CIRP Network Collection: Advances in Co-clinical Quantitative Imaging Research

A special issue of Tomography (ISSN 2379-139X).

Deadline for manuscript submissions: closed (1 February 2023) | Viewed by 33549

Special Issue Editors

Michigan Medicine, University of Michigan, Ann Arbor, MI 48109, USA
Interests: quantitative diffusion imaging; cancer imaging biomarkers; technical bias correction; clinical trial translation; quantitative diffusion models
Departments of Molecular and Cellular Biology Radiology, Baylor College of Medicine, Houston, TX 77030, USA
Interests: breast cancer detection; cancer genetic regulation; genetically engineered animal models; therapeutic and preventive agents; genomic and imaging integration
Cancer Imaging Program, Division of Cancer Treatment & Diagnosis, National Cancer Institute, Bethesda, MD 20892, USA
Interests: MRI/MRSI; molecular imaging; quantitative imaging technology; co-clinical quantitative imaging; image-based biomarkers; cancer therapy response assessment
Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University Medical Center, Durham, NC 27710, USA
Interests: computed tomography; micro-computed tomography; quantitative imaging; theranostics; image reconstruction

Special Issue Information

Dear Colleagues,

The National Cancer Institute’s Co-Clinical Imaging Research Resource Program (CIRP) promotes the development of quantitative imaging resources for therapeutic or prevention co-clinical trials that study both patients and human-in-mouse models. The program facilitates consensus on quantitative imaging methods and standard operating procedures for co-clinical applications. CIRP is committed to the development of freely accessible, comprehensive information resources to guide co-clinical imaging investigations in the context of experimental design, protocol and software development, modeling and information extraction, biological and pathological validations, multiscale data integration, and preclinical–clinical correlations. 

All papers submitted to this Special Issue are reviewed by independent referees, and the final decision is made by a Tomography Editorial Board Member who does not have any conflict of interest with the submission.

Dr. Dariya Malyarenko
Prof. Dr. Michael Lewis
Dr. Huiming Zhang
Prof. Dr. Cristian Badea
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Tomography is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • quantitative cancer imaging
  • pre-clinical animal imaging
  • co-clinical data integration
  • tumor mouse models
  • co-clinical trial informatics

Published Papers (13 papers)

