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Dickie BR, Ahmed Z, Arvidsson J, Bell LC, Buckley DL, Debus C, Fedorov A, Floca R, Gutmann I, van der Heijden RA, van Houdt PJ, Sourbron S, Thrippleton MJ, Quarles C, Kompan IN. A community-endorsed open-source lexicon for contrast agent-based perfusion MRI: A consensus guidelines report from the ISMRM Open Science Initiative for Perfusion Imaging (OSIPI). Magn Reson Med 2024; 91:1761-1773. [PMID: 37831600 DOI: 10.1002/mrm.29840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 07/25/2023] [Accepted: 08/04/2023] [Indexed: 10/15/2023]
Abstract
This manuscript describes the ISMRM OSIPI (Open Science Initiative for Perfusion Imaging) lexicon for dynamic contrast-enhanced and dynamic susceptibility-contrast MRI. The lexicon was developed by Taskforce 4.2 of OSIPI to provide standardized definitions of commonly used quantities, models, and analysis processes with the aim of reducing reporting variability. The taskforce was established in February 2020 and consists of medical physicists, engineers, clinicians, data and computer scientists, and DICOM (Digital Imaging and Communications in Medicine) standard experts. Members of the taskforce collaborated via a slack channel and quarterly virtual meetings. Members participated by defining lexicon items and reporting formats that were reviewed by at least two other members of the taskforce. Version 1.0.0 of the lexicon was subject to open review from the wider perfusion imaging community between January and March 2022, and endorsed by the Perfusion Study Group of the ISMRM in the summer of 2022. The initial scope of the lexicon was set by the taskforce and defined such that it contained a basic set of quantities, processes, and models to enable users to report an end-to-end analysis pipeline including kinetic model fitting. We also provide guidance on how to easily incorporate lexicon items and definitions into free-text descriptions (e.g., in manuscripts and other documentation) and introduce an XML-based pipeline encoding format to encode analyses using lexicon definitions in standardized and extensible machine-readable code. The lexicon is designed to be open-source and extendable, enabling ongoing expansion of its content. We hope that widespread adoption of lexicon terminology and reporting formats described herein will increase reproducibility within the field.
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Affiliation(s)
- Ben R Dickie
- Division of Informatics, Imaging, and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- Geoffrey Jefferson Brain Research Center, Manchester Academic Health Science Center, The University of Manchester, Manchester, UK
| | - Zaki Ahmed
- Corewell Health William Beaumont University Hospital, Royal Oak, Michigan, USA
| | - Jonathan Arvidsson
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Laura C Bell
- Clinical Imaging Group, Genentech, Inc., South San Francisco, California, USA
| | | | | | - Andrey Fedorov
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ralf Floca
- National Center for Radiation Research in Oncology, Heidelberg Institute for Radiation Oncology, Heidelberg, Germany
| | - Ingomar Gutmann
- Faculty of Physics, Physics of Functional Materials, University of Vienna, Vienna, Austria
| | - Rianne A van der Heijden
- Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Petra J van Houdt
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Steven Sourbron
- Department of Infection, Immunity, and Cardiovascular Diseases, University of Sheffield, Sheffield, UK
| | - Michael J Thrippleton
- Edinburgh Imaging and Center for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Chad Quarles
- Department of Cancer Systems Imaging, UT MD Anderson Cancer Center, Houston, Texas, USA
| | - Ina N Kompan
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
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van Houdt PJ, Ragunathan S, Berks M, Ahmed Z, Kershaw LE, Gurney-Champion OJ, Tadimalla S, Arvidsson J, Sun Y, Kallehauge J, Dickie B, Lévy S, Bell L, Sourbron S, Thrippleton MJ. Contrast-agent-based perfusion MRI code repository and testing framework: ISMRM Open Science Initiative for Perfusion Imaging (OSIPI). Magn Reson Med 2024; 91:1774-1786. [PMID: 37667526 DOI: 10.1002/mrm.29826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 06/30/2023] [Accepted: 07/25/2023] [Indexed: 09/06/2023]
Abstract
PURPOSE Software has a substantial impact on quantitative perfusion MRI values. The lack of generally accepted implementations, code sharing and transparent testing reduces reproducibility, hindering the use of perfusion MRI in clinical trials. To address these issues, the ISMRM Open Science Initiative for Perfusion Imaging (OSIPI) aimed to establish a community-led, centralized repository for sharing open-source code for processing contrast-based perfusion imaging, incorporating an open-source testing framework. METHODS A repository was established on the OSIPI GitHub website. Python was chosen as the target software language. Calls for code contributions were made to OSIPI members, the ISMRM Perfusion Study Group, and publicly via OSIPI websites. An automated unit-testing framework was implemented to evaluate the output of code contributions, including visual representation of the results. RESULTS The repository hosts 86 implementations of perfusion processing steps contributed by 12 individuals or teams. These cover all core aspects of DCE- and DSC-MRI processing, including multiple implementations of the same functionality. Tests were developed for 52 implementations, covering five analysis steps. For T1 mapping, signal-to-concentration conversion and population AIF functions, different implementations resulted in near-identical output values. For the five pharmacokinetic models tested (Tofts, extended Tofts-Kety, Patlak, two-compartment exchange, and two-compartment uptake), differences in output parameters were observed between contributions. CONCLUSIONS The OSIPI DCE-DSC code repository represents a novel community-led model for code sharing and testing. The repository facilitates the re-use of existing code and the benchmarking of new code, promoting enhanced reproducibility in quantitative perfusion imaging.
