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Malyarenko D, Ono S, Lynch TJE, Swanson SD. Technical note: hydrogel-based mimics of prostate cancer with matched relaxation, diffusion and kurtosis for validating multi-parametric MRI. Med Phys 2024; 51:3590-3596. [PMID: 38128027 PMCID: PMC11138133 DOI: 10.1002/mp.16908] [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: 09/14/2023] [Revised: 11/16/2023] [Accepted: 12/10/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Protocol standardization and optimization for clinical translation of emerging quantitative multiparametric (mp)MRI biomarkers of high-risk prostate cancer requires imaging references that mimic realistic tissue value combinations for bias assessment in derived relaxation and diffusion parameters. PURPOSE This work aimed to develop a novel class of hydrogel-based synthetic materials with simultaneously controlled quantitative relaxation, diffusion, and kurtosis parameters that mimic in vivo prostate value combinations in the same spatial compartment and allow stable assemblies of adjacent structures. METHODS A set of materials with tunable T2, diffusion, and kurtosis were assembled to create quantitative biomimetic (mp)MRI references. T2 was controlled with variable agarose concentration, monoexponential diffusion by polyvinylpyrrolidone (PVP), and kurtosis by addition of lamellar vesicles. The materials were mechanically stabilized by UV cross-linked polyacrylamide gels (PAG) to allow biomimetic morphologies. The reference T2 were measured on a 3T scanner using multi-echo CPMG, and diffusion kurtosis-with multi-b DWI. RESULTS Agarose concentration controls T2 values which are nominally independent of PVP or vesicle concentration. For agarose PVP hydrogels, monoexponential diffusion values are a function of PVP concentration and independent of agarose concentration. Compared to free vesicles, for agarose-PAG combined with vesicles, diffusion was predominantly controlled by vesicles and PAG, while kurtosis was affected by agarose and vesicle concentration. Both hydrogel classes achieved image voxel parameter values (T2, Da, Ka) for relaxation (T2: 65-255 ms), apparent diffusion (Da: 0.8-1.7 μm2/ms), and kurtosis (Ka: 0.5-1.25) within the target literature ranges for normal prostate zones and cancer lesions. Relaxation and diffusion parameters remained stable for over 6 months for layered material assemblies. CONCLUSION A stable biomimetic mpMR reference based on hydrogels has been developed with a range of multi-compartment diffusion and relaxation parameter combinations observed in cancerous and healthy prostate tissue.
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Affiliation(s)
- Dariya Malyarenko
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Shigeto Ono
- Computerized Imaging Reference Systems (Sun Nuclear), Mirion Technologies Inc., Norfolk, VA 23513, USA
| | - Ted J. E. Lynch
- Computerized Imaging Reference Systems (Sun Nuclear), Mirion Technologies Inc., Norfolk, VA 23513, USA
| | - Scott D. Swanson
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
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Friedli I, Baid-Agrawal S, Unwin R, Morell A, Johansson L, Hockings PD. Magnetic Resonance Imaging in Clinical Trials of Diabetic Kidney Disease. J Clin Med 2023; 12:4625. [PMID: 37510740 PMCID: PMC10380287 DOI: 10.3390/jcm12144625] [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: 05/29/2023] [Revised: 06/28/2023] [Accepted: 07/04/2023] [Indexed: 07/30/2023] Open
Abstract
Chronic kidney disease (CKD) associated with diabetes mellitus (DM) (known as diabetic kidney disease, DKD) is a serious and growing healthcare problem worldwide. In DM patients, DKD is generally diagnosed based on the presence of albuminuria and a reduced glomerular filtration rate. Diagnosis rarely includes an invasive kidney biopsy, although DKD has some characteristic histological features, and kidney fibrosis and nephron loss cause disease progression that eventually ends in kidney failure. Alternative sensitive and reliable non-invasive biomarkers are needed for DKD (and CKD in general) to improve timely diagnosis and aid disease monitoring without the need for a kidney biopsy. Such biomarkers may also serve as endpoints in clinical trials of new treatments. Non-invasive magnetic resonance imaging (MRI), particularly multiparametric MRI, may achieve these goals. In this article, we review emerging data on MRI techniques and their scientific, clinical, and economic value in DKD/CKD for diagnosis, assessment of disease pathogenesis and progression, and as potential biomarkers for clinical trial use that may also increase our understanding of the efficacy and mode(s) of action of potential DKD therapeutic interventions. We also consider how multi-site MRI studies are conducted and the challenges that should be addressed to increase wider application of MRI in DKD.