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Research

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15 pages, 1430 KiB  
Article
Co-Clinical Imaging Metadata Information (CIMI) for Cancer Research to Promote Open Science, Standardization, and Reproducibility in Preclinical Imaging
by Stephen M. Moore, James D. Quirk, Andrew W. Lassiter, Richard Laforest, Gregory D. Ayers, Cristian T. Badea, Andriy Y. Fedorov, Paul E. Kinahan, Matthew Holbrook, Peder E. Z. Larson, Renuka Sriram, Thomas L. Chenevert, Dariya Malyarenko, John Kurhanewicz, A. McGarry Houghton, Brian D. Ross, Stephen Pickup, James C. Gee, Rong Zhou, Seth T. Gammon, Henry Charles Manning, Raheleh Roudi, Heike E. Daldrup-Link, Michael T. Lewis, Daniel L. Rubin, Thomas E. Yankeelov and Kooresh I. Shoghiadd Show full author list remove Hide full author list
Tomography 2023, 9(3), 995-1009; https://doi.org/10.3390/tomography9030081 - 11 May 2023
Cited by 1 | Viewed by 2726
Abstract
Preclinical imaging is a critical component in translational research with significant complexities in workflow and site differences in deployment. Importantly, the National Cancer Institute’s (NCI) precision medicine initiative emphasizes the use of translational co-clinical oncology models to address the biological and molecular bases [...] Read more.
Preclinical imaging is a critical component in translational research with significant complexities in workflow and site differences in deployment. Importantly, the National Cancer Institute’s (NCI) precision medicine initiative emphasizes the use of translational co-clinical oncology models to address the biological and molecular bases of cancer prevention and treatment. The use of oncology models, such as patient-derived tumor xenografts (PDX) and genetically engineered mouse models (GEMMs), has ushered in an era of co-clinical trials by which preclinical studies can inform clinical trials and protocols, thus bridging the translational divide in cancer research. Similarly, preclinical imaging fills a translational gap as an enabling technology for translational imaging research. Unlike clinical imaging, where equipment manufacturers strive to meet standards in practice at clinical sites, standards are neither fully developed nor implemented in preclinical imaging. This fundamentally limits the collection and reporting of metadata to qualify preclinical imaging studies, thereby hindering open science and impacting the reproducibility of co-clinical imaging research. To begin to address these issues, the NCI co-clinical imaging research program (CIRP) conducted a survey to identify metadata requirements for reproducible quantitative co-clinical imaging. The enclosed consensus-based report summarizes co-clinical imaging metadata information (CIMI) to support quantitative co-clinical imaging research with broad implications for capturing co-clinical data, enabling interoperability and data sharing, as well as potentially leading to updates to the preclinical Digital Imaging and Communications in Medicine (DICOM) standard. Full article
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14 pages, 30303 KiB  
Article
Metabolite-Specific Echo Planar Imaging for Preclinical Studies with Hyperpolarized 13C-Pyruvate MRI
by Sule I. Sahin, Xiao Ji, Shubhangi Agarwal, Avantika Sinha, Ivina Mali, Jeremy W. Gordon, Mark Mattingly, Sukumar Subramaniam, John Kurhanewicz, Peder E. Z. Larson and Renuka Sriram
Tomography 2023, 9(2), 736-749; https://doi.org/10.3390/tomography9020059 - 27 Mar 2023
Cited by 3 | Viewed by 1893
Abstract
Metabolite-specific echo-planar imaging (EPI) sequences with spectral–spatial (spsp) excitation are commonly used in clinical hyperpolarized [1-13C]pyruvate studies because of their speed, efficiency, and flexibility. In contrast, preclinical systems typically rely on slower spectroscopic methods, such as chemical shift imaging (CSI). In [...] Read more.
Metabolite-specific echo-planar imaging (EPI) sequences with spectral–spatial (spsp) excitation are commonly used in clinical hyperpolarized [1-13C]pyruvate studies because of their speed, efficiency, and flexibility. In contrast, preclinical systems typically rely on slower spectroscopic methods, such as chemical shift imaging (CSI). In this study, a 2D spspEPI sequence was developed for use on a preclinical 3T Bruker system and tested on in vivo mice experiments with patient-derived xenograft renal cell carcinoma (RCC) or prostate cancer tissues implanted in the kidney or liver. Compared to spspEPI sequences, CSI were found to have a broader point spread function via simulations and exhibited signal bleeding between vasculature and tumors in vivo. Parameters for the spspEPI sequence were optimized using simulations and verified with in vivo data. The expected lactate SNR and pharmacokinetic modeling accuracy increased with lower pyruvate flip angles (less than 15°), intermediate lactate flip angles (25° to 40°), and temporal resolution of 3 s. Overall SNR was also higher with coarser spatial resolution (4 mm isotropic vs. 2 mm isotropic). Pharmacokinetic modelling used to fit kPL maps showed results consistent with the previous literature and across different sequences and tumor xenografts. This work describes and justifies the pulse design and parameter choices for preclinical spspEPI hyperpolarized 13C-pyruvate studies and shows superior image quality to CSI. Full article