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Affiliation(s)
- Petra J van Houdt
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | - Michael Berks
- Quantitative Biomedical Imaging Laboratory, Division of Cancer Sciences, The University of Manchester, Manchester, UK
| | - Zaki Ahmed
- Corewell Health William Beaumont University Hospital, Diagnostic Radiology, Royal Oak, USA
| | - Lucy E Kershaw
- Edinburgh Imaging and Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Oliver J Gurney-Champion
- Department of Radiology and Nuclear Medicine, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Sirisha Tadimalla
- Institute of Medical Physics, The University of Sydney, Sydney, Australia
| | - Jonathan Arvidsson
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Yu Sun
- Institute of Medical Physics, The University of Sydney, Sydney, Australia
| | - Jesper Kallehauge
- Aarhus University Hospital, Danish Centre for Particle Therapy, Aarhus, Denmark
- Aarhus University, Department of Clinical Medicine, Aarhus, Denmark
| | - Ben Dickie
- Division of Informatics, Imaging, and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- Geoffrey Jefferson Brain Research Centre, Manchester Academic Health Science Centre, Northern Care Alliance NHS Group, The University of Manchester, Manchester, UK
| | - Simon Lévy
- MR Research Collaborations, Siemens Healthcare Pty Ltd, Melbourne, Australia
| | - Laura Bell
- Genentech, Inc, Clinical Imaging Group, South San Francisco, USA
| | - Steven Sourbron
- University of Sheffield, Department of Infection, Immunity and Cardiovascular Disease, Sheffield, UK
| | - Michael J Thrippleton
- University of Edinburgh, Edinburgh Imaging and Centre for Clinical Brain Sciences, Edinburgh, UK
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Hu LS, Smits M, Kaufmann TJ, Knutsson L, Rapalino O, Galldiks N, Sundgrene PC, Cha S. Advanced Imaging in the Diagnosis and Response Assessment of High-Grade Glioma: AJR Expert Panel Narrative Review. AJR Am J Roentgenol 2024. [PMID: 38477525 DOI: 10.2214/ajr.23.30612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2024]
Abstract
This AJR Expert Panel Narrative explores the current status of advanced MRI and PET techniques for the post-therapeutic response assessment of high-grade adult-type gliomas, focusing on ongoing clinical controversies in current practice. Discussed techniques that complement conventional MRI and aid the differentiation of recurrent tumor from post-treatment effects include DWI and diffusion tensor imaging; perfusion MRI techniques including dynamic susceptibility contrast (DSC), dynamic contrast-enhanced MRI, and arterial spin labeling; MR spectroscopy including assessment of 2-hydroxyglutarate (2HG) concentration; glucose- and amino acid (AA)-based PET; and amide proton transfer imaging. Updated criteria for Response Assessment in Neuro-Oncology are presented. Given the abundant supporting clinical evidence, the panel supports a recommendation that routine response assessment after HGG treatment should include perfusion MRI, particularly given the development of a consensus recommended DSC-MRI protocol. Although published studies support 2HG MRS and AA PET, these techniques' widespread adoption will likely require increased availability (for 2HG MRS) or increased insurance funding in the United States (for AA PET). The article concludes with a series of consensus opinions from the author panel, centered on the clinical integration of the advanced imaging techniques into posttreatment surveillance protocols.
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Affiliation(s)
- Leland S Hu
- Department of Radiology, Mayo Clinic, Phoenix, AZ
- Department of Cancer Biology, Mayo Clinic, Phoenix, AZ
- Department of Neurological Surgery, Mayo Clinic, Phoenix, AZ
| | - Marion Smits
- Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
- Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
- Medical Delta, Delft, The Netherlands
| | | | - Linda Knutsson
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University, Baltimore, MD, USA
- Department of Medical Radiation Physics, Lund University, Lund, Sweden
| | - Otto Rapalino
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Norbert Galldiks
- Dept. of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany
- Inst. of Neuroscience and Medicine (INM-3), Research Center Juelich, Juelich, Germany
- Center of Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne, and Duesseldorf, Cologne, Germany
| | - Pia C Sundgrene
- Institution of Clinical Sciences Lund/Radiology, Lund University, Lund Sweden
- Lund BioImaging Center, Lund University, Lud, Sweden
- Department of Medical Imaging and Function Skane University hospital, Lund, Sweden
| | - Soonmee Cha
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
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Wu C, Hormuth DA, Easley T, Pineda F, Karczmar GS, Yankeelov TE. Systematic evaluation of MRI-based characterization of tumor-associated vascular morphology and hemodynamics via a dynamic digital phantom. J Med Imaging (Bellingham) 2024; 11:024002. [PMID: 38463607 PMCID: PMC10921778 DOI: 10.1117/1.jmi.11.2.024002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 01/26/2024] [Accepted: 02/19/2024] [Indexed: 03/12/2024] Open
Abstract
Purpose Validation of quantitative imaging biomarkers is a challenging task, due to the difficulty in measuring the ground truth of the target biological process. A digital phantom-based framework is established to systematically validate the quantitative characterization of tumor-associated vascular morphology and hemodynamics based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Approach A digital phantom is employed to provide a ground-truth vascular system within which 45 synthetic tumors are simulated. Morphological analysis is performed on high-spatial resolution DCE-MRI data (spatial/temporal resolution = 30 to 300 μ m / 60 s ) to determine the accuracy of locating the arterial inputs of tumor-associated vessels (TAVs). Hemodynamic analysis is then performed on the combination of high-spatial resolution and high-temporal resolution (spatial/temporal resolution = 60 to 300 μ m / 1 to 10 s) DCE-MRI data, determining the accuracy of estimating tumor-associated blood pressure, vascular extraction rate, interstitial pressure, and interstitial flow velocity. Results The observed effects of acquisition settings demonstrate that, when optimizing the DCE-MRI protocol for the morphological analysis, increasing the spatial resolution is helpful but not necessary, as the location and arterial input of TAVs can be recovered with high accuracy even with the lowest investigated spatial resolution. When optimizing the DCE-MRI protocol for hemodynamic analysis, increasing the spatial resolution of the images used for vessel segmentation is essential, and the spatial and temporal resolutions of the images used for the kinetic parameter fitting require simultaneous optimization. Conclusion An in silico validation framework was generated to systematically quantify the effects of image acquisition settings on the ability to accurately estimate tumor-associated characteristics.
