: 10.1016/j.mric.2015.08.002] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The authors discuss eight areas of quantitative MR imaging that are currently used (RECIST, DCE-MR imaging, DSC-MR imaging, diffusion MR imaging) in clinical trials or emerging (CEST, elastography, hyperpolarized MR imaging, multiparameter MR imaging) as promising techniques in diagnosing cancer and assessing or predicting response of cancer to therapy. Illustrative applications of the techniques in the clinical setting are summarized before describing the current limitations of the methods.
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Affiliation(s)
- Richard G Abramson
- Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University, VUIIS 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA
| | - Lori R Arlinghaus
- Department of Radiology and Radiological Sciences, Vanderbilt University, 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA
| | - Adrienne N Dula
- Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University, VUIIS 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA
| | - C Chad Quarles
- Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University, VUIIS 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA; Department of Biomedical Engineering, Institute of Imaging Science, Vanderbilt University, VUIIS 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA; Department of Cancer Biology, Institute of Imaging Science, Vanderbilt University, 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA
| | - Ashley M Stokes
- Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University, VUIIS 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA
| | - Jared A Weis
- Department of Biomedical Engineering, Vanderbilt University, VUIIS 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA
| | - Jennifer G Whisenant
- Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University, VUIIS 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA
| | - Eduard Y Chekmenev
- Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University, VUIIS 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA; Department of Biomedical Engineering, Institute of Imaging Science, Vanderbilt University, VUIIS 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA; Department of Biochemistry, Institute of Imaging Science, Vanderbilt University, 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA
| | - Igor Zhukov
- National Research Nuclear University MEPhI, Kashirskoye highway, 31, Moscow 115409, Russia
| | - Jason M Williams
- Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University, 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA
| | - Thomas E Yankeelov
- Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University, VUIIS 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA; Department of Biomedical Engineering, Institute of Imaging Science, Vanderbilt University, VUIIS 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA; Department of Cancer Biology, Institute of Imaging Science, Vanderbilt University, 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA; Department of Physics, Institute of Imaging Science, Vanderbilt University, VUIIS 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA.
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Stokes AM, Hart CP, Quarles CC. Hypoxia Imaging With PET Correlates With Antitumor Activity of the Hypoxia-Activated Prodrug Evofosfamide (TH-302) in Rodent Glioma Models. Tomography 2016; 2:229-237. [PMID: 27752544 PMCID: PMC5065246 DOI: 10.18383/j.tom.2016.00259] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
High-grade gliomas are often characterized by hypoxia, which is associated with both poor long-term prognosis and therapy resistance. The adverse role hypoxia plays in treatment resistance and disease progression has led to the development of hypoxia imaging methods and hypoxia-targeted treatments. Here, we determined the tumor hypoxia and vascular perfusion characteristics of 2 rat orthotopic glioma models using 18-fluoromisonidozole positron emission tomography. In addition, we determined tumor response to the hypoxia-activated prodrug evofosfamide (TH-302) in these rat glioma models. C6 tumors exhibited more hypoxia and were less perfused than 9L tumors. On the basis of these differences in their tumor hypoxic burden, treatment with evofosfamide resulted in 4- and 2-fold decreases in tumor growth rates of C6 and 9L tumors, respectively. This work shows that imaging methods sensitive to tumor hypoxia and perfusion are able to predict response to hypoxia-targeted agents. This has implications for improved patient selection, particularly in clinical trials, for treatment with hypoxia-activated cytotoxic prodrugs, such as evofosfamide.