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14 pages, 2564 KiB  
Article
Improved Repeatability of Mouse Tibia Volume Segmentation in Murine Myelofibrosis Model Using Deep Learning
by Aman Kushwaha, Rami F. Mourad, Kevin Heist, Humera Tariq, Heang-Ping Chan, Brian D. Ross, Thomas L. Chenevert, Dariya Malyarenko and Lubomir M. Hadjiiski
Tomography 2023, 9(2), 589-602; https://doi.org/10.3390/tomography9020048 - 07 Mar 2023
Cited by 2 | Viewed by 1827
Abstract
A murine model of myelofibrosis in tibia was used in a co-clinical trial to evaluate segmentation methods for application of image-based biomarkers to assess disease status. The dataset (32 mice with 157 3D MRI scans including 49 test–retest pairs scanned on consecutive days) [...] Read more.
A murine model of myelofibrosis in tibia was used in a co-clinical trial to evaluate segmentation methods for application of image-based biomarkers to assess disease status. The dataset (32 mice with 157 3D MRI scans including 49 test–retest pairs scanned on consecutive days) was split into approximately 70% training, 10% validation, and 20% test subsets. Two expert annotators (EA1 and EA2) performed manual segmentations of the mouse tibia (EA1: all data; EA2: test and validation). Attention U-net (A-U-net) model performance was assessed for accuracy with respect to EA1 reference using the average Jaccard index (AJI), volume intersection ratio (AVI), volume error (AVE), and Hausdorff distance (AHD) for four training scenarios: full training, two half-splits, and a single-mouse subsets. The repeatability of computer versus expert segmentations for tibia volume of test–retest pairs was assessed by within-subject coefficient of variance (%wCV). A-U-net models trained on full and half-split training sets achieved similar average accuracy (with respect to EA1 annotations) for test set: AJI = 83–84%, AVI = 89–90%, AVE = 2–3%, and AHD = 0.5 mm–0.7 mm, exceeding EA2 accuracy: AJ = 81%, AVI = 83%, AVE = 14%, and AHD = 0.3 mm. The A-U-net model repeatability wCV [95% CI]: 3 [2, 5]% was notably better than that of expert annotators EA1: 5 [4, 9]% and EA2: 8 [6, 13]%. The developed deep learning model effectively automates murine bone marrow segmentation with accuracy comparable to human annotators and substantially improved repeatability. Full article
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15 pages, 2034 KiB  
Article
Repeatability of Quantitative Magnetic Resonance Imaging Biomarkers in the Tibia Bone Marrow of a Murine Myelofibrosis Model
by Brian D. Ross, Dariya Malyarenko, Kevin Heist, Ghoncheh Amouzandeh, Youngsoon Jang, Christopher A. Bonham, Cyrus Amirfazli, Gary D. Luker and Thomas L. Chenevert
Tomography 2023, 9(2), 552-566; https://doi.org/10.3390/tomography9020045 - 28 Feb 2023
Cited by 2 | Viewed by 1785
Abstract
Quantitative MRI biomarkers are sought to replace painful and invasive sequential bone-marrow biopsies routinely used for myelofibrosis (MF) cancer monitoring and treatment assessment. Repeatability of MRI-based quantitative imaging biomarker (QIB) measurements was investigated for apparent diffusion coefficient (ADC), proton density fat fraction (PDFF), [...] Read more.
Quantitative MRI biomarkers are sought to replace painful and invasive sequential bone-marrow biopsies routinely used for myelofibrosis (MF) cancer monitoring and treatment assessment. Repeatability of MRI-based quantitative imaging biomarker (QIB) measurements was investigated for apparent diffusion coefficient (ADC), proton density fat fraction (PDFF), and magnetization transfer ratio (MTR) in a JAK2 V617F hematopoietic transplant model of MF. Repeatability coefficients (RCs) were determined for three defined tibia bone-marrow sections (2–9 mm; 10–12 mm; and 12.5–13.5 mm from the knee joint) across 15 diseased mice from 20–37 test-retest pairs. Scans were performed on consecutive days every two weeks for a period of 10 weeks starting 3–4 weeks after transplant. The mean RC with (95% confidence interval (CI)) for these sections, respectively, were for ADC: 0.037 (0.031, 0.050), 0.087 (0.069, 0.116), and 0.030 (0.022, 0.044) μm2/ms; for PDFF: 1.6 (1.3, 2.0), 15.5 (12.5, 20.2), and 25.5 (12.0, 33.0)%; and for MTR: 0.16 (0.14, 0.19), 0.11 (0.09, 0.15), and 0.09 (0.08, 0.15). Change-trend analysis of these QIBs identified a dynamic section within the mid-tibial bone marrow in which confident changes (exceeding RC) could be observed after a four-week interval between scans across all measured MRI-based QIBs. Our results demonstrate the capability to derive quantitative imaging metrics from mouse tibia bone marrow for monitoring significant longitudinal MF changes. Full article
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12 pages, 2419 KiB  
Article
Feasibility of [18F]FSPG PET for Early Response Assessment to Combined Blockade of EGFR and Glutamine Metabolism in Wild-Type KRAS Colorectal Cancer
by Seong-Woo Bae, Jianbo Wang, Dimitra K. Georgiou, Xiaoxia Wen, Allison S. Cohen, Ling Geng, Mohammed Noor Tantawy and H. Charles Manning
Tomography 2023, 9(2), 497-508; https://doi.org/10.3390/tomography9020041 - 24 Feb 2023