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Affiliation(s)
- Iris Friedli
- Antaros Medical, BioVenture Hub, 43183 Mölndal, Sweden
| | - Seema Baid-Agrawal
- Transplant Center, Sahlgrenska University Hospital, University of Gothenburg, 41345 Gothenburg, Sweden
| | - Robert Unwin
- AstraZeneca R&D BioPharmaceuticals, Translational Science and Experimental Medicine, Early Cardiovascular, Renal & Metabolic Diseases (CVRM), Granta Park, Cambridge CB21 6GH, UK
| | - Arvid Morell
- Antaros Medical, BioVenture Hub, 43183 Mölndal, Sweden
| | | | - Paul D Hockings
- Antaros Medical, BioVenture Hub, 43183 Mölndal, Sweden
- MedTech West, Chalmers University of Technology, 41345 Gothenburg, Sweden
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Gammon ST, Cohen AS, Lehnert AL, Sullivan DC, Malyarenko D, Manning HC, Hormuth DA, Daldrup-Link HE, An H, Quirk JD, Shoghi K, Pagel MD, Kinahan PE, Miyaoka RS, Houghton AM, Lewis MT, Larson P, Sriram R, Blocker SJ, Pickup S, Badea A, Badea CT, Yankeelov TE, Chenevert TL. An Online Repository for Pre-Clinical Imaging Protocols (PIPs). Tomography 2023; 9:750-758. [PMID: 37104131 PMCID: PMC10145184 DOI: 10.3390/tomography9020060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 03/15/2023] [Accepted: 03/16/2023] [Indexed: 03/29/2023] Open
Abstract
Providing method descriptions that are more detailed than currently available in typical peer reviewed journals has been identified as an actionable area for improvement. In the biochemical and cell biology space, this need has been met through the creation of new journals focused on detailed protocols and materials sourcing. However, this format is not well suited for capturing instrument validation, detailed imaging protocols, and extensive statistical analysis. Furthermore, the need for additional information must be counterbalanced by the additional time burden placed upon researchers who may be already overtasked. To address these competing issues, this white paper describes protocol templates for positron emission tomography (PET), X-ray computed tomography (CT), and magnetic resonance imaging (MRI) that can be leveraged by the broad community of quantitative imaging experts to write and self-publish protocols in protocols.io. Similar to the Structured Transparent Accessible Reproducible (STAR) or Journal of Visualized Experiments (JoVE) articles, authors are encouraged to publish peer reviewed papers and then to submit more detailed experimental protocols using this template to the online resource. Such protocols should be easy to use, readily accessible, readily searchable, considered open access, enable community feedback, editable, and citable by the author.
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Affiliation(s)
- Seth T. Gammon
- Department of Cancer Systems Imaging, University of MD Anderson Cancer Center, 1881 E. Road, Houston, TX 77030, USA
- Correspondence: ; Tel.: +713-745-3705
| | - Allison S. Cohen
- Department of Cancer Systems Imaging, University of MD Anderson Cancer Center, 1881 E. Road, Houston, TX 77030, USA
| | | | - Daniel C. Sullivan
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
| | - Dariya Malyarenko
- Department of Radiology, University of Michigan, Ann Arbor, MI 48108, USA
| | - Henry Charles Manning
- Department of Cancer Systems Imaging, University of MD Anderson Cancer Center, 1881 E. Road, Houston, TX 77030, USA
| | - David A. Hormuth
- Oden Institute for Computational Engineering and Sciences, and Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA
| | - Heike E. Daldrup-Link
- Department of Radiology, Molecular Imaging Program at Stanford, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Hongyu An
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO 63110, USA
| | - James D. Quirk
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO 63110, USA
| | - Kooresh Shoghi
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO 63110, USA
| | - Mark David Pagel
- Department of Cancer Systems Imaging, University of MD Anderson Cancer Center, 1881 E. Road, Houston, TX 77030, USA
| | - Paul E. Kinahan
- Department of Radiology, University of Washington, Seattle, WA 98105, USA
| | - Robert S. Miyaoka
- Department of Radiology, University of Washington, Seattle, WA 98105, USA
| | | | - Michael T. Lewis
- Lester and Sue Smith Breast Center, Dan L Duncan Comprehensive Cancer Center, Houston, TX 77030, USA
| | - Peder Larson
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
| | - Renuka Sriram
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA
| | - Stephanie J. Blocker
- Center for In Vivo Microscopy, Department of Radiology, Duke University School of Medicine, Durham, NC 27710, USA
| | - Stephen Pickup
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Alexandra Badea
- Department of Radiology, Duke University, Durham, NC 27708, USA
| | | | - Thomas E. Yankeelov
- Department of Biomedical Engineering, Diagnostic Medicine, and Oncology, Oden Institute for Computational Engineering and Sciences, Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX 77030, USA
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