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Liu Y, Kong Y, Yan Y, Hui P. Explore the value of carotid ultrasound radiomics nomogram in predicting ischemic stroke risk in patients with type 2 diabetes mellitus. Front Endocrinol (Lausanne) 2024; 15:1357580. [PMID: 38706699 PMCID: PMC11066235 DOI: 10.3389/fendo.2024.1357580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 04/08/2024] [Indexed: 05/07/2024] Open
Abstract
Background and objective Type 2 Diabetes Mellitus (T2DM) with insulin resistance (IR) is prone to damage the vascular endothelial, leading to the formation of vulnerable carotid plaques and increasing ischemic stroke (IS) risk. The purpose of this study is to develop a nomogram model based on carotid ultrasound radiomics for predicting IS risk in T2DM patients. Methods 198 T2DM patients were enrolled and separated into study and control groups based on IS history. After manually delineating carotid plaque region of interest (ROI) from images, radiomics features were identified and selected using the least absolute shrinkage and selection operator (LASSO) regression to calculate the radiomics score (RS). A combinatorial logistic machine learning model and nomograms were created using RS and clinical features like the triglyceride-glucose index. The three models were assessed using area under curve (AUC) and decision curve analysis (DCA). Results Patients were divided into the training set and the testing set by the ratio of 0.7. 4 radiomics features were selected. RS and clinical variables were all statically significant in the training set and were used to create a combination model and a prediction nomogram. The combination model (radiomics + clinical nomogram) had the largest AUC in both the training set and the testing set (0.898 and 0.857), and DCA analysis showed that it had a higher overall net benefit compared to the other models. Conclusions This study created a carotid ultrasound radiomics machine-learning-based IS risk nomogram for T2DM patients with carotid plaques. Its diagnostic performance and clinical prediction capabilities enable accurate, convenient, and customized medical care.
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Affiliation(s)
| | | | | | - Pinjing Hui
- Department of Stroke Center, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
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Hou C, Li S, Zheng S, Liu LP, Nie F, Zhang W, He W. Quality assessment of radiomics models in carotid plaque: a systematic review. Quant Imaging Med Surg 2024; 14:1141-1154. [PMID: 38223070 PMCID: PMC10784017 DOI: 10.21037/qims-23-712] [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: 05/22/2023] [Accepted: 10/17/2023] [Indexed: 01/16/2024]
Abstract
Background Although imaging techniques provide information about the morphology and stability of carotid plaque, they are operator dependent and may miss certain subtleties. A variety of radiomics models for carotid plaque have recently been proposed for identifying vulnerable plaques and predicting cardiovascular and cerebrovascular diseases. The purpose of this review was to assess the risk of bias, reporting, and methodological quality of radiomics models for carotid atherosclerosis plaques. Methods A systematic search was carried out to identify available literature published in PubMed, Web of Science, and the Cochrane Library up to March 2023. Studies that developed and/or validated machine learning models based on radiomics data to identify and/or predict unfavorable cerebral and cardiovascular events in carotid plaque were included. The basic information of each piece of included literature was identified, and the reporting quality, risk of bias, and radiomics methodology quality were assessed according the TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) checklist, the Prediction Model Risk of Bias Assessment Tool (PROBAST), and the radiomics quality score (RQS), respectively. Results A total of 2,738 patients from 19 studies were included. The mean overall TRIPOD adherence rate was 66.1% (standard deviation 12.8%), with a range of 45-87%. All studies had a high overall risk of bias, with the analysis domain being the most common source of bias. The mean RQS was 9.89 (standard deviation 5.70), accounting for 27.4% of the possible maximum value of 36. The mean area under the curve for diagnostic or predictive properties of these included radiomics models was 0.876±0.09, with a range of 0.741-0.989. Conclusions Radiomics models may have value in the assessment of carotid plaque, the overall scientific validity and reporting quality of current carotid plaque radiomics reports are still lacking, and many barriers must be overcome before these models can be applied in clinical practice.
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Affiliation(s)
- Chao Hou
- Department of Ultrasound, Lanzhou University Second Hospital, Lanzhou, China
- Department of Ultrasound, the Affiliated Hospital of Southwest Medical University, Luzhou, China
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Shuo Li
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Shuai Zheng
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Lu-Ping Liu
- Department of Ultrasound, Lanzhou University Second Hospital, Lanzhou, China
| | - Fang Nie
- Department of Ultrasound, Lanzhou University Second Hospital, Lanzhou, China
| | - Wei Zhang
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wen He
- Department of Ultrasound, Lanzhou University Second Hospital, Lanzhou, China
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
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Karlas A, Fasoula NA, Kallmayer M, Schäffer C, Angelis G, Katsouli N, Reidl M, Duelmer F, Al Adem K, Hadjileontiadis L, Eckstein HH, Ntziachristos V. Optoacoustic biomarkers of lipids, hemorrhage and inflammation in carotid atherosclerosis. Front Cardiovasc Med 2023; 10:1210032. [PMID: 38028502 PMCID: PMC10666780 DOI: 10.3389/fcvm.2023.1210032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 10/18/2023] [Indexed: 12/01/2023] Open
Abstract
Imaging plays a critical role in exploring the pathophysiology and enabling the diagnostics and therapy assessment in carotid artery disease. Ultrasonography, computed tomography, magnetic resonance imaging and nuclear medicine techniques have been used to extract of known characteristics of plaque vulnerability, such as inflammation, intraplaque hemorrhage and high lipid content. Despite the plethora of available techniques, there is still a need for new modalities to better characterize the plaque and provide novel biomarkers that might help to detect the vulnerable plaque early enough and before a stroke occurs. Optoacoustics, by providing a multiscale characterization of the morphology and pathophysiology of the plaque could offer such an option. By visualizing endogenous (e.g., hemoglobin, lipids) and exogenous (e.g., injected dyes) chromophores, optoacoustic technologies have shown great capability in imaging lipids, hemoglobin and inflammation in different applications and settings. Herein, we provide an overview of the main optoacoustic systems and scales of detail that enable imaging of carotid plaques in vitro, in small animals and humans. Finally, we discuss the limitations of this novel set of techniques while investigating their potential to enable a deeper understanding of carotid plaque pathophysiology and possibly improve the diagnostics in future patients with carotid artery disease.