Cited by 4 | Viewed by 1714
Abstract
Early response assessment is critical for personalizing cancer therapy. Emerging therapeutic regimens with encouraging results in the wild-type (WT) KRAS colorectal cancer (CRC) setting include inhibitors of epidermal growth factor receptor (EGFR) and glutaminolysis. Towards predicting clinical outcome, this preclinical study evaluated non-invasive [...] Read more.
Early response assessment is critical for personalizing cancer therapy. Emerging therapeutic regimens with encouraging results in the wild-type (WT) KRAS colorectal cancer (CRC) setting include inhibitors of epidermal growth factor receptor (EGFR) and glutaminolysis. Towards predicting clinical outcome, this preclinical study evaluated non-invasive positron emission tomography (PET) with (4S)-4-(3-[18F]fluoropropyl)-L-glutamic acid ([18F]FSPG) in treatment-sensitive and treatment-resistant WT KRAS CRC patient-derived xenografts (PDXs). Tumor-bearing mice were imaged with [18F]FSPG PET before and one week following the initiation of treatment with either EGFR-targeted monoclonal antibody (mAb) therapy, glutaminase inhibitor therapy, or the combination. Imaging was correlated with tumor volume and histology. In PDX that responded to therapy, [18F]FSPG PET was significantly decreased from baseline at 1-week post-therapy, prior to changes in tumor volume. In contrast, [18F]FSPG PET was not decreased in non-responding PDX. These data suggest that [18F]FSPG PET may serve as an early metric of response to EGFR and glutaminase inhibition in the WT KRAS CRC setting. Full article
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12 pages, 1651 KiB  
Article
Evaluation of Apparent Diffusion Coefficient Repeatability and Reproducibility for Preclinical MRIs Using Standardized Procedures and a Diffusion-Weighted Imaging Phantom
by Dariya Malyarenko, Ghoncheh Amouzandeh, Stephen Pickup, Rong Zhou, Henry Charles Manning, Seth T. Gammon, Kooresh I. Shoghi, James D. Quirk, Renuka Sriram, Peder Larson, Michael T. Lewis, Robia G. Pautler, Paul E. Kinahan, Mark Muzi and Thomas L. Chenevert
Tomography 2023, 9(1), 375-386; https://doi.org/10.3390/tomography9010030 - 07 Feb 2023
Cited by 4 | Viewed by 1946
Abstract
Relevant to co-clinical trials, the goal of this work was to assess repeatability, reproducibility, and bias of the apparent diffusion coefficient (ADC) for preclinical MRIs using standardized procedures for comparison to performance of clinical MRIs. A temperature-controlled phantom provided an absolute reference standard [...] Read more.
Relevant to co-clinical trials, the goal of this work was to assess repeatability, reproducibility, and bias of the apparent diffusion coefficient (ADC) for preclinical MRIs using standardized procedures for comparison to performance of clinical MRIs. A temperature-controlled phantom provided an absolute reference standard to measure spatial uniformity of these performance metrics. Seven institutions participated in the study, wherein diffusion-weighted imaging (DWI) data were acquired over multiple days on 10 preclinical scanners, from 3 vendors, at 6 field strengths. Centralized versus site-based analysis was compared to illustrate incremental variance due to processing workflow. At magnet isocenter, short-term (intra-exam) and long-term (multiday) repeatability were excellent at within-system coefficient of variance, wCV [±CI] = 0.73% [0.54%, 1.12%] and 1.26% [0.94%, 1.89%], respectively. The cross-system reproducibility coefficient, RDC [±CI] = 0.188 [0.129, 0.343] µm2/ms, corresponded to 17% [12%, 31%] relative to the reference standard. Absolute bias at isocenter was low (within 4%) for 8 of 10 systems, whereas two high-bias (>10%) scanners were primary contributors to the relatively high RDC. Significant additional variance (>2%) due to site-specific analysis was observed for 2 of 10 systems. Base-level technical bias, repeatability, reproducibility, and spatial uniformity patterns were consistent with human MRIs (scaled for bore size). Well-calibrated preclinical MRI systems are capable of highly repeatable and reproducible ADC measurements. Full article
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16 pages, 2650 KiB  
Article
Dynamic Contrast-Enhanced MRI in the Abdomen of Mice with High Temporal and Spatial Resolution Using Stack-of-Stars Sampling and KWIC Reconstruction
by Stephen Pickup, Miguel Romanello, Mamta Gupta, Hee Kwon Song and Rong Zhou
Tomography 2022, 8(5), 2113-2128; https://doi.org/10.3390/tomography8050178 - 24 Aug 2022
Cited by 3 | Viewed by 1771
Abstract
Application of quantitative dynamic contrast-enhanced (DCE) MRI in mouse models of abdominal cancer is challenging due to the effects of RF inhomogeneity, image corruption from rapid respiratory motion and the need for high spatial and temporal resolutions. Here we demonstrate a DCE protocol [...] Read more.
