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Mukherjee S, Bhaduri S, Harwood R, Murray P, Wilm B, Bearon R, Poptani H. Multiparametric MRI based assessment of kidney injury in a mouse model of ischemia reperfusion injury. Sci Rep 2024; 14:19922. [PMID: 39198525 PMCID: PMC11358484 DOI: 10.1038/s41598-024-70401-x] [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: 02/29/2024] [Accepted: 08/16/2024] [Indexed: 09/01/2024] Open
Abstract
Kidney diseases pose a global healthcare burden, with millions requiring renal replacement therapy. Ischemia/reperfusion injury (IRI) is a common pathology of acute kidney injury, causing hypoxia and subsequent inflammation-induced kidney damage. Accurate detection of acute kidney injury due to IRI is crucial for timely intervention. We used longitudinal, multi-parametric magnetic resonance imaging (MRI) employing arterial spin labelling (ASL), diffusion weighted imaging (DWI), and dynamic contrast enhanced (DCE)-MRI to assess IRI induced changes in both the injured and healthy contralateral kidney, in a unilateral IRI mouse model (n = 9). Multi-parametric MRI demonstrated significant differences in kidney volume (p = 0.001), blood flow (p = 0.002), filtration coefficient (p = 0.038), glomerular filtration rate (p = 0.005) and apparent diffusion coefficient (p = 0.048) between the injured kidney and contralateral kidney on day 1 post-IRI surgery. Identification of the injured kidney using principal component analysis including most of the imaging parameters demonstrated an area under the curve (AUC) of 0.97. These results point to the utility of multi-parametric MRI in early detection of IRI-induced kidney damage suggesting that the combination of various MRI parameters may be suitable for monitoring the extent of injury in this model.
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Affiliation(s)
- Soham Mukherjee
- Centre for Pre-Clinical Imaging, Molecular and Integrative Biology, Institute of Systems, University of Liverpool, Crown Street, Liverpool, L69 3BX, UK
| | - Sourav Bhaduri
- Centre for Pre-Clinical Imaging, Molecular and Integrative Biology, Institute of Systems, University of Liverpool, Crown Street, Liverpool, L69 3BX, UK
- Institute for Advancing Intelligence (IAI), TCG CREST, Kolkata, India
| | - Rachel Harwood
- Department of Women's and Children's Health, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK
| | - Patricia Murray
- Department of Women's and Children's Health, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK
| | - Bettina Wilm
- Department of Women's and Children's Health, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK
| | - Rachel Bearon
- Department of Mathematical Science, University of Liverpool, Liverpool, UK
- Department of Mathematics, Kings College, London, UK
| | - Harish Poptani
- Centre for Pre-Clinical Imaging, Molecular and Integrative Biology, Institute of Systems, University of Liverpool, Crown Street, Liverpool, L69 3BX, UK.
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2
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Ghasemi A, Ahlawat S, Fayad LM. Magnetic Resonance Imaging Biomarkers of Bone and Soft Tissue Tumors. Semin Musculoskelet Radiol 2024; 28:39-48. [PMID: 38330969 DOI: 10.1055/s-0043-1776433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2024]
Abstract
Magnetic resonance imaging (MRI) is essential in the management of musculoskeletal (MSK) tumors. This review delves into the diverse MRI modalities, focusing on anatomical, functional, and metabolic sequences that provide essential biomarkers for tumor detection, characterization, disease extent determination, and assessment of treatment response. MRI's multimodal capabilities offer a range of biomarkers that enhance MSK tumor evaluation, aiding in better patient management.
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Affiliation(s)
- Ali Ghasemi
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Shivani Ahlawat
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Laura Marie Fayad
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins Medical Institutions, Baltimore, Maryland
- Department of Orthopaedic Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland
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3
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Sherminie LPG, Jayatilake ML. Fractal Dimension Analysis of Pixel Dynamic Contrast Enhanced-Magnetic Resonance Imaging Pharmacokinetic Parameters for Discrimination of Benign and Malignant Breast Lesions. JCO Clin Cancer Inform 2023; 7:e2200101. [PMID: 36745858 DOI: 10.1200/cci.22.00101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
PURPOSE Breast cancer is the most frequent cancer in women worldwide. However, its diagnosis mostly depends on visual examination of radiologic images, leading to an overdiagnosis with substantial costs. Therefore, a quantitative approach such as dynamic contrast enhanced (DCE)-magnetic resonance imaging (MRI) through pharmacokinetic (PK) modeling is required for reliable analysis. As PK parameters lack information on parameter heterogeneity, texture-based analysis is required to quantify PK parameter heterogeneity. Therefore, this study focused on determining the usefulness of fractal dimension (FD) as a potential imaging biomarker of tumor heterogeneity for discriminating benign and malignant breast lesions. METHODS Parametric maps for PK parameters, extravasation rate of contrast agent from blood plasma to extravascular extracellular space (Ktrans) and volume fraction of extravascular extracellular space (ve), were generated for the regions of interest (ROIs) under the standard model using 18 lesions. Then, tumor ROI and pixel DCE-MRI time-course data were analyzed to extract pixel values of Ktrans and ve. For each ROI, FD values of Ktrans and ve were computed using the blanket method. RESULTS The FD values of Ktrans for benign and malignant lesions varied from 2.96 to 3.49 and from 2.37 to 3.16, respectively, whereas FD values of ve for benign and malignant lesions varied from 3.01 to 5.15 and 2.42 to 3.44, respectively. There were significant differences in FD values derived from Ktrans parametric maps (P = .0053) and ve parametric maps (P = .0271) between benign and malignant lesions according to the statistical analysis. CONCLUSION Incorporating texture heterogeneity changes in breast lesions captured by FD with quantitative DCE-MRI parameters generated under the standard model is a potential marker for prediction of malignant lesions.
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Affiliation(s)
- Lahanda Purage G Sherminie
- Department of Nuclear Science, Faculty of Science, University of Colombo, Colombo, Sri Lanka.,Department of Radiography/Radiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya, Sri Lanka
| | - Mohan L Jayatilake
- Department of Radiography/Radiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya, Sri Lanka
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4
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Jarrett AM, Kazerouni AS, Wu C, Virostko J, Sorace AG, DiCarlo JC, Hormuth DA, Ekrut DA, Patt D, Goodgame B, Avery S, Yankeelov TE. 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] [MESH Headings] [Grants] [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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Affiliation(s)
- Angela M Jarrett
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA
- Livestrong Cancer Institutes, Austin, TX, USA
| | - Anum S Kazerouni
- Departments of Biomedical Engineering, Austin, TX, USA
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Chengyue Wu
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA
| | - John Virostko
- Livestrong Cancer Institutes, Austin, TX, USA
- Departments of Diagnostic Medicine, Austin, TX, USA
- Departments of Oncology, Austin, TX, USA
| | - Anna G Sorace
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA
- Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, USA
- O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Julie C DiCarlo
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA
- Livestrong Cancer Institutes, Austin, TX, USA
| | - David A Hormuth
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA
- Livestrong Cancer Institutes, Austin, TX, USA
| | - David A Ekrut
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA
| | | | - Boone Goodgame
- Departments of Oncology, Austin, TX, USA
- Departments of Internal Medicine, The University of Texas at Austin, Austin, Texas, USA
- Seton Hospital, Austin, TX, USA
| | - Sarah Avery
- Austin Radiological Association, Austin, TX, USA
| | - Thomas E Yankeelov
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA.
- Livestrong Cancer Institutes, Austin, TX, USA.
- Departments of Biomedical Engineering, Austin, TX, USA.
- Departments of Diagnostic Medicine, Austin, TX, USA.
- Departments of Oncology, Austin, TX, USA.
