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Mansouri Z, Salimi Y, Amini M, Hajianfar G, Oveisi M, Shiri I, Zaidi H. Development and validation of survival prognostic models for head and neck cancer patients using machine learning and dosiomics and CT radiomics features: a multicentric study. Radiat Oncol 2024; 19:12. [PMID: 38254203 PMCID: PMC10804728 DOI: 10.1186/s13014-024-02409-6] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 01/17/2024] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND This study aimed to investigate the value of clinical, radiomic features extracted from gross tumor volumes (GTVs) delineated on CT images, dose distributions (Dosiomics), and fusion of CT and dose distributions to predict outcomes in head and neck cancer (HNC) patients. METHODS A cohort of 240 HNC patients from five different centers was obtained from The Cancer Imaging Archive. Seven strategies, including four non-fusion (Clinical, CT, Dose, DualCT-Dose), and three fusion algorithms (latent low-rank representation referred (LLRR),Wavelet, weighted least square (WLS)) were applied. The fusion algorithms were used to fuse the pre-treatment CT images and 3-dimensional dose maps. Overall, 215 radiomics and Dosiomics features were extracted from the GTVs, alongside with seven clinical features incorporated. Five feature selection (FS) methods in combination with six machine learning (ML) models were implemented. The performance of the models was quantified using the concordance index (CI) in one-center-leave-out 5-fold cross-validation for overall survival (OS) prediction considering the time-to-event. RESULTS The mean CI and Kaplan-Meier curves were used for further comparisons. The CoxBoost ML model using the Minimal Depth (MD) FS method and the glmnet model using the Variable hunting (VH) FS method showed the best performance with CI = 0.73 ± 0.15 for features extracted from LLRR fused images. In addition, both glmnet-Cindex and Coxph-Cindex classifiers achieved a CI of 0.72 ± 0.14 by employing the dose images (+ incorporated clinical features) only. CONCLUSION Our results demonstrated that clinical features, Dosiomics and fusion of dose and CT images by specific ML-FS models could predict the overall survival of HNC patients with acceptable accuracy. Besides, the performance of ML methods among the three different strategies was almost comparable.
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Affiliation(s)
- Zahra Mansouri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Mehdi Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Ghasem Hajianfar
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Mehrdad Oveisi
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland.
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
- University Research and Innovation Center, Óbuda University, Budapest, Hungary.
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Sanaat A, Amini M, Arabi H, Zaidi H. The quest for multifunctional and dedicated PET instrumentation with irregular geometries. Ann Nucl Med 2024; 38:31-70. [PMID: 37952197 PMCID: PMC10766666 DOI: 10.1007/s12149-023-01881-6] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 10/09/2023] [Indexed: 11/14/2023]
Abstract
We focus on reviewing state-of-the-art developments of dedicated PET scanners with irregular geometries and the potential of different aspects of multifunctional PET imaging. First, we discuss advances in non-conventional PET detector geometries. Then, we present innovative designs of organ-specific dedicated PET scanners for breast, brain, prostate, and cardiac imaging. We will also review challenges and possible artifacts by image reconstruction algorithms for PET scanners with irregular geometries, such as non-cylindrical and partial angular coverage geometries and how they can be addressed. Then, we attempt to address some open issues about cost/benefits analysis of dedicated PET scanners, how far are the theoretical conceptual designs from the market/clinic, and strategies to reduce fabrication cost without compromising performance.
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Affiliation(s)
- Amirhossein Sanaat
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Mehdi Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland.
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9700 RB, Groningen, The Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, 500, Odense, Denmark.
- University Research and Innovation Center, Óbuda University, Budapest, Hungary.
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Shiri I, Amini M, Yousefirizi F, Vafaei Sadr A, Hajianfar G, Salimi Y, Mansouri Z, Jenabi E, Maghsudi M, Mainta I, Becker M, Rahmim A, Zaidi H. Information fusion for fully automated segmentation of head and neck tumors from PET and CT images. Med Phys 2024; 51:319-333. [PMID: 37475591 DOI: 10.1002/mp.16615] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 05/16/2023] [Accepted: 06/19/2023] [Indexed: 07/22/2023] Open
Abstract
BACKGROUND PET/CT images combining anatomic and metabolic data provide complementary information that can improve clinical task performance. PET image segmentation algorithms exploiting the multi-modal information available are still lacking. PURPOSE Our study aimed to assess the performance of PET and CT image fusion for gross tumor volume (GTV) segmentations of head and neck cancers (HNCs) utilizing conventional, deep learning (DL), and output-level voting-based fusions. METHODS The current study is based on a total of 328 histologically confirmed HNCs from six different centers. The images were automatically cropped to a 200 × 200 head and neck region box, and CT and PET images were normalized for further processing. Eighteen conventional image-level fusions were implemented. In addition, a modified U2-Net architecture as DL fusion model baseline was used. Three different input, layer, and decision-level information fusions were used. Simultaneous truth and performance level estimation (STAPLE) and majority voting to merge different segmentation outputs (from PET and image-level and network-level fusions), that is, output-level information fusion (voting-based fusions) were employed. Different networks were trained in a 2D manner with a batch size of 64. Twenty percent of the dataset with stratification concerning the centers (20% in each center) were used for final result reporting. Different standard segmentation metrics and conventional PET metrics, such as SUV, were calculated. RESULTS In single modalities, PET had a reasonable performance with a Dice score of 0.77 ± 0.09, while CT did not perform acceptably and reached a Dice score of only 0.38 ± 0.22. Conventional fusion algorithms obtained a Dice score range of [0.76-0.81] with guided-filter-based context enhancement (GFCE) at the low-end, and anisotropic diffusion and Karhunen-Loeve transform fusion (ADF), multi-resolution singular value decomposition (MSVD), and multi-level image decomposition based on latent low-rank representation (MDLatLRR) at the high-end. All DL fusion models achieved Dice scores of 0.80. Output-level voting-based models outperformed all other models, achieving superior results with a Dice score of 0.84 for Majority_ImgFus, Majority_All, and Majority_Fast. A mean error of almost zero was achieved for all fusions using SUVpeak , SUVmean and SUVmedian . CONCLUSION PET/CT information fusion adds significant value to segmentation tasks, considerably outperforming PET-only and CT-only methods. In addition, both conventional image-level and DL fusions achieve competitive results. Meanwhile, output-level voting-based fusion using majority voting of several algorithms results in statistically significant improvements in the segmentation of HNC.
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Affiliation(s)
- Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Mehdi Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Fereshteh Yousefirizi
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, British Columbia, Canada
| | - Alireza Vafaei Sadr
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany
- Department of Public Health Sciences, College of Medicine, The Pennsylvania State University, Hershey, USA
| | - Ghasem Hajianfar
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Zahra Mansouri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Elnaz Jenabi
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Mehdi Maghsudi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Ismini Mainta
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Minerva Becker
- Service of Radiology, Geneva University Hospital, Geneva, Switzerland
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, British Columbia, Canada
- Department of Radiology and Physics, University of British Columbia, Vancouver, Canada
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
- Geneva University Neurocenter, Geneva University, Geneva, Switzerland
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
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Hajianfar G, Khorgami M, Rezaei Y, Amini M, Samiei N, Tabib A, Borji BK, Kalayinia S, Shiri I, Hosseini S, Oveisi M. Comparison of Machine Learning Algorithms Using Manual/Automated Features on 12-Lead Signal Electrocardiogram Classification: A Large Cohort Study on Students Aged Between 6 to 18 Years Old. Cardiovasc Eng Technol 2023; 14:786-800. [PMID: 37848737 DOI: 10.1007/s13239-023-00687-x] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 09/13/2023] [Indexed: 10/19/2023]
Abstract
PROPOSE An electrocardiogram (ECG) has been extensively used to detect rhythm disturbances. We sought to determine the accuracy of different machine learning in distinguishing abnormal ECGs from normal ones in children who were examined using a resting 12-Lead ECG machine, and we also compared the manual and automated measurement using the modular ECG Analysis System (MEANS) algorithm of ECG features. METHODS Altogether, 10745 ECGs were recorded for students aged 6 to 18. Manual and automatic ECG features were extracted for each participant. Features were normalized using Z-score normalization and went through the student's t-test and chi-squared test to measure their relevance. We applied the Boruta algorithm for feature selection and then implemented eight classifier algorithms. The dataset was split into training (80%) and test (20%) partitions. The performance of the classifiers was evaluated on the test data (unseen data) by 1000 bootstrap, and sensitivity (SEN), specificity (SPE), AUC, and accuracy (ACC) were reported. RESULTS In univariate analysis, the highest performance was heart rate and RR interval in the manual dataset and heart rate in an automated dataset with AUC of 0.72 and 0.71, respectively. The best classifiers in the manual dataset were random forest (RF) and quadratic-discriminant-analysis (QDA) with AUC, ACC, SEN, and SPE equal to 0.93, 0.98, 0.69, 0.99, and 0.90, 0.95, 0.75, 0.96, respectively. In the automated dataset, QDA (AUC: 0.89, ACC:0.92, SEN:0.71, SPE:0.93) and stack learning (SL) (AUC:0.89, ACC:0.96, SEN:0.61, SPE:0.99) reached best performances. CONCLUSION This study demonstrated that the manual measurement of 12-Lead ECG features had better performance than the automated measurement (MEANS algorithm), but some classifiers had promising results in discriminating between normal and abnormal cases. Further studies can help us evaluate the applicability and efficacy of machine-learning approaches for distinguishing abnormal ECGs in community-based investigations in both adults and children.