Application of quantitative dynamic contrast-enhanced (DCE) MRI in mouse models of abdominal cancer is challenging due to the effects of RF inhomogeneity, image corruption from rapid respiratory motion and the need for high spatial and temporal resolutions. Here we demonstrate a DCE protocol optimized for such applications. The method consists of three acquisitions: (1) actual flip-angle B1 mapping, (2) variable flip-angle T1 mapping and (3) acquisition of the DCE series using a motion-robust radial strategy with k-space weighted image contrast (KWIC) reconstruction. All three acquisitions employ spoiled radial imaging with stack-of-stars sampling (SoS) and golden-angle increments between the views. This scheme is shown to minimize artifacts due to respiratory motion while simultaneously facilitating view-sharing image reconstruction for the dynamic series. The method is demonstrated in a genetically engineered mouse model of pancreatic ductal adenocarcinoma and yielded mean perfusion parameters of Ktrans = 0.23 ± 0.14 min−1 and ve = 0.31 ± 0.17 (n = 22) over a wide range of tumor sizes. The SoS-sampled DCE method is shown to produce artifact-free images with good SNR leading to robust estimation of DCE parameters. Full article
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10 pages, 2295 KiB  
Article
Web-Based Application for Biomedical Image Registry, Analysis, and Translation (BiRAT)
by Rahul Pemmaraju, Robert Minahan, Elise Wang, Kornel Schadl, Heike Daldrup-Link and Frezghi Habte
Tomography 2022, 8(3), 1453-1462; https://doi.org/10.3390/tomography8030117 - 30 May 2022
Cited by 5 | Viewed by 3830
Abstract
Imaging has become an invaluable tool in preclinical research for its capability to non-invasively detect and monitor disease and assess treatment response. With the increased use of preclinical imaging, large volumes of image data are being generated requiring critical data management tools. Due [...] Read more.
Imaging has become an invaluable tool in preclinical research for its capability to non-invasively detect and monitor disease and assess treatment response. With the increased use of preclinical imaging, large volumes of image data are being generated requiring critical data management tools. Due to proprietary issues and continuous technology development, preclinical images, unlike DICOM-based images, are often stored in an unstructured data file in company-specific proprietary formats. This limits the available DICOM-based image management database to be effectively used for preclinical applications. A centralized image registry and management tool is essential for advances in preclinical imaging research. Specifically, such tools may have a high impact in generating large image datasets for the evolving artificial intelligence applications and performing retrospective analyses of previously acquired images. In this study, a web-based server application is developed to address some of these issues. The application is designed to reflect the actual experimentation workflow maintaining detailed records of both individual images and experimental data relevant to specific studies and/or projects. The application also includes a web-based 3D/4D image viewer to easily and quickly view and evaluate images. This paper briefly describes the initial implementation of the web-based application. Full article
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14 pages, 4882 KiB  
Article
Photon Counting CT and Radiomic Analysis Enables Differentiation of Tumors Based on Lymphocyte Burden
by Alex J. Allphin, Yvonne M. Mowery, Kyle J. Lafata, Darin P. Clark, Alex M. Bassil, Rico Castillo, Diana Odhiambo, Matthew D. Holbrook, Ketan B. Ghaghada and Cristian T. Badea
Tomography 2022, 8(2), 740-753; https://doi.org/10.3390/tomography8020061 - 10 Mar 2022
Cited by 6 | Viewed by 5249
Abstract
The purpose of this study was to investigate if radiomic analysis based on spectral micro-CT with nanoparticle contrast-enhancement can differentiate tumors based on lymphocyte burden. High mutational load transplant soft tissue sarcomas were initiated in Rag2+/− and Rag2−/− mice to model [...] Read more.
The purpose of this study was to investigate if radiomic analysis based on spectral micro-CT with nanoparticle contrast-enhancement can differentiate tumors based on lymphocyte burden. High mutational load transplant soft tissue sarcomas were initiated in Rag2+/− and Rag2−/− mice to model varying lymphocyte burden. Mice received radiation therapy (20 Gy) to the tumor-bearing hind limb and were injected with a liposomal iodinated contrast agent. Five days later, animals underwent conventional micro-CT imaging using an energy integrating detector (EID) and spectral micro-CT imaging using a photon-counting detector (PCD). Tumor volumes and iodine uptakes were measured. The radiomic features (RF) were grouped into feature-spaces corresponding to EID, PCD, and spectral decomposition images. The RFs were ranked to reduce redundancy and increase relevance based on TL burden. A stratified repeated cross validation strategy was used to assess separation using a logistic regression classifier. Tumor iodine concentration was the only significantly different conventional tumor metric between Rag2+/− (TLs present) and Rag2−/− (TL-deficient) tumors. The RFs further enabled differentiation between Rag2+/− and Rag2−/− tumors. The PCD-derived RFs provided the highest accuracy (0.68) followed by decomposition-derived RFs (0.60) and the EID-derived RFs (0.58). Such non-invasive approaches could aid in tumor stratification for cancer therapy studies. Full article
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Review