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Affiliation(s)
- Ashley M. Stokes
- Institute of Imaging Science, Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee
- Department of Imaging Research, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, Arizona; and
| | - Charles P. Hart
- Threshold Pharmaceuticals Inc., South San Francisco, California
| | - C. Chad Quarles
- Institute of Imaging Science, Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee
- Department of Imaging Research, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, Arizona; and
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Hu LS, Ning S, Eschbacher JM, Baxter LC, Gaw N, Ranjbar S, Plasencia J, Dueck AC, Peng S, Smith KA, Nakaji P, Karis JP, Quarles CC, Wu T, Loftus JC, Jenkins RB, Sicotte H, Kollmeyer TM, O'Neill BP, Elmquist W, Hoxworth JM, Frakes D, Sarkaria J, Swanson KR, Tran NL, Li J, Mitchell JR. Radiogenomics to characterize regional genetic heterogeneity in glioblastoma. Neuro Oncol 2016; 19:128-137. [PMID: 27502248 DOI: 10.1093/neuonc/now135] [Citation(s) in RCA: 137] [Impact Index Per Article: 17.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Glioblastoma (GBM) exhibits profound intratumoral genetic heterogeneity. Each tumor comprises multiple genetically distinct clonal populations with different therapeutic sensitivities. This has implications for targeted therapy and genetically informed paradigms. Contrast-enhanced (CE)-MRI and conventional sampling techniques have failed to resolve this heterogeneity, particularly for nonenhancing tumor populations. This study explores the feasibility of using multiparametric MRI and texture analysis to characterize regional genetic heterogeneity throughout MRI-enhancing and nonenhancing tumor segments. METHODS We collected multiple image-guided biopsies from primary GBM patients throughout regions of enhancement (ENH) and nonenhancing parenchyma (so called brain-around-tumor, [BAT]). For each biopsy, we analyzed DNA copy number variants for core GBM driver genes reported by The Cancer Genome Atlas. We co-registered biopsy locations with MRI and texture maps to correlate regional genetic status with spatially matched imaging measurements. We also built multivariate predictive decision-tree models for each GBM driver gene and validated accuracies using leave-one-out-cross-validation (LOOCV). RESULTS We collected 48 biopsies (13 tumors) and identified significant imaging correlations (univariate analysis) for 6 driver genes: EGFR, PDGFRA, PTEN, CDKN2A, RB1, and TP53. Predictive model accuracies (on LOOCV) varied by driver gene of interest. Highest accuracies were observed for PDGFRA (77.1%), EGFR (75%), CDKN2A (87.5%), and RB1 (87.5%), while lowest accuracy was observed in TP53 (37.5%). Models for 4 driver genes (EGFR, RB1, CDKN2A, and PTEN) showed higher accuracy in BAT samples (n = 16) compared with those from ENH segments (n = 32). CONCLUSION MRI and texture analysis can help characterize regional genetic heterogeneity, which offers potential diagnostic value under the paradigm of individualized oncology.
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Affiliation(s)
- Leland S Hu
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Shuluo Ning
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Jennifer M Eschbacher
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Leslie C Baxter
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Nathan Gaw
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Sara Ranjbar
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Jonathan Plasencia
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Amylou C Dueck
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Sen Peng
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Kris A Smith
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Peter Nakaji
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - John P Karis
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - C Chad Quarles
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Teresa Wu
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Joseph C Loftus
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Robert B Jenkins
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Hugues Sicotte
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Thomas M Kollmeyer
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Brian P O'Neill
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - William Elmquist
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Joseph M Hoxworth
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - David Frakes
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Jann Sarkaria
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Kristin R Swanson
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Nhan L Tran
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - Jing Li
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
| | - J Ross Mitchell
- Department of Radiology, Mayo Clinic, Phoenix, Arizona (L.S.H., T.W., J.M.H.); Department of Biostatistics, Mayo Clinic, Phoenix, Arizona (A.C.D.); Department of Research, Mayo Clinic, Arizona (J.R.M., K.S.); Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona (K.R.S.); Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, Arizona (J.C.L.); Department of Pathology, Mayo Clinic, Rochester, Minnesota (R.B.J., T.M.K.); Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota (H.S.); Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota (B.P.O.); Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota (J.S.); Department of Pharmaceutics, University of Minnesota, Minneapolis, Minnesota (W.E.); Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona (S.P., N.L.T.); School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona (J.L., T.W., S.N., N.G.); Department of Biomedical Informatics, Arizona State University, Tempe, Arizona (S.R.); School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona (J.P., D.F.); Department of Pathology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (J.M.E.); Department of Neurosurgery, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (K.A.S., P.N.); Department of Radiology, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (L.C.B., J.P. K., L.S.H.); Department of Imaging Research, Barrow Neurological Institute - St. Joseph's Hospital and Medical Center, Phoenix, Arizona (C.C.Q.)