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Affiliation(s)
- Angelos Karlas
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany
- Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany
- Department for Vascular and Endovascular Surgery, Klinikum Rechts der Isar, Technical University of Munich (TUM), Munich, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany
| | - Nikolina-Alexia Fasoula
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany
- Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany
| | - Michael Kallmayer
- Department for Vascular and Endovascular Surgery, Klinikum Rechts der Isar, Technical University of Munich (TUM), Munich, Germany
| | - Christoph Schäffer
- Department for Vascular and Endovascular Surgery, Klinikum Rechts der Isar, Technical University of Munich (TUM), Munich, Germany
| | - Georgios Angelis
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany
- Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany
| | - Nikoletta Katsouli
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany
- Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany
| | - Mario Reidl
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany
- Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany
| | - Felix Duelmer
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany
- Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany
- Chair for Computer Aided Medical Procedures and Augmented Reality, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Kenana Al Adem
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany
- Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany
| | - Leontios Hadjileontiadis
- Department of Biomedical Engineering, Healthcare Engineering Innovation Center (HEIC), Khalifa University, Abu Dhabi, United Arab Emirates
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Hans-Henning Eckstein
- Department for Vascular and Endovascular Surgery, Klinikum Rechts der Isar, Technical University of Munich (TUM), Munich, Germany
| | - Vasilis Ntziachristos
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany
- Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany
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Kadoglou NPE, Stasinopoulou M, Gkougkoudi E, Christodoulou E, Kostomitsopoulos N, Valsami G. The Complementary Effects of Dabigatran Etexilate and Exercise Training on the Development and Stability of the Atherosclerotic Lesions in Diabetic ApoE Knockout Mice. Pharmaceuticals (Basel) 2023; 16:1396. [PMID: 37895867 PMCID: PMC10609840 DOI: 10.3390/ph16101396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 09/27/2023] [Accepted: 09/29/2023] [Indexed: 10/29/2023] Open
Abstract
Aim: To determine the complementary effects of dabigatran etexilate (DE), exercise training (ET), and combination (DE + ET) on the development and stability of the atherosclerotic lesions in diabetic apoE knockout (apoE-/-) mice. Methods: In 48 male apoE-/- diabetic mice, streptozotocin (STZ) was induced for 5 consecutive days. Mice received a high-fat diet (HFD) for 8 weeks and then were randomized into four groups (1. Control/CG, 2. DEG: HFD with DE, 3. ETG: ET on treadmill, 4. DE + ETG: combination DE and ET treatment). At the end of the eighth week, all mice were euthanatized and morphometry of the aortic lesions at the level of aortic valve was obtained. Collagen, elastin, MCP-1, TNF-a, matrix metalloproteinases (MMP-2,-3,-9), and TIMP-1 concentrations within plaques at the aortic valve were determined. Results: All active groups had significantly smaller aorta stenosis (DEG:7.9 ± 2.2%, ETG:17.3 ± 5.3%, DE + ETG:7.1 ± 2.7%) compared to CG (23.3 ± 5.5% p < 0.05), reduced the relative intra-plaque content of MCP-1, macrophages, MMP-3, and MMP-9, and considerably increased collagen, elastin, and TIMP-1 (p < 0.05). Group 4 showed the most pronounced results (p < 0.05). Both DEG and DE + ETG significantly reduced MMP-2 and TNF-a concentrations compared to ETG and CG (p < 0.010). Conclusion: DE and ET treatment of diabetic apoE-/- mice resulted in complementary amelioration of atherosclerotic lesions development and stability, mediated by the anti-inflammatory modulation of both DE and ET.
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Affiliation(s)
| | - Marianna Stasinopoulou
- Center of Experimental Surgery, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
| | | | - Eirini Christodoulou
- Laboratory of Biopharmaceutics & Pharmacokinetics, Department of Pharmacy, School of Health Sciences, National & Kapodistrian University of Athens, 15771 Athens, Greece (G.V.)
| | - Nikolaos Kostomitsopoulos
- Center of Experimental Surgery, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
| | - Georgia Valsami
- Laboratory of Biopharmaceutics & Pharmacokinetics, Department of Pharmacy, School of Health Sciences, National & Kapodistrian University of Athens, 15771 Athens, Greece (G.V.)
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