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11 pages, 735 KiB  
Review
The National Cancer Institute’s Co-Clinical Quantitative Imaging Research Resources for Precision Medicine in Preclinical and Clinical Settings
by Huiming Zhang
Tomography 2023, 9(3), 931-941; https://doi.org/10.3390/tomography9030076 - 30 Apr 2023
Viewed by 1600
Abstract
Genetically engineered mouse models (GEMMs) and patient-derived xenograft mouse models (PDXs) can recapitulate important biological features of cancer. They are often part of precision medicine studies in a co-clinical setting, in which therapeutic investigations are conducted in patients and in parallel (or sequentially) [...] Read more.
Genetically engineered mouse models (GEMMs) and patient-derived xenograft mouse models (PDXs) can recapitulate important biological features of cancer. They are often part of precision medicine studies in a co-clinical setting, in which therapeutic investigations are conducted in patients and in parallel (or sequentially) in cohorts of GEMMs or PDXs. Employing radiology-based quantitative imaging in these studies allows in vivo assessment of disease response in real time, providing an important opportunity to bridge precision medicine from the bench to the bedside. The Co-Clinical Imaging Research Resource Program (CIRP) of the National Cancer Institute focuses on the optimization of quantitative imaging methods to improve co-clinical trials. The CIRP supports 10 different co-clinical trial projects, spanning diverse tumor types, therapeutic interventions, and imaging modalities. Each CIRP project is tasked to deliver a unique web resource to support the cancer community with the necessary methods and tools to conduct co-clinical quantitative imaging studies. This review provides an update of the CIRP web resources, network consensus, technology advances, and a perspective on the future of the CIRP. The presentations in this special issue of Tomography were contributed by the CIRP working groups, teams, and associate members. Full article
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24 pages, 3249 KiB  
Review
Animal Models and Their Role in Imaging-Assisted Co-Clinical Trials
by Donna M. Peehl, Cristian T. Badea, Thomas L. Chenevert, Heike E. Daldrup-Link, Li Ding, Lacey E. Dobrolecki, A. McGarry Houghton, Paul E. Kinahan, John Kurhanewicz, Michael T. Lewis, Shunqiang Li, Gary D. Luker, Cynthia X. Ma, H. Charles Manning, Yvonne M. Mowery, Peter J. O'Dwyer, Robia G. Pautler, Mark A. Rosen, Raheleh Roudi, Brian D. Ross, Kooresh I. Shoghi, Renuka Sriram, Moshe Talpaz, Richard L. Wahl and Rong Zhouadd Show full author list remove Hide full author list
Tomography 2023, 9(2), 657-680; https://doi.org/10.3390/tomography9020053 - 16 Mar 2023
Cited by 3 | Viewed by 2913
Abstract
The availability of high-fidelity animal models for oncology research has grown enormously in recent years, enabling preclinical studies relevant to prevention, diagnosis, and treatment of cancer to be undertaken. This has led to increased opportunities to conduct co-clinical trials, which are studies on [...] Read more.
The availability of high-fidelity animal models for oncology research has grown enormously in recent years, enabling preclinical studies relevant to prevention, diagnosis, and treatment of cancer to be undertaken. This has led to increased opportunities to conduct co-clinical trials, which are studies on patients that are carried out parallel to or sequentially with animal models of cancer that mirror the biology of the patients’ tumors. Patient-derived xenografts (PDX) and genetically engineered mouse models (GEMM) are considered to be the models that best represent human disease and have high translational value. Notably, one element of co-clinical trials that still needs significant optimization is quantitative imaging. The National Cancer Institute has organized a Co-Clinical Imaging Resource Program (CIRP) network to establish best practices for co-clinical imaging and to optimize translational quantitative imaging methodologies. This overview describes the ten co-clinical trials of investigators from eleven institutions who are currently supported by the CIRP initiative and are members of the Animal Models and Co-clinical Trials (AMCT) Working Group. Each team describes their corresponding clinical trial, type of cancer targeted, rationale for choice of animal models, therapy, and imaging modalities. The strengths and weaknesses of the co-clinical trial design and the challenges encountered are considered. The rich research resources generated by the members of the AMCT Working Group will benefit the broad research community and improve the quality and translational impact of imaging in co-clinical trials. Full article
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Other