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Stokes AM, Skinner JT, Yankeelov T, Quarles CC. Assessment of a simplified spin and gradient echo (sSAGE) approach for human brain tumor perfusion imaging. Magn Reson Imaging 2016; 34:1248-1255. [PMID: 27457801 DOI: 10.1016/j.mri.2016.07.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Accepted: 07/17/2016] [Indexed: 10/21/2022]
Abstract
The goal of this study was to validate a simplified spin- and gradient-echo (sSAGE) approach to obtain T1-corrected dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) data in a clinical brain tumor population. A five-echo SAGE sequence was used to acquire DSC-MRI data (n=8 patients, 3 primary glioma, and 5 brain metastases). The ΔR2⁎ and ΔR2 time series obtained from a nonlinear fit of all echoes (SAGE) were compared to ΔR2⁎ and ΔR2 time series obtained analytically (sSAGE) using three echoes (two GEs and one SE). Through the use of multiple echoes, both methods removed T1 leakage effects from the ΔR2⁎ and ΔR2 time series, and the sSAGE ΔR2⁎ and ΔR2 time series were highly correlated with those from SAGE, with average correlations of 0.9. The resulting hemodynamic parameters included GE and SE cerebral blood volume (CBV), cerebral blood flow (CBF), mean vessel diameter (mVD), volume transfer constant (Ktrans), and volume fraction of the extravascular extracellular space (ve). For each metric, there was good correlation (>0.86) between sSAGE and SAGE, with no significant differences. The sSAGE method provides T1-corrected GE and SE DSC-MRI parameters in an efficient and clinically feasible manner.
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Affiliation(s)
- Ashley M Stokes
- Institute of Imaging Science, Vanderbilt University, 1161 21st Ave. S, Nashville, TN 37232, USA
| | - Jack T Skinner
- Institute of Imaging Science, Vanderbilt University, 1161 21st Ave. S, Nashville, TN 37232, USA
| | - Thomas Yankeelov
- Institute of Imaging Science, Vanderbilt University, 1161 21st Ave. S, Nashville, TN 37232, USA
| | - C Chad Quarles
- Institute of Imaging Science, Vanderbilt University, 1161 21st Ave. S, Nashville, TN 37232, USA.
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Sorace AG, Quarles CC, Sanchez V, Yankeelov TE. Abstract 4237: Decreased hypoxia in a HER2+ breast cancer model following trastuzumab therapy. Cancer Res 2016. [DOI: 10.1158/1538-7445.am2016-4237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Introduction: Hypoxic tumors demonstrate treatment resistance with an increased risk of metastasis. Therefore, alleviating hypoxia has potential to improve efficiency of combination therapies. The primary goal of this study is to quantify alterations in hypoxia in response to trastuzumab in a murine model of HER2+ breast cancer through imaging and histology.
Experimental Design: Mice were implanted subcutaneously with BT474 breast cancer cells (107) and randomly assigned into treated (10 mg/kg trastuzumab) or control (saline) groups. After tumors reached ∼250 mm3, animals (n = 32) were utilized to identify longitudinal changes in functional vascular through an intravenous injection of Hoechst 33342 nuclear stain (immediately prior to sacrifice) and hypoxia through pimonidazole intravenous injection (one hour prior to sacrifice). Tumors were extracted for immunofluorescence between days 0 through 7. Tumor sections were flash frozen and stained with anti-pimonidazole and propidium iodide (nuclear counterstain). Additionally, a set of tumors (n = 36) were sacrificed for immunohistochemistry (days 0 through 7), formalin fixed and stained for CA-IX. All stained sections were scanned in high resolution (20×) and quantitatively analyzed with Leica SCN400 software. Another cohort of animals (n = 10) were imaged with 18F-FMISO (fluoromisonidazole) PET between days 0 through 7 and quantified via mean tracer concentration (%ID/g).
Results: Immunohistochemistry revealed significantly increased hypoxia in the control group compared to treated on days 3 (p = 0.03) and 7 (p = 0.002), as measured through CA-IX staining. Additionally, on day 4, functional vascular delivery was increased while hypoxia (pimonidazole) decreased in treated tumors compared to control. 18F-FMISO PET imaging corroborated histology findings with significantly decreased hypoxia in treated tumors compared to control tumors on day 7 (-47%; p<0.0001).
Conclusion: Trastuzumab has been shown to decrease hypoxia, as measured through gold-standard immunofluorescence. Additionally, this trastuzumab-induced improvement in tumor hypoxia can be measured via clinically relevant, noninvasive imaging (18F-FMISO PET). Temporarily improving the tumor's functional vasculature and decreasing hypoxia during trastuzumab treatment has potential to enhance the effectiveness of combination therapies. Identifying windows of improved vascular and cellular normalization with noninvasive medical imaging provide opportunity to decrease resistance to standard-of-care treatments and optimize therapeutic timing and regimens.