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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5
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Characterizing Errors in Pharmacokinetic Parameters from Analyzing Quantitative Abbreviated DCE-MRI Data in Breast Cancer. ACTA ACUST UNITED AC 2021; 7:253-267. [PMID: 34201654 PMCID: PMC8293327 DOI: 10.3390/tomography7030023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 06/15/2021] [Accepted: 06/21/2021] [Indexed: 12/13/2022]
Abstract
This study characterizes the error that results when performing quantitative analysis of abbreviated dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data of the breast with the Standard Kety-Tofts (SKT) model and its Patlak variant. More specifically, we used simulations and patient data to determine the accuracy with which abbreviated time course data could reproduce the pharmacokinetic parameters, Ktrans (volume transfer constant) and ve (extravascular/extracellular volume fraction), when compared to the full time course data. SKT analysis of simulated abbreviated time courses (ATCs) based on the imaging parameters from two available datasets (collected with a 3T MRI scanner) at a temporal resolution of 15 s (N = 15) and 7.23 s (N = 15) found a concordance correlation coefficient (CCC) greater than 0.80 for ATCs of length 3.0 and 2.5 min, respectively, for the Ktrans parameter. Analysis of the experimental data found that at least 90% of patients met this CCC cut-off of 0.80 for the ATCs of the aforementioned lengths. Patlak analysis of experimental data found that 80% of patients from the 15 s resolution dataset and 90% of patients from the 7.27 s resolution dataset met the 0.80 CCC cut-off for ATC lengths of 1.25 and 1.09 min, respectively. This study provides evidence for both the feasibility and potential utility of performing a quantitative analysis of abbreviated breast DCE-MRI in conjunction with acquisition of current standard-of-care high resolution scans without significant loss of information in the community setting.
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6
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Pedersen M, Irrera P, Dastrù W, Zöllner FG, Bennett KM, Beeman SC, Bretthorst GL, Garbow JR, Longo DL. Dynamic Contrast Enhancement (DCE) MRI-Derived Renal Perfusion and Filtration: Basic Concepts. Methods Mol Biol 2021; 2216:205-227. [PMID: 33476002 DOI: 10.1007/978-1-0716-0978-1_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Dynamic contrast-enhanced (DCE) MRI monitors the transit of contrast agents, typically gadolinium chelates, through the intrarenal regions, the renal cortex, the medulla, and the collecting system. In this way, DCE-MRI reveals the renal uptake and excretion of the contrast agent. An optimal DCE-MRI acquisition protocol involves finding a good compromise between whole-kidney coverage (i.e., 3D imaging), spatial and temporal resolution, and contrast resolution. By analyzing the enhancement of the renal tissues as a function of time, one can determine indirect measures of clinically important single-kidney parameters as the renal blood flow, glomerular filtration rate, and intrarenal blood volumes. Gadolinium-containing contrast agents may be nephrotoxic in patients suffering from severe renal dysfunction, but otherwise DCE-MRI is clearly useful for diagnosis of renal functions and for assessing treatment response and posttransplant rejection.Here we introduce the concept of renal DCE-MRI, describe the existing methods, and provide an overview of preclinical DCE-MRI applications to illustrate the utility of this technique to measure renal perfusion and glomerular filtration rate in animal models.This publication is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This introduction is complemented by two separate publications describing the experimental procedure and data analysis.
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Affiliation(s)
- Michael Pedersen
- Department of Clinical Medicine - Comparative Medicine Lab, Aarhus University, Aarhus, Denmark
| | - Pietro Irrera
- University of Campania "Luigi Vanvitelli", Napoli, Italy
| | - Walter Dastrù
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy
| | - Frank G Zöllner
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Kevin M Bennett
- Washington University School of Medicine, St. Louis, MO, USA
| | - Scott C Beeman
- Washington University School of Medicine, St. Louis, MO, USA
| | | | - Joel R Garbow
- Washington University School of Medicine, St. Louis, MO, USA
| | - Dario Livio Longo
- Institute of Biostructures and Bioimaging (IBB), Italian National Research Council (CNR), Torino, Italy.
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7
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Harrington KA, Shukla-Dave A, Paudyal R, Do RKG. MRI of the Pancreas. J Magn Reson Imaging 2020; 53:347-359. [PMID: 32302044 DOI: 10.1002/jmri.27148] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 03/01/2020] [Accepted: 03/02/2020] [Indexed: 02/06/2023] Open
Abstract
MRI has played a critical role in the evaluation of patients with pancreatic pathologies, from screening of patients at high risk for pancreatic cancer to the evaluation of pancreatic cysts and indeterminate pancreatic lesions. The high mortality associated with pancreatic adenocarcinomas has spurred much interest in developing effective screening tools, with MRI using magnetic resonance cholangiopancreatography (MRCP) playing a central role in the hopes of identifying cancers at earlier stages amenable to curative resection. Ongoing efforts to improve the resolution and robustness of imaging of the pancreas using MRI may thus one day reduce the mortality of this deadly disease. However, the increasing use of cross-sectional imaging has also generated a concomitant clinical conundrum: How to manage incidental pancreatic cystic lesions that are found in over a quarter of patients who undergo MRCP. Efforts to improve the specificity of MRCP for patients with pancreatic cysts and with indeterminate pancreatic masses may be achieved with continued technical advances in MRI, including diffusion-weighted and T1 -weighted dynamic contrast-enhanced MRI. However, developments in quantitative MRI of the pancreas remain challenging, due to the small size of the pancreas and its upper abdominal location, adjacent to bowel and below the diaphragm. Further research is needed to improve MRI of the pancreas as a clinical tool, to positively affect the lives of patients with pancreatic abnormalities. This review focuses on various MR techniques such as MRCP, quantitative imaging, and dynamic contrast-enhanced imaging and their clinical applicability in the imaging of the pancreas, with an emphasis on pancreatic malignant and premalignant lesions. Level of Evidence 5 Technical Efficacy Stage 3 J. MAGN. RESON. IMAGING 2021;53:347-359.
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Affiliation(s)
- Kate A Harrington
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Amita Shukla-Dave
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ramesh Paudyal
- Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Richard K G Do
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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8
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Paudyal R, Lu Y, Hatzoglou V, Moreira A, Stambuk HE, Oh JH, Cunanan KM, Nunez DA, Mazaheri Y, Gonen M, Ho A, Fagin JA, Wong RJ, Shaha A, Tuttle RM, Shukla-Dave A. Dynamic contrast-enhanced MRI model selection for predicting tumor aggressiveness in papillary thyroid cancers. NMR IN BIOMEDICINE 2020; 33:e4166. [PMID: 31680360 PMCID: PMC7687051 DOI: 10.1002/nbm.4166] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 07/04/2019] [Accepted: 07/17/2019] [Indexed: 06/10/2023]
Abstract
The purpose of this study was to identify the optimal tracer kinetic model from T1 -weighted dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data and evaluate whether parameters estimated from the optimal model predict tumor aggressiveness determined from histopathology in patients with papillary thyroid carcinoma (PTC) prior to surgery. In this prospective study, 18 PTC patients underwent pretreatment DCE-MRI on a 3 T MR scanner prior to thyroidectomy. This study was approved by the institutional review board and informed consent was obtained from all patients. The two-compartment exchange model, compartmental tissue uptake model, extended Tofts model (ETM) and standard Tofts model were compared on a voxel-wise basis to determine the optimal model using the corrected Akaike information criterion (AICc) for PTC. The optimal model is the one with the lowest AICc. Statistical analysis included paired and unpaired t-tests and a one-way analysis of variance. Bonferroni correction was applied for multiple comparisons. Receiver operating characteristic (ROC) curves were generated from the optimal model parameters to differentiate PTC with and without aggressive features, and AUCs were compared. ETM performed best with the lowest AICc and the highest Akaike weight (0.44) among the four models. ETM was preferred in 44% of all 3419 voxels. The ETM estimates of Ktrans in PTCs with the aggressive feature extrathyroidal extension (ETE) were significantly higher than those without ETE (0.78 ± 0.29 vs. 0.34 ± 0.18 min-1 , P = 0.005). From ROC analysis, cut-off values of Ktrans , ve and vp , which discriminated between PTCs with and without ETE, were determined at 0.45 min-1 , 0.28 and 0.014 respectively. The sensitivities and specificities were 86 and 82% (Ktrans ), 71 and 82% (ve ), and 86 and 55% (vp ), respectively. Their respective AUCs were 0.90, 0.71 and 0.71. We conclude that ETM Ktrans has shown potential to classify tumors with and without aggressive ETE in patients with PTC.