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19 pages, 2601 KiB  
Concept Paper
Toward Practical Integration of Omic and Imaging Data in Co-Clinical Trials
by Emel Alkim, Heidi Dowst, Julie DiCarlo, Lacey E. Dobrolecki, Anadulce Hernández-Herrera, David A. Hormuth II, Yuxing Liao, Apollo McOwiti, Robia Pautler, Mothaffar Rimawi, Ashley Roark, Ramakrishnan Rajaram Srinivasan, Jack Virostko, Bing Zhang, Fei Zheng, Daniel L. Rubin, Thomas E. Yankeelov and Michael T. Lewis
Tomography 2023, 9(2), 810-828; https://doi.org/10.3390/tomography9020066 - 10 Apr 2023
Cited by 1 | Viewed by 1708
Abstract
Co-clinical trials are the concurrent or sequential evaluation of therapeutics in both patients clinically and patient-derived xenografts (PDX) pre-clinically, in a manner designed to match the pharmacokinetics and pharmacodynamics of the agent(s) used. The primary goal is to determine the degree to which [...] Read more.
Co-clinical trials are the concurrent or sequential evaluation of therapeutics in both patients clinically and patient-derived xenografts (PDX) pre-clinically, in a manner designed to match the pharmacokinetics and pharmacodynamics of the agent(s) used. The primary goal is to determine the degree to which PDX cohort responses recapitulate patient cohort responses at the phenotypic and molecular levels, such that pre-clinical and clinical trials can inform one another. A major issue is how to manage, integrate, and analyze the abundance of data generated across both spatial and temporal scales, as well as across species. To address this issue, we are developing MIRACCL (molecular and imaging response analysis of co-clinical trials), a web-based analytical tool. For prototyping, we simulated data for a co-clinical trial in “triple-negative” breast cancer (TNBC) by pairing pre- (T0) and on-treatment (T1) magnetic resonance imaging (MRI) from the I-SPY2 trial, as well as PDX-based T0 and T1 MRI. Baseline (T0) and on-treatment (T1) RNA expression data were also simulated for TNBC and PDX. Image features derived from both datasets were cross-referenced to omic data to evaluate MIRACCL functionality for correlating and displaying MRI-based changes in tumor size, vascularity, and cellularity with changes in mRNA expression as a function of treatment. Full article
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9 pages, 895 KiB  
Perspective
An Online Repository for Pre-Clinical Imaging Protocols (PIPs)
by Seth T. Gammon, Allison S. Cohen, Adrienne L. Lehnert, Daniel C. Sullivan, Dariya Malyarenko, Henry Charles Manning, David A. Hormuth, Heike E. Daldrup-Link, Hongyu An, James D. Quirk, Kooresh Shoghi, Mark David Pagel, Paul E. Kinahan, Robert S. Miyaoka, A. McGarry Houghton, Michael T. Lewis, Peder Larson, Renuka Sriram, Stephanie J. Blocker, Stephen Pickup, Alexandra Badea, Cristian T. Badea, Thomas E. Yankeelov and Thomas L. Chenevertadd Show full author list remove Hide full author list
Tomography 2023, 9(2), 750-758; https://doi.org/10.3390/tomography9020060 - 27 Mar 2023
Cited by 2 | Viewed by 1885
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 [...] Read more.
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. Full article
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