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Raunig DL, Pennello GA, Delfino JG, Buckler AJ, Hall TJ, Guimaraes AR, Wang X, Huang EP, Barnhart HX, deSouza N, Obuchowski N. Multiparametric Quantitative Imaging Biomarker as a Multivariate Descriptor of Health: A Roadmap. Acad Radiol 2023; 30:159-182. [PMID: 36464548 PMCID: PMC9825667 DOI: 10.1016/j.acra.2022.10.026] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 10/24/2022] [Accepted: 10/29/2022] [Indexed: 12/02/2022]
Abstract
Multiparametric quantitative imaging biomarkers (QIBs) offer distinct advantages over single, univariate descriptors because they provide a more complete measure of complex, multidimensional biological systems. In disease, where structural and functional disturbances occur across a multitude of subsystems, multivariate QIBs are needed to measure the extent of system malfunction. This paper, the first Use Case in a series of articles on multiparameter imaging biomarkers, considers multiple QIBs as a multidimensional vector to represent all relevant disease constructs more completely. The approach proposed offers several advantages over QIBs as multiple endpoints and avoids combining them into a single composite that obscures the medical meaning of the individual measurements. We focus on establishing statistically rigorous methods to create a single, simultaneous measure from multiple QIBs that preserves the sensitivity of each univariate QIB while incorporating the correlation among QIBs. Details are provided for metrological methods to quantify the technical performance. Methods to reduce the set of QIBs, test the superiority of the mp-QIB model to any univariate QIB model, and design study strategies for generating precision and validity claims are also provided. QIBs of Alzheimer's Disease from the ADNI merge data set are used as a case study to illustrate the methods described.
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Affiliation(s)
- David L Raunig
- Department of Statistical and Quantitative Sciences, Data Science Institute, Takeda Pharmaceuticals, Cambridge, Massachusetts.
| | - Gene A Pennello
- Center for Devices and Radiological Health, US Food and Drug Administration Division of Imaging, Diagnostic and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland
| | - Jana G Delfino
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland
| | | | - Timothy J Hall
- Department of Medical Physics, University of Wisconsin, Madison, Wisconsin
| | - Alexander R Guimaraes
- Department of Diagnostic Radiology, Oregon Health & Sciences University, Portland, Oregon
| | - Xiaofeng Wang
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland, Ohio
| | - Erich P Huang
- Biometric Research Program, Division of Cancer Treatment and Diagnosis - National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Huiman X Barnhart
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina
| | - Nandita deSouza
- Division of Radiotherapy and Imaging, the Insitute of Cancer Research and Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Nancy Obuchowski
- Department of Quantitative Health Sciences, Lerner Research Institute Cleveland Clinic Foundation, Cleveland, Ohio
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Calcagno C, David JA, Motaal AG, Coolen BF, Beldman T, Corbin A, Kak A, Ramachandran S, Pruzan A, Sridhar A, Soler R, Faries CM, Fayad ZA, Mulder WJM, Strijkers GJ. Self-gated, dynamic contrast-enhanced magnetic resonance imaging with compressed-sensing reconstruction for evaluating endothelial permeability in the aortic root of atherosclerotic mice. NMR IN BIOMEDICINE 2023; 36:e4823. [PMID: 36031706 PMCID: PMC10078106 DOI: 10.1002/nbm.4823] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 08/01/2022] [Accepted: 08/21/2022] [Indexed: 05/16/2023]
Abstract
High-risk atherosclerotic plaques are characterized by active inflammation and abundant leaky microvessels. We present a self-gated, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquisition with compressed sensing reconstruction and apply it to assess longitudinal changes in endothelial permeability in the aortic root of Apoe-/- atherosclerotic mice during natural disease progression. Twenty-four, 8-week-old, female Apoe-/- mice were divided into four groups (n = 6 each) and imaged with self-gated DCE-MRI at 4, 8, 12, and 16 weeks after high-fat diet initiation, and then euthanized for CD68 immunohistochemistry for macrophages. Eight additional mice were kept on a high-fat diet and imaged longitudinally at the same time points. Aortic-root pseudo-concentration curves were analyzed using a validated piecewise linear model. Contrast agent wash-in and washout slopes (b1 and b2 ) were measured as surrogates of aortic root endothelial permeability and compared with macrophage density by immunohistochemistry. b2 , indicating contrast agent washout, was significantly higher in mice kept on an high-fat diet for longer periods of time (p = 0.03). Group comparison revealed significant differences between mice on a high-fat diet for 4 versus 16 weeks (p = 0.03). Macrophage density also significantly increased with diet duration (p = 0.009). Spearman correlation between b2 from DCE-MRI and macrophage density indicated a weak relationship between the two parameters (r = 0.28, p = 0.20). Validated piecewise linear modeling of the DCE-MRI data showed that the aortic root contrast agent washout rate is significantly different during disease progression. Further development of this technique from a single-slice to a 3D acquisition may enable better investigation of the relationship between in vivo imaging of endothelial permeability and atherosclerotic plaques' genetic, molecular, and cellular makeup in this important model of disease.
