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Mallabone M, Labib D, Abdelhaleem A, Dykstra S, Thompson RB, Paterson DI, Thompson SK, Hasanzadeh F, Mikami Y, Rivest S, Flewitt J, Feng Y, Macdonald M, King M, Bristow M, Kolman L, Howarth AG, Lydell CP, Miller RJ, Fine NM, White JA. Sex-based Differences in the Phenotypic Expression and Prognosis of Idiopathic Non-ischemic Cardiomyopathy: A Cardiovascular Magnetic Resonance Study. Eur Heart J Cardiovasc Imaging 2024:jeae014. [PMID: 38236156 DOI: 10.1093/ehjci/jeae014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 12/16/2023] [Accepted: 01/03/2024] [Indexed: 01/19/2024] Open
Abstract
AIMS We sought to characterize sex-related differences in CMR-based cardiovascular phenotypes and prognosis in patients with idiopathic non-ischemic cardiomyopathy (NICM). METHODS AND RESULTS Patients with NICM enrolled in the Cardiovascular Imaging Registry of Calgary (CIROC) between 2015 and 2021 were identified. Z-score values for chamber volumes and function were calculated as standard deviation from mean values of 157 sex-matched healthy volunteers, ensuring reported differences were independent of known sex-dependencies. Patients were followed for the composite outcome of all-cause mortality, heart failure admission, or ventricular arrhythmia.A total of 747 patients were studied, 531 (71%) males. By Z-score values, females showed significantly higher left ventricular (LV) ejection fraction (EF; median difference 1 SD) and right ventricular (RV) EF (difference 0.6 SD) with greater LV mass (difference 2.1 SD; p-value<0.01 for all) versus males despite similar chamber volumes. Females had a significantly lower prevalence of mid-wall striae (MWS) fibrosis (23% versus 36%; p-value<0.001). Over a median follow-up of 4.7 years, 173 patients (23%) developed the composite outcome, with equal distribution in males and females. LV EF and MWS were significant independent predictors of the outcome (respective HR [95% CI] 0.97 [0.95-0.99] and 1.6 [1.2-2.3]; p-value=0.003 and 0.005). There was no association of sex with the outcome. CONCLUSIONS In a large contemporary cohort, NICM was uniquely expressed in females versus males. Despite similar chamber dilation, females demonstrated greater concentric remodelling, lower reductions in bi-ventricular function, and a lower burden of replacement fibrosis. Overall, their prognosis remained similar to male patients with NICM.
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Affiliation(s)
- Maggie Mallabone
- Stephenson Cardiac Imaging Centre, University of Calgary, Calgary, Alberta, Canada
| | - Dina Labib
- Stephenson Cardiac Imaging Centre, University of Calgary, Calgary, Alberta, Canada
- Libin Cardiovascular Institute of Alberta, Calgary, AB, Canada
- Department of Cardiovascular Medicine, Cairo University, Cairo, Egypt
| | | | - Steven Dykstra
- Stephenson Cardiac Imaging Centre, University of Calgary, Calgary, Alberta, Canada
- Libin Cardiovascular Institute of Alberta, Calgary, AB, Canada
| | - Richard B Thompson
- Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Canada
| | - D Ian Paterson
- Ottawa Heart Institute, Faculty of Medicine, University of Ottawa, Ottawa, Canada
| | - Sam K Thompson
- Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Canada
| | - Fereshteh Hasanzadeh
- Stephenson Cardiac Imaging Centre, University of Calgary, Calgary, Alberta, Canada
- Libin Cardiovascular Institute of Alberta, Calgary, AB, Canada
| | - Yoko Mikami
- Stephenson Cardiac Imaging Centre, University of Calgary, Calgary, Alberta, Canada
- Libin Cardiovascular Institute of Alberta, Calgary, AB, Canada
| | - Sandra Rivest
- Stephenson Cardiac Imaging Centre, University of Calgary, Calgary, Alberta, Canada
| | - Jacqueline Flewitt
- Stephenson Cardiac Imaging Centre, University of Calgary, Calgary, Alberta, Canada
- Libin Cardiovascular Institute of Alberta, Calgary, AB, Canada
| | - Yuanchao Feng
- Libin Cardiovascular Institute of Alberta, Calgary, AB, Canada
| | | | - Melanie King
- Libin Cardiovascular Institute of Alberta, Calgary, AB, Canada
| | - Michael Bristow