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Affiliation(s)
- Ramesh Paudyal
- Department of Medical Physics, Memorial Sloan Kettering
Cancer Center, New York, USA
| | - Yonggang Lu
- Department of Radiology, Medical College of Wisconsin,
Milwaukee, Wisconsin, USA
| | - Vaios Hatzoglou
- Department of Radiology, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - Andre Moreira
- Department of Pathology, NYU Langone Medical Center, New
York, USA
| | - Hilda E. Stambuk
- Department of Radiology, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering
Cancer Center, New York, USA
| | - Kristen M. Cunanan
- Department of Epidemiology and Biostatistics, Memorial
Sloan Kettering Cancer Center, New York, USA
| | - David Aramburu Nunez
- Department of Medical Physics, Memorial Sloan Kettering
Cancer Center, New York, USA
| | - Yousef Mazaheri
- Department of Medical Physics, Memorial Sloan Kettering
Cancer Center, New York, USA
- Department of Radiology, Medical College of Wisconsin,
Milwaukee, Wisconsin, USA
| | - Mithat Gonen
- Department of Epidemiology and Biostatistics, Memorial
Sloan Kettering Cancer Center, New York, USA
| | - Alan Ho
- Department of Medicine, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - James A. Fagin
- Department of Medicine, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - Richard J. Wong
- Department of Surgery, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - Ashok Shaha
- Department of Surgery, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - R. Michael Tuttle
- Department of Medicine, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan Kettering
Cancer Center, New York, USA
- Department of Radiology, Memorial Sloan Kettering Cancer
Center, New York, USA
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9
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Karolak A, Markov DA, McCawley LJ, Rejniak KA. Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues. J R Soc Interface 2019; 15:rsif.2017.0703. [PMID: 29367239 DOI: 10.1098/rsif.2017.0703] [Citation(s) in RCA: 71] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Accepted: 01/02/2018] [Indexed: 02/06/2023] Open
Abstract
A main goal of mathematical and computational oncology is to develop quantitative tools to determine the most effective therapies for each individual patient. This involves predicting the right drug to be administered at the right time and at the right dose. Such an approach is known as precision medicine. Mathematical modelling can play an invaluable role in the development of such therapeutic strategies, since it allows for relatively fast, efficient and inexpensive simulations of a large number of treatment schedules in order to find the most effective. This review is a survey of mathematical models that explicitly take into account the spatial architecture of three-dimensional tumours and address tumour development, progression and response to treatments. In particular, we discuss models of epithelial acini, multicellular spheroids, normal and tumour spheroids and organoids, and multi-component tissues. Our intent is to showcase how these in silico models can be applied to patient-specific data to assess which therapeutic strategies will be the most efficient. We also present the concept of virtual clinical trials that integrate standard-of-care patient data, medical imaging, organ-on-chip experiments and computational models to determine personalized medical treatment strategies.
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Affiliation(s)
- Aleksandra Karolak
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Dmitry A Markov
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.,Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN, USA
| | - Lisa J McCawley
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.,Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN, USA
| | - Katarzyna A Rejniak
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA .,Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
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10
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Hansen MB, Tietze A, Haack S, Kallehauge J, Mikkelsen IK, Østergaard L, Mouridsen K. Robust estimation of hemo-dynamic parameters in traditional DCE-MRI models. PLoS One 2019; 14:e0209891. [PMID: 30605459 PMCID: PMC6317807 DOI: 10.1371/journal.pone.0209891] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Accepted: 11/09/2018] [Indexed: 01/04/2023] Open
Abstract
PURPOSE In dynamic contrast enhanced (DCE) MRI, separation of signal contributions from perfusion and leakage requires robust estimation of parameters in a pharmacokinetic model. We present and quantify the performance of a method to compute tissue hemodynamic parameters from DCE data using established pharmacokinetic models. METHODS We propose a Bayesian scheme to obtain perfusion metrics from DCE MRI data. Initial performance is assessed through digital phantoms of the extended Tofts model (ETM) and the two-compartment exchange model (2CXM), comparing the Bayesian scheme to the standard Levenberg-Marquardt (LM) algorithm. Digital phantoms are also invoked to identify limitations in the pharmacokinetic models related to measurement conditions. Using computed maps of the extra vascular volume (ve) from 19 glioma patients, we analyze differences in the number of un-physiological high-intensity ve values for both ETM and 2CXM, using a one-tailed paired t-test assuming un-equal variance. RESULTS The Bayesian parameter estimation scheme demonstrated superior performance over the LM technique in the digital phantom simulations. In addition, we identified limitations in parameter reliability in relation to scan duration for the 2CXM. DCE data for glioma and cervical cancer patients was analyzed with both algorithms and demonstrated improvement in image readability for the Bayesian method. The Bayesian method demonstrated significantly fewer non-physiological high-intensity ve values for the ETM (p<0.0001) and the 2CXM (p<0.0001). CONCLUSION We have demonstrated substantial improvement of the perceptive quality of pharmacokinetic parameters from advanced compartment models using the Bayesian parameter estimation scheme as compared to the LM technique.
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Affiliation(s)
- Mikkel B. Hansen
- Center of Functionally Integrative Neuroscience and MINDLab, Institute of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Anna Tietze
- Center of Functionally Integrative Neuroscience and MINDLab, Institute of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Inst. of Neuroradiology, Charité University Medicine Berlin, Berlin, Germany
| | - Søren Haack
- Department of Clinical Engineering, Aarhus University Hospital, Aarhus, Denmark
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Jesper Kallehauge
- Department of Medical Physics, Aarhus University Hospital, Aarhus, Denmark
| | - Irene K. Mikkelsen
- Center of Functionally Integrative Neuroscience and MINDLab, Institute of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Leif Østergaard
- Center of Functionally Integrative Neuroscience and MINDLab, Institute of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Medical Physics, Aarhus University Hospital, Aarhus, Denmark
| | - Kim Mouridsen
- Center of Functionally Integrative Neuroscience and MINDLab, Institute of Clinical Medicine, Aarhus University, Aarhus, Denmark
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11
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Inglese M, Cavaliere C, Monti S, Forte E, Incoronato M, Nicolai E, Salvatore M, Aiello M. A multi-parametric PET/MRI study of breast cancer: Evaluation of DCE-MRI pharmacokinetic models and correlation with diffusion and functional parameters. NMR IN BIOMEDICINE 2019; 32:e4026. [PMID: 30379384 DOI: 10.1002/nbm.4026] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2018] [Revised: 09/04/2018] [Accepted: 09/11/2018] [Indexed: 06/08/2023]
Abstract
46 patients with histologically confirmed breast cancer were enrolled and imaged with a 3T hybrid PET/MRI system, at staging. Diffusion, functional and perfusion parameters (measured by Tofts and shutter speed models) were compared. Results showed a good correlation between pharmacokinetic parameters and the SUV.