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Affiliation(s)
- Claudia Calcagno
- Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - John A David
- Amsterdam University Medical Centers, Department of Medical Biochemistry, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam, The Netherlands
| | - Abdallah G Motaal
- Siemens Healthineers, Cardiovascular Care Group, Advanced Therapies Business, Erlangen, Germany
| | - Bram F Coolen
- Amsterdam University Medical Centers, Department of Biomedical Engineering and Physics, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam, The Netherlands
| | - Thijs Beldman
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
- Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Alexandra Corbin
- Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Arnav Kak
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Sarayu Ramachandran
- Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Alison Pruzan
- Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Arthi Sridhar
- Department of Hematology/Oncology, UTHealth McGovern Medical School, Houston, TX, USA
| | - Raphael Soler
- CNRS, CRMBM, Marseille, France
- Department of Vascular and Endovascular Surgery, Hôpital Universitaire de la Timone, APHM, Marseille, France
| | - Christopher M Faries
- Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Zahi A Fayad
- Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Willem J M Mulder
- Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
- Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Gustav J Strijkers
- Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, USA
- Amsterdam University Medical Centers, Department of Biomedical Engineering and Physics, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam, The Netherlands
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Wan T, Wu C, Meng M, Liu T, Li C, Ma J, Qin Z. Radiomic Features on Multiparametric MRI for Preoperative Evaluation of Pituitary Macroadenomas Consistency: Preliminary Findings. J Magn Reson Imaging 2021; 55:1491-1503. [PMID: 34549842 DOI: 10.1002/jmri.27930] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 09/05/2021] [Accepted: 09/10/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Preoperative assessment of the consistency of pituitary macroadenomas (PMA) might be needed for surgical planning. PURPOSE To investigate the diagnostic performance of radiomics models based on multiparametric magnetic resonance imaging (mpMRI) for preoperatively evaluating the tumor consistency of PMA. STUDY TYPE Retrospective. POPULATION One hundred and fifty-six PMA patients (soft consistency, N = 104 vs. hard consistency, N = 52), divided into training (N = 108) and test (N = 48) cohorts. The tumor consistency was determined on surgical findings. FIELD STRENGTH/SEQUENCE T1-weighted imaging (T1WI), contrast-enhanced T1WI (T1CE), and T2-weighted imaging (T2WI) using spin-echo sequences with a 3.0-T scanner. ASSESSMENT An automated three-dimensional (3D) segmentation was performed to generate the volume of interest (VOI) on T2WI, then T1WI/T1CE were coregistered to T2WI. A total of 388 radiomic features were extracted on each VOI of mpMRI. The top-discriminative features were identified using the minimum-redundancy maximum-relevance method and 0.632+ bootstrapping. The radiomics models based on each sequence and their combinations were established via the random forest (RF) and support vector machine (SVM), and independently evaluated for their ability in distinguishing PMA consistency. STATISTICAL TESTS Mann-Whitney U-test and Chi-square test were used for comparison analysis. The area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), specificity (SPE), and relative standard deviation (RSD) were calculated to evaluate each model's performance. ACC with P-value<0.05 was considered statistically significant. RESULTS Eleven mpMRI-based features exhibited statistically significant differences between soft and hard PMA in the training cohort. The radiomics model built on combined T1WI/T1CE/T2WI demonstrated the best performance among all the radiomics models with an AUC of 0.90 (95% confidence interval [CI]: 0.87-0.92), ACC of 0.87 (CI: 0.84-0.89), SEN of 0.83 (CI: 0.81-0.85), and SPE of 0.87 (CI: 0.85-0.99) in the test cohort. DATA CONCLUSION Radiomic features based on mpMRI have good performance in the presurgical evaluation of PMA consistency. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Tao Wan
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Chunxue Wu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Ming Meng
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Tao Liu
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Chuzhong Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jun Ma
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zengchang Qin