- Stephenson Cardiac Imaging Centre, University of Calgary, Calgary, Alberta, Canada
- Libin Cardiovascular Institute of Alberta, Calgary, AB, Canada
- Department of Diagnostic Imaging, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Louis Kolman
- Stephenson Cardiac Imaging Centre, University of Calgary, Calgary, Alberta, Canada
- Libin Cardiovascular Institute of Alberta, Calgary, AB, Canada
- Department of Cardiac Science, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Andrew G Howarth
- Stephenson Cardiac Imaging Centre, University of Calgary, Calgary, Alberta, Canada
- Libin Cardiovascular Institute of Alberta, Calgary, AB, Canada
- Department of Cardiac Science, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Carmen P Lydell
- Stephenson Cardiac Imaging Centre, University of Calgary, Calgary, Alberta, Canada
- Libin Cardiovascular Institute of Alberta, Calgary, AB, Canada
- Department of Diagnostic Imaging, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Robert Jh Miller
- Libin Cardiovascular Institute of Alberta, Calgary, AB, Canada
- Department of Cardiac Science, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Nowell M Fine
- Libin Cardiovascular Institute of Alberta, Calgary, AB, Canada
- Department of Cardiac Science, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - James A White
- Stephenson Cardiac Imaging Centre, University of Calgary, Calgary, Alberta, Canada
- Libin Cardiovascular Institute of Alberta, Calgary, AB, Canada
- Department of Diagnostic Imaging, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Cardiac Science, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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Cella E, Mambriani S, Donniaquio A, Hasanzadeh F, P. Nozza, Bennicelli E, Arnaldi D. 293P Molecular biomarkers correlation in symptomatic tumor-related epilepsy: A preliminary study on 151 cases of brain tumors. Ann Oncol 2022. [DOI: 10.1016/j.annonc.2022.07.427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022] Open
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Joloudari JH, Saadatfar H, GhasemiGol M, Alizadehsani R, Sani ZA, Hasanzadeh F, Hassannataj E, Sharifrazi D, Mansor Z. FCM-DNN: diagnosing coronary artery disease by deep accuracy fuzzy C-means clustering model. Math Biosci Eng 2022; 19:3609-3635. [PMID: 35341267 DOI: 10.3934/mbe.2022167] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Cardiovascular disease is one of the most challenging diseases in middle-aged and older people, which causes high mortality. Coronary artery disease (CAD) is known as a common cardiovascular disease. A standard clinical tool for diagnosing CAD is angiography. The main challenges are dangerous side effects and high angiography costs. Today, the development of artificial intelligence-based methods is a valuable achievement for diagnosing disease. Hence, in this paper, artificial intelligence methods such as neural network (NN), deep neural network (DNN), and fuzzy C-means clustering combined with deep neural network (FCM-DNN) are developed for diagnosing CAD on a cardiac magnetic resonance imaging (CMRI) dataset. The original dataset is used in two different approaches. First, the labeled dataset is applied to the NN and DNN to create the NN and DNN models. Second, the labels are removed, and the unlabeled dataset is clustered via the FCM method, and then, the clustered dataset is fed to the DNN to create the FCM-DNN model. By utilizing the second clustering and modeling, the training process is improved, and consequently, the accuracy is increased. As a result, the proposed FCM-DNN model achieves the best performance with a 99.91% accuracy specifying 10 clusters, i.e., 5 clusters for healthy subjects and 5 clusters for sick subjects, through the 10-fold cross-validation technique compared to the NN and DNN models reaching the accuracies of 92.18% and 99.63%, respectively. To the best of our knowledge, no study has been conducted for CAD diagnosis on the CMRI dataset using artificial intelligence methods. The results confirm that the proposed FCM-DNN model can be helpful for scientific and research centers.