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Affiliation(s)
- Ghasem Hajianfar
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Niayesh Highway, Valiasr Ave., Tehran, 19969111541, Iran
| | - Mohammadrafie Khorgami
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Niayesh Highway, Valiasr Ave., Tehran, 19969111541, Iran.
| | - Yousef Rezaei
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Niayesh Highway, Valiasr Ave., Tehran, 19969111541, Iran
- Behyan Clinic, Pardis New Town, Tehran, Iran
| | - Mehdi Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Niloufar Samiei
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Niayesh Highway, Valiasr Ave., Tehran, 19969111541, Iran
| | - Avisa Tabib
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Niayesh Highway, Valiasr Ave., Tehran, 19969111541, Iran
| | - Bahareh Kazem Borji
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Niayesh Highway, Valiasr Ave., Tehran, 19969111541, Iran
| | - Samira Kalayinia
- Cardiogenetic Research Center, Rajaie Cardiovascular, Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
- Department of Cardiology, Inselspital, University of Bern, Bern, Switzerland
| | - Saeid Hosseini
- Heart Valve Disease Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Mehrdad Oveisi
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
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Khodabakhshi Z, Amini M, Hajianfar G, Oveisi M, Shiri I, Zaidi H. Dual-Centre Harmonised Multimodal Positron Emission Tomography/Computed Tomography Image Radiomic Features and Machine Learning Algorithms for Non-small Cell Lung Cancer Histopathological Subtype Phenotype Decoding. Clin Oncol (R Coll Radiol) 2023; 35:713-725. [PMID: 37599160 DOI: 10.1016/j.clon.2023.08.003] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 06/10/2023] [Accepted: 08/05/2023] [Indexed: 08/22/2023]
Abstract
AIMS We aimed to build radiomic models for classifying non-small cell lung cancer (NSCLC) histopathological subtypes through a dual-centre dataset and comprehensively evaluate the effect of ComBat harmonisation on the performance of single- and multimodality radiomic models. MATERIALS AND METHODS A public dataset of NSCLC patients from two independent centres was used. Two image fusion methods, namely guided filtering-based fusion and image fusion based on visual saliency map and weighted least square optimisation, were used. Radiomic features were extracted from each scan, including first-order, texture and moment-invariant features. Subsequently, ComBat harmonisation was applied to the extracted features from computed tomography (CT), positron emission tomography (PET) and fused images to correct the centre effect. For feature selection, least absolute shrinkage and selection operator (Lasso) and recursive feature elimination (RFE) were investigated. For machine learning, logistic regression (LR), support vector machine (SVM) and AdaBoost were evaluated for classifying NSCLC subtypes. Training and evaluation of the models were carried out in a robust framework to offset plausible errors and performance was reported using area under the curve, balanced accuracy, sensitivity and specificity before and after harmonisation. N-way ANOVA was used to assess the effect of different factors on the performance of the models. RESULTS Support vector machine fed with selected features by recursive feature elimination from a harmonised PET feature set achieved the highest performance (area under the curve = 0.82) in classifying NSCLC histopathological subtypes. Although the performance of the models did not significantly improve for CT images after harmonisation, the performance of PET and guided filtering-based fusion feature signatures significantly improved for almost all models. Although the selection of the image modality and feature selection methods was effective on the performance of the model (ANOVA P-values <0.001), machine learning and harmonisation did not change the performance significantly (ANOVA P-values = 0.839 and 0.292, respectively). CONCLUSION This study confirmed the potential of radiomic analysis on PET, CT and hybrid images for histopathological classification of NSCLC subtypes.
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Affiliation(s)
- Z Khodabakhshi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - M Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - G Hajianfar
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - M Oveisi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran; Comprehensive Cancer Centre, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences & Medicine, Kings College London, London, UK; Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - I Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - H Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland; Geneva University Neurocenter, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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Shiri I, Razeghi B, Vafaei Sadr A, Amini M, Salimi Y, Ferdowsi S, Boor P, Gündüz D, Voloshynovskiy S, Zaidi H. Multi-institutional PET/CT image segmentation using federated deep transformer learning. Comput Methods Programs Biomed 2023; 240:107706. [PMID: 37506602 DOI: 10.1016/j.cmpb.2023.107706] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 07/02/2023] [Indexed: 07/30/2023]
Abstract
BACKGROUND AND OBJECTIVE Generalizable and trustworthy deep learning models for PET/CT image segmentation necessitates large diverse multi-institutional datasets. However, legal, ethical, and patient privacy issues challenge sharing of datasets between different centers. To overcome these challenges, we developed a federated learning (FL) framework for multi-institutional PET/CT image segmentation. METHODS A dataset consisting of 328 FL (HN) cancer patients who underwent clinical PET/CT examinations gathered from six different centers was enrolled. A pure transformer network was implemented as fully core segmentation algorithms using dual channel PET/CT images. We evaluated different frameworks (single center-based, centralized baseline, as well as seven different FL algorithms) using 68 PET/CT images (20% of each center data). In particular, the implemented FL algorithms include clipping with the quantile estimator (ClQu), zeroing with the quantile estimator (ZeQu), federated averaging (FedAvg), lossy compression (LoCo), robust aggregation (RoAg), secure aggregation (SeAg), and Gaussian differentially private FedAvg with adaptive quantile clipping (GDP-AQuCl). RESULTS The Dice coefficient was 0.80±0.11 for both centralized and SeAg FL algorithms. All FL approaches achieved centralized learning model performance with no statistically significant differences. Among the FL algorithms, SeAg and GDP-AQuCl performed better than the other techniques. However, there was no statistically significant difference. All algorithms, except the center-based approach, resulted in relative errors less than 5% for SUVmax and SUVmean for all FL and centralized methods. Centralized and FL algorithms significantly outperformed the single center-based baseline. CONCLUSIONS The developed FL-based (with centralized method performance) algorithms exhibited promising performance for HN tumor segmentation from PET/CT images.
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Affiliation(s)
- Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Behrooz Razeghi
- Department of Computer Science, University of Geneva, Geneva, Switzerland
| | - Alireza Vafaei Sadr
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany; Department of Public Health Sciences, College of Medicine, The Pennsylvania State University, Hershey, PA 17033, USA
| | - Mehdi Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Sohrab Ferdowsi
- Department of Computer Science, University of Geneva, Geneva, Switzerland
| | - Peter Boor
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany
| | - Deniz Gündüz
- Department of Electrical and Electronic Engineering, Imperial College London, UK
| | | | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland; Geneva University Neurocenter, University of Geneva, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, Groningen, The Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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Amini M, Pursamimi M, Hajianfar G, Salimi Y, Saberi A, Mehri-Kakavand G, Nazari M, Ghorbani M, Shalbaf A, Shiri I, Zaidi H. Machine learning-based diagnosis and risk classification of coronary artery disease using myocardial perfusion imaging SPECT: A radiomics study. Sci Rep 2023; 13:14920. [PMID: 37691039 PMCID: PMC10493219 DOI: 10.1038/s41598-023-42142-w] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 09/06/2023] [Indexed: 09/12/2023] Open
Abstract
This study aimed to investigate the diagnostic performance of machine learning-based radiomics analysis to diagnose coronary artery disease status and risk from rest/stress Myocardial Perfusion Imaging (MPI) single-photon emission computed tomography (SPECT). A total of 395 patients suspicious of coronary artery disease who underwent 2-day stress-rest protocol MPI SPECT were enrolled in this study. The left ventricle myocardium, excluding the cardiac cavity, was manually delineated on rest and stress images to define a volume of interest. Added to clinical features (age, sex, family history, diabetes status, smoking, and ejection fraction), a total of 118 radiomics features, were extracted from rest and stress MPI SPECT images to establish different feature sets, including Rest-, Stress-, Delta-, and Combined-radiomics (all together) feature sets. The data were randomly divided into 80% and 20% subsets for training and testing, respectively. The performance of classifiers built from combinations of three feature selections, and nine machine learning algorithms was evaluated for two different diagnostic tasks, including 1) normal/abnormal (no CAD vs. CAD) classification, and 2) low-risk/high-risk CAD classification. Different metrics, including the area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE), were reported for models' evaluation. Overall, models built on the Stress feature set (compared to other feature sets), and models to diagnose the second task (compared to task 1 models) revealed better performance. The Stress-mRMR-KNN (feature set-feature selection-classifier) reached the highest performance for task 1 with AUC, ACC, SEN, and SPE equal to 0.61, 0.63, 0.64, and 0.6, respectively. The Stress-Boruta-GB model achieved the highest performance for task 2 with AUC, ACC, SEN, and SPE of 0.79, 0.76, 0.75, and 0.76, respectively. Diabetes status from the clinical feature family, and dependence count non-uniformity normalized, from the NGLDM family, which is representative of non-uniformity in the region of interest were the most frequently selected features from stress feature set for CAD risk classification. This study revealed promising results for CAD risk classification using machine learning models built on MPI SPECT radiomics. The proposed models are helpful to alleviate the labor-intensive MPI SPECT interpretation process regarding CAD status and can potentially expedite the diagnostic process.
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Affiliation(s)
- Mehdi Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Mohamad Pursamimi
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ghasem Hajianfar
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Abdollah Saberi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Ghazal Mehri-Kakavand
- Department of Medical Physics, School of Medicine, Semnan University of Medical Sciences, Semnan, Iran
| | - Mostafa Nazari
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mahdi Ghorbani
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
- Department of Cardiology, Inselspital, University of Bern, Bern, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland.
- University Research and Innovation Center, Obuda University, Budapest, Hungary.