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
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Gong X, Liang Z, Wang Y, Zhang C, Xie S, Fan Y. Comparative study on hemodynamic environments around patient-specific carotid atherosclerotic plaques with different symmetrical features. MEDICINE IN NOVEL TECHNOLOGY AND DEVICES 2021. [DOI: 10.1016/j.medntd.2021.100079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
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Varga-Szemes A, Schoepf UJ, Maurovich-Horvat P, Wang R, Xu L, Dargis DM, Emrich T, Buckler AJ. Coronary plaque assessment of Vasodilative capacity by CT angiography effectively estimates fractional flow reserve. Int J Cardiol 2021; 331:307-315. [PMID: 33529657 DOI: 10.1016/j.ijcard.2021.01.040] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 01/15/2021] [Accepted: 01/22/2021] [Indexed: 12/23/2022]
Abstract
BACKGROUND To evaluate the feasibility of non-invasive fractional flow reserve (FFR) estimation using histologically-validated assessment of plaque morphology on coronary CTA (CCTA) as inputs to a predictive model further validated against invasive FFR. METHODS Patients (n = 113, 59 ± 8.9 years, 77% male) with suspected coronary artery disease (CAD) who had undergone CCTA and invasive FFR between August 2013 and May 2018 were included. Commercially available software was used to extract quantitative plaque morphology inclusive of both vessel structure and composition. The extracted plaque morphology was then fed as inputs to an optimized artificial neural network to predict lesion-specific ischemia/hemodynamically significant CAD with performance validated by invasive FFR. RESULTS A total of 122 lesions were considered, 59 (48%) had low FFR values. Plaque morphology-based FFR assessment achieved an area under the curve, sensitivity and specificity of 0.94, 0.90 and 0.81, respectively, versus 0.71, 0.71, and 0.50, respectively, for an optimized threshold applied to degree of stenosis. The optimized ridge regression model for continuous value estimation of FFR achieved a cross-correlation coefficient of 0.56 and regression slope of 0.59 using cross validation, versus 0.18 and 0.10 for an optimized threshold applied to degree of stenosis. CONCLUSIONS Our results show that non-invasive plaque morphology-based FFR assessment may be used to predict lesion-specific ischemia resulting in hemodynamically significant CAD. This substantially outperforms degree of stenosis interpretation and has a comparable level of sensitivity and specificity relative to publicly reported results from computational fluid dynamics-based approaches.
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Affiliation(s)
- Akos Varga-Szemes
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - U Joseph Schoepf
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA.
| | - Pal Maurovich-Horvat
- MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | - Rui Wang
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Lei Xu
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Danielle M Dargis
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - Tilman Emrich
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA; Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University, Mainz, Germany; German Centre for Cardiovascular Research, Partner site Rhine-Main, Mainz, Germany
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Murgia A, Balestrieri A, Francone M, Lucatelli P, Scapin E, Buckler A, Micheletti G, Faa G, Conti M, Suri JS, Guglielmi G, Carriero A, Saba L. Plaque imaging volume analysis: technique and application. Cardiovasc Diagn Ther 2020; 10:1032-1047. [PMID: 32968659 PMCID: PMC7487381 DOI: 10.21037/cdt.2020.03.01] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Accepted: 02/15/2020] [Indexed: 12/12/2022]
Abstract
The prevention and management of atherosclerosis poses a tough challenge to public health organizations worldwide. Together with myocardial infarction, stroke represents its main manifestation, with up to 25% of all ischemic strokes being caused by thromboembolism arising from the carotid arteries. Therefore, a vast number of publications have focused on the characterization of the culprit lesion, the atherosclerotic plaque. A paradigm shift appears to be taking place at the current state of research, as the attention is gradually moving from the classically defined degree of stenosis to the identification of features of plaque vulnerability, which appear to be more reliable predictors of recurrent cerebrovascular events. The present review will offer a perspective on the present state of research in the field of carotid atherosclerotic disease, focusing on the imaging modalities currently used in the study of the carotid plaque and the impact that such diagnostic means are having in the clinical setting.