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Affiliation(s)
- Marianna Inglese
- IRCCS SDN, Naples, Italy
- Department of Computer, Control and Management Engineering Antonio Ruberti, University of Rome 'La Sapienza', Italy
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12
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Meenambal R, Poojar P, Geethanath S, Anitha TS, Kannan S. Lanthanide phosphate (LnPO 4 ) rods as bio-probes: A systematic investigation on structural, optical, magnetic, and biological characteristics. J Biomed Mater Res B Appl Biomater 2018; 107:1372-1383. [PMID: 30265773 DOI: 10.1002/jbm.b.34229] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2018] [Revised: 08/14/2018] [Accepted: 08/18/2018] [Indexed: 01/11/2023]
Abstract
The proposed work involves an exclusive study on the synthesis protocol, crystal structure analysis, and imaging contrast features of unique lanthanide phosphates (LnPO4 ). XRD and Raman spectra affirmed the ability of the proposed synthesis technique to achieve unique LnPO4 devoid of impurities. The crystal structure analysis confirms the P121/c1 space setting of NdPO4 , EuPO4 , GdPO4 , and TbPO4 that all uniformly crystallizes in monoclinic unit cell. In a similar manner, the tetragonal crystal setting of DyPO4 , ErPO4 , HoPO4 , and YbPO4 that unvaryingly possess the I41/amd space setting is confirmed. Under the same synthesis conditions, the monoclinic (Eu) and tetragonal (Ho) lanthanide phosphates displayed uniform rod-like morphologies. Absorption and luminescence properties of unique LnPO4 were determined. In vitro biological studies demonstrated low toxicity levels of LnPO4 and clearly distinguished fluorescence of TbPO4 and EuPO4 in Y79, retinoblastoma cell lines. The paramagnetic response of GdPO4 , NdPO4 , DyPO4 , TbPO4 , and HoPO4 facilitated excellent magnetic resonance imaging (MRI) contrast features. Meanwhile, GdPO4 , DyPO4 , HoPO4 , and YbPO4 possessing higher X-ray absorption coefficient than clinical contrast Omnipaque™ exhibited high computed tomography (CT) efficiency. © 2018 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater 107B: 1372-1383, 2019.
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Affiliation(s)
- Rugmani Meenambal
- Centre for Nanoscience and Technology, Pondicherry University, 605014, Puducherry, India
| | - Pavan Poojar
- Medical Imaging Research Centre, Dayananda Sagar Institutions, Bangalore, India
| | - Sairam Geethanath
- Medical Imaging Research Centre, Dayananda Sagar Institutions, Bangalore, India
| | - T S Anitha
- Central Inter-Disciplinary Research Facility, Mahatma Gandhi Medical College and Research Institute, 607403, Puducherry, India
| | - S Kannan
- Centre for Nanoscience and Technology, Pondicherry University, 605014, Puducherry, India
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13
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Sun M, Kang L, Cui Y, Li G. Application of a novel targeting nanoparticle contrast agent combined with magnetic resonance imaging in the diagnosis of intraductal papillary mucinous neoplasm. Exp Ther Med 2018; 16:1216-1224. [PMID: 30116372 PMCID: PMC6090224 DOI: 10.3892/etm.2018.6349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2016] [Accepted: 11/24/2017] [Indexed: 11/06/2022] Open
Abstract
Intraductal papillary mucinous neoplasm (IPMN) is a severe disease with macroscopic visible mucin secretion that primarily occurs in biliary tracts or pancreatic ducts. In comparison with standard diagnostic imaging, probing the molecular abnormalities associated with the initial stages of diseases rather than imaging the end effects markedly improves the accuracy of diagnosis. In the present study, magnetic resonance imaging (MRI) in combination with the contrast agent PEGylated magnetoliposome consisting of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) and target molecules of IPMN were investigated in the diagnosis of patients with suspected IPMN. The present investigation indicated that the novel targeting nanoparticle contrast agent targeted platelet-derived growth factor receptor-β and RET, and maintained a high affinity with tumor markers located on the IPMN surface. The novel targeting nanoparticle contrast agent combined with MRI exhibited increased sensitivity in diagnosing early-stage patients with IPMN. Furthermore, image quality was improved following the use of the novel targeting nanoparticle contrast agent combined with MRI compared with standard MRI. The targeting nanoparticle contrast agent retained sufficient affinity and was present for an adequate amount of time to observe the tumor mass in papillae using MRI. Notably, the targeting nanoparticle contrast agent was metabolized at 12 h post-injection. In conclusion, these outcomes indicate that the novel targeting nanoparticle contrast agent combined with MRI improved image quality and sensitivity compared with standard MRI, which suggests that this approach may be promising for clinical detection in patients with suspected IPMN.
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Affiliation(s)
- Min Sun
- NMR Department, Cangzhou Central Hospital, Cangzhou, Hebei 061000, P.R. China
| | - Liqing Kang
- NMR Department, Cangzhou Central Hospital, Cangzhou, Hebei 061000, P.R. China
| | - Yanchao Cui
- Emergency Department, Beijing University of Chinese Medicine, The Third Affiliated Hospital, Beijing 100029, P.R. China
| | - Guoce Li
- NMR Department, Cangzhou Central Hospital, Cangzhou, Hebei 061000, P.R. China
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Nagasaka K, Satake H, Ishigaki S, Kawai H, Naganawa S. Histogram analysis of quantitative pharmacokinetic parameters on DCE-MRI: correlations with prognostic factors and molecular subtypes in breast cancer. Breast Cancer 2018; 26:113-124. [PMID: 30069785 DOI: 10.1007/s12282-018-0899-8] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Accepted: 07/26/2018] [Indexed: 12/14/2022]
Abstract
BACKGROUND Breast cancer heterogeneity influences poor prognoses thorough therapy resistance. This study quantitatively evaluated intratumoral heterogeneity through a histogram analysis of dynamic contrast-enhanced MRI (DCE-MRI) pharmacokinetic parameters, and determined correlations with prognostic factors and molecular subtypes. METHODS We retrospectively investigated 101 invasive ductal breast cancers from 99 women who underwent preoperative DCE-MRI between July 2012 and November 2014. Pharmacokinetic parameters (Ktrans, kep, and ve) were obtained by the Tofts model. For each parameter, the mean, standard deviation, coefficient of variation, skewness, and kurtosis values of tumor were calculated, and prognostic factors and subtypes associations were assessed. RESULTS The mean of ve was lower in cancers with high Ki-67 than in cancers with low Ki-67 (P = 0.002). The coefficient of variation of ve was higher in cancers with estrogen receptor negativity than in cancers with estrogen receptor positivity (P < 0.001). The coefficient of variation of ve was also higher in cancers with high Ki-67 than in cancers with low Ki-67 (P < 0.001). The skewness of ve was higher in cancers with high nuclear grade than in cancers with low nuclear grade (P = 0.006). Triple-negative cancers showed higher ve coefficient of variation than did those with luminal A (P < 0.001) and B (P = 0.006). CONCLUSIONS Various ve parameters correlated with breast cancer prognostic factors and molecular subtypes.