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University of Medical Center Groningen, Groningen, The Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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Mohebi M, Amini M, Alemzadeh-Ansari MJ, Alizadehasl A, Rajabi AB, Shiri I, Zaidi H, Orooji M. Post-revascularization Ejection Fraction Prediction for Patients Undergoing Percutaneous Coronary Intervention Based on Myocardial Perfusion SPECT Imaging Radiomics: a Preliminary Machine Learning Study. J Digit Imaging 2023; 36:1348-1363. [PMID: 37059890 PMCID: PMC10407007 DOI: 10.1007/s10278-023-00820-1] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 03/24/2023] [Accepted: 03/27/2023] [Indexed: 04/16/2023] Open
Abstract
In this study, the ability of radiomics features extracted from myocardial perfusion imaging with SPECT (MPI-SPECT) was investigated for the prediction of ejection fraction (EF) post-percutaneous coronary intervention (PCI) treatment. A total of 52 patients who had undergone pre-PCI MPI-SPECT were enrolled in this study. After normalization of the images, features were extracted from the left ventricle, initially automatically segmented by k-means and active contour methods, and finally edited and approved by an expert radiologist. More than 1700 2D and 3D radiomics features were extracted from each patient's scan. A cross-combination of three feature selections and seven classifier methods was implemented. Three classes of no or dis-improvement (class 1), improved EF from 0 to 5% (class 2), and improved EF over 5% (class 3) were predicted by using tenfold cross-validation. Lastly, the models were evaluated based on accuracy, AUC, sensitivity, specificity, precision, and F-score. Neighborhood component analysis (NCA) selected the most predictive feature signatures, including Gabor, first-order, and NGTDM features. Among the classifiers, the best performance was achieved by the fine KNN classifier, which yielded mean accuracy, AUC, sensitivity, specificity, precision, and F-score of 0.84, 0.83, 0.75, 0.87, 0.78, and 0.76, respectively, in 100 iterations of classification, within the 52 patients with 10-fold cross-validation. The MPI-SPECT-based radiomic features are well suited for predicting post-revascularization EF and therefore provide a helpful approach for deciding on the most appropriate treatment.
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Affiliation(s)
- Mobin Mohebi
- Department of Biomedical Engineering, Tarbiat Modares University, Tehran, Iran
| | - Mehdi Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland
| | | | - Azin Alizadehasl
- Echocardiography Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
- Cardio-Oncology Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Ahmad Bitarafan Rajabi
- Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
- Echocardiography Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland
- Geneva University Neuro Center, Geneva University, Geneva, Switzerland
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
| | - Mahdi Orooji
- Department of Biomedical Engineering, Tarbiat Modares University, Tehran, Iran
- Department of Electrical and Computer Engineering, University of California–Davis, Davis, CA USA
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9
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Sabouri M, Hajianfar G, Hosseini Z, Amini M, Mohebi M, Ghaedian T, Madadi S, Rastgou F, Oveisi M, Bitarafan Rajabi A, Shiri I, Zaidi H. Myocardial Perfusion SPECT Imaging Radiomic Features and Machine Learning Algorithms for Cardiac Contractile Pattern Recognition. J Digit Imaging 2023; 36:497-509. [PMID: 36376780 PMCID: PMC10039187 DOI: 10.1007/s10278-022-00705-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 08/31/2022] [Accepted: 09/15/2022] [Indexed: 11/16/2022] Open
Abstract
A U-shaped contraction pattern was shown to be associated with a better Cardiac resynchronization therapy (CRT) response. The main goal of this study is to automatically recognize left ventricular contractile patterns using machine learning algorithms trained on conventional quantitative features (ConQuaFea) and radiomic features extracted from Gated single-photon emission computed tomography myocardial perfusion imaging (GSPECT MPI). Among 98 patients with standard resting GSPECT MPI included in this study, 29 received CRT therapy and 69 did not (also had CRT inclusion criteria but did not receive treatment yet at the time of data collection, or refused treatment). A total of 69 non-CRT patients were employed for training, and the 29 were employed for testing. The models were built utilizing features from three distinct feature sets (ConQuaFea, radiomics, and ConQuaFea + radiomics (combined)), which were chosen using Recursive feature elimination (RFE) feature selection (FS), and then trained using seven different machine learning (ML) classifiers. In addition, CRT outcome prediction was assessed by different treatment inclusion criteria as the study's final phase. The MLP classifier had the highest performance among ConQuaFea models (AUC, SEN, SPE = 0.80, 0.85, 0.76). RF achieved the best performance in terms of AUC, SEN, and SPE with values of 0.65, 0.62, and 0.68, respectively, among radiomic models. GB and RF approaches achieved the best AUC, SEN, and SPE values of 0.78, 0.92, and 0.63 and 0.74, 0.93, and 0.56, respectively, among the combined models. A promising outcome was obtained when using radiomic and ConQuaFea from GSPECT MPI to detect left ventricular contractile patterns by machine learning.
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Affiliation(s)
- Maziar Sabouri
- Department of Medical Physics, School of Medicine, Iran University of Medical Science, Tehran, Iran
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Ghasem Hajianfar
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Zahra Hosseini
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Mehdi Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Mobin Mohebi
- Department of Biomedical Engineering, Tarbiat Modares University, Tehran, Iran
| | - Tahereh Ghaedian
- Nuclear Medicine and Molecular Imaging Research Center, School of Medicine, Namazi Teaching Hospital, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Shabnam Madadi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Fereydoon Rastgou
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Mehrdad Oveisi
- Comprehensive Cancer Centre, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences & Medicine, King's College London, London, UK
- Department of Computer Science, University of British Columbia, Vancouver BC, Canada
| | - Ahmad Bitarafan Rajabi
- Echocardiography Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran.
- Cardiovascular Interventional Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran.
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland.
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland.
- Geneva University Neurocenter, Geneva University, Geneva, Switzerland.
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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10
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Hajianfar G, Sabouri M, Salimi Y, Amini M, Bagheri S, Jenabi E, Hekmat S, Maghsudi M, Mansouri Z, Khateri M, Hosein Jamshidi M, Jafari E, Bitarafan Rajabi A, Assadi M, Oveisi M, Shiri I, Zaidi H. Artificial intelligence-based analysis of whole-body bone scintigraphy: The quest for the optimal deep learning algorithm and comparison with human observer performance. Z Med Phys 2023:S0939-3889(23)00008-9. [PMID: 36932023 DOI: 10.1016/j.zemedi.2023.01.008] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/22/2022] [Accepted: 01/18/2023] [Indexed: 03/17/2023]
Abstract
PURPOSE Whole-body bone scintigraphy (WBS) is one of the most widely used modalities in diagnosing malignant bone diseases during the early stages. However, the procedure is time-consuming and requires vigour and experience. Moreover, interpretation of WBS scans in the early stages of the disorders might be challenging because the patterns often reflect normal appearance that is prone to subjective interpretation. To simplify the gruelling, subjective, and prone-to-error task of interpreting WBS scans, we developed deep learning (DL) models to automate two major analyses, namely (i) classification of scans into normal and abnormal and (ii) discrimination between malignant and non-neoplastic bone diseases, and compared their performance with human observers. MATERIALS AND METHODS After applying our exclusion criteria on 7188 patients from three different centers, 3772 and 2248 patients were enrolled for the first and second analyses, respectively. Data were split into two parts, including training and testing, while a fraction of training data were considered for validation. Ten different CNN models were applied to single- and dual-view input (posterior and anterior views) modes to find the optimal model for each analysis. In addition, three different methods, including squeeze-and-excitation (SE), spatial pyramid pooling (SPP), and attention-augmented (AA), were used to aggregate the features for dual-view input models. Model performance was reported through area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and specificity and was compared with the DeLong test applied to ROC curves. The test dataset was evaluated by three nuclear medicine physicians (NMPs) with different levels of experience to compare the performance of AI and human observers. RESULTS DenseNet121_AA (DensNet121, with dual-view input aggregated by AA) and InceptionResNetV2_SPP achieved the highest performance (AUC = 0.72) for the first and second analyses, respectively. Moreover, on average, in the first analysis, Inception V3 and InceptionResNetV2 CNN models and dual-view input with AA aggregating method had superior performance. In addition, in the second analysis, DenseNet121 and InceptionResNetV2 as CNN methods and dual-view input with AA aggregating method achieved the best results. Conversely, the performance of AI models was significantly higher than human observers for the first analysis, whereas their performance was comparable in the second analysis, although the AI model assessed the scans in a drastically lower time. CONCLUSION Using the models designed in this study, a positive step can be taken toward improving and optimizing WBS interpretation. By training DL models with larger and more diverse cohorts, AI could potentially be used to assist physicians in the assessment of WBS images.