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Affiliation(s)
- Alessandro Murgia
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari – Polo di Monserrato, s.s. 554 Monserrato (Cagliari) 09045, Italy
| | - Antonella Balestrieri
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari – Polo di Monserrato, s.s. 554 Monserrato (Cagliari) 09045, Italy
| | - Marco Francone
- Department of Radiological, Oncological and Anatomopathological Sciences-Radiology, ‘Sapienza’ University of Rome, Rome, Italy
| | - Pierleone Lucatelli
- Department of Radiological, Oncological and Anatomopathological Sciences-Radiology, ‘Sapienza’ University of Rome, Rome, Italy
| | - Elisa Scapin
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari – Polo di Monserrato, s.s. 554 Monserrato (Cagliari) 09045, Italy
| | | | - Giulio Micheletti
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari – Polo di Monserrato, s.s. 554 Monserrato (Cagliari) 09045, Italy
| | - Gavino Faa
- Department of Pathology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari – Polo San Giovanni di Dio, Cagliari (Cagliari) 09045, Italy
| | - Maurizio Conti
- Diagnostic and Monitoring Division, AtheroPoint™ LLC, Roseville, CA, USA
- Department of Electrical Engineering, U of Idaho (Affl.), Idaho, USA
| | - Jasjit S. Suri
- Diagnostic and Monitoring Division, AtheroPoint™ LLC, Roseville, CA, USA
- Department of Electrical Engineering, U of Idaho (Affl.), Idaho, USA
| | | | - Alessandro Carriero
- Department of Radiology, Maggiore della Carità Hospital, Università del Piemonte Orientale, Novara, Italy
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari – Polo di Monserrato, s.s. 554 Monserrato (Cagliari) 09045, Italy
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Wan T, Shang X, Yang W, Chen J, Li D, Qin Z. Automated coronary artery tree segmentation in X-ray angiography using improved Hessian based enhancement and statistical region merging. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 157:179-190. [PMID: 29477426 DOI: 10.1016/j.cmpb.2018.01.002] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2017] [Revised: 12/02/2017] [Accepted: 01/08/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE Coronary artery segmentation is a fundamental step for a computer-aided diagnosis system to be developed to assist cardiothoracic radiologists in detecting coronary artery diseases. Manual delineation of the vasculature becomes tedious or even impossible with a large number of images acquired in the daily life clinic. A new computerized image-based segmentation method is presented for automatically extracting coronary arteries from angiography images. METHODS A combination of a multiscale-based adaptive Hessian-based enhancement method and a statistical region merging technique provides a simple and effective way to improve the complex vessel structures as well as thin vessel delineation which often missed by other segmentation methods. The methodology was validated on 100 patients who underwent diagnostic coronary angiography. The segmentation performance was assessed via both qualitative and quantitative evaluations. RESULTS Quantitative evaluation shows that our method is able to identify coronary artery trees with an accuracy of 93% and outperforms other segmentation methods in terms of two widely used segmentation metrics of mean absolute difference and dice similarity coefficient. CONCLUSIONS The comparison to the manual segmentations from three human observers suggests that the presented automated segmentation method is potential to be used in an image-based computerized analysis system for early detection of coronary artery disease.