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Affiliation(s)
- Chengyue Wu
- University of Texas at Austin, Oden Institute for Computational Engineering and Sciences, Austin, Texas, United States
- MD Anderson Cancer Center, Department of Imaging Physics, Houston, Texas, United States
- MD Anderson Cancer Center, Department of Breast Imaging, Houston, Texas, United States
- MD Anderson Cancer Center, Department of Biostatistics, Houston, Texas, United States
| | - David A. Hormuth
- University of Texas at Austin, Oden Institute for Computational Engineering and Sciences, Austin, Texas, United States
- University of Texas at Austin, Livestrong Cancer Institutes, Austin, Texas, United States
| | - Ty Easley
- Washington University in St. Louis, Department of Biomedical Engineering, St. Louis, Missouri, United States
| | - Federico Pineda
- University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Gregory S. Karczmar
- University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Thomas E. Yankeelov
- University of Texas at Austin, Oden Institute for Computational Engineering and Sciences, Austin, Texas, United States
- MD Anderson Cancer Center, Department of Imaging Physics, Houston, Texas, United States
- University of Texas at Austin, Livestrong Cancer Institutes, Austin, Texas, United States
- University of Texas at Austin, Department of Biomedical Engineering, Austin, Texas, United States
- University of Texas at Austin, Department of Diagnostic Medicine, Austin, Texas, United States
- University of Texas at Austin, Department of Oncology, Austin, Texas, United States
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Hu LS, D'Angelo F, Weiskittel TM, Caruso FP, Fortin Ensign SP, Blomquist MR, Flick MJ, Wang L, Sereduk CP, Meng-Lin K, De Leon G, Nespodzany A, Urcuyo JC, Gonzales AC, Curtin L, Lewis EM, Singleton KW, Dondlinger T, Anil A, Semmineh NB, Noviello T, Patel RA, Wang P, Wang J, Eschbacher JM, Hawkins-Daarud A, Jackson PR, Grunfeld IS, Elrod C, Mazza GL, McGee SC, Paulson L, Clark-Swanson K, Lassiter-Morris Y, Smith KA, Nakaji P, Bendok BR, Zimmerman RS, Krishna C, Patra DP, Patel NP, Lyons M, Neal M, Donev K, Mrugala MM, Porter AB, Beeman SC, Jensen TR, Schmainda KM, Zhou Y, Baxter LC, Plaisier CL, Li J, Li H, Lasorella A, Quarles CC, Swanson KR, Ceccarelli M, Iavarone A, Tran NL. Integrated molecular and multiparametric MRI mapping of high-grade glioma identifies regional biologic signatures. Nat Commun 2023; 14:6066. [PMID: 37770427 PMCID: PMC10539500 DOI: 10.1038/s41467-023-41559-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 09/06/2023] [Indexed: 09/30/2023] Open
Abstract
Sampling restrictions have hindered the comprehensive study of invasive non-enhancing (NE) high-grade glioma (HGG) cell populations driving tumor progression. Here, we present an integrated multi-omic analysis of spatially matched molecular and multi-parametric magnetic resonance imaging (MRI) profiling across 313 multi-regional tumor biopsies, including 111 from the NE, across 68 HGG patients. Whole exome and RNA sequencing uncover unique genomic alterations to unresectable invasive NE tumor, including subclonal events, which inform genomic models predictive of geographic evolution. Infiltrative NE tumor is alternatively enriched with tumor cells exhibiting neuronal or glycolytic/plurimetabolic cellular states, two principal transcriptomic pathway-based glioma subtypes, which respectively demonstrate abundant private mutations or enrichment in immune cell signatures. These NE phenotypes are non-invasively identified through normalized K2 imaging signatures, which discern cell size heterogeneity on dynamic susceptibility contrast (DSC)-MRI. NE tumor populations predicted to display increased cellular proliferation by mean diffusivity (MD) MRI metrics are uniquely associated with EGFR amplification and CDKN2A homozygous deletion. The biophysical mapping of infiltrative HGG potentially enables the clinical recognition of tumor subpopulations with aggressive molecular signatures driving tumor progression, thereby informing precision medicine targeting.
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Affiliation(s)
- Leland S Hu
- Department of Radiology, Mayo Clinic Arizona, Phoenix, AZ, USA.
- Department of Cancer Biology, Mayo Clinic Arizona, Scottsdale, AZ, USA.
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA.
| | - Fulvio D'Angelo
- Department of Neurological Surgery, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, USA.
| | - Taylor M Weiskittel
- Mayo Clinic Alix School of Medicine Minnesota, Rochester, MN, USA
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Francesca P Caruso
- Department of Electrical Engineering and Information Technologies, University of Naples, "Federico II", I-80128, Naples, Italy
- BIOGEM Institute of Molecular Biology and Genetics, I-83031, Ariano Irpino, Italy
| | - Shannon P Fortin Ensign
- Department of Cancer Biology, Mayo Clinic Arizona, Scottsdale, AZ, USA
- Department of Hematology and Oncology, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - Mylan R Blomquist
- Department of Cancer Biology, Mayo Clinic Arizona, Scottsdale, AZ, USA
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
- Mayo Clinic Alix School of Medicine Arizona, Scottsdale, AZ, USA
| | - Matthew J Flick
- Department of Radiology, Mayo Clinic Arizona, Phoenix, AZ, USA
- Department of Cancer Biology, Mayo Clinic Arizona, Scottsdale, AZ, USA
- Mayo Clinic Alix School of Medicine Arizona, Scottsdale, AZ, USA
| | - Lujia Wang
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Christopher P Sereduk
- Department of Cancer Biology, Mayo Clinic Arizona, Scottsdale, AZ, USA
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Kevin Meng-Lin
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Gustavo De Leon
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Ashley Nespodzany
- Department of Neuroimaging Research, Barrow Neurological Institute, Dignity Health, Phoenix, AZ, USA
| | - Javier C Urcuyo
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Ashlyn C Gonzales
- Department of Neuroimaging Research, Barrow Neurological Institute, Dignity Health, Phoenix, AZ, USA
| | - Lee Curtin
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Erika M Lewis
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Kyle W Singleton
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | | | - Aliya Anil
- Department of Neuroimaging Research, Barrow Neurological Institute, Dignity Health, Phoenix, AZ, USA
| | - Natenael B Semmineh
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Teresa Noviello
- Department of Electrical Engineering and Information Technologies, University of Naples, "Federico II", I-80128, Naples, Italy
- BIOGEM Institute of Molecular Biology and Genetics, I-83031, Ariano Irpino, Italy
| | - Reyna A Patel
- Department of Radiology, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - Panwen Wang
- Quantitative Health Sciences, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Junwen Wang
- Division of Applied Oral Sciences & Community Dental Care, The University of Hong Kong, Hong Kong SAR, China
| | - Jennifer M Eschbacher
- Department of Neuropathology, Barrow Neurological Institute, Dignity Health, Phoenix, AZ, USA
| | | | - Pamela R Jackson
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Itamar S Grunfeld
- Department of Psychology, Hunter College, The City University of New York, New York, NY, USA
- Department of Psychology, The Graduate Center, The City University of New York, New York, NY, USA
| | | | - Gina L Mazza
- Quantitative Health Sciences, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Sam C McGee
- Department of Speech and Hearing Science, Arizona State University, Tempe, AZ, USA
| | - Lisa Paulson
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | | | | | - Kris A Smith
- Department of Neurosurgery, Barrow Neurological Institute, Dignity Health, Phoenix, AZ, USA
| | - Peter Nakaji
- Department of Neurosurgery, Banner University Medical Center, University of Arizona, Phoenix, AZ, USA
| | - Bernard R Bendok
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Richard S Zimmerman
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Chandan Krishna
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Devi P Patra
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Naresh P Patel
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Mark Lyons
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Matthew Neal
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Kliment Donev
- Department of Pathology, Mayo Clinic Arizona, Phoenix, AZ, USA
| | | | - Alyx B Porter
- Department of Neurology, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - Scott C Beeman
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, USA
| | | | - Kathleen M Schmainda
- Departments of Biophysics and Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Yuxiang Zhou
- Department of Radiology, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - Leslie C Baxter
- Department of Radiology, Mayo Clinic Arizona, Phoenix, AZ, USA
- Departments of Psychiatry and Psychology, Mayo Clinic, AZ, USA
| | - Christopher L Plaisier
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Jing Li
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Hu Li
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Anna Lasorella
- Department of Biochemistry and Molecular Biology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - C Chad Quarles
- Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Kristin R Swanson
- Department of Cancer Biology, Mayo Clinic Arizona, Scottsdale, AZ, USA
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Michele Ceccarelli
- Department of Public Health Sciences, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, USA.
| | - Antonio Iavarone
- Department of Neurological Surgery, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, USA.
| | - Nhan L Tran
- Department of Cancer Biology, Mayo Clinic Arizona, Scottsdale, AZ, USA.