Citation Format: Anna G. Sorace, C. Chad Quarles, Violeta Sanchez, Thomas E. Yankeelov. Decreased hypoxia in a HER2+ breast cancer model following trastuzumab therapy. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4237.
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Newton AT, Pruthi S, Stokes AM, Skinner JT, Quarles CC. Improving Perfusion Measurement in DSC-MR Imaging with Multiecho Information for Arterial Input Function Determination. AJNR Am J Neuroradiol 2016; 37:1237-43. [PMID: 26988812 DOI: 10.3174/ajnr.a4700] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2015] [Accepted: 12/14/2015] [Indexed: 01/12/2023]
Abstract
BACKGROUND AND PURPOSE Clinical measurements of cerebral perfusion have been increasingly performed with multiecho dynamic susceptibility contrast-MR imaging techniques due to their ability to remove confounding T1 effects of contrast agent extravasation from perfusion quantification. However, to this point, the extra information provided by multiecho techniques has not been used to improve the process of estimating the arterial input function, which is critical to accurate perfusion quantification. The purpose of this study is to investigate methods by which multiecho DSC-MRI data can be used to automatically avoid voxels whose signal decreases to the level of noise when calculating the arterial input function. MATERIALS AND METHODS Here we compare postprocessing strategies for clinical multiecho DSC-MR imaging data to test whether arterial input function measures could be improved by automatically identifying and removing voxels exhibiting signal attenuation (truncation) artifacts. RESULTS In a clinical pediatric population, we found that the Pearson correlation coefficient between ΔR2* time-series calculated from each TE individually was a valuable criterion for automated estimation of the arterial input function, resulting in higher peak arterial input function values while maintaining smooth and reliable arterial input function shapes. CONCLUSIONS This work is the first to demonstrate that multiecho information may be useful in clinically important automatic arterial input function estimation because it can be used to improve automatic selection of voxels from which the arterial input function should be measured.
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Affiliation(s)
- A T Newton
- From the Department of Radiology and Radiological Sciences (A.T.N., S.P.), Vanderbilt University Medical Center, Nashville, Tennessee Institute of Imaging Science (A.T.N.), Vanderbilt University, Nashville, Tennessee
| | - S Pruthi
- From the Department of Radiology and Radiological Sciences (A.T.N., S.P.), Vanderbilt University Medical Center, Nashville, Tennessee Monroe Carell Jr. Children's Hospital at Vanderbilt (S.P.), Nashville, Tennessee
| | - A M Stokes
- Barrow Neurological Institute (A.M.S., C.C.Q.), Phoenix, Arizona Saint Joseph's Hospital and Medical Center (A.M.S., C.C.Q.), Phoenix, Arizona
| | - J T Skinner
- National Comprehensive Cancer Network (J.T.S.), Fort Washington, Pennsylvania
| | - C C Quarles
- Barrow Neurological Institute (A.M.S., C.C.Q.), Phoenix, Arizona Saint Joseph's Hospital and Medical Center (A.M.S., C.C.Q.), Phoenix, Arizona
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Sorace AG, Quarles CC, Yankeelov TE. Abstract P6-01-03: Trastuzumab-induced hypoxia changes in a HER2+ murine model of breast cancer. Cancer Res 2016. [DOI: 10.1158/1538-7445.sabcs15-p6-01-03] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Introduction: The primary goal of this study is to quantitatively map the changes in perfusion and hypoxia in response to trastuzumab treatment through imaging and immunohistochemical studies in a murine model of HER2+ breast cancer. Correlating quantitative imaging data with pathological validation of vessel architecture will identify the temporal changes in vascular function, which will enable optimizing the order and timing of multi-modal (i.e., trastuzumab, chemo- and radiation) therapy, leading to significantly improved anticancer response.
Experimental Design: Mice were implanted subcutaneously with BT474 breast cancer cells (1×107) and randomly assorted into groups: treated (10 mg/kg trastuzumab) or control (saline). After tumors reached ∼225 mm3, animals (n = 20) were imaged with dynamic contrast enhanced-MRI (7.0 T MRI) before treatment (day 0), and 24 hours after each treatment (day 1 and day 4). Pharmacokinetic parameters, Ktrans and ve, were extracted. Subgroups of animals were sacrificed for histology between days 0 through 7 (n = 36). Tumor sections were paraffin-embedded and stained with CA-IX, CD31, α-SMA, Ki67 and H&E. Slides were scanned in high resolution (20×) and quantitatively analyzed with Leica SCN400 software. Another cohort of animals (n = 32) was utilized to identify longitudinal changes in functional vascular (Hoechst 33342) and hypoxia (pimonidazole) through immunofluorescence. Agents were injected prior to sacrifice between days 0 through 7 and tumors were immediately frozen for processing.