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Affiliation(s)
- Tao Wan
- Medical Image Analysis Lab, School of Biomedical Science and Medical Engineering, Beihang University, Beijing 100191, China.
| | - Xiaoqing Shang
- Medical Image Analysis Lab, School of Biomedical Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Weilin Yang
- School of Biomedical Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Jianhui Chen
- No. 91 Central Hospital of PLA, Henan 454003, China
| | - Deyu Li
- School of Biomedical Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Zengchang Qin
- Intelligent Computing and Machine Learning Lab, School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.
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Sheahan M, Ma X, Paik D, Obuchowski NA, St. Pierre S, Newman WP, Rae G, Perlman ES, Rosol M, Keith JC, Buckler AJ. Atherosclerotic Plaque Tissue: Noninvasive Quantitative Assessment of Characteristics with Software-aided Measurements from Conventional CT Angiography. Radiology 2018; 286:622-631. [PMID: 28858564 PMCID: PMC5790306 DOI: 10.1148/radiol.2017170127] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Purpose To (a) evaluate whether plaque tissue characteristics determined with conventional computed tomographic (CT) angiography could be quantitated at higher levels of accuracy by using image processing algorithms that take characteristics of the image formation process coupled with biologic insights on tissue distributions into account by comparing in vivo results and ex vivo histologic findings and (b) assess reader variability. Materials and Methods Thirty-one consecutive patients aged 43-85 years (average age, 64 years) known to have or suspected of having atherosclerosis who underwent CT angiography and were referred for endarterectomy were enrolled. Surgical specimens were evaluated with histopathologic examination to serve as standard of reference. Two readers used lumen boundary to determine scanner blur and then optimized component densities and subvoxel boundaries to best fit the observed image by using semiautomatic software. The accuracy of the resulting in vivo quantitation of calcification, lipid-rich necrotic core (LRNC), and matrix was assessed with statistical estimates of bias and linearity relative to ex vivo histologic findings. Reader variability was assessed with statistical estimates of repeatability and reproducibility. Results A total of 239 cross sections obtained with CT angiography and histologic examination were matched. Performance on held-out data showed low levels of bias and high Pearson correlation coefficients for calcification (-0.096 mm2 and 0.973, respectively), LRNC (1.26 mm2 and 0.856), and matrix (-2.44 mm2 and 0.885). Intrareader variability was low (repeatability coefficient ranged from 1.50 mm2 to 1.83 mm2 among tissue characteristics), as was interreader variability (reproducibility coefficient ranged from 2.09 mm2 to 4.43 mm2). Conclusion There was high correlation and low bias between the in vivo software image analysis and ex vivo histopathologic quantitative measures of atherosclerotic plaque tissue characteristics, as well as low reader variability. Software algorithms can mitigate the blurring and partial volume effects of routine CT angiography acquisitions to produce accurate quantification to enhance current clinical practice. Clinical trial registration no. NCT02143102 © RSNA, 2017 Online supplemental material is available for this article. An earlier incorrect version of this article appeared online. This article was corrected on September 15, 2017.
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Affiliation(s)
- Malachi Sheahan
- From the Louisiana State University Health Sciences Center, New Orleans, La (M.S., W.P.N., G.R.); Elucid Bioimaging, 225 Main St, Wenham, MA 01984 (X.M., D.P., S.S.P., M.R., J.C.K., A.J.B.); Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, Ohio (N.A.O.); and Perlman Advisory Group, Boynton Beach, Fla (E.S.P.)
| | - Xiaonan Ma
- From the Louisiana State University Health Sciences Center, New Orleans, La (M.S., W.P.N., G.R.); Elucid Bioimaging, 225 Main St, Wenham, MA 01984 (X.M., D.P., S.S.P., M.R., J.C.K., A.J.B.); Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, Ohio (N.A.O.); and Perlman Advisory Group, Boynton Beach, Fla (E.S.P.)
| | - David Paik
- From the Louisiana State University Health Sciences Center, New Orleans, La (M.S., W.P.N., G.R.); Elucid Bioimaging, 225 Main St, Wenham, MA 01984 (X.M., D.P., S.S.P., M.R., J.C.K., A.J.B.); Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, Ohio (N.A.O.); and Perlman Advisory Group, Boynton Beach, Fla (E.S.P.)