- Department of Neurological Surgery, Mayo Clinic Arizona, Scottsdale, AZ, USA.
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Vossough A. Newer MRI Techniques in Pediatric Neuroimaging. Semin Roentgenol 2023; 58:131-144. [PMID: 36732007 DOI: 10.1053/j.ro.2022.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 10/27/2022] [Indexed: 11/23/2022]
Affiliation(s)
- Arastoo Vossough
- Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA..
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Bernstock JD, Gary SE, Klinger N, Valdes PA, Ibn Essayed W, Olsen HE, Chagoya G, Elsayed G, Yamashita D, Schuss P, Gessler FA, Peruzzi PP, Bag A, Friedman GK. Standard clinical approaches and emerging modalities for glioblastoma imaging. Neurooncol Adv 2022; 4:vdac080. [PMID: 35821676 PMCID: PMC9268747 DOI: 10.1093/noajnl/vdac080] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Glioblastoma (GBM) is the most common primary adult intracranial malignancy and carries a dismal prognosis despite an aggressive multimodal treatment regimen that consists of surgical resection, radiation, and adjuvant chemotherapy. Radiographic evaluation, largely informed by magnetic resonance imaging (MRI), is a critical component of initial diagnosis, surgical planning, and post-treatment monitoring. However, conventional MRI does not provide information regarding tumor microvasculature, necrosis, or neoangiogenesis. In addition, traditional MRI imaging can be further confounded by treatment-related effects such as pseudoprogression, radiation necrosis, and/or pseudoresponse(s) that preclude clinicians from making fully informed decisions when structuring a therapeutic approach. A myriad of novel imaging modalities have been developed to address these deficits. Herein, we provide a clinically oriented review of standard techniques for imaging GBM and highlight emerging technologies utilized in disease characterization and therapeutic development.
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Affiliation(s)
- Joshua D Bernstock
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School , Boston, Massachusetts, USA
| | - Sam E Gary
- Medical Scientist Training Program, University of Alabama at Birmingham, Birmingham , AL, USA
| | - Neil Klinger
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School , Boston, Massachusetts, USA
| | - Pablo A Valdes
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School , Boston, Massachusetts, USA
| | - Walid Ibn Essayed
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School , Boston, Massachusetts, USA
| | - Hannah E Olsen
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School , Boston, Massachusetts, USA
| | - Gustavo Chagoya
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham , AL, USA
| | - Galal Elsayed
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham , AL, USA
| | - Daisuke Yamashita
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham , AL, USA
| | - Patrick Schuss
- Department of Neurosurgery, Unfallkrankenhaus Berlin , Berlin, Germany
| | | | - Pier Paolo Peruzzi
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School , Boston, Massachusetts, USA
| | - Asim Bag
- Department of Diagnostic Imaging, St. Jude Children’s Research Hospital , Memphis, TN USA
| | - Gregory K Friedman
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham , AL, USA
- Division of Pediatric Hematology and Oncology, Department of Pediatrics, University of Alabama at Birmingham , Birmingham, AL, USA
- Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham , AL, USA
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8
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Malik DG, Rath TJ, Urcuyo Acevedo JC, Canoll PD, Swanson KR, Boxerman JL, Quarles CC, Schmainda KM, Burns TC, Hu LS. Advanced MRI Protocols to Discriminate Glioma From Treatment Effects: State of the Art and Future Directions. FRONTIERS IN RADIOLOGY 2022; 2:809373. [PMID: 37492687 PMCID: PMC10365126 DOI: 10.3389/fradi.2022.809373] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 03/01/2022] [Indexed: 07/27/2023]
Abstract
In the follow-up treatment of high-grade gliomas (HGGs), differentiating true tumor progression from treatment-related effects, such as pseudoprogression and radiation necrosis, presents an ongoing clinical challenge. Conventional MRI with and without intravenous contrast serves as the clinical benchmark for the posttreatment surveillance imaging of HGG. However, many advanced imaging techniques have shown promise in helping better delineate the findings in indeterminate scenarios, as posttreatment effects can often mimic true tumor progression on conventional imaging. These challenges are further confounded by the histologic admixture that can commonly occur between tumor growth and treatment-related effects within the posttreatment bed. This review discusses the current practices in the surveillance imaging of HGG and the role of advanced imaging techniques, including perfusion MRI and metabolic MRI.