Results: Treated tumors exhibited a significant increase in Ktrans (p = 0.03) on day 4 compared to controls, indicative of heightened vessel perfusion and/or permeability. Additionally on day 4, treated tumors exhibited a significant increase in ve (p = 0.01), the extravascular extracellular volume fraction, indicating increased cell death. Significant decreases in Ki67 proliferation staining (p = 0.02) and tumor volume at day 7 (p = 0.04) in the treated group confirmed tumor response to trastuzumab. Immunohistochemical analyses revealed treated tumors have a significant decrease in CD31 microvessel density staining (p = 0.01), with a simultaneous significant increase in α-SMA pericyte coverage staining (p = 0.05) on day 4; thus, there was an overall increase in the "vessel maturation index" (ratio of α-SMA to CD31 staining) compared to controls (p = 0.01). CA-IX staining demonstrated increased hypoxia in the control group compared to treated, showing significant increases on day 3 (p = 0.03) and day 7 (p = 0.002). Qualitative differences were noted between control and treated groups for both functional vasculature and pimonidazole hypoxia fluorescent staining.
Conclusion: Increased intratumoral vascular delivery (Ktrans) with a simultaneous increase in vessel maturation (immunohistochemistry) are exhibited on day 4 post trastuzumab treatment. Additionally, decreased hypoxia is revealed in treated tumors compared to controls. Hypoxic tumor cells show resistance to both radiation and chemotherapy, therefore temporarily improving the tumor's functional vasculature and decreasing hypoxia during trastuzumab treatment has potential to enhance the effectiveness of these combination therapies.
Citation Format: Sorace AG, Quarles CC, Yankeelov TE. Trastuzumab-induced hypoxia changes in a HER2+ murine model of breast cancer. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P6-01-03.
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Affiliation(s)
- AG Sorace
- Vanderbilt University, Nashville, TN
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Skinner JT, Moots PL, Ayers GD, Quarles CC. On the Use of DSC-MRI for Measuring Vascular Permeability. AJNR Am J Neuroradiol 2016; 37:80-7. [PMID: 26427833 DOI: 10.3174/ajnr.a4478] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2015] [Accepted: 05/12/2015] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Contrast agent extravasation has been shown to confound brain tumor perfusion measurements with DSC-MR imaging, necessitating the use of correction techniques (eg, Weisskoff, Bjornerud). Leakage parameters (K2 and K(a)) postulated to reflect vessel permeability can be extracted from these correction methods; however, the biophysical interpretation of these parameters and their relationship to commonly used MR imaging measures of vascular permeability (eg, contrast agent volume transfer constant, [K(trans)]) remain unclear. Given that vascular density, as assessed by blood volume, and vascular permeability, as reflected by K(trans) (and potentially K2 or K(a)), report on unique and clinically informative vascular characteristics, there is a compelling interest to simultaneously assess these features. MATERIALS AND METHODS We acquired multiecho DSC-MR imaging data, allowing the simultaneous computation and voxelwise comparison of single- and dual-echo derived measures of K2, K(a) and K(trans) in patients with glioma. This acquisition enabled the investigation of competing T1 and T2* leakage effects and TE dependency on these parameters. RESULTS K2 and K(a) displayed nonsignificant (P = .150 and P = .060, respectively) voxelwise linear correlations with K(trans), while a significant (P < .001) inverse relationship was observed between K2 and Ka (coefficient of determination [r(2)] = 0.466-0.984). Significantly different (P < .005) mean estimates were found between voxels exhibiting predominately T1 and T2* effects for K2 and K(a). K(trans), however, was observed to be similar between these voxels (0.109 versus 0.092 minutes(-1)). Significant differences (P < .001) in extracellular-extravascular volume fraction (v(e)) (0.285 versus 0.167) were also observed between cohorts. Additionally, K2 and K(a) were found to have a significant quadratic relationship (P = .031 and P = .005, respectively) with v(e). CONCLUSIONS Estimates of vascular permeability in brain tumors may be simultaneously acquired from multiple-echo DSC-MR imaging via K(trans); however, caution should be used in assuming a similar relationship for K2 and K(a).