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Affiliation(s)
- Ken Nagasaka
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Shouwa-ku, Nagoya, 466-8550, Japan.
| | - Hiroko Satake
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Shouwa-ku, Nagoya, 466-8550, Japan
| | - Satoko Ishigaki
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Shouwa-ku, Nagoya, 466-8550, Japan
| | - Hisashi Kawai
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Shouwa-ku, Nagoya, 466-8550, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Shouwa-ku, Nagoya, 466-8550, Japan
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15
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Gaass T, Schneider MJ, Dietrich O, Ingrisch M, Dinkel J. Technical Note: Quantitative dynamic contrast-enhanced MRI of a 3-dimensional artificial capillary network. Med Phys 2017; 44:1462-1469. [PMID: 28235128 DOI: 10.1002/mp.12162] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Revised: 01/23/2017] [Accepted: 02/08/2017] [Indexed: 12/24/2022] Open
Abstract
PURPOSE Variability across devices, patients, and time still hinders widespread recognition of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) as quantitative biomarker. The purpose of this work was to introduce and characterize a dedicated microchannel phantom as a model for quantitative DCE-MRI measurements. METHODS A perfusable, MR-compatible microchannel network was constructed on the basis of sacrificial melt-spun sugar fibers embedded in a block of epoxy resin. Structural analysis was performed on the basis of light microscopy images before DCE-MRI experiments. During dynamic acquisition the capillary network was perfused with a standard contrast agent injection system. Flow-dependency, as well as inter- and intrascanner reproducibility of the computed DCE parameters were evaluated using a 3.0 T whole-body MRI. RESULTS Semi-quantitative and quantitative flow-related parameters exhibited the expected proportionality to the set flow rate (mean Pearson correlation coefficient: 0.991, P < 2.5e-5). The volume fraction was approximately independent from changes of the applied flow rate through the phantom. Repeatability and reproducibility experiments yielded maximum intrascanner coefficients of variation (CV) of 4.6% for quantitative parameters. All evaluated parameters were well in the range of known in vivo results for the applied flow rates. CONCLUSION The constructed phantom enables reproducible, flow-dependent, contrast-enhanced MR measurements with the potential to facilitate standardization and comparability of DCE-MRI examinations.
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Affiliation(s)
- Thomas Gaass
- Josef Lissner Laboratory for Biomedical Imaging, Institute for Clinical Radiology, Ludwig-Maximilians-University Hospital Munich, Munich, Germany.,Comprehensive Pneumology Center, German Center for Lung Research, Munich, Germany
| | - Moritz Jörg Schneider
- Josef Lissner Laboratory for Biomedical Imaging, Institute for Clinical Radiology, Ludwig-Maximilians-University Hospital Munich, Munich, Germany.,Comprehensive Pneumology Center, German Center for Lung Research, Munich, Germany
| | - Olaf Dietrich
- Josef Lissner Laboratory for Biomedical Imaging, Institute for Clinical Radiology, Ludwig-Maximilians-University Hospital Munich, Munich, Germany
| | - Michael Ingrisch
- Josef Lissner Laboratory for Biomedical Imaging, Institute for Clinical Radiology, Ludwig-Maximilians-University Hospital Munich, Munich, Germany
| | - Julien Dinkel
- Comprehensive Pneumology Center, German Center for Lung Research, Munich, Germany.,Institute for Clinical Radiology, Ludwig-Maximilians-University Hospital Munich, Munich, Germany
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16
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Meenambal R, Poojar P, Geethanath S, Kannan S. Substitutional limit of gadolinium in β-tricalcium phosphate and its magnetic resonance imaging characteristics. J Biomed Mater Res B Appl Biomater 2016; 105:2545-2552. [PMID: 27690186 DOI: 10.1002/jbm.b.33775] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Accepted: 08/09/2016] [Indexed: 11/11/2022]
Abstract
To compensate the limitations of bone tissue magnetic resonance imaging (MRI), a series of gadolinium (Gd3+ ) substituted β-Tricalcium phosphate [β-TCP, β-Ca3 (PO4 )2 ] were developed. All the powders were characterized using XRD, Raman spectroscopy, Rietveld refinement of the XRD data and the studies confirmed the Gd3+ occupancy at Ca2+ (1), Ca2+ (2) and Ca2+ (3) lattice sites of β-Ca3 (PO4 )2. HR-TEM analysis revealed the spherical nature of particles with diameter about 100 nm. The Gd3+ doped β-Ca3 (PO4 )2 exhibited non-toxic behaviour to MG-63 cells in vitro and the room temperature magnetic field versus magnetization measurements confirmed its paramagnetic behaviour. MRI analysis revelas that it shorten both T1 and T2 proton relaxation times, thus influencing both r1 and r2 relaxivity values that reach 61.97 mM-1 s-1 and 73.35 mM-1 s-1 . © 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 105B: 2545-2552, 2017.
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Affiliation(s)
- Rugmani Meenambal
- Centre for Nanoscience and Technology, Pondicherry University, Puducherry, 605 014, India
| | - Pavan Poojar
- Medical Imaging Research Centre, Dayananda Sagar Institutions, Bangalore, India
| | - Sairam Geethanath
- Medical Imaging Research Centre, Dayananda Sagar Institutions, Bangalore, India
| | - S Kannan
- Centre for Nanoscience and Technology, Pondicherry University, Puducherry, 605 014, India
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17
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Ryu JK, Rhee SJ, Song JY, Cho SH, Jahng GH. Characteristics of quantitative perfusion parameters on dynamic contrast-enhanced MRI in mammographically occult breast cancer. J Appl Clin Med Phys 2016; 17:377-390. [PMID: 27685105 PMCID: PMC5874120 DOI: 10.1120/jacmp.v17i5.6091] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2015] [Revised: 04/27/2016] [Accepted: 04/25/2016] [Indexed: 12/12/2022] Open
Abstract
The purpose of this study was to compare the characteristics of quantitative per-fusion parameters obtained from dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) in patients with mammographically occult (MO) breast cancers and those with mammographically visible (MV) breast cancers. Quantitative parameters (AUC, Ktrans, kep, ve, vp, and wi) from 13 MO breast cancers and 16 MV breast cancers were mapped after the DCE-MRI data were acquired. Various prog-nostic factors, including axillary nodal status, estrogen receptor (ER), progesterone receptor (PR), Ki-67, p53, E-cadherin, and human epidermal growth factor receptor 2 (HER2) were obtained in each group. Fisher's exact test was used to compare any differences of the various prognostic factors between the two groups. The Mann- Whitney U test was applied to compare the quantitative parameters between these two groups. Finally, Spearman's correlation was used to investigate the relation-ships between perfusion indices and four factors - age, tumor size, Ki-67, and p53 - for each group. Although age, tumor size, and the prognostic factors were not statistically different between the two groups, the mean values of the quantitative parameters, except wi in the MV group, were higher than those in the MO group without statistical significance (p = 0.219). The kep value was significantly differ-ent between the two groups (p = 0.048), but the other parameters were not. In the MO group, vp with size, ve with p53, and Ktrans and vp with Ki-67 had significant correlations (p < 0.05). However, in the MV group, only kep showed significant correlation with age. The kep value was only the perfusion parameter of statistical significance between MO and MV breast cancers.
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Affiliation(s)
- Jung Kyu Ryu
- Kyung Hee University Hospital at Gandong, College of Medicine, Kyung Hee University.