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Affiliation(s)
- Dania G. Malik
- Department of Radiology, Mayo Clinic, Phoenix, AZ, United States
| | - Tanya J. Rath
- Department of Radiology, Mayo Clinic, Phoenix, AZ, United States
| | - Javier C. Urcuyo Acevedo
- Mathematical Neurooncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ, United States
| | - Peter D. Canoll
- Departments of Pathology and Cell Biology, Columbia University, New York, NY, United States
| | - Kristin R. Swanson
- Mathematical Neurooncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ, United States
| | - Jerrold L. Boxerman
- Department of Diagnostic Imaging, Brown University, Providence, RI, United States
| | - C. Chad Quarles
- Department of Neuroimaging Research & Barrow Neuroimaging Innovation Center, Barrow Neurologic Institute, Phoenix, AZ, United States
| | - Kathleen M. Schmainda
- Department of Biophysics & Radiology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Terry C. Burns
- Departments of Neurologic Surgery and Neuroscience, Mayo Clinic, Rochester, MN, United States
| | - Leland S. Hu
- Department of Radiology, Mayo Clinic, Phoenix, AZ, United States
- Mathematical Neurooncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ, United States
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9
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Stokes AM, Bergamino M, Alhilali L, Hu LS, Karis JP, Baxter LC, Bell LC, Quarles CC. Evaluation of single bolus, dual-echo dynamic susceptibility contrast MRI protocols in brain tumor patients. J Cereb Blood Flow Metab 2021; 41:3378-3390. [PMID: 34415211 PMCID: PMC8669280 DOI: 10.1177/0271678x211039597] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Relative cerebral blood volume (rCBV) obtained from dynamic susceptibility contrast (DSC) MRI is adversely impacted by contrast agent leakage in brain tumors. Using simulations, we previously demonstrated that multi-echo DSC-MRI protocols provide improvements in contrast agent dosing, pulse sequence flexibility, and rCBV accuracy. The purpose of this study is to assess the in-vivo performance of dual-echo acquisitions in patients with brain tumors (n = 59). To verify pulse sequence flexibility, four single-dose dual-echo acquisitions were tested with variations in contrast agent dose, flip angle, and repetition time, and the resulting dual-echo rCBV was compared to standard single-echo rCBV obtained with preload (double-dose). Dual-echo rCBV was comparable to standard double-dose single-echo protocols (mean (standard deviation) tumor rCBV 2.17 (1.28) vs. 2.06 (1.20), respectively). High rCBV similarity was observed (CCC = 0.96), which was maintained across both flip angle (CCC = 0.98) and repetition time (CCC = 0.96) permutations, demonstrating that dual-echo acquisitions provide flexibility in acquisition parameters. Furthermore, a single dual-echo acquisition was shown to enable quantification of both perfusion and permeability metrics. In conclusion, single-dose dual-echo acquisitions provide similar rCBV to standard double-dose single-echo acquisitions, suggesting contrast agent dose can be reduced while providing significant pulse sequence flexibility and complementary tumor perfusion and permeability metrics.
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Affiliation(s)
- Ashley M Stokes
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Maurizio Bergamino
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Lea Alhilali
- Neuroradiology, Southwest Neuroimaging at Barrow Neurological Institute, Phoenix, AZ, USA
| | - Leland S Hu
- Department of Radiology, Division of Neuroradiology, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - John P Karis
- Neuroradiology, Southwest Neuroimaging at Barrow Neurological Institute, Phoenix, AZ, USA
| | - Leslie C Baxter
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, USA.,Department of Radiology, Division of Neuroradiology, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - Laura C Bell
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, USA
| | - C Chad Quarles
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, USA
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10
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Quantitative magnetic resonance imaging and tumor forecasting of breast cancer patients in the community setting. Nat Protoc 2021; 16:5309-5338. [PMID: 34552262 PMCID: PMC9753909 DOI: 10.1038/s41596-021-00617-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 08/12/2021] [Indexed: 02/07/2023]
Abstract
This protocol describes a complete data acquisition, analysis and computational forecasting pipeline for employing quantitative MRI data to predict the response of locally advanced breast cancer to neoadjuvant therapy in a community-based care setting. The methodology has previously been successfully applied to a heterogeneous patient population. The protocol details how to acquire the necessary images followed by registration, segmentation, quantitative perfusion and diffusion analysis, model calibration, and prediction. The data collection portion of the protocol requires ~25 min of scanning, postprocessing requires 2-3 h, and the model calibration and prediction components require ~10 h per patient depending on tumor size. The response of individual breast cancer patients to neoadjuvant therapy is forecast by application of a biophysical, reaction-diffusion mathematical model to these data. Successful application of the protocol results in coregistered MRI data from at least two scan visits that quantifies an individual tumor's size, cellularity and vascular properties. This enables a spatially resolved prediction of how a particular patient's tumor will respond to therapy. Expertise in image acquisition and analysis, as well as the numerical solution of partial differential equations, is required to carry out this protocol.
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11
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Wu C, Hormuth DA, Easley T, Eijkhout V, Pineda F, Karczmar GS, Yankeelov TE. An in silico validation framework for quantitative DCE-MRI techniques based on a dynamic digital phantom. Med Image Anal 2021; 73:102186. [PMID: 34329903 PMCID: PMC8453106 DOI: 10.1016/j.media.2021.102186] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 07/08/2021] [Accepted: 07/16/2021] [Indexed: 10/20/2022]
Abstract
Quantitative evaluation of an image processing method to perform as designed is central to both its utility and its ability to guide the data acquisition process. Unfortunately, these tasks can be quite challenging due to the difficulty of experimentally obtaining the "ground truth" data to which the output of a given processing method must be compared. One way to address this issue is via "digital phantoms", which are numerical models that provide known biophysical properties of a particular object of interest. In this contribution, we propose an in silico validation framework for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquisition and analysis methods that employs a novel dynamic digital phantom. The phantom provides a spatiotemporally-resolved representation of blood-interstitial flow and contrast agent delivery, where the former is solved by a 1D-3D coupled computational fluid dynamic system, and the latter described by an advection-diffusion equation. Furthermore, we establish a virtual simulator which takes as input the digital phantom, and produces realistic DCE-MRI data with controllable acquisition parameters. We assess the performance of a simulated standard-of-care acquisition (Protocol A) by its ability to generate contrast-enhanced MR images that separate vasculature from surrounding tissue, as measured by the contrast-to-noise ratio (CNR). We find that the CNR significantly decreases as the spatial resolution (SRA, where the subscript indicates Protocol A) or signal-to-noise ratio (SNRA) decreases. Specifically, with an SNRA / SRA = 75 dB / 30 μm, the median CNR is 77.30, whereas an SNRA / SRA = 5 dB / 300 μm reduces the CNR to 6.40. Additionally, we assess the performance of simulated ultra-fast acquisition (Protocol B) by its ability to generate DCE-MR images that capture contrast agent pharmacokinetics, as measured by error in the signal-enhancement ratio (SER) compared to ground truth (PESER). We find that PESER significantly decreases the as temporal resolution (TRB) increases. Similar results are reported for the effects of spatial resolution and signal-to-noise ratio on PESER. For example, with an SNRB / SRB / TRB = 5 dB / 300 μm / 10 s, the median PESER is 21.00%, whereas an SNRB / SRB / TRB = 75 dB / 60 μm / 1 s, yields a median PESER of 0.90%. These results indicate that our in silico framework can generate virtual MR images that capture effects of acquisition parameters on the ability of generated images to capture morphological or pharmacokinetic features. This validation framework is not only useful for investigations of perfusion-based MRI techniques, but also for the systematic evaluation and optimization new MRI acquisition, reconstruction, and image processing techniques.