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Affiliation(s)
- J T Skinner
- From the Vanderbilt University Institute of Imaging Science (J.T.S., C.C.Q.) Departments of Radiology and Radiological Sciences (J.T.S., C.C.Q.)
| | | | - G D Ayers
- Biostatistics (G.D.A.), Vanderbilt University School of Medicine, Nashville, Tennessee
| | - C C Quarles
- From the Vanderbilt University Institute of Imaging Science (J.T.S., C.C.Q.) Departments of Cancer Biology (C.C.Q.) Biomedical Engineering (C.C.Q.), Vanderbilt University, Nashville, Tennessee Departments of Radiology and Radiological Sciences (J.T.S., C.C.Q.)
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Sorace AG, Quarles CC, Whisenant JG, Hanker AB, McIntyre JO, Sanchez VM, Yankeelov TE. Trastuzumab improves tumor perfusion and vascular delivery of cytotoxic therapy in a murine model of HER2+ breast cancer: preliminary results. Breast Cancer Res Treat 2016; 155:273-84. [PMID: 26791520 PMCID: PMC4833210 DOI: 10.1007/s10549-016-3680-8] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2015] [Accepted: 01/04/2016] [Indexed: 01/17/2023]
Abstract
To employ in vivo imaging and histological techniques to identify and quantify vascular changes early in the course of treatment with trastuzumab in a murine model of HER2+ breast cancer. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) was used to quantitatively characterize vessel perfusion/permeability (via the parameter K (trans) ) and the extravascular extracellular volume fraction (v e ) in the BT474 mouse model of HER2+ breast cancer (N = 20) at baseline, day one, and day four following trastuzumab treatment (10 mg/kg). Additional cohorts of mice were used to quantify proliferation (Ki67), microvessel density (CD31), pericyte coverage (α-SMA) by immunohistochemistry (N = 44), and to quantify human VEGF-A expression (N = 29) throughout the course of therapy. Longitudinal assessment of combination doxorubicin ± trastuzumab (N = 42) tested the hypothesis that prior treatment with trastuzumab will increase the efficacy of subsequent doxorubicin therapy. Compared to control tumors, trastuzumab-treated tumors exhibited a significant increase in K (trans) (P = 0.035) on day four, indicating increased perfusion and/or vessel permeability and a simultaneous significant increase in v e (P = 0.01), indicating increased cell death. Immunohistochemical and ELISA analyses revealed that by day four the trastuzumab-treated tumors had a significant increase in vessel maturation index (i.e., the ratio of α-SMA to CD31 staining) compared to controls (P < 0.001) and a significant decrease in VEGF-A (P = 0.03). Additionally, trastuzumab dosing prior to doxorubicin improved the overall effectiveness of the therapies (P < 0.001). This study identifies and validates improved perfusion characteristics following trastuzumab therapy, resulting in an improvement in trastuzumab-doxorubicin combination therapy in a murine model of HER2+ breast cancer. This data suggests properties of vessel maturation. In particular, the use of DCE-MRI, a clinically available imaging method, following treatment with trastuzumab may provide an opportunity to optimize the scheduling and improve delivery of subsequent cytotoxic therapy.
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Affiliation(s)
- Anna G. Sorace
- Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, USA,Vanderbilt Institute of Imaging Science, Vanderbilt University Medical Center, AA-1105 Medical Center North, 1161 21st Ave South, Nashville, TN 37232-2310, USA
| | - C. Chad Quarles
- Division of Neuroimaging Research, Barrow Neurological Institute, Dignity Health, St. Joseph’s Hospital and Medical Center, Phoenix, AZ 85013, USA
| | - Jennifer G. Whisenant
- Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, USA,Vanderbilt Institute of Imaging Science, Vanderbilt University Medical Center, AA-1105 Medical Center North, 1161 21st Ave South, Nashville, TN 37232-2310, USA
| | - Ariella B. Hanker
- Department of Cancer Biology, Vanderbilt University, Nashville, TN, USA
| | - J. Oliver McIntyre
- Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, USA,Vanderbilt Institute of Imaging Science, Vanderbilt University Medical Center, AA-1105 Medical Center North, 1161 21st Ave South, Nashville, TN 37232-2310, USA,Department of Cancer Biology, Vanderbilt University, Nashville, TN, USA
| | - Violeta M. Sanchez
- Department of Hematology Oncology, Vanderbilt University, Nashville, TN, USA
| | - Thomas E. Yankeelov
- Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, USA,Vanderbilt Institute of Imaging Science, Vanderbilt University Medical Center, AA-1105 Medical Center North, 1161 21st Ave South, Nashville, TN 37232-2310, USA,Department of Cancer Biology, Vanderbilt University, Nashville, TN, USA,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA,Vanderbilt Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA,Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, USA