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Affiliation(s)
| | - Hamid Saadatfar
- Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran
| | - Mohammad GhasemiGol
- Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC 3216, Australia
| | - Zahra Alizadeh Sani
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
- Omid hospital, Iran University of Medical Sciences, Tehran, Iran
| | | | - Edris Hassannataj
- Department of Nursing, School of Nursing and Allied Medical Sciences, Maragheh Faculty of Medical Sciences, Maragheh, Iran
| | - Danial Sharifrazi
- Department of Computer Engineering, School of Technical and Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran
| | - Zulkefli Mansor
- Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, UKM Bangi 43600, Malaysia
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Sharifrazi D, Alizadehsani R, Joloudari JH, Band SS, Hussain S, Sani ZA, Hasanzadeh F, Shoeibi A, Dehzangi A, Sookhak M, Alinejad-Rokny H. CNN-KCL: Automatic myocarditis diagnosis using convolutional neural network combined with k-means clustering. Math Biosci Eng 2022; 19:2381-2402. [PMID: 35240789 DOI: 10.3934/mbe.2022110] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Myocarditis is the form of an inflammation of the middle layer of the heart wall which is caused by a viral infection and can affect the heart muscle and its electrical system. It has remained one of the most challenging diagnoses in cardiology. Myocardial is the prime cause of unexpected death in approximately 20% of adults less than 40 years of age. Cardiac MRI (CMR) has been considered a noninvasive and golden standard diagnostic tool for suspected myocarditis and plays an indispensable role in diagnosing various cardiac diseases. However, the performance of CMR depends heavily on the clinical presentation and features such as chest pain, arrhythmia, and heart failure. Besides, other imaging factors like artifacts, technical errors, pulse sequence, acquisition parameters, contrast agent dose, and more importantly qualitatively visual interpretation can affect the result of the diagnosis. This paper introduces a new deep learning-based model called Convolutional Neural Network-Clustering (CNN-KCL) to diagnose Myocarditis. In this study, we used 47 subjects with a total number of 98,898 images to diagnose myocarditis disease. Our results demonstrate that the proposed method achieves an accuracy of 97.41% based on 10 fold-cross validation technique with 4 clusters for diagnosis of Myocarditis. To the best of our knowledge, this research is the first to use deep learning algorithms for the diagnosis of myocarditis.
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Affiliation(s)
- Danial Sharifrazi
- Department of Computer Engineering, School of Technical and Engineering, Shiraz Branch, Islamic Azad University, Shiraz, IR
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC 3216, AU
| | | | - Shahab S Band
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology 123 University Road, Section 3, Douliou, Yunlin 64002, TW
| | - Sadiq Hussain
- System Administrator, Dibrugarh University, Assam 786004, IN
| | - Zahra Alizadeh Sani
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
- Omid hospital, Iran University of Medical Sciences, Tehran, IR
| | | | - Afshin Shoeibi
- FPGA Laboratory, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, IR
| | - Abdollah Dehzangi
- Department of Computer Science, Rutgers University, Camden, NJ 08102, USA
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA
| | - Mehdi Sookhak
- Department of Computer Science, Texas A & M University at Corpus Christi, Corpus Christi, TX 78412, USA
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, AU
- Health Data Analytics Program, AI-enabled Processes (AIP) Research Centre, Macquarie University, Sydney 2109, AU
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Hosseinian SA, Hasanzadeh F. Impact of high dietary energy on obesity and oxidative stress in domestic pigeons. Vet Med Sci 2021; 7:1391-1399. [PMID: 33811747 PMCID: PMC8294395 DOI: 10.1002/vms3.478] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 03/09/2021] [Indexed: 12/25/2022] Open