| | - Nancy A. Obuchowski
- From the Louisiana State University Health Sciences Center, New Orleans, La (M.S., W.P.N., G.R.); Elucid Bioimaging, 225 Main St, Wenham, MA 01984 (X.M., D.P., S.S.P., M.R., J.C.K., A.J.B.); Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, Ohio (N.A.O.); and Perlman Advisory Group, Boynton Beach, Fla (E.S.P.)
| | - Samantha St. Pierre
- From the Louisiana State University Health Sciences Center, New Orleans, La (M.S., W.P.N., G.R.); Elucid Bioimaging, 225 Main St, Wenham, MA 01984 (X.M., D.P., S.S.P., M.R., J.C.K., A.J.B.); Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, Ohio (N.A.O.); and Perlman Advisory Group, Boynton Beach, Fla (E.S.P.)
| | - William P. Newman
- From the Louisiana State University Health Sciences Center, New Orleans, La (M.S., W.P.N., G.R.); Elucid Bioimaging, 225 Main St, Wenham, MA 01984 (X.M., D.P., S.S.P., M.R., J.C.K., A.J.B.); Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, Ohio (N.A.O.); and Perlman Advisory Group, Boynton Beach, Fla (E.S.P.)
| | - Guenevere Rae
- From the Louisiana State University Health Sciences Center, New Orleans, La (M.S., W.P.N., G.R.); Elucid Bioimaging, 225 Main St, Wenham, MA 01984 (X.M., D.P., S.S.P., M.R., J.C.K., A.J.B.); Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, Ohio (N.A.O.); and Perlman Advisory Group, Boynton Beach, Fla (E.S.P.)
| | - Eric S. Perlman
- From the Louisiana State University Health Sciences Center, New Orleans, La (M.S., W.P.N., G.R.); Elucid Bioimaging, 225 Main St, Wenham, MA 01984 (X.M., D.P., S.S.P., M.R., J.C.K., A.J.B.); Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, Ohio (N.A.O.); and Perlman Advisory Group, Boynton Beach, Fla (E.S.P.)
| | - Michael Rosol
- From the Louisiana State University Health Sciences Center, New Orleans, La (M.S., W.P.N., G.R.); Elucid Bioimaging, 225 Main St, Wenham, MA 01984 (X.M., D.P., S.S.P., M.R., J.C.K., A.J.B.); Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, Ohio (N.A.O.); and Perlman Advisory Group, Boynton Beach, Fla (E.S.P.)
| | - James C. Keith
- From the Louisiana State University Health Sciences Center, New Orleans, La (M.S., W.P.N., G.R.); Elucid Bioimaging, 225 Main St, Wenham, MA 01984 (X.M., D.P., S.S.P., M.R., J.C.K., A.J.B.); Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, Ohio (N.A.O.); and Perlman Advisory Group, Boynton Beach, Fla (E.S.P.)
| | - Andrew J. Buckler
- From the Louisiana State University Health Sciences Center, New Orleans, La (M.S., W.P.N., G.R.); Elucid Bioimaging, 225 Main St, Wenham, MA 01984 (X.M., D.P., S.S.P., M.R., J.C.K., A.J.B.); Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, Ohio (N.A.O.); and Perlman Advisory Group, Boynton Beach, Fla (E.S.P.)
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Coolen BF, Calcagno C, van Ooij P, Fayad ZA, Strijkers GJ, Nederveen AJ. Vessel wall characterization using quantitative MRI: what's in a number? MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2017; 31:201-222. [PMID: 28808823 PMCID: PMC5813061 DOI: 10.1007/s10334-017-0644-x] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2017] [Revised: 07/04/2017] [Accepted: 07/18/2017] [Indexed: 12/15/2022]
Abstract
The past decade has witnessed the rapid development of new MRI technology for vessel wall imaging. Today, with advances in MRI hardware and pulse sequences, quantitative MRI of the vessel wall represents a real alternative to conventional qualitative imaging, which is hindered by significant intra- and inter-observer variability. Quantitative MRI can measure several important morphological and functional characteristics of the vessel wall. This review provides a detailed introduction to novel quantitative MRI methods for measuring vessel wall dimensions, plaque composition and permeability, endothelial shear stress and wall stiffness. Together, these methods show the versatility of non-invasive quantitative MRI for probing vascular disease at several stages. These quantitative MRI biomarkers can play an important role in the context of both treatment response monitoring and risk prediction. Given the rapid developments in scan acceleration techniques and novel image reconstruction, we foresee the possibility of integrating the acquisition of multiple quantitative vessel wall parameters within a single scan session.