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Affiliation(s)
- Ghasem Hajianfar
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland
| | - Maziar Sabouri
- Department of Medical Physics, School of Medicine, Iran University of Medical Science, Tehran, Iran; Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland
| | - Mehdi Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland
| | - Soroush Bagheri
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Elnaz Jenabi
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Sepideh Hekmat
- Hasheminejad Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Mehdi Maghsudi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Zahra Mansouri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland
| | - Maziar Khateri
- Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Mohammad Hosein Jamshidi
- Department of Medical Imaging and Radiation Sciences, School of Allied Medical Sciences, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Esmail Jafari
- The Persian Gulf Nuclear Medicine Research Center, Department of Molecular Imaging and Radionuclide Therapy, Bushehr Medical University Hospital, School of Medicine, Bushehr University of Medical Sciences, Bushehr, Iran
| | - Ahmad Bitarafan Rajabi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Majid Assadi
- The Persian Gulf Nuclear Medicine Research Center, Department of Molecular Imaging and Radionuclide Therapy, Bushehr Medical University Hospital, School of Medicine, Bushehr University of Medical Sciences, Bushehr, Iran
| | - Mehrdad Oveisi
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland; Geneva University Neurocenter, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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11
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Shiri I, Vafaei Sadr A, Akhavan A, Salimi Y, Sanaat A, Amini M, Razeghi B, Saberi A, Arabi H, Ferdowsi S, Voloshynovskiy S, Gündüz D, Rahmim A, Zaidi H. Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning. Eur J Nucl Med Mol Imaging 2023; 50:1034-1050. [PMID: 36508026 PMCID: PMC9742659 DOI: 10.1007/s00259-022-06053-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Accepted: 11/18/2022] [Indexed: 12/15/2022]
Abstract
PURPOSE Attenuation correction and scatter compensation (AC/SC) are two main steps toward quantitative PET imaging, which remain challenging in PET-only and PET/MRI systems. These can be effectively tackled via deep learning (DL) methods. However, trustworthy, and generalizable DL models commonly require well-curated, heterogeneous, and large datasets from multiple clinical centers. At the same time, owing to legal/ethical issues and privacy concerns, forming a large collective, centralized dataset poses significant challenges. In this work, we aimed to develop a DL-based model in a multicenter setting without direct sharing of data using federated learning (FL) for AC/SC of PET images. METHODS Non-attenuation/scatter corrected and CT-based attenuation/scatter corrected (CT-ASC) 18F-FDG PET images of 300 patients were enrolled in this study. The dataset consisted of 6 different centers, each with 50 patients, with scanner, image acquisition, and reconstruction protocols varying across the centers. CT-based ASC PET images served as the standard reference. All images were reviewed to include high-quality and artifact-free PET images. Both corrected and uncorrected PET images were converted to standardized uptake values (SUVs). We used a modified nested U-Net utilizing residual U-block in a U-shape architecture. We evaluated two FL models, namely sequential (FL-SQ) and parallel (FL-PL) and compared their performance with the baseline centralized (CZ) learning model wherein the data were pooled to one server, as well as center-based (CB) models where for each center the model was built and evaluated separately. Data from each center were divided to contribute to training (30 patients), validation (10 patients), and test sets (10 patients). Final evaluations and reports were performed on 60 patients (10 patients from each center). RESULTS In terms of percent SUV absolute relative error (ARE%), both FL-SQ (CI:12.21-14.81%) and FL-PL (CI:11.82-13.84%) models demonstrated excellent agreement with the centralized framework (CI:10.32-12.00%), while FL-based algorithms improved model performance by over 11% compared to CB training strategy (CI: 22.34-26.10%). Furthermore, the Mann-Whitney test between different strategies revealed no significant differences between CZ and FL-based algorithms (p-value > 0.05) in center-categorized mode. At the same time, a significant difference was observed between the different training approaches on the overall dataset (p-value < 0.05). In addition, voxel-wise comparison, with respect to reference CT-ASC, exhibited similar performance for images predicted by CZ (R2 = 0.94), FL-SQ (R2 = 0.93), and FL-PL (R2 = 0.92), while CB model achieved a far lower coefficient of determination (R2 = 0.74). Despite the strong correlations between CZ and FL-based methods compared to reference CT-ASC, a slight underestimation of predicted voxel values was observed. CONCLUSION Deep learning-based models provide promising results toward quantitative PET image reconstruction. Specifically, we developed two FL models and compared their performance with center-based and centralized models. The proposed FL-based models achieved higher performance compared to center-based models, comparable with centralized models. Our work provided strong empirical evidence that the FL framework can fully benefit from the generalizability and robustness of DL models used for AC/SC in PET, while obviating the need for the direct sharing of datasets between clinical imaging centers.
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Affiliation(s)
- Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Alireza Vafaei Sadr
- Department of Theoretical Physics and Center for Astroparticle Physics, University of Geneva, Geneva, Switzerland.,Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany
| | - Azadeh Akhavan
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Amirhossein Sanaat
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Mehdi Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Behrooz Razeghi
- Department of Computer Science, University of Geneva, Geneva, Switzerland
| | - Abdollah Saberi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | | | | | - Deniz Gündüz
- Department of Electrical and Electronic Engineering, Imperial College London, London, UK
| | - Arman Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, Canada.,Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland. .,Geneva University Neurocenter, Geneva University, Geneva, Switzerland. .,Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands. .,Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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12
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Mohtashami-Borzadaran V, Amini M, Ahmadi J. Estimating the parameters of a dependent model and applying it to environmental data set. J Appl Stat 2023; 50:984-1016. [PMID: 36925902 PMCID: PMC10013524 DOI: 10.1080/02664763.2021.2006613] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
In this paper, a new dependent model is introduced. The model is motivated using the structure of series-parallel systems consisting of two series-parallel systems with a random number of parallel sub-systems that have fixed components connected in series. The dependence properties of the proposed model are studied. Two estimation methods, namely the moment method, and the maximum likelihood method are applied to estimate the parameters of the distributions of the components based on observing the system's lifetime data. A Monte Carlo simulation study is used to evaluate the performance of the estimators. Two real data sets are used to illustrate the proposed method. The results are useful for researchers and practitioners interested in analyzing bivariate data related to extreme events.
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Affiliation(s)
| | - M Amini
- Department of Statistics, Ferdowsi University of Mashhad, Mashhad, Iran
| | - J Ahmadi
- Department of Statistics, Ferdowsi University of Mashhad, Mashhad, Iran
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13
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Arian F, Amini M, Mostafaei S, Rezaei Kalantari K, Haddadi Avval A, Shahbazi Z, Kasani K, Bitarafan Rajabi A, Chatterjee S, Oveisi M, Shiri I, Zaidi H. Myocardial Function Prediction After Coronary Artery Bypass Grafting Using MRI Radiomic Features and Machine Learning Algorithms. J Digit Imaging 2022; 35:1708-1718. [PMID: 35995896 DOI: 10.1007/s10278-022-00681-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 06/21/2022] [Accepted: 07/12/2022] [Indexed: 01/02/2023] Open
Abstract
The main aim of the present study was to predict myocardial function improvement in cardiac MR (LGE-CMR) images in patients after coronary artery bypass grafting (CABG) using radiomics and machine learning algorithms. Altogether, 43 patients who had visible scars on short-axis LGE-CMR images and were candidates for CABG surgery were selected and enrolled in this study. MR imaging was performed preoperatively using a 1.5-T MRI scanner. All images were segmented by two expert radiologists (in consensus). Prior to extraction of radiomics features, all MR images were resampled to an isotropic voxel size of 1.8 × 1.8 × 1.8 mm3. Subsequently, intensities were quantized to 64 discretized gray levels and a total of 93 features were extracted. The applied algorithms included a smoothly clipped absolute deviation (SCAD)-penalized support vector machine (SVM) and the recursive partitioning (RP) algorithm as a robust classifier for binary classification in this high-dimensional and non-sparse data. All models were validated with repeated fivefold cross-validation and 10,000 bootstrapping resamples. Ten and seven features were selected with SCAD-penalized SVM and RP algorithm, respectively, for CABG responder/non-responder classification. Considering univariate analysis, the GLSZM gray-level non-uniformity-normalized feature achieved the best performance (AUC: 0.62, 95% CI: 0.53-0.76) with SCAD-penalized SVM. Regarding multivariable modeling, SCAD-penalized SVM obtained an AUC of 0.784 (95% CI: 0.64-0.92), whereas the RP algorithm achieved an AUC of 0.654 (95% CI: 0.50-0.82). In conclusion, different radiomics texture features alone or combined in multivariate analysis using machine learning algorithms provide prognostic information regarding myocardial function in patients after CABG.
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Affiliation(s)
- Fatemeh Arian
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Mehdi Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4, CH-1211, Switzerland
| | - Shayan Mostafaei
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Kiara Rezaei Kalantari
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran.,Cardio-Oncology Research Center, Rajaei Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | | | - Zahra Shahbazi
- Department of Biostatistics, School of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Kianosh Kasani
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Ahmad Bitarafan Rajabi
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran. .,Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran. .,Echocardiography Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran. .,Cardiovascular interventional research center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran.
| | - Saikat Chatterjee
- School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Brinellvägen 8, Stockholm, Sweden
| | - Mehrdad Oveisi
- Comprehensive Cancer Centre, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences & Medicine, Kings College London, London, UK.,Department of Computer Science, University of British Columbia, Vancouver BC, Canada
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4, CH-1211, Switzerland.