Abstract
Obesity is associated with increased risk of oxidative stress in humans and laboratory animals but information regarding obesity-induced oxidative stress in birds is lacking. Therefore, this study aimed to investigate the influence of high-energy diets (HED) on obesity and oxidative stress in domestic pigeons. Forty-five adult clinically healthy-domestic male pigeons were randomly assigned to three equal dietary groups including low (2,850 kcal/kg), medium (3,150 kcal/kg) and high (3,450 kcal/kg) energy diets (named low energy diet, medium-energy diet and HED, respectively). All birds received formulated diets for 60 consecutive days. Several parameters such as feed intake, body weight (BW), average weight gain (AWG) and total weight gain were determined. Serum concentrations of triglyceride (TG), total cholesterol (TC), high-, low- and very-low-density lipoprotein cholesterols, alanine aminotransferase (ALT), aspartate aminotransferase (AST) and alkaline phosphatase (ALP) were evaluated at days 0, 30 and 60; and serum levels of total antioxidant capacity (T-AOC), malondialdehyde (MDA) and cortisol were also measured at day 60. On day 60, five pigeons from each group were randomly euthanized and some parameters such as weight and relative weight of liver, breast muscle, and abdominal fat were determined. Furthermore, hepatic total fat content was also evaluated. BW, AWG, total weight, and circulating TG, TC, ALT, AST, ALP, MDA and cortisol in HED were significantly higher than other groups. Serum T-AOC in HED was significantly lower than the other groups. In conclusion, this study showed that increasing dietary energy up to 3,450 kcal/kg in pigeons led to obesity and oxidative stress in them. Accordingly, it could be stated that HED and obesity induce oxidative stress in pigeon and controlling the dietary energy intake of pigeons is necessary to prevent oxidative stress in them.
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Affiliation(s)
| | - Fereshteh Hasanzadeh
- Department of Clinical ScienceSchool of Veterinary MedicineShiraz UniversityShirazIran
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Alizadehsani R, Alizadeh Sani Z, Behjati M, Roshanzamir Z, Hussain S, Abedini N, Hasanzadeh F, Khosravi A, Shoeibi A, Roshanzamir M, Moradnejad P, Nahavandi S, Khozeimeh F, Zare A, Panahiazar M, Acharya UR, Islam SMS. Risk factors prediction, clinical outcomes, and mortality in COVID-19 patients. J Med Virol 2020; 93:2307-2320. [PMID: 33247599 PMCID: PMC7753243 DOI: 10.1002/jmv.26699] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 11/06/2020] [Accepted: 11/20/2020] [Indexed: 12/24/2022]
Abstract
Preventing communicable diseases requires understanding the spread, epidemiology, clinical features, progression, and prognosis of the disease. Early identification of risk factors and clinical outcomes might help in identifying critically ill patients, providing appropriate treatment, and preventing mortality. We conducted a prospective study in patients with flu-like symptoms referred to the imaging department of a tertiary hospital in Iran between March 3, 2020, and April 8, 2020. Patients with COVID-19 were followed up after two months to check their health condition. The categorical data between groups were analyzed by Fisher's exact test and continuous data by Wilcoxon rank-sum test. Three hundred and nineteen patients (mean age 45.48 ± 18.50 years, 177 women) were enrolled. Fever, dyspnea, weakness, shivering, C-reactive protein, fatigue, dry cough, anorexia, anosmia, ageusia, dizziness, sweating, and age were the most important symptoms of COVID-19 infection. Traveling in the past 3 months, asthma, taking corticosteroids, liver disease, rheumatological disease, cough with sputum, eczema, conjunctivitis, tobacco use, and chest pain did not show any relationship with COVID-19. To the best of our knowledge, a number of factors associated with mortality due to COVID-19 have been investigated for the first time in this study. Our results might be helpful in early prediction and risk reduction of mortality in patients infected with COVID-19.