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Affiliation(s)
- Bram F Coolen
- Department of Biomedical Engineering and Physics, Academic Medical Center, PO BOX 22660, 1100 DD, Amsterdam, The Netherlands. .,Department of Radiology, Academic Medical Center, Amsterdam, The Netherlands.
| | - Claudia Calcagno
- Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Pim van Ooij
- Department of Radiology, Academic Medical Center, Amsterdam, The Netherlands
| | - Zahi A Fayad
- Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Gustav J Strijkers
- Department of Biomedical Engineering and Physics, Academic Medical Center, PO BOX 22660, 1100 DD, Amsterdam, The Netherlands
| | - Aart J Nederveen
- Department of Radiology, Academic Medical Center, Amsterdam, The Netherlands
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Wan T, Cao J, Chen J, Qin Z. Automated grading of breast cancer histopathology using cascaded ensemble with combination of multi-level image features. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.05.084] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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11
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van Hoof RHM, Heeneman S, Wildberger JE, Kooi ME. Dynamic Contrast-Enhanced MRI to Study Atherosclerotic Plaque Microvasculature. Curr Atheroscler Rep 2016; 18:33. [PMID: 27115144 PMCID: PMC4846686 DOI: 10.1007/s11883-016-0583-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Rupture of a vulnerable atherosclerotic plaque of the carotid artery is an important underlying cause of clinical ischemic events, such as stroke. Abundant microvasculature has been identified as an important aspect contributing to plaque vulnerability. Plaque microvasculature can be studied non-invasively with dynamic contrast-enhanced (DCE-)MRI in animals and patients. In recent years, several DCE-MRI studies have been published evaluating the association between microvasculature and other key features of plaque vulnerability (e.g., inflammation and intraplaque hemorrhage), as well as the effects of novel therapeutic interventions. The present paper reviews this literature, focusing on DCE-MRI methods of acquisition and analysis of atherosclerotic plaques, the current state and future potential of DCE-MRI in the evaluation of plaque microvasculature in clinical and preclinical settings.
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Affiliation(s)
- Raf H. M. van Hoof
- />Department of Radiology, Maastricht University Medical Center (MUMC), P.O. Box 5800, 6202 AZ Maastricht, The Netherlands
- />CARIM School for Cardiovascular Diseases, Maastricht University, P.O. Box 616, Maastricht, 6200 MD The Netherlands
| | - Sylvia Heeneman
- />CARIM School for Cardiovascular Diseases, Maastricht University, P.O. Box 616, Maastricht, 6200 MD The Netherlands
- />Department of Pathology, Maastricht University Medical Center (MUMC), P.O. Box 5800, Maastricht, 6202 AZ The Netherlands
| | - Joachim E. Wildberger
- />Department of Radiology, Maastricht University Medical Center (MUMC), P.O. Box 5800, 6202 AZ Maastricht, The Netherlands
- />CARIM School for Cardiovascular Diseases, Maastricht University, P.O. Box 616, Maastricht, 6200 MD The Netherlands
| | - M. Eline Kooi
- />Department of Radiology, Maastricht University Medical Center (MUMC), P.O. Box 5800, 6202 AZ Maastricht, The Netherlands
- />CARIM School for Cardiovascular Diseases, Maastricht University, P.O. Box 616, Maastricht, 6200 MD The Netherlands
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12
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Decano JL, Mattson PC, Aikawa M. Macrophages in Vascular Inflammation: Origins and Functions. Curr Atheroscler Rep 2016; 18:34. [DOI: 10.1007/s11883-016-0585-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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