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4, CH-1211, Switzerland. .,Geneva University Neurocenter, Geneva University, Geneva, Switzerland. .,Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands. .,Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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14
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Mohammadi M, Emadi M, Amini M. Testing bivariate independence based on α-divergence by improved probit transformation method for copula density estimation. COMMUN STAT-SIMUL C 2022. [DOI: 10.1080/03610918.2022.2025836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
- M. Mohammadi
- Department of Statistics, Ferdowsi University of Mashhad, Mashhad, Iran
- Department of Statistics, University of Zabol, Zabol, Iran
| | - M. Emadi
- Department of Statistics, Ferdowsi University of Mashhad, Mashhad, Iran
| | - M. Amini
- Department of Statistics, Ordered Data, Reliability and Dependency Center of Excellence, Ferdowsi University of Mashhad, Mashhad, Iran
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15
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Amini M, Nazari M, Shiri I, Hajianfar G, Deevband MR, Abdollahi H, Arabi H, Rahmim A, Zaidi H. Multi-level multi-modality (PET and CT) fusion radiomics: prognostic modeling for non-small cell lung carcinoma. Phys Med Biol 2021; 66. [PMID: 34544053 DOI: 10.1088/1361-6560/ac287d] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 09/20/2021] [Indexed: 12/23/2022]
Abstract
We developed multi-modality radiomic models by integrating information extracted from18F-FDG PET and CT images using feature- and image-level fusions, toward improved prognosis for non-small cell lung carcinoma (NSCLC) patients. Two independent cohorts of NSCLC patients from two institutions (87 and 95 patients) were cycled as training and testing datasets. Fusion approaches were applied at two levels, namely feature- and image-levels. For feature-level fusion, radiomic features were extracted individually from CT and PET images and concatenated. Alternatively, radiomic features extracted separately from CT and PET images were averaged. For image-level fusion, wavelet fusion was utilized and tuned with two parameters, namely CT weight and Wavelet Band Pass Filtering Ratio. Clinical and combined clinical + radiomic models were developed. Gray level discretization was performed at 3 different levels (16, 32 and 64) and 225 radiomics features were extracted. Overall survival (OS) was considered as the endpoint. For feature reduction, correlated (redundant) features were excluded using Spearman's correlation, and best combination of top ten features with highest concordance-indices (via univariate Cox model) were selected in each model for further multivariate Cox model. Moreover, prognostic score's median, obtained from the training cohort, was used intact in the testing cohort as a threshold to classify patients into low- versus high-risk groups, and log-rank test was applied to assess differences between the Kaplan-Meier curves. Overall, while models based on feature-level fusion strategy showed limited superiority over single-modalities, image-level fusion strategy significantly outperformed both single-modality and feature-level fusion strategies. As such, the clinical model (C-index = 0.656) outperformed all models from single-modality and feature-level strategies, but was outperformed by certain models from image-level fusion strategy. Our findings indicated that image-level fusion multi-modality radiomics models outperformed single-modality, feature-level fusion, and clinical models for OS prediction of NSCLC patients.
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Affiliation(s)
- Mehdi Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1205 Geneva, Switzerland.,Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mostafa Nazari
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1205 Geneva, Switzerland
| | - Ghasem Hajianfar
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Mohammad Reza Deevband
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamid Abdollahi
- Department of Radiologic Technology, School of Allied Medicine, Kerman University of Medical Sciences, Kerman, Iran
| | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1205 Geneva, Switzerland
| | - Arman Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver BC, Canada.,Department of Integrative Oncology, BC Cancer Research Institute, Vancouver BC, Canada
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1205 Geneva, Switzerland.,Geneva University Neurocenter, Geneva University, CH-1211 Geneva, Switzerland.,Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands.,Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
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16
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Saberi F, Kouhsari F, Abbasi S, Rosell CM, Amini M. Effect of baking in different ovens on the quality and structural characteristics of saltine crackers. Int J Food Sci Technol 2021. [DOI: 10.1111/ijfs.15372] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Farzad Saberi
- Department of Food Science and Technology, Science and Research Branch Islamic Azad University Tehran Iran
- Department of Research and Development Zarkam Company Zar Industrial and Research Group Hashtgerd Iran
| | - Fatemeh Kouhsari
- Department of Research and Development Zarkam Company Zar Industrial and Research Group Hashtgerd Iran
- Department of Food Science, Engineering and Technology College of Agriculture and Natural Resources University of Tehran Karaj Iran
| | - Samaneh Abbasi
- Department of Research and Development Zarkam Company Zar Industrial and Research Group Hashtgerd Iran
- Department of Food Science and Technology Varamin‐Pishva Branch Islamic Azad University Varamin Iran
| | - Cristina M. Rosell
- Institute of Agrochemistry and Food Technology (IATA‐CSIC) C/Agustin Escardino, 7 Paterna Valencia 46980 Spain
| | - Mehdi Amini
- Department of Research and Development Zarkam Company Zar Industrial and Research Group Hashtgerd Iran
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17
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Khodabakhshi Z, Amini M, Mostafaei S, Haddadi Avval A, Nazari M, Oveisi M, Shiri I, Zaidi H. Overall Survival Prediction in Renal Cell Carcinoma Patients Using Computed Tomography Radiomic and Clinical Information. J Digit Imaging 2021. [PMID: 34382117 DOI: 10.1007/s10278-021-00500-y/figures/5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/01/2023] Open
Abstract
The aim of this work is to investigate the applicability of radiomic features alone and in combination with clinical information for the prediction of renal cell carcinoma (RCC) patients' overall survival after partial or radical nephrectomy. Clinical studies of 210 RCC patients from The Cancer Imaging Archive (TCIA) who underwent either partial or radical nephrectomy were included in this study. Regions of interest (ROIs) were manually defined on CT images. A total of 225 radiomic features were extracted and analyzed along with the 59 clinical features. An elastic net penalized Cox regression was used for feature selection. Accelerated failure time (AFT) with the shared frailty model was used to determine the effects of the selected features on the overall survival time. Eleven radiomic and twelve clinical features were selected based on their non-zero coefficients. Tumor grade, tumor malignancy, and pathology t-stage were the most significant predictors of overall survival (OS) among the clinical features (p < 0.002, < 0.02, and < 0.018, respectively). The most significant predictors of OS among the selected radiomic features were flatness, area density, and median (p < 0.02, < 0.02, and < 0.05, respectively). Along with important clinical features, such as tumor heterogeneity and tumor grade, imaging biomarkers such as tumor flatness, area density, and median are significantly correlated with OS of RCC patients.
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Affiliation(s)
- Zahra Khodabakhshi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Mehdi Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Shayan Mostafaei
- Department of Biostatistics, School of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran
- Epidemiology and Biostatistics Unit, Rheumatology Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Mostafa Nazari
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mehrdad Oveisi
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
- Comprehensive Cancer Centre, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences & Medicine , Kings College London, London, UK
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland.
- Geneva University Neurocenter, Geneva University, Geneva, Switzerland.
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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Goodarzi F, Amini M. Reliability and expectation bounds based on Hardy’s inequality. COMMUN STAT-THEOR M 2021. [DOI: 10.1080/03610926.2021.1966037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- F. Goodarzi
- Department of Statistics, University of Kashan, Kashan, Iran
| | - M. Amini
- Department of Statistics, Ordered Data, Reliability and Dependency Center of Excellence, Ferdowsi University of Mashhad, Mashhad, Iran
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Nasabi M, Naderi B, Akbari M, Aktar T, Kieliszek M, Amini M. Physical, structural and sensory properties of wafer batter and wafer sheets influenced by various sources of grains. Lebensm Wiss Technol 2021. [DOI: 10.1016/j.lwt.2021.111826] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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20
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Ramezani Darmian P, Memarzadeh Z, Aryan R, Nahidi Y, Mehri Z, Taghipour A, Samimi N, Amini M, Layegh P. Cutaneous manifestations of patients hospitalized with coronavirus disease 2019 (COVID-19). J Eur Acad Dermatol Venereol 2021; 35:e837-e839. [PMID: 34309940 PMCID: PMC8447134 DOI: 10.1111/jdv.17557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- P Ramezani Darmian
- Cutaneous Leishmaniasis Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Z Memarzadeh
- Cutaneous Leishmaniasis Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - R Aryan
- Cutaneous Leishmaniasis Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Y Nahidi
- Cutaneous Leishmaniasis Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Z Mehri
- Cutaneous Leishmaniasis Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - A Taghipour
- Health Sciences Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - N Samimi
- Department of Dermatology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - M Amini
- Lung Diseases Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - P Layegh
- Cutaneous Leishmaniasis Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
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Saberi Zafarghandi MB, Eshrati S, Arezoomandan R, Farnia M, Mohammadi H, Vahed N, Javaheri A, Amini M, Heidari S. Review, Documentation, Assessment of Treatment, and Harm Reduction Programs of Substance Use Disorder in Iranian Prisons. IJPCP 2021. [DOI: 10.32598/ijpcp.27.1.3324.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Objectives: This study aims to assess the implementation of drug-related harm reduction programs in Iranian prisons and suggest solutions for their improvement. Methods: This study was conducted in three steps. First, library method was used for collecting data from the central library of Iran’s Prisons, Security and Corrective Measures Organization. In the second step, performance indicators were extracted based on the results of first step and two researcher-made checklists were designed. Finally, a field visit and a semi-structured interview with the authorities involved in the treatment and harm reduction services were carried out. Results: In most of prisons, drug-related harm reduction programs were underway. Despite a lack of human resources and budget at the beginning, the quality of measures was gradually increased and the attitude of authorities was improved. Methadone Maintenance Treatment and Triangular Clinics were the most common harm reduction programs, in addition to HIV and tuberculosis screening programs in collaboration with medical sciences universities. The program continued despite the change of officials. Conclusion: Harm reduction programs are able to reduce infection diseases, self-harm and violent behaviors in prisons of Iran. Cultural programs along with other harm reduction programs, briefings and seeking support from the authorities can greatly help with continuation of the programs in prisons. By eliminating the shortage of manpower and redefining the security areas for ordinary prisoners, it will be possible to make better use of the facilities of universities and research centers.