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18
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Duan C, Kallehauge JF, Bretthorst GL, Tanderup K, Ackerman JJH, Garbow JR. Are complex DCE-MRI models supported by clinical data? Magn Reson Med 2016; 77:1329-1339. [PMID: 26946317 DOI: 10.1002/mrm.26189] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2015] [Revised: 02/02/2016] [Accepted: 02/08/2016] [Indexed: 12/11/2022]
Abstract
PURPOSE To ascertain whether complex dynamic contrast enhanced (DCE) MRI tracer kinetic models are supported by data acquired in the clinic and to determine the consequences of limited contrast-to-noise. METHODS Generically representative in silico and clinical (cervical cancer) DCE-MRI data were examined. Bayesian model selection evaluated support for four compartmental DCE-MRI models: the Tofts model (TM), Extended Tofts model, Compartmental Tissue Uptake model (CTUM), and Two-Compartment Exchange model. RESULTS Complex DCE-MRI models were more sensitive to noise than simpler models with respect to both model selection and parameter estimation. Indeed, as contrast-to-noise decreased, complex DCE models became less probable and simpler models more probable. The less complex TM and CTUM were the optimal models for the DCE-MRI data acquired in the clinic. [In cervical tumors, Ktrans, Fp, and PS increased after radiotherapy (P = 0.004, 0.002, and 0.014, respectively)]. CONCLUSION Caution is advised when considering application of complex DCE-MRI kinetic models to data acquired in the clinic. It follows that data-driven model selection is an important prerequisite to DCE-MRI analysis. Model selection is particularly important when high-order, multiparametric models are under consideration. (Parameters obtained from kinetic modeling of cervical cancer clinical DCE-MRI data showed significant changes at an early stage of radiotherapy.) Magn Reson Med 77:1329-1339, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Chong Duan
- Department of Chemistry, Washington University, Saint Louis, Missouri, USA
| | - Jesper F Kallehauge
- Department of Medical Physics, Aarhus University, Aarhus, Denmark.,Department of Oncology, Aarhus University, Aarhus, Denmark
| | - G Larry Bretthorst
- Department of Radiology, Washington University, Saint Louis, Missouri, USA
| | - Kari Tanderup
- Department of Oncology, Aarhus University, Aarhus, Denmark.,Department of Radiation Oncology, Washington University, Saint Louis, Missouri, USA.,Institute of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Joseph J H Ackerman
- Department of Chemistry, Washington University, Saint Louis, Missouri, USA.,Department of Radiology, Washington University, Saint Louis, Missouri, USA.,Department of Medicine, Washington University, Saint Louis, Missouri, USA.,Alvin J Siteman Cancer Center, Washington University, Saint Louis, Missouri, USA
| | - Joel R Garbow
- Department of Radiology, Washington University, Saint Louis, Missouri, USA.,Alvin J Siteman Cancer Center, Washington University, Saint Louis, Missouri, USA
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Venkatesha N, Poojar P, Ashwini R, Qurishi Y, Geethanath S, Srivastava C. Ultrafine graphene oxide–CoFe2O4 nanoparticle composite as T1 and T2 contrast agent for magnetic resonance imaging. RSC Adv 2016. [DOI: 10.1039/c5ra27186j] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Graphene oxide–CoFe2O4 nanoparticle composites were synthesized using a two step synthesis method in which graphene oxide was initially synthesized followed by precipitation of CoFe2O4 nanoparticles in a reaction mixture containing graphene oxide.
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Affiliation(s)
- N. Venkatesha
- Department of Materials Engineering
- Indian Institute of Science
- Bangalore-560012
- India
| | - Pavan Poojar
- Medical Imaging Research Centre
- Dayananda Sagar Institutions
- Bangalore-560078
- India
| | - R. Ashwini
- Department of Materials Engineering
- Indian Institute of Science
- Bangalore-560012
- India
| | - Yasrib Qurishi
- Department of Molecular Reproduction
- Development and Genetics
- Indian Institute of Science
- Bangalore-560012
- India
| | - Sairam Geethanath
- Medical Imaging Research Centre
- Dayananda Sagar Institutions
- Bangalore-560078
- India
| | - Chandan Srivastava
- Department of Materials Engineering
- Indian Institute of Science
- Bangalore-560012
- India
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Multiparametric magnetic resonance imaging for predicting pathological response after the first cycle of neoadjuvant chemotherapy in breast cancer. Invest Radiol 2015; 50:195-204. [PMID: 25360603 DOI: 10.1097/rli.0000000000000100] [Citation(s) in RCA: 110] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
OBJECTIVES The purpose of this study was to determine whether multiparametric magnetic resonance imaging (MRI) using dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DWI), obtained before and after the first cycle of neoadjuvant chemotherapy (NAC), is superior to single-parameter measurements for predicting pathologic complete response (pCR) in patients with breast cancer. MATERIALS AND METHODS Patients with stage II/III breast cancer were enrolled in an institutional review board-approved study in which 3-T DCE-MRI and DWI data were acquired before (n = 42) and after 1 cycle (n = 36) of NAC. Estimates of the volume transfer rate (K), extravascular extracellular volume fraction (ve), blood plasma volume fraction (vp), and the efflux rate constant (kep = K/ve) were generated from the DCE-MRI data using the Extended Tofts-Kety model. The apparent diffusion coefficient (ADC) was estimated from the DWI data. The derived parameter kep/ADC was compared with single-parameter measurements for its ability to predict pCR after the first cycle of NAC. RESULTS The kep/ADC after the first cycle of NAC discriminated patients who went on to achieve a pCR (P < 0.001) and achieved a sensitivity, specificity, positive predictive value, and area under the receiver operator curve (AUC) of 0.92, 0.78, 0.69, and 0.88, respectively. These values were superior to the single parameters kep (AUC, 0.76) and ADC (AUC, 0.82). The AUCs between kep/ADC and kep were significantly different on the basis of the bootstrapped 95% confidence intervals (0.018-0.23), whereas the AUCs between kep/ADC and ADC trended toward significance (-0.11 to 0.24). CONCLUSIONS The multiparametric analysis of DCE-MRI and DWI was superior to the single-parameter measurements for predicting pCR after the first cycle of NAC.
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Khalifa F, Soliman A, El-Baz A, Abou El-Ghar M, El-Diasty T, Gimel'farb G, Ouseph R, Dwyer AC. Models and methods for analyzing DCE-MRI: a review. Med Phys 2015; 41:124301. [PMID: 25471985 DOI: 10.1118/1.4898202] [Citation(s) in RCA: 199] [Impact Index Per Article: 22.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
PURPOSE To present a review of most commonly used techniques to analyze dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), discusses their strengths and weaknesses, and outlines recent clinical applications of findings from these approaches. METHODS DCE-MRI allows for noninvasive quantitative analysis of contrast agent (CA) transient in soft tissues. Thus, it is an important and well-established tool to reveal microvasculature and perfusion in various clinical applications. In the last three decades, a host of nonparametric and parametric models and methods have been developed in order to quantify the CA's perfusion into tissue and estimate perfusion-related parameters (indexes) from signal- or concentration-time curves. These indexes are widely used in various clinical applications for the detection, characterization, and therapy monitoring of different diseases. RESULTS Promising theoretical findings and experimental results for the reviewed models and techniques in a variety of clinical applications suggest that DCE-MRI is a clinically relevant imaging modality, which can be used for early diagnosis of different diseases, such as breast and prostate cancer, renal rejection, and liver tumors. CONCLUSIONS Both nonparametric and parametric approaches for DCE-MRI analysis possess the ability to quantify tissue perfusion.
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Affiliation(s)
- Fahmi Khalifa
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, Kentucky 40292 and Electronics and Communication Engineering Department, Mansoura University, Mansoura 35516, Egypt
| | - Ahmed Soliman
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, Kentucky 40292
| | - Ayman El-Baz
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, Kentucky 40292
| | - Mohamed Abou El-Ghar
- Radiology Department, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt
| | - Tarek El-Diasty
- Radiology Department, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt
| | - Georgy Gimel'farb
- Department of Computer Science, University of Auckland, Auckland 1142, New Zealand
| | - Rosemary Ouseph
- Kidney Transplantation-Kidney Disease Center, University of Louisville, Louisville, Kentucky 40202
| | - Amy C Dwyer
- Kidney Transplantation-Kidney Disease Center, University of Louisville, Louisville, Kentucky 40202
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Texture analysis on MR images helps predicting non-response to NAC in breast cancer. BMC Cancer 2015; 15:574. [PMID: 26243303 PMCID: PMC4526309 DOI: 10.1186/s12885-015-1563-8] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2014] [Accepted: 07/16/2015] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND To assess the performance of a predictive model of non-response to neoadjuvant chemotherapy (NAC) in patients with breast cancer based on texture, kinetic, and BI-RADS parameters measured from dynamic MRI. METHODS Sixty-nine patients with invasive ductal carcinoma of the breast who underwent pre-treatment MRI were studied. Morphological parameters and biological markers were measured. Pathological complete response was defined as the absence of invasive and in situ cancer in breast and nodes. Pathological non-responders, partial and complete responders were identified. Dynamic imaging was performed at 1.5 T with a 3D axial T1W GRE fat-suppressed sequence. Visual texture, kinetic and BI-RADS parameters were measured in each lesion. ROC analysis and leave-one-out cross-validation were used to assess the performance of individual parameters, then the performance of multi-parametric models in predicting non-response to NAC. RESULTS A model based on four pre-NAC parameters (inverse difference moment, GLN, LRHGE, wash-in) and k-means clustering as statistical classifier identified non-responders with 84 % sensitivity. BI-RADS mass/non-mass enhancement, biological markers and histological grade did not contribute significantly to the prediction. CONCLUSION Pre-NAC texture and kinetic parameters help predicting non-benefit to NAC. Further testing including larger groups of patients with different tumor subtypes is needed to improve the generalization properties and validate the performance of the predictive model.