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Affiliation(s)
- Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, Australia
| | - Zahra Alizadeh Sani
- Rajaei Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran.,Department of Cardiac MRI, Omid Hospital, Tehran, Iran
| | - Mohaddeseh Behjati
- Rajaei Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Zahra Roshanzamir
- Pediatric Respiratory and Sleep Medicine Research Center, Children's Medical Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Sadiq Hussain
- System Administrator at Dibrugarh University, Dibrugarh, Assam, India
| | - Niloofar Abedini
- Tehran University of Medical Science, Imam Khomeini Hospital Complex, Tehran, Iran
| | | | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, Australia
| | - Afshin Shoeibi
- Faculty of Electrical and Computer Engineering, Biomedical Data Acquisition Lab, K. N. Toosi University of Technology, Tehran, Iran.,Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
| | | | - Pardis Moradnejad
- Rajaei Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, Australia
| | - Fahime Khozeimeh
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, Australia
| | - Assef Zare
- Faculty of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad, Iran
| | - Maryam Panahiazar
- Institute for Computational Health Sciences, University of California San Francisco, San Francisco, CA, USA
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore.,Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan.,Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore
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Hasanzadeh F, Behbahani FK. Synthesis of 8-Aryl-7H-acenaphtho[1,2-d]imidazoles Using Fe3O4 NPs@GO@C4H8SO3H as a Green and Recyclable Magnetic Nanocatalyst. Russ J Org Chem 2020. [DOI: 10.1134/s1070428020060160] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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Hasanzadeh F, Faeghi F, Valizadeh A, Bayani L. Diagnostic Value of Diffusion Weighted Magnetic Resonance Imaging in Evaluation of Metastatic Axillary Lymph Nodes in a Sample of Iranian Women with Breast Cancer. Asian Pac J Cancer Prev 2017; 18:1265-1270. [PMID: 28610412 PMCID: PMC5555533 DOI: 10.22034/apjcp.2017.18.5.1265] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
Objective: To evaluate the diagnostic value of diffusion weighted magnetic resonance imaging (DW-MRI) in assessment of metastases in axillary lymph nodes (ALNs) in a sample of Iranian women with breast cancer. Methods: A total of 50 axillary lymph nodes from 30 female patients with histologically verified breast cancer were assessed by 1.5 T MRI. DWI was implemented at b-values of 50, 400 and 800 s/mm2. Short axis diameter, presence of fatty hilum and apparent diffusion coefficient (ADC) values (min, max and mean) of metastatic and non-metastatic ALNs was compared. Cutoff ADC values to discriminate between benign and malignant axillary lymph nodes were analyzed with receiver coefficient characteristic (ROC) curves. Result: The final histopathological examination revealed 46% (n=23) metastatic and 54% (n=27) non-metastatic ALNs. There was no statistically significant difference in short axis diameter between the two groups (p = 0.537). However there was significantly correlation between loss of fatty hilum and presence of metastases (p < 0.001) and ADC values (0.255 ± 0.19×10-3 mm2/s vs 0.616 ±0.3×10-3 mm2/s (ADC min), 1.088 ± 0.22×10-3 mm2/s vs 1.497 ± 0.24×10-3 mm2/s (ADC max) and 0.824 ± 0.103 ×10-3 mm2/s vs 1.098 ± 0.23 ×10-3 mm2/s (ADC mean)) of metastatic ALNs were significantly lower than those of non-metastatic ALNs (p < 0.001). The optimal mean ADC cut-off value for differentiation between metastatic and non-metastatic ALNs was 0.904×10-3 mm2/s which had a higher specificity (88.9%) and accuracy (91.8%) as compared with ADC min and ADC max. Conclusion: DWI-MRI and ADC values are promising imaging methods which can assess metastatic ALNs in breast cancer with high sensitivity, specificity and accuracy.
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Affiliation(s)
- Fereshteh Hasanzadeh
- Radiology Technology Department, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Lakouraj MM, Hasanzadeh F, Zare EN. Nanogel and super-paramagnetic nanocomposite of thiacalix[4]arene functionalized chitosan: synthesis, characterization and heavy metal sorption. Iran Polym J 2014. [DOI: 10.1007/s13726-014-0287-y] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Hasanzadeh F, Hoseini Azizi T, Esmaily H, Ehsaee MR. Impact of familiar sensory stimulation on level of Consciousness in patients with head injury in ICU. ACTA ACUST UNITED AC 2012. [DOI: 10.29252/jnkums.4.1.121] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
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Samini M, Mohagheghi M, Hasanzadeh F, dehpour A. Anti-ulcer Effect of Bromocriptine on Indomethacin-induced Gastric Damage in Rats. ACTA ACUST UNITED AC 2000. [DOI: 10.1211/146080800128736286] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
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