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22
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Amini M, Abdi A, Abbassi Daloii A. Synergistic Effects of Aerobic Training and Momordica Charantia L. on Serum Lipocalins in Men with Type 2 Diabetes. J Ardabil Univ Med Sci 2020. [DOI: 10.29252/jarums.20.1.7] [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: 10/31/2022] Open
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Kheyri A, Amini M, Jabbari H, Bozorgnia A, Volodin A. Exponential convergence rates for the kernel bivariate distribution function estimator under NSD assumption with application to hydrology data. COMMUN STAT-THEOR M 2020. [DOI: 10.1080/03610926.2020.1808900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- A. Kheyri
- Department of Statistics, Ordered and Spatial Data Center of Excellence, Ferdowsi University of Mashhad, Mashhad, Iran
| | - M. Amini
- Department of Statistics, Ordered and Spatial Data Center of Excellence, Ferdowsi University of Mashhad, Mashhad, Iran
| | - H. Jabbari
- Department of Statistics, Ordered and Spatial Data Center of Excellence, Ferdowsi University of Mashhad, Mashhad, Iran
| | - A. Bozorgnia
- Department of Statistics, Khayyam University, Mashhad, Iran
| | - A. Volodin
- Department of Mathematics and Statistics, University of Regina, Regina, Canada
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Amini M, Niemi E, Hisdal J, Kalvøy H, Tronstad C, Scholz H, Rosales A, Martinsen ØG. Monitoring the quality of frozen-thawed venous segments using bioimpedance spectroscopy. Physiol Meas 2020; 41:044008. [PMID: 32235072 DOI: 10.1088/1361-6579/ab85b7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
OBJECTIVE Storage at temperatures as low as -80 °C and below (cryopreservation) is considered a method for long-term preservation of cells and tissues, and especially blood vessel segments, which are to be used for clinical operations such as transplantation. However, the freezing and thawing processes themselves can induce injuries to the cells and tissue by damaging the structure and consequently functionality of the cryopreserved tissue. In addition, the level of damage is dependent on the rate of cooling and warming used during the freezing-thawing process. Current methods for monitoring the viability and integrity of cells and tissues after going through the freezing-thawing cycle are usually invasive and destructive to the cells and tissues. Therefore, employing monitoring methods which are not destructive to the cryopreserved tissues, such as bioimpedance measurement techniques, is necessary. In this study we aimed to design a bioimpedance measurement setup to detect changes in venous segments after freezing-thawing cycles in a noninvasive manner. APPROACH A bioimpedance spectroscopy measurement technique with a two-electrode setup was employed to monitor ovine jugular vein segments after each cycle during a process of seven freezing-thawing cycles. MAIN RESULTS The results demonstrated changes in the impedance spectra of the measured venous segments after each freezing-thawing cycle. SIGNIFICANCE This indicates that bioimpedance spectroscopy has the potential to be developed into a novel method for non-invasive and non-destructive monitoring of the viability of complex tissue after cryopreservation.
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Affiliation(s)
- M Amini
- Department of Physics, University of Oslo, Oslo, Norway
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25
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Mehrparvar B, Chashmniam S, Nobakht F, Amini M, Javidi A, Minai-Tehrani A, Arjmand B, Gilany K. Metabolic profiling of seminal plasma from teratozoospermia patients. J Pharm Biomed Anal 2020; 178:112903. [DOI: 10.1016/j.jpba.2019.112903] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2019] [Revised: 09/28/2019] [Accepted: 09/30/2019] [Indexed: 10/25/2022]
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26
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Amini M, Kalvøy H, Martinsen Ø. Finite Element Simulation of the Impedance Response of a Vascular Segment as a Function of Changes in Electrode Configuration. J Electr Bioimpedance 2020; 11:112-131. [PMID: 33584912 PMCID: PMC7851985 DOI: 10.2478/joeb-2020-0017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Indexed: 06/12/2023]
Abstract
Monitoring a biological tissue as a three dimensional (3D) model is of high importance. Both the measurement technique and the measuring electrode play substantial roles in providing accurate 3D measurements. Bioimpedance spectroscopy has proven to be a noninvasive method providing the possibility of monitoring a 3D construct in a real time manner. On the other hand, advances in electrode fabrication has made it possible to use flexible electrodes with different configurations, which makes 3D measurements possible. However, designing an experimental measurement set-up for monitoring a 3D construct can be costly and time consuming and would require many tissue models. Finite element modeling methods provide a simple alternative for studying the performance of the electrode and the measurement set-up before starting with the experimental measurements. Therefore, in this study we employed the COMSOL Multiphysics finite element modeling method for simulating the effects of changing the electrode configuration on the impedance spectroscopy measurements of a venous segment. For this purpose, the simulations were performed for models with different electrode configurations. The simulation results provided us with the possibility of finding the optimal electrode configuration including the geometry, number and dimensions of the electrodes, which can be later employed in the experimental measurement set-up.
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Affiliation(s)
- M. Amini
- Department of Physics, University of Oslo, Oslo, Norway
| | - H. Kalvøy
- Department of Clinical and Biomedical Engineering, Rikshospitalet, Oslo University Hospital, Oslo, Norway
| | - Ø.G. Martinsen
- Department of Physics, University of Oslo, Oslo, Norway
- Department of Clinical and Biomedical Engineering, Rikshospitalet, Oslo University Hospital, Oslo, Norway
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27
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Naderi H, Matuła P, Amini M, Ahmadzade H. A version of the Kolmogrov–Feller weak law of large numbers for maximal weighted sums of random variables. COMMUN STAT-THEOR M 2019. [DOI: 10.1080/03610926.2018.1513146] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- H. Naderi
- Department of Statistics, University of Sistan and Baluchestan, Zahedan, Iran
| | - P. Matuła
- Institute of Mathematics, Marie Curie-Skłodowska University, pl. M.C.-Skłodowskiej 1, 20-031 Lublin, Poland
| | - M. Amini
- Department of Statistics, Faculty of Mathematical Sciences, Ferdowsi University, Mashhad, Iran
| | - H. Ahmadzade
- Department of Statistics, University of Sistan and Baluchestan, Zahedan, Iran
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28
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Kassaian N, Feizi A, Aminorroaya A, Amini M, Ataei B, Rostami S. OR34: Effects of Probiotics and Synbiotic on Lipid Profiles in Adults at Risk of Type 2 Diabetes: A Double-Blind Randomized Controlled Clinical Trial. Clin Nutr 2019. [DOI: 10.1016/s0261-5614(19)32506-3] [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: 12/01/2022]
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29
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Amini M, Soltani M. Quantum transport through the edge states of zigzag phosphorene nanoribbons in presence of a single point defect: analytic Green's function method. J Phys Condens Matter 2019; 31:215301. [PMID: 30794998 DOI: 10.1088/1361-648x/ab09b8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Zigzag phosphorene nanoribbons have quasi-flat band edge modes entirely detached from bulk states. We analytically study the electronic transport through such edge states in the presence of a localized defect for semi-infinite and finite ribbon widths. Using the tight-binding model, we derive analytical expressions for the Green's function and transmission amplitude of both pristine and defective nanoribbons. We find that the transmission of ribbons with both semi-infinite and finite width is sensitive to the location of a single impurity defect with respect to the edge. By the presence of an impurity on the outermost edge site of the ribbon, the transmission through the edge channel, similar to a one-dimensional chain, strongly suppresses for the entire energy spectrum of the quasi-flat band. In contrast, the transmission of low-energy [Formula: see text] states, is robust as the impurity is moved one position far away from the edge on the same sub-lattice. The analytical calculations are also complemented by exact numerical transport computations using the Landauer approach.
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Affiliation(s)
- M Amini
- Department of Physics, University of Isfahan (UI), Hezar Jerib, 81746-73441, Isfahan, Iran
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30
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Kheyri A, Amini M, Jabbari H, Bozorgnia A. Kernel density estimation under negative superadditive dependence and its application for real data. J STAT COMPUT SIM 2019. [DOI: 10.1080/00949655.2019.1619738] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- A. Kheyri
- Department of Statistics, Ordered and Spatial Data Center of Excellence, Ferdowsi University of Mashhad, Mashhad, Iran
| | - M. Amini
- Department of Statistics, Ordered and Spatial Data Center of Excellence, Ferdowsi University of Mashhad, Mashhad, Iran
| | - H. Jabbari
- Department of Statistics, Ordered and Spatial Data Center of Excellence, Ferdowsi University of Mashhad, Mashhad, Iran
| | - A. Bozorgnia
- Department of Statistics, Ordered and Spatial Data Center of Excellence, Ferdowsi University of Mashhad, Mashhad, Iran
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31
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Sani HRN, Amini M, Bozorgnia A. Complete convergence for weighted sums of arrays of APND random variables. COMMUN STAT-THEOR M 2018. [DOI: 10.1080/03610926.2016.1205623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- H. R. Nili Sani
- Department of Statistics, University of Birjand, Birjand, Iran
| | - M. Amini
- Department of Statistics, Ferdowsi University of Mashhad, Mashhad, Iran
| | - A. Bozorgnia
- Department of Statistics, University of Khayyam, Mashhad, Iran
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32
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Amini M, Mobli M, Khalili M, Ebadi-Dehaghani H. Assessment of Compatibility in Polypropylene/Poly(lactic acid)/Ethylene Vinyl Alcohol Ternary Blends: Relating Experiments and Molecular Dynamics Simulation Results. J MACROMOL SCI B 2018. [DOI: 10.1080/00222348.2018.1460153] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- M. Amini
- Department of Polymer Engineering, Shahreza Branch, Islamic Azad University, Shahreza, Isfahan Province, Iran
| | - M. Mobli
- Department of Polymer Engineering, Shahreza Branch, Islamic Azad University, Shahreza, Isfahan Province, Iran
| | - M. Khalili
- Department of Polymer Engineering, Shahreza Branch, Islamic Azad University, Shahreza, Isfahan Province, Iran
| | - H. Ebadi-Dehaghani
- Department of Polymer Engineering, Shahreza Branch, Islamic Azad University, Shahreza, Isfahan Province, Iran
- Baspar Farayand Arya Co., Science and Technology Park, Islamic Azad University, Shahreza Branch, Pasdaran Ave, Shahreza, Isfahan Province, Iran
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Bisceglia M, Sickel JZ, Giangaspero F, Gomes V, Amini M, Michal M. Littoral Cell Angioma of the Spleen: An Additional Report of Four Cases with Emphasis on the Association with Visceral Organ Cancers. Tumori 2018; 84:595-9. [PMID: 9862523 DOI: 10.1177/030089169808400516] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Aims and background Littoral cell angioma (LCA) is an uncommon vascular tumor of the spleen recently described and interpreted as the tumoral counterpart of the normally present littoral cells lining the splenic sinus channels of red pulp. The diagnosis of LCA is suggested by a quite characteristic morphology and confirmed by the demonstration of a hybrid endothelial/histiocytic phenotype. Methods Four original and previously unreported cases of LCA are presented. All four splenic vascular tumors were investigated by light microscopy and immunohistochemistry for endothelial and histiocytic markers. Results All four cases were associated with visceral epithelial malignancies (colorectal adenocarcinoma in two cases, renal and pancreatic adenocarcinoma in one case each). One case was also associated with an intracranial tentorial meningioma. Conclusions We consider our findings as a novelty and signal the possible existence of a clinical syndrome. Five of a total of 21 previously reported cases in the literature were also described as being associated with other cancers (non-Hodgkin's lymphoma in two cases, two not further specified tumors of the liver and brain, an epithelial ovarian cancer, and a non-small cell lung cancer in one case each). Close follow-up and careful investigation in search of a second visceral neoplasm are strongly recommended in cases of LCA, but further clinical observations and more in-depth genetic and molecular studies are needed before any valid conclusions can be drawn.