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Wang F, Kopylov D, Zu Z, Takahashi K, Wang S, Quarles CC, Gore JC, Harris RC, Takahashi T. Mapping murine diabetic kidney disease using chemical exchange saturation transfer MRI. Magn Reson Med 2015; 76:1531-1541. [PMID: 26608660 DOI: 10.1002/mrm.26045] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2015] [Revised: 10/17/2015] [Accepted: 10/21/2015] [Indexed: 12/27/2022]
Abstract
PURPOSE Diabetic nephropathy (DN) is the leading cause of renal failure; however, current clinical tests are insufficient for assessing this disease. DN is associated with changes in renal metabolites, so we evaluated the utility of chemical exchange saturation transfer (CEST) imaging to detect changes characteristic of this disease. METHODS Sensitivity of CEST imaging at 7 Tesla to DN was evaluated by imaging diabetic mice [db/db, db/db endothelial nitric oxide synthase (eNOS)-/-] that show different levels of nephropathy as well as by longitudinal imaging (8 to 24 weeks). Nondiabetic (db/m) mice were used as controls. RESULTS Compared with nondiabetic mice, the CEST contrasts of hydroxyl metabolites that correspond to glucose and glycogen were significantly increased in papilla (P), inner medulla (IM), and outer medulla (OM) in db/db and db/db eNOS-/- kidneys at 16 weeks. The db/db eNOS-/- mice that showed advanced nephropathy exhibited greater CEST effects in OM and significant CEST contrasts were also observed in cortex. Longitudinally, db/db mice exhibited progressive increases in hydroxyl signals in IM+P and OM from 12 to 24 weeks and an increase was also observed in cortex at 24 weeks. CONCLUSION CEST MRI can be used to measure changes of hydroxyl metabolites in kidney during progression of DN. Magn Reson Med 76:1531-1541, 2016. © 2015 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Feng Wang
- Vanderbilt University Institute of Imaging Science, Tennessee, USA. .,Radiology and Radiological Sciences, Vanderbilt University, Tennessee, USA.
| | - David Kopylov
- Drexel University College of Medicine, Pennsylvania, USA
| | - Zhongliang Zu
- Vanderbilt University Institute of Imaging Science, Tennessee, USA.,Radiology and Radiological Sciences, Vanderbilt University, Tennessee, USA
| | - Keiko Takahashi
- Division of Nephrology and Hypertension, Vanderbilt University, Tennessee, USA
| | - Suwan Wang
- Division of Nephrology and Hypertension, Vanderbilt University, Tennessee, USA
| | - C Chad Quarles
- Vanderbilt University Institute of Imaging Science, Tennessee, USA.,Radiology and Radiological Sciences, Vanderbilt University, Tennessee, USA
| | - John C Gore
- Vanderbilt University Institute of Imaging Science, Tennessee, USA.,Radiology and Radiological Sciences, Vanderbilt University, Tennessee, USA.,Biomedical Engineering, Vanderbilt University, Tennessee, USA
| | - Raymond C Harris
- Division of Nephrology and Hypertension, Vanderbilt University, Tennessee, USA
| | - Takamune Takahashi
- Division of Nephrology and Hypertension, Vanderbilt University, Tennessee, USA
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Khan IS, Odom M, Ehtesham M, Colvin D, Quarles CC, McLaughlin B, Singer RJ. Intraarterial administration of norcantharidin attenuates ischemic stroke damage in rodents when given at the time of reperfusion: novel uses of endovascular capabilities. J Neurosurg 2015; 125:152-9. [PMID: 26544777 DOI: 10.3171/2015.4.jns142400] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECT Matrix metalloprotease-9 (MMP-9) plays a critical role in infarct progression, blood-brain barrier (BBB) disruption, and vasogenic edema. While systemic administration of MMP-9 inhibitors has shown neuroprotective promise in ischemic stroke, there has been little effort to incorporate these drugs into endovascular modalities. By modifying the rodent middle cerebral artery occlusion (MCAO) model to allow local intraarterial delivery of drugs, one has the ability to mimic endovascular delivery of therapeutics. Using this model, the authors sought to maximize the protective potential of MMP-9 inhibition by intraarterial administration of an MMP-9 inhibitor, norcantharidin (NCTD). METHODS Spontaneously hypertensive rats were subjected to 90-minute MCAO followed immediately by local intraarterial administration of NCTD. The rats' neurobehavioral performances were scored according to the ladder rung walking test results and the Garcia neurological test for as long as 7 days after stroke. MRI was also conducted 24 hours after the stroke to assess infarct volume and BBB disruption. At the end of the experimental protocol, rat brains were used for active MMP-9 immunohistochemical analysis to assess the degree of MMP-9 inhibition. RESULTS NCTD-treated rats showed significantly better neurobehavioral scores for all days tested. MR images also depicted significantly decreased infarct volumes and BBB disruption 24 hours after stroke. Inhibition of MMP-9 expression in the ischemic region was depicted on immunohistochemical analysis, wherein treated rats showed decreased active MMP-9 staining compared with controls. CONCLUSIONS Intraarterial NCTD significantly improved outcome when administered at the time of reperfusion in a spontaneously hypertensive rat stroke model. This study suggests that supplementing endovascular revascularization with local neuroprotective drug therapy may be a viable therapeutic strategy.