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Affiliation(s)
- Chengyue Wu
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 E 24th St, Austin, TX 78712, United States.
| | - David A Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 E 24th St, Austin, TX 78712, United States; Livestrong Cancer Institutes, United States
| | - Ty Easley
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, United States
| | | | - Federico Pineda
- Department of Radiology, The University of Chicago, Chicago, IL 60637, United States
| | - Gregory S Karczmar
- Department of Radiology, The University of Chicago, Chicago, IL 60637, United States
| | - Thomas E Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 E 24th St, Austin, TX 78712, United States; Livestrong Cancer Institutes, United States; Departments of Biomedical Engineering, United States; Departments of Diagnostic Medicine, United States; Departments of Oncology, The University of Texas at Austin, Austin, TX 78712, United States; Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX 77030, United States
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12
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Abstract
The central role of MRI in neuro-oncology is undisputed. The technique is used, both in clinical practice and in clinical trials, to diagnose and monitor disease activity, support treatment decision-making, guide the use of focused treatments and determine response to treatment. Despite recent substantial advances in imaging technology and image analysis techniques, clinical MRI is still primarily used for the qualitative subjective interpretation of macrostructural features, as opposed to quantitative analyses that take into consideration multiple pathophysiological features. However, the field of quantitative imaging and imaging biomarker development is maturing. The European Imaging Biomarkers Alliance (EIBALL) and Quantitative Imaging Biomarkers Alliance (QIBA) are setting standards for biomarker development, validation and implementation, as well as promoting the use of quantitative imaging and imaging biomarkers by demonstrating their clinical value. In parallel, advanced imaging techniques are reaching the clinical arena, providing quantitative, commonly physiological imaging parameters that are driving the discovery, validation and implementation of quantitative imaging and imaging biomarkers in the clinical routine. Additionally, computational analysis techniques are increasingly being used in the research setting to convert medical images into objective high-dimensional data and define radiomic signatures of disease states. Here, I review the definition and current state of MRI biomarkers in neuro-oncology, and discuss the clinical potential of quantitative image analysis techniques.
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13
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Chen HSM, Jen ML, Hou P, Stafford RJ, Liu HL. A dynamic susceptibility contrast MRI digital reference object for testing software with leakage correction: Effect of background simulation. Med Phys 2021; 48:6051-6059. [PMID: 34293208 DOI: 10.1002/mp.15125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 05/22/2021] [Accepted: 07/17/2021] [Indexed: 11/10/2022] Open
Abstract
PURPOSE Dynamic susceptibility contrast (DSC)-MRI is a perfusion imaging technique from which useful quantitative imaging biomarkers can be derived. Relative cerebral blood volume (rCBV) is such a biomarker commonly used for evaluating brain tumors. To account for the extravasation of contrast agents in tumors, post-processing leakage correction is often applied to improve rCBV accuracy. Digital reference objects (DRO) are ideal for testing the post-processing software, because the biophysical model used to generate the DRO can be matched to the one that the software uses. This study aims to develop DROs to validate the leakage correction of software using Weisskoff model and to examine the effect of background signal time curves that are required by the model. METHODS Three DROs were generated using the Weisskoff model, each composed of nine foreground lesion objects with combinations of different levels of rCBV and contrast leakage parameter (K2). Three types of background were implemented for these DROs: (1) a multi-compartment brain-like background, (2) a sphere background with a constant signal time curve, and (3) a sphere background with signal time curve identical to that of the brain-like DRO's white matter (WM). The DROs were then analyzed with an FDA-cleared software with and without leakage correction. Leakage correction was tested with and without brain segmentation. RESULTS Accuracy of leakage correction was able to be verified using the brain-like phantom and the sphere phantom with WM background. The sphere with constant background did not perform well with leakage correction with or without brain segmentation. The DROs were able to verify that for the particular software tested, leakage correction with brain segmentation achieved the lowest error. CONCLUSIONS DSC-MRI DROs with biophysical model matched to that of the post-processing software can be well used for the software's validation, provided that the background signals are also properly simulated for generating the reference time curve required by the model. Care needs to be taken to consider the interaction of the design of the DRO with the software's implementation of brain segmentation to extract the reference time curve.
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Affiliation(s)
- Henry Szu-Meng Chen
- Departments of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Mu-Lan Jen
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Ping Hou
- Departments of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - R Jason Stafford
- Departments of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ho-Ling Liu
- Departments of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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14
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Zhou L, Wang Y, Pinho MC, Pan E, Xi Y, Maldjian JA, Madhuranthakam AJ. Intrasession Reliability of Arterial Spin-Labeled MRI-Measured Noncontrast Perfusion in Glioblastoma at 3 T. ACTA ACUST UNITED AC 2021; 6:139-147. [PMID: 32548290 PMCID: PMC7289238 DOI: 10.18383/j.tom.2020.00010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Arterial spin-labeled magnetic resonance imaging can provide quantitative perfusion measurements in the brain and can be potentially used to evaluate therapy response assessment in glioblastoma (GBM). The reliability and reproducibility of this method to measure noncontrast perfusion in GBM, however, are lacking. We evaluated the intrasession reliability of brain and tumor perfusion in both healthy volunteers and patients with GBM at 3 T using pseudocontinuous labeling (pCASL) and 3D turbo spin echo (TSE) using Cartesian acquisition with spiral profile reordering (CASPR). Two healthy volunteers at a single time point and 6 newly diagnosed patients with GBM at multiple time points (before, during, and after chemoradiation) underwent scanning (total, 14 sessions). Compared with 3D GraSE, 3D TSE-CASPR generated cerebral blood flow maps with better tumor-to-normal background tissue contrast and reduced image distortions. The intraclass correlation coefficient between the 2 runs of 3D pCASL with TSE-CASPR was consistently high (≥0.90) across all normal-appearing gray matter (NAGM) regions of interest (ROIs), and was particularly high in tumors (0.98 with 95% confidence interval [CI]: 0.97-0.99). The within-subject coefficients of variation were relatively low in all normal-appearing gray matter regions of interest (3.40%-7.12%), and in tumors (4.91%). Noncontrast perfusion measured using 3D pCASL with TSE-CASPR provided robust cerebral blood flow maps in both healthy volunteers and patients with GBM with high intrasession repeatability at 3 T. This approach can be an appropriate noncontrast and noninvasive quantitative perfusion imaging method for longitudinal assessment of therapy response and management of patients with GBM.