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Affiliation(s)
- M Bisceglia
- Servizio di Anatomia Patologica, IRCCS-Ospedale Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
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Abdi A, Ramezani N, Amini M. FNDC5 Gene Expression and Irisin Protein Level of Visceral Fat Tissue after Eight Weeks of Resistance Training in Type 2 Diabetic Rats. J Ardabil Univ Med Sci 2018. [DOI: 10.29252/jarums.18.1.80] [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: 10/31/2022] Open
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Affiliation(s)
- S. Mirzaei
- Department of Statistics, Payame Noor University, Tehran, Iran
| | - G. R. Mohtashami Borzadaran
- Department of Statistics, Ordered and Spatial Data Center of Excellence, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad, Iran
| | - M. Amini
- Department of Statistics, Ordered and Spatial Data Center of Excellence, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad, Iran
| | - H. Jabbari
- Department of Statistics, Ordered and Spatial Data Center of Excellence, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad, Iran
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Amini M, Hisdal J, Kalvøy H. Applications of Bioimpedance Measurement Techniques in Tissue Engineering. J Electr Bioimpedance 2018; 9:142-158. [PMID: 33584930 PMCID: PMC7852004 DOI: 10.2478/joeb-2018-0019] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Indexed: 05/19/2023]
Abstract
Rapid development in the field of tissue engineering necessitates implementation of monitoring methods for evaluation of the viability and characteristics of the cell cultures in a real-time, non-invasive and non-destructive manner. Current monitoring techniques are mainly histological and require labeling and involve destructive tests to characterize cell cultures. Bioimpedance measurement technique which benefits from measurement of electrical properties of the biological tissues, offers a non-invasive, label-free and real-time solution for monitoring tissue engineered constructs. This review outlines the fundamentals of bioimpedance, as well as electrical properties of the biological tissues, different types of cell culture constructs and possible electrode configuration set ups for performing bioimpedance measurements on these cell cultures. In addition, various bioimpedance measurement techniques and their applications in the field of tissue engineering are discussed.
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Affiliation(s)
- M. Amini
- Department of Physics, University of Oslo, Oslo, Norway
- E-mail:
| | - J. Hisdal
- Vascular Investigations and Circulation lab, Aker Hospital, Oslo University Hospital, Oslo, Norway
| | - H. Kalvøy
- Department of Clinical and Biomedical Engineering, Rikshospitalet, Oslo University Hospital, Oslo, Norway
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Gilany K, Jafarzadeh N, Mani-Varnosfaderani A, Minai-Tehrani A, Sadeghi MR, Darbandi M, Darbandi S, Amini M, Arjmand B, Pahlevanzadeh Z. Metabolic Fingerprinting of Seminal Plasma from Non-obstructive Azoospermia Patients: Positive Versus Negative Sperm Retrieval. J Reprod Infertil 2018; 19:109-114. [PMID: 30009145 PMCID: PMC6010822] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Non-obstructive azoospermia (NOA) occurs in approximately 10% of infertile men. Retrieval of the spermatozoa from the testicle of NOA patients is an invasive approach. Seminal plasma is an excellent source for exploring to find the biomarkers for presence of spermatozoa in testicular tissue. The present discovery phase study aimed to use metabolic fingerprinting to detect spermatogenesis from seminal plasma in NOA patients as a non-invasive method. METHODS In this study, 20 men with NOA were identified based on histological analysis who had their first testicular biopsy in 2015 at Avicenna Fertility Center, Tehran, Iran. They were divided into two groups, a positive testicular sperm extraction (TESE(+)) and a negative testicular sperm extraction (TESE(-)). Seminal plasma of NOA patients was collected before they underwent testicular sperm extraction (TESE) operation. The metabolomic fingerprinting was evaluated by Raman spectrometer. Principal component analysis (PCA) and an unsupervised statistical method, was used to detect outliers and find the structure of the data. The PCA was analyzed by MATLAB software. RESULTS Metabolic fingerprinting of seminal plasma from NOA showed that TESE (+) versus TESE(-) patients were classified by PCA. Furthermore, a possible subdivision of TESE(-) group was observed. Additionally, TESE(-) patients were in extreme oxidative imbalance compared to TESE(+) patients. CONCLUSION Metabolic fingerprinting of seminal plasma can be considered as a breakthrough, an easy and cheap method for prediction presence of spermatogenesis in NOA.
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Affiliation(s)
- Kambiz Gilany
- Reproductive Biotechnology Research Center, Avicenna Research Institute, ACECR, Tehran, Iran, Metabolomics and Genomics Research Center, Endocrinology and Metabolism Molecular Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran,Corresponding Author: Kambiz Gilany, Reproductive Biotechnology Research Center, Avicenna Research Institute, ACECR, Tehran, Iran, E-mail:
| | - Naser Jafarzadeh
- Department of Medical Physics, Tarbiat Modares University, Tehran, Iran
| | - Ahmad Mani-Varnosfaderani
- Chemometrics and Chemoinformatics Laboratory, Department of Chemistry, Faculty of Sciences, Tarbiat Modares University, Tehran, Iran
| | - Arash Minai-Tehrani
- Nanobiotechnology Research Center, Avicenna Research Institute, ACECR, Tehran, Iran
| | - Mohammed Reza Sadeghi
- Reproductive Biotechnology Research Center, Avicenna Research Institute, ACECR, Tehran, Iran
| | - Mahsa Darbandi
- Reproductive Biotechnology Research Center, Avicenna Research Institute, ACECR, Tehran, Iran
| | - Sara Darbandi
- Reproductive Biotechnology Research Center, Avicenna Research Institute, ACECR, Tehran, Iran
| | - Mehdi Amini
- Reproductive Biotechnology Research Center, Avicenna Research Institute, ACECR, Tehran, Iran
| | - Babak Arjmand
- Cell Therapy and Regenerative Medicine Research Center, Endocrinology and Metabolism Molecular Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran, Metabolomics and Genomics Research Center, Endocrinology and Metabolism Molecular Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Zhamak Pahlevanzadeh
- Reproductive Biotechnology Research Center, Avicenna Research Institute, ACECR, Tehran, Iran
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Amini M, Heravi F, Zandi B, Eslami S, Mohajerzadeh M, Rohani M. The effect of mandibular advancement device on physiologic parameters and volumetric MRI in mild to moderate obstructive sleep apnea-A randomized controlled trial. Sleep Med 2017. [DOI: 10.1016/j.sleep.2017.11.035] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Amini M. Mild Steel Corrosion Inhibition by Benzotriazole in 0.5M Sulfuric Acid Solution on Rough and Smooth Surfaces. INT J ELECTROCHEM SC 2017. [DOI: 10.20964/2017.09.70] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Zamani Z, Borzadaran GRM, Amini M. On a new positive dependence concept based on the conditional mean inactivity time order. COMMUN STAT-THEOR M 2017. [DOI: 10.1080/03610926.2015.1030417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Z. Zamani
- Department of Statistics, Ferdowsi University of Mashhad, Mashhad, Iran
| | | | - M. Amini
- Department of Statistics, Ferdowsi University of Mashhad, Mashhad, Iran
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Gilany K, Mani-Varnosfaderani A, Minai-Tehrani A, Mirzajani F, Ghassempour A, Sadeghi MR, Amini M, Rezadoost H. Untargeted metabolomic profiling of seminal plasma in nonobstructive azoospermia men: A noninvasive detection of spermatogenesis. Biomed Chromatogr 2017; 31. [DOI: 10.1002/bmc.3931] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2016] [Revised: 12/11/2016] [Accepted: 01/03/2017] [Indexed: 11/08/2022]
Affiliation(s)
- Kambiz Gilany
- Reproductive Biotechnology Research Center; Avicenna Research Institute, ACECR; Tehran Iran
| | - Ahmad Mani-Varnosfaderani
- Department of Chemistry, Faculty of Sciences, Chemometrics Laboratory; Tarbiat Modares University; Tehran Iran
| | - Arash Minai-Tehrani
- Nanobiotechnology Research Center; Avicenna Research Institute, ACECR; Tehran Iran
| | - Fateme Mirzajani
- Department of Biotechnology; The Faculty of Renewable Emergies and New Technologies; Tehran Iran
- Department of Nanobiotechnology; Protein Research Institute, Shahid Beheshti Universtiy; Tehran Iran
| | - Alireza Ghassempour
- Department of Phytochemistry; Medicinal Plants and Drugs Research Institute, Shahid Beheshti University; Tehran Iran
| | - Mohammed Reza Sadeghi
- Reproductive Biotechnology Research Center; Avicenna Research Institute, ACECR; Tehran Iran
| | - Mehdi Amini
- Reproductive Biotechnology Research Center; Avicenna Research Institute, ACECR; Tehran Iran
| | - Hassan Rezadoost
- Department of Phytochemistry; Medicinal Plants and Drugs Research Institute, Shahid Beheshti University; Tehran Iran
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Zamani Z, Mohtashami Borzadaran GR, Amini M. Some Results on Stochastic Orderings of Generalized Order Statistics and Spacings. JSTA 2017. [DOI: 10.2991/jsta.2017.16.3.3] [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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Gilany K, Minai-Tehrani A, Amini M, Agharezaee N, Arjmand B. The Challenge of Human Spermatozoa Proteome: A Systematic Review. J Reprod Infertil 2017; 18:267-279. [PMID: 29062791 PMCID: PMC5641436] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Currently, there are 20,197 human protein-coding genes in the most expertly curated database (UniProtKB/Swiss-Pro). Big efforts have been made by the international consortium, the Chromosome-Centric Human Proteome Project (C-HPP) and independent researchers, to map human proteome. In brief, anno 2017 the human proteome was outlined. The male factor contributes to 50% of infertility in couples. However, there are limited human spermatozoa proteomic studies. Firstly, the development of the mapping of the human spermatozoa was analyzed. The human spermatozoa have been used as a model for missing proteins. It has been shown that human spermatozoa are excellent sources for finding missing proteins. Y chromosome proteome mapping is led by Iran. However, it seems that it is extremely challenging to map the human spermatozoa Y chromosome proteins based on current mass spectrometry-based proteomics technology. Post-translation modifications (PTMs) of human spermatozoa proteome are the most unexplored area and currently the exact role of PTMs in male infertility is unknown. Additionally, the clinical human spermatozoa proteomic analysis, anno 2017 was done in this study.