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Atuegwu NC, Li X, Arlinghaus LR, Abramson RG, Williams JM, Chakravarthy AB, Abramson VG, Yankeelov TE. Longitudinal, intermodality registration of quantitative breast PET and MRI data acquired before and during neoadjuvant chemotherapy: preliminary results. Med Phys 2014; 41:052302. [PMID: 24784395 DOI: 10.1118/1.4870966] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE The authors propose a method whereby serially acquired DCE-MRI, DW-MRI, and FDG-PET breast data sets can be spatially and temporally coregistered to enable the comparison of changes in parameter maps at the voxel level. METHODS First, the authors aligned the PET and MR images at each time point rigidly and nonrigidly. To register the MR images longitudinally, the authors extended a nonrigid registration algorithm by including a tumor volume-preserving constraint in the cost function. After the PET images were aligned to the MR images at each time point, the authors then used the transformation obtained from the longitudinal registration of the MRI volumes to register the PET images longitudinally. The authors tested this approach on ten breast cancer patients by calculating a modified Dice similarity of tumor size between the PET and MR images as well as the bending energy and changes in the tumor volume after the application of the registration algorithm. RESULTS The median of the modified Dice in the registered PET and DCE-MRI data was 0.92. For the longitudinal registration, the median tumor volume change was -0.03% for the constrained algorithm, compared to -32.16% for the unconstrained registration algorithms (p = 8 × 10(-6)). The medians of the bending energy were 0.0092 and 0.0001 for the unconstrained and constrained algorithms, respectively (p = 2.84 × 10(-7)). CONCLUSIONS The results indicate that the proposed method can accurately spatially align DCE-MRI, DW-MRI, and FDG-PET breast images acquired at different time points during therapy while preventing the tumor from being substantially distorted or compressed.
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Affiliation(s)
- Nkiruka C Atuegwu
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee 37232-2310 and Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee 37232-2675
| | - Xia Li
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee 37232-2310
| | - Lori R Arlinghaus
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee 37232-2310
| | - Richard G Abramson
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee 37232-2310; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee 37232-2675; and Vanderbilt Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee 37232-6838
| | - Jason M Williams
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee 37232-2310 and Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee 37232-2675
| | - A Bapsi Chakravarthy
- Department of Radiation Oncology, Vanderbilt University Medical Center, Nashville, Tennessee 37232-5671 and Vanderbilt Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee 37232-6838
| | - Vandana G Abramson
- Department of Medical Oncology, Vanderbilt University Medical Center, Nashville, Tennessee 37232-6307 and Vanderbilt Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee 37232-6838
| | - Thomas E Yankeelov
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee 37232-2310; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee 37232-2675; Department of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee 37240-1807; Department of Cancer Biology, Vanderbilt University Medical Center, Nashville, Tennessee 37232-6838; Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee 37235-1631; and Vanderbilt Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee 37232-6838
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Wang CH, Yin FF, Horton J, Chang Z. Review of treatment assessment using DCE-MRI in breast cancer radiation therapy. World J Methodol 2014; 4:46-58. [PMID: 25332905 PMCID: PMC4202481 DOI: 10.5662/wjm.v4.i2.46] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2013] [Revised: 12/31/2013] [Accepted: 02/18/2014] [Indexed: 02/06/2023] Open
Abstract
As a noninvasive functional imaging technique, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is being used in oncology to measure properties of tumor microvascular structure and permeability. Studies have shown that parameters derived from certain pharmacokinetic models can be used as imaging biomarkers for tumor treatment response. The use of DCE-MRI for quantitative and objective assessment of radiation therapy has been explored in a variety of methods and tumor types. However, due to the complexity in imaging technology and divergent outcomes from different pharmacokinetic approaches, the method of using DCE-MRI in treatment assessment has yet to be standardized, especially for breast cancer. This article reviews the basic principles of breast DCE-MRI and recent studies using DCE-MRI in treatment assessment. Technical and clinical considerations are emphasized with specific attention to assessment of radiation treatment response.
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Qin Q, Huang AJ, Hua J, Desmond JE, Stevens RD, van Zijl PC. Three-dimensional whole-brain perfusion quantification using pseudo-continuous arterial spin labeling MRI at multiple post-labeling delays: accounting for both arterial transit time and impulse response function. NMR IN BIOMEDICINE 2014; 27:116-28. [PMID: 24307572 PMCID: PMC3947417 DOI: 10.1002/nbm.3040] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2013] [Revised: 08/26/2013] [Accepted: 08/27/2013] [Indexed: 05/12/2023]
Abstract
Measurement of the cerebral blood flow (CBF) with whole-brain coverage is challenging in terms of both acquisition and quantitative analysis. In order to fit arterial spin labeling-based perfusion kinetic curves, an empirical three-parameter model which characterizes the effective impulse response function (IRF) is introduced, which allows the determination of CBF, the arterial transit time (ATT) and T(1,eff). The accuracy and precision of the proposed model were compared with those of more complicated models with four or five parameters through Monte Carlo simulations. Pseudo-continuous arterial spin labeling images were acquired on a clinical 3-T scanner in 10 normal volunteers using a three-dimensional multi-shot gradient and spin echo scheme at multiple post-labeling delays to sample the kinetic curves. Voxel-wise fitting was performed using the three-parameter model and other models that contain two, four or five unknown parameters. For the two-parameter model, T(1,eff) values close to tissue and blood were assumed separately. Standard statistical analysis was conducted to compare these fitting models in various brain regions. The fitted results indicated that: (i) the estimated CBF values using the two-parameter model show appreciable dependence on the assumed T(1,eff) values; (ii) the proposed three-parameter model achieves the optimal balance between the goodness of fit and model complexity when compared among the models with explicit IRF fitting; (iii) both the two-parameter model using fixed blood T1 values for T(1,eff) and the three-parameter model provide reasonable fitting results. Using the proposed three-parameter model, the estimated CBF (46 ± 14 mL/100 g/min) and ATT (1.4 ± 0.3 s) values averaged from different brain regions are close to the literature reports; the estimated T(1,eff) values (1.9 ± 0.4 s) are higher than the tissue T1 values, possibly reflecting a contribution from the microvascular arterial blood compartment.