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Affiliation(s)
| | | | - Marco C Pinho
- Department of Radiology.,Advanced Imaging Research Center
| | - Edward Pan
- Department of Neurology and Neurotherapeutics.,Department of Neurological Surgery.,Harold C. Simmons Cancer Center; and
| | - Yin Xi
- Department of Radiology.,Department of Population and Data Sciences, University of Texas Southwestern Medical Center at Dallas, Dallas, TX
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15
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Abstract
PURPOSE OF REVIEW To highlight some of the recent advances in magnetic resonance imaging (MRI), in terms of acquisition, analysis, and interpretation for primary diagnosis, treatment planning, and surveillance of patients with a brain tumour. RECENT FINDINGS The rapidly emerging field of radiomics associates large numbers of imaging features with clinical characteristics. In the context of glioma, attempts are made to correlate such imaging features with the tumour genotype, using so-called radiogenomics. The T2-fluid attenuated inversion recovery (FLAIR) mismatch sign is an easy to apply imaging feature for identifying isocitrate dehydrogenase-mutant 1p/19q intact glioma with very high specificity.For treatment planning, resting state functional MRI (fMRI) may become as powerful as task-based fMRI. Functional ultrasound has shown the potential to identify functionally active cortex during surgery.For tumour response assessment automated techniques have been developed. Multiple new guidelines have become available, including those for adult and paediatric glioma and for leptomeningeal metastases, as well as on brain metastasis and perfusion imaging. SUMMARY Neuroimaging plays a central role but still often falls short on essential questions. Advanced imaging acquisition and analysis techniques hold great promise for answering such questions, and are expected to change the role of neuroimaging for patient management substantially in the near future.
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16
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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] [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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17
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Boxerman JL, Quarles CC, Hu LS, Erickson BJ, Gerstner ER, Smits M, Kaufmann TJ, Barboriak DP, Huang RH, Wick W, Weller M, Galanis E, Kalpathy-Cramer J, Shankar L, Jacobs P, Chung C, van den Bent MJ, Chang S, Al Yung WK, Cloughesy TF, Wen PY, Gilbert MR, Rosen BR, Ellingson BM, Schmainda KM. Consensus recommendations for a dynamic susceptibility contrast MRI protocol for use in high-grade gliomas. Neuro Oncol 2020; 22:1262-1275. [PMID: 32516388 PMCID: PMC7523451 DOI: 10.1093/neuonc/noaa141] [Citation(s) in RCA: 103] [Impact Index Per Article: 25.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Despite the widespread clinical use of dynamic susceptibility contrast (DSC) MRI, DSC-MRI methodology has not been standardized, hindering its utilization for response assessment in multicenter trials. Recently, the DSC-MRI Standardization Subcommittee of the Jumpstarting Brain Tumor Drug Development Coalition issued an updated consensus DSC-MRI protocol compatible with the standardized brain tumor imaging protocol (BTIP) for high-grade gliomas that is increasingly used in the clinical setting and is the default MRI protocol for the National Clinical Trials Network. After reviewing the basis for controversy over DSC-MRI protocols, this paper provides evidence-based best practices for clinical DSC-MRI as determined by the Committee, including pulse sequence (gradient echo vs spin echo), BTIP-compliant contrast agent dosing (preload and bolus), flip angle (FA), echo time (TE), and post-processing leakage correction. In summary, full-dose preload, full-dose bolus dosing using intermediate (60°) FA and field strength-dependent TE (40-50 ms at 1.5 T, 20-35 ms at 3 T) provides overall best accuracy and precision for cerebral blood volume estimates. When single-dose contrast agent usage is desired, no-preload, full-dose bolus dosing using low FA (30°) and field strength-dependent TE provides excellent performance, with reduced contrast agent usage and elimination of potential systematic errors introduced by variations in preload dose and incubation time.
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Affiliation(s)
- Jerrold L Boxerman
- Department of Diagnostic Imaging, Warren Alpert Medical School, Brown University, Providence, Rhode Island, USA
- Representative of the Eastern Cooperative Oncology Group–American College of Radiology Imaging Network (ECOG-ACRIN) Cancer Research Group
- Representative of the American Society of Neuroradiology (ASNR)
- Representative of the American Society of Functional Neuroradiology (ASFNR)
| | - Chad C Quarles
- Department of Neuroimaging Research and Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Leland S Hu
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, USA
- Representative of the Alliance for Clinical Trials in Oncology
- Representative of the American Society of Neuroradiology (ASNR)
| | - Bradley J Erickson
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
- Representative of the Alliance for Clinical Trials in Oncology
- Representative of the RSNA Quantitative Imaging Biomarker Alliance (QIBA)
- Representative of the American Society of Neuroradiology (ASNR)
| | - Elizabeth R Gerstner
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Representative of the Adult Brain Tumor Consortium (ABTC)
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC–University Medical Center Rotterdam, Rotterdam, Netherlands
- Representative of the European Organisation for Research and Treatment of Cancer (EORTC)
| | - Timothy J Kaufmann
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
- Representative of the Alliance for Clinical Trials in Oncology
| | - Daniel P Barboriak
- Department of Radiology, Duke University School of Medicine, Durham, North Carolina, USA
- Representative of the Eastern Cooperative Oncology Group–American College of Radiology Imaging Network (ECOG-ACRIN) Cancer Research Group
- Representative of the RSNA Quantitative Imaging Biomarker Alliance (QIBA)
- Representative of the American Society of Neuroradiology (ASNR)
| | - Raymond H Huang
- Department of Radiology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
- Center for Neuro-Oncology, Dana-Farber/Brigham and Women’s Cancer Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Wolfgang Wick
- Department of Neurooncology, National Center of Tumor Disease, University Clinic Heidelberg, Heidelberg, Germany
- Representative of the European Organisation for Research and Treatment of Cancer (EORTC)
| | - Michael Weller
- Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland
- Representative of the European Organisation for Research and Treatment of Cancer (EORTC)
| | - Evanthia Galanis
- Division of Medical Oncology, Department of Oncology, Mayo Clinic, Rochester, Minnesota, USA
- Representative of the Alliance for Clinical Trials in Oncology
| | - Jayashree Kalpathy-Cramer
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Lalitha Shankar
- Division of Cancer Treatment and Diagnosis, National Cancer Institute (NCI), Bethesda, Maryland, USA
| | - Paula Jacobs
- Division of Cancer Treatment and Diagnosis, National Cancer Institute (NCI), Bethesda, Maryland, USA
| | - Caroline Chung
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- Representative of the Alliance for Clinical Trials in Oncology
| | - Martin J van den Bent
- Department of Neuro-Oncology, Erasmus MC Cancer Institute, Rotterdam, Netherlands
- Representative of the European Organisation for Research and Treatment of Cancer (EORTC)
| | - Susan Chang
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA
| | - W K Al Yung
- Department of Neuro-Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program and UCLA Brain Tumor Imaging Laboratory (BTIL), David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Patrick Y Wen
- Center for Neuro-Oncology, Dana-Farber/Brigham and Women’s Cancer Center, Harvard Medical School, Boston, Massachusetts, USA
- Representative of the Adult Brain Tumor Consortium (ABTC)
| | - Mark R Gilbert
- Neuro-Oncology Branch, National Cancer Institute (NCI), Bethesda, Maryland, USA
- Representative of the Radiation Therapy Oncology Group (RTOG)
| | - Bruce R Rosen
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Benjamin M Ellingson
- UCLA Neuro-Oncology Program and UCLA Brain Tumor Imaging Laboratory (BTIL), David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
- Departments of Radiological Sciences, Psychiatry, and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
- Representative of the Adult Brain Tumor Consortium (ABTC)
- Representative of the Ivy Consortium for Early Phase Clinical Trials
- Representative of the Eastern Cooperative Oncology Group–American College of Radiology Imaging Network (ECOG-ACRIN) Cancer Research Group
- Representative of the RSNA Quantitative Imaging Biomarker Alliance (QIBA)
- Representative of the American Society of Neuroradiology (ASNR)
| | - Kathleen M Schmainda
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
- Representative of the Eastern Cooperative Oncology Group–American College of Radiology Imaging Network (ECOG-ACRIN) Cancer Research Group
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Abstract
The National Cancer Institute's Quantitative Imaging Network (QIN) has thrived over the past 12 years with an emphasis on the development of image-based decision support software tools for improving measurements of imaging metrics. An overarching goal has been to develop advanced tools that could be translated into clinical trials to provide for improved prediction of response to therapeutic interventions. This article provides an overview of the successes in development and translation of new algorithms into the clinical workflow by the many research teams of the Quantitative Imaging Network.