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Affiliation(s)
- Kambiz Gilany
- Reproductive Biotechnology Research Center, Avicenna Research Institute, ACECR, Tehran, Iran, Metabolomics and Genomics Research Center, Endocrinology and Metabolism Molecular Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran,Corresponding Author: Kambiz Gilany, Reproductive Biotechnology Research Center, Avicenna Research Institute, ACECR, Tehran, Iran, P.O. Box: 19615-1177 E-mail:
| | - Arash Minai-Tehrani
- Nanobiotechnology Research Center, Avicenna Research Institute, ACECR, Tehran, Iran
| | - Mehdi Amini
- Reproductive Biotechnology Research Center, Avicenna Research Institute, ACECR, Tehran, Iran
| | - Niloofar Agharezaee
- Reproductive Biotechnology Research Center, Avicenna Research Institute, ACECR, Tehran, Iran, Department of Genetics, Tehran Medical Sciences Branch, Islamic Azad University, Tehran, Iran
| | - Babak Arjmand
- Metabolomics and Genomics Research Center, Endocrinology and Metabolism Molecular Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran, Cell Therapy and Regenerative Medicine Research Center, Endocrinology and Metabolism Molecular Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
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Zargar M, Jabbari Nooghabi H, Amini M. Test of Independence for Baker’s Bivariate Distributions. COMMUN STAT-SIMUL C 2016. [DOI: 10.1080/03610918.2014.917676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- M. Zargar
- Department of Statistics, Ordered and Spatial Data Center of Excellence, Ferdowsi University of Mashhad, Mashhad, Iran
| | - H. Jabbari Nooghabi
- Department of Statistics, Ordered and Spatial Data Center of Excellence, Ferdowsi University of Mashhad, Mashhad, Iran
| | - M. Amini
- Department of Statistics, Ordered and Spatial Data Center of Excellence, Ferdowsi University of Mashhad, Mashhad, Iran
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Saadatjoo N, Javaheri M, Saemian N, Amini M. Preparation of a carbon-14 analog of 2-[2-(4-(dibenzo[b, f][1,4]thiazepin-11-yl)piperazin-1-yl)ethoxy]ethanol. Radiochemistry 2016. [DOI: 10.1134/s1066362216050131] [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: 11/23/2022]
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Saadatjoo N, Javaheri M, Saemian N, Amini M. Synthesis of a carbon-14 analog of 8-chloro-11- (4-methyl-1-piperazinyl)-11-[14C]-5H-dibenzo[b,e][1,4]diazepine (clozapine) using microwave irradiation. Radiochemistry 2016. [DOI: 10.1134/s1066362216040123] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Bayat A, Sadeghi AM, Avadi MR, Amini M, Rafiee-Tehrani M, Shafiee A, Majlesi R, Junginger HE. Synthesis of N, N-dimethyl N-ethyl Chitosan as a Carrier for Oral Delivery of Peptide Drugs. J BIOACT COMPAT POL 2016. [DOI: 10.1177/0883911506068679] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
N, N-dimethyl N-ethyl chitosan (DMEC), a quanternized derivative of chitosan was synthesized based on a modified two-step method via a 22 factorial design to optimize the preparative conditions. The degree of deacetylation of the starting chitosan was determined by FTIR and NMR methods and was 95%. In the first step of the synthesis, mono-ethyl chitosan was prepared by introducing an ethyl group onto the amine group of chitosan via a Schiff base and in the next step methyl iodide was added to produce DMEC which was water soluble in a pH range of 4-8. The DMEC polymers with different degrees of quaternization were obtained and fully characterized using FTIR and 1H-NMR spectroscopic methods. Based on 1H-NMR calculations, the degree of quaternization was 52% by optimizing the two-step process.
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Affiliation(s)
- A. Bayat
- Pharmacy and Pharmaceutical Sciences Research Center, Tehran University of Medical Sciences, Tehran, Iran, Cosar Pharmaceutical Co., Tehran, Iran
| | - A. M.M. Sadeghi
- Department of Pharmaceutical Technology, Leiden/Amsterdam Center for Drug Research, Leiden, The Netherlands
| | - M. R. Avadi
- Pharmacy and Pharmaceutical Sciences Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - M. Amini
- Pharmacy and Pharmaceutical Sciences Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - M. Rafiee-Tehrani
- Pharmacy and Pharmaceutical Sciences Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - A. Shafiee
- Pharmacy and Pharmaceutical Sciences Research Center, Tehran University of Medical Sciences, Tehran, Iran,
| | | | - H. E. Junginger
- Department of Pharmaceutical Technology, Leiden/Amsterdam Center for Drug Research, Leiden, The Netherlands, Pharmaceutical Sciences, Naresuan University, Phitsanulok, Thailand
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Abstract
Chitosan exhibits poor solubility at pH values above 6 which prevents its enhancing effects at drugs absorption of sites. In the present work, N-triethylated chitosan (TEC) was prepared based on a modified one-step process via a 22 factorial design to optimize the preparative conditions. TEC polymer with different degree of quaternization for pharmacological and pharmaceutical experiments was achieved. Ethyl iodide and sodium hydroxide concentrations were chosen as independent variables. The degree of deacetylation of the starting chitosan was predetermined by pH-metric titration, infrared, and NMR methods. TEC chloride was fully characterized using FTIR and 1H-NMR spectroscopies. Based on NMR calculations, a high degree of quaternization was achieved through the optimized one-step process. These highly N-triethylated chitosan chlorides were soluble in water at room temperature.
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Affiliation(s)
- M. R. Avadi
- Faculty of Pharmacy Shahid Beheshti University of Medical Sciences Tehran, Iran and Hakim Pharmaceutical Company P.O. Box 11365-5465, Tehran, Iran
| | - M. J. Zohuriaan-Mehr
- Iran Polymer and Petrochemical Institute (IPPI) P.O. Box 14965-115, Tehran, Iran
| | | | | | | | - A. Shafiee
- Tehran University of Medical Sciences, Tehran, Iran
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Zargar M, Jabbari H, Amini M. Comparing the empirical powers of several independence tests in generalized FGM family. CSAM 2016. [DOI: 10.5351/csam.2016.23.3.215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- M. Zargar
- Department of Statistics, Ferdowsi University of Mashhad, Iran
| | - H. Jabbari
- Department of Statistics, Ferdowsi University of Mashhad, Iran
| | - M. Amini
- Department of Statistics, Ferdowsi University of Mashhad, Iran
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Hendriks L, Brouns A, Amini M, Uyterlinde W, Wijsman R, Biesma B, Stigt J, Ruysscher DD, Heuvel MVD, Dingemans AM. 115PD: Brain metastases (BM) development after chemoradiation (CRT) for stage III non-small cell lung cancer (NSCLC): Does the type of chemotherapy matter? J Thorac Oncol 2016. [DOI: 10.1016/s1556-0864(16)30228-3] [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: 10/21/2022]
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