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Affiliation(s)
- Imad S Khan
- J.B. Marshall Laboratory for Neurovascular Therapeutics at Dartmouth;,Division of Neurosurgery, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire
| | - Mitchell Odom
- Department of Neurosurgery, Vanderbilt University Medical Center
| | - Moneeb Ehtesham
- Department of Neurosurgery, Vanderbilt University Medical Center
| | - Daniel Colvin
- Vanderbilt University Institute of Imaging Sciences; and
| | - C Chad Quarles
- Vanderbilt University Institute of Imaging Sciences; and
| | - BethAnn McLaughlin
- Department of Neurology and Pharmacology, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Robert J Singer
- J.B. Marshall Laboratory for Neurovascular Therapeutics at Dartmouth;,Division of Neurosurgery, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire
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Li K, Li H, Zhang XY, Stokes AM, Jiang X, Kang H, Quarles CC, Zu Z, Gochberg DF, Gore JC, Xu J. Influence of water compartmentation and heterogeneous relaxation on quantitative magnetization transfer imaging in rodent brain tumors. Magn Reson Med 2015; 76:635-44. [PMID: 26375875 DOI: 10.1002/mrm.25893] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2015] [Revised: 07/24/2015] [Accepted: 07/25/2015] [Indexed: 12/16/2022]
Abstract
PURPOSE The goal of this study was to investigate the influence of water compartmentation and heterogeneous relaxation properties on quantitative magnetization transfer (qMT) imaging in tissues, and in particular whether a two-pool model is sufficient to describe qMT data in brain tumors. METHODS Computer simulations and in vivo experiments with a series of qMT measurements before and after injection of Gd-DTPA were performed. Both off-resonance pulsed saturation (pulsed) and on-resonance selective inversion recovery (SIR) qMT methods were used, and all data were fit with a two-pool model only. RESULTS Simulations indicated that a two-pool fitting of four-pool data yielded accurate measures of pool size ratio (PSR) of macromolecular versus free water protons when there were fast transcytolemmal exchange and slow R1 recovery. The fitted in vivo PSR of both pulsed and SIR qMT methods showed no dependence on R1 variations caused by different concentrations of Gd-DTPA during wash-out, whereas the fitted kex (magnetization transfer exchange rate) changed significantly with R1 . CONCLUSION A two-pool model provides reproducible estimates of PSR in brain tumors independent of relaxation properties in the presence of relatively fast transcytolemmal exchange, whereas estimates of kex are biased by relaxation variations. In addition, estimates of PSR in brain tumors using the pulsed and SIR qMT methods agree well with one another. Magn Reson Med 76:635-644, 2016. © 2015 Wiley Periodicals, Inc.
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Affiliation(s)
- Ke Li
- Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee, USA
| | - Hua Li
- Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA.,Department of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee, USA
| | - Xiao-Yong Zhang
- Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee, USA
| | - Ashley M Stokes
- Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee, USA
| | - Xiaoyu Jiang
- Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee, USA
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University, Nashville, Tennessee, USA
| | - C Chad Quarles
- Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Zhongliang Zu
- Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee, USA
| | - Daniel F Gochberg
- Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee, USA.,Department of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee, USA
| | - John C Gore
- Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee, USA.,Department of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA.,Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, USA
| | - Junzhong Xu
- Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee, USA.,Department of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee, USA
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