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Affiliation(s)
- Qin Qin
- The Russell H. Morgan Department of Radiology and Radiological
Science, Division of MR Research, The Johns Hopkins University School of Medicine,
Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy
Krieger Institute, Baltimore, MD, USA
| | - Alan J. Huang
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy
Krieger Institute, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University,
Baltimore, MD, USA
| | - Jun Hua
- The Russell H. Morgan Department of Radiology and Radiological
Science, Division of MR Research, The Johns Hopkins University School of Medicine,
Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy
Krieger Institute, Baltimore, MD, USA
| | - John E. Desmond
- Department of Neurology and Neurosurgery, The Johns Hopkins
University, Baltimore, MD, USA
| | - Robert D. Stevens
- The Russell H. Morgan Department of Radiology and Radiological
Science, Division of MR Research, The Johns Hopkins University School of Medicine,
Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy
Krieger Institute, Baltimore, MD, USA
- Department of Neurology and Neurosurgery, The Johns Hopkins
University, Baltimore, MD, USA
- Department of Anesthesiology and Critical Care Medicine, The Johns
Hopkins University, Baltimore, MD, USA
| | - Peter C.M. van Zijl
- The Russell H. Morgan Department of Radiology and Radiological
Science, Division of MR Research, The Johns Hopkins University School of Medicine,
Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy
Krieger Institute, Baltimore, MD, USA
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Cuenod C, Balvay D. Perfusion and vascular permeability: Basic concepts and measurement in DCE-CT and DCE-MRI. Diagn Interv Imaging 2013; 94:1187-204. [DOI: 10.1016/j.diii.2013.10.010] [Citation(s) in RCA: 138] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Anderson SW, Barry B, Soto J, Ozonoff A, O'Brien M, Jara H. Characterizing non-gaussian, high b-value diffusion in liver fibrosis: Stretched exponential and diffusional kurtosis modeling. J Magn Reson Imaging 2013; 39:827-34. [PMID: 24259401 DOI: 10.1002/jmri.24234] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2012] [Accepted: 04/30/2013] [Indexed: 02/06/2023] Open
Abstract
PURPOSE To employ the stretched exponential and diffusional kurtosis models to study the non-Gaussian behavior of diffusion-related signal decay of the liver in an animal model of hepatic fibrosis. MATERIALS AND METHODS High b-value diffusion imaging data (up to 3500 s/mm(2) ) of ex vivo murine liver specimens was acquired using a 9.4 T MRI scanner. A simple monoexponential model as well as the stretched exponential and diffusional kurtosis models were employed to analyze the diffusion data, the results of which were correlated with liver histopathology. RESULTS Strong correlations between histopathological assessments of hepatic fibrosis and parameters derived from the stretched exponential and diffusional kurtosis models were found. Using Akaike's Information Criterion (AIC) analyses, the kurtosis model was found to result in an improved fit of the high b-value diffusion data when compared to both the monoexponential and stretched exponential models. CONCLUSION The use of diffusional kurtosis or stretched exponential models, applied to the characterization of the non-Gaussian behavior of the molecular diffusion of liver exhibited over an extended b-factor range, affords the potential for an increased capability of magnetic resonance imaging (MRI) in the characterization of chronic liver disease.
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Affiliation(s)
- Stephan W Anderson
- Boston University Medical Center, Department of Radiology, Boston, Massachusetts, USA
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Li X, Arlinghaus LR, Ayers GD, Chakravarthy AB, Abramson RG, Abramson VG, Atuegwu N, Farley J, Mayer IA, Kelley MC, Meszoely IM, Means-Powell J, Grau AM, Sanders M, Bhave SR, Yankeelov TE. DCE-MRI analysis methods for predicting the response of breast cancer to neoadjuvant chemotherapy: pilot study findings. Magn Reson Med 2013; 71:1592-602. [PMID: 23661583 DOI: 10.1002/mrm.24782] [Citation(s) in RCA: 89] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2012] [Revised: 03/13/2013] [Accepted: 04/02/2013] [Indexed: 01/14/2023]
Abstract
PURPOSE The purpose of this pilot study is to determine (1) if early changes in both semiquantitative and quantitative DCE-MRI parameters, observed after the first cycle of neoadjuvant chemotherapy in breast cancer patients, show significant difference between responders and nonresponders and (2) if these parameters can be used as a prognostic indicator of the eventual response. METHODS Twenty-eight patients were examined using DCE-MRI pre-, post-one cycle, and just prior to surgery. The semiquantitative parameters included longest dimension, tumor volume, initial area under the curve, and signal enhancement ratio related parameters, while quantitative parameters included K(trans), v(e), k(ep), v(p), and τ(i) estimated using the standard Tofts-Kety, extended Tofts-Kety, and fast exchange regime models. RESULTS Our preliminary results indicated that the signal enhancement ratio washout volume and k(ep) were significantly different between pathologic complete responders from nonresponders (P < 0.05) after a single cycle of chemotherapy. Receiver operator characteristic analysis showed that the AUC of the signal enhancement ratio washout volume was 0.75, and the AUCs of k(ep) estimated by three models were 0.78, 0.76, and 0.73, respectively. CONCLUSION In summary, the signal enhancement ratio washout volume and k(ep) appear to predict breast cancer response after one cycle of neoadjuvant chemotherapy. This observation should be confirmed with additional prospective studies.
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Affiliation(s)
- Xia Li
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University, Nashville, Tennessee, USA
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Anderson SW, Barry B, Soto JA, Ozonoff A, O'Brien M, Jara H. Quantifying hepatic fibrosis using a biexponential model of diffusion weighted imaging in ex vivo liver specimens. Magn Reson Imaging 2012; 30:1475-82. [PMID: 22921938 DOI: 10.1016/j.mri.2012.05.010] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2012] [Revised: 04/04/2012] [Accepted: 05/14/2012] [Indexed: 12/15/2022]
Abstract
The purpose of this study was to evaluate the non-Gaussian behavior of diffusion related signal decay of the ex vivo murine liver tissues from a dietary model of hepatic fibrosis. To this end, a biexponential formalism was used to model high b-value diffusion imaging (up to 3500 s/mm(2)), the findings of which were correlated with liver histopathology and compared to a simple monoexponential model. The presence of a major, fast diffusing component and a minor, slow diffusing component was demonstrated. With increasing hepatic fibrosis, the fractional contribution of the fast diffusing component decreased, as did the diffusion coefficient of the fast diffusing component. Strong correlation between the degrees of liver fibrosis and a two-predictor regression model incorporating parameters of the biexponential model was found. Using Akaike's Information Criterion analyses, the biexponential model resulted in an improved fit of the high b-value diffusion data when compared to the monoexponential model.
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Affiliation(s)
- Stephan W Anderson
- Department of Radiology, Boston University Medical Center, Boston, MA 02218, USA.
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Barnes SL, Whisenant JG, Loveless ME, Ayers GD, Yankeelov TE. Assessing the reproducibility of dynamic contrast enhanced magnetic resonance imaging in a murine model of breast cancer. Magn Reson Med 2012; 69:1721-34. [PMID: 22847762 DOI: 10.1002/mrm.24422] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2012] [Revised: 06/18/2012] [Accepted: 06/26/2012] [Indexed: 12/29/2022]
Abstract
Quantitative dynamic contrast enhanced magnetic resonance imaging estimates parameters related to tissue vascularity and volume fractions; additionally, semiquantitative parameters such as the initial area under the curve can be utilized to describe tissue behavior. The aim of this study was to establish the reproducibility of quantitative and semiquantitative analysis of dynamic contrast enhanced magnetic resonance imaging in a murine model of breast cancer. For each animal, a T1-weighted, gradient-echo sequence was used to acquire two sets of dynamic contrast enhanced magnetic resonance imaging data separated by 5 h. Data were acquired at both a 0.05 mm3 (128(2) , n=12) and a 0.2 mm3 (64(2), n=12) resolution, and analysis was performed using both the Tofts-Kety (to estimate Ktrans and ve) and extended Tofts-Kety (Ktrans, ve, and vp) models. Reproducibility analysis was performed for both the center slice and the total tumor volume for all parameters. For the total volume analysis, the repeatability index for Ktrans is 0.073 min(-1) in the standard model analysis and 0.075 min(-1) in the extended model analysis at the 128(2) acquisition. For the 64(2) acquisition, the values are 0.089 and 0.063 min(-1) for the standard and extended models, respectively. The repeatability index for initial area under the curve was 0.0039 and 0.0042 mM min for the 128(2) and 64(2) acquisitions, respectively.
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Affiliation(s)
- Stephanie L Barnes
- Institute of Imaging Science, Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee 37232-2675, USA
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