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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] [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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20
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Optimal Control Theory for Personalized Therapeutic Regimens in Oncology: Background, History, Challenges, and Opportunities. J Clin Med 2020; 9:jcm9051314. [PMID: 32370195 PMCID: PMC7290915 DOI: 10.3390/jcm9051314] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 04/25/2020] [Accepted: 04/28/2020] [Indexed: 12/13/2022] Open
Abstract
Optimal control theory is branch of mathematics that aims to optimize a solution to a dynamical system. While the concept of using optimal control theory to improve treatment regimens in oncology is not novel, many of the early applications of this mathematical technique were not designed to work with routinely available data or produce results that can eventually be translated to the clinical setting. The purpose of this review is to discuss clinically relevant considerations for formulating and solving optimal control problems for treating cancer patients. Our review focuses on two of the most widely used cancer treatments, radiation therapy and systemic therapy, as they naturally lend themselves to optimal control theory as a means to personalize therapeutic plans in a rigorous fashion. To provide context for optimal control theory to address either of these two modalities, we first discuss the major limitations and difficulties oncologists face when considering alternate regimens for their patients. We then provide a brief introduction to optimal control theory before formulating the optimal control problem in the context of radiation and systemic therapy. We also summarize examples from the literature that illustrate these concepts. Finally, we present both challenges and opportunities for dramatically improving patient outcomes via the integration of clinically relevant, patient-specific, mathematical models and optimal control theory.
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21
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Hori M. Distinguishing Benign From Malignant Soft Tissue Tumors By Dynamic Susceptibility Contrast Magnetic Resonance Imaging. Acad Radiol 2020; 27:361-362. [PMID: 31734116 DOI: 10.1016/j.acra.2019.10.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 10/02/2019] [Indexed: 02/02/2023]
Affiliation(s)
- Masaaki Hori
- Department of Radiology, Toho University Omori Medical Center, 6-11-1 Omorinishi, Ota-ku, Tokyo, 143-8541, Japan.
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22
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Steidl E, Müller M, Müller A, Herrlinger U, Hattingen E. Longitudinal, leakage corrected and uncorrected rCBV during the first-line treatment of glioblastoma: a prospective study. J Neurooncol 2019; 144:409-417. [PMID: 31321614 DOI: 10.1007/s11060-019-03244-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Accepted: 07/15/2019] [Indexed: 12/15/2022]
Abstract
PURPOSE Dynamic susceptibility contrast (DSC) MR-perfusion is becoming a standard of care for the monitoring of glioblastoma. Yet, technical standards are lacking and measurements without leakage correction are still common. Also, data on leakage corrected measurements during stable disease is scarce. In this study we hypothesized that basic leakage correction would significantly enhance data quality during stable disease and improve progress detection. We furthermore investigated whether longitudinal data could increase diagnostic performance. METHODS Patients with histologically proven glioblastoma undergoing first-line therapy were prospectively recruited. We conducted DSC perfusion measurements without prebolus administration in 6-week intervals from the end of radiotherapy until progression. Maximum relative cerebral volume values (rCBVmax) with and without leakage correction were calculated using Philips IntelliSpace®. RESULTS We recruited 16 patients and conducted 82 MRI scans with a mean follow up of 7.2 month. During stable disease, corrected rCBVmax was significantly more stable than uncorrected rCBVmax. Detection of progression with a rCBVmax cutoff was better for corrected (specificity 86%) than for uncorrected rCBVmax (specificity 41%). Interestingly, the increase of corrected rCBVmax upon progression also had a good diagnostic performance with a combination of both cutoffs delivering the best result (sensitivity/specificity 89%/93%). CONCLUSION Corrected rCBVmax supports the imaging finding of a stable disease and large increases during longitudinal observation support the diagnosis of tumor progression. rCBV values without prebolus or leakage correction are not reliable to monitor glioblastomas. Further studies to investigate the value of longitudinal rCBV dynamics for the differentiation of real tumor progression from pseudoprogression are warranted.
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Affiliation(s)
- Eike Steidl
- Institute of Neuroradiology, University Hospital Frankfurt, Schleusenweg 2-16, 60528, Frankfurt, Germany.
- Department of Radiology, Neuroradiology, University Hospital Bonn, Sigmund-Freud-Str. 25, 53127, Bonn, Germany.
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Mathias Müller
- Department of Radiology, Neuroradiology, University Hospital Bonn, Sigmund-Freud-Str. 25, 53127, Bonn, Germany
| | - Andreas Müller
- Department of Radiology, Neuroradiology, University Hospital Bonn, Sigmund-Freud-Str. 25, 53127, Bonn, Germany
| | - Ulrich Herrlinger
- Division of Clinical Neurooncology, Department of Neurology, University Hospital Bonn, Sigmund-Freud-Str. 25, 53127, Bonn, Germany
| | - Elke Hattingen
- Institute of Neuroradiology, University Hospital Frankfurt, Schleusenweg 2-16, 60528, Frankfurt, Germany
- Department of Radiology, Neuroradiology, University Hospital Bonn, Sigmund-Freud-Str. 25, 53127, Bonn, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
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