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Makhlouf Y, Singh VK, Craig S, McArdle A, French D, Loughrey MB, Oliver N, Acevedo JB, O’Reilly P, James JA, Maxwell P, Salto-Tellez M. True-T - Improving T-cell response quantification with holistic artificial intelligence based prediction in immunohistochemistry images. Comput Struct Biotechnol J 2024; 23:174-185. [PMID: 38146436 PMCID: PMC10749253 DOI: 10.1016/j.csbj.2023.11.048] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 11/24/2023] [Accepted: 11/24/2023] [Indexed: 12/27/2023] Open
Abstract
The immune response associated with oncogenesis and potential oncological ther- apeutic interventions has dominated the field of cancer research over the last decade. T-cell lymphocytes in the tumor microenvironment are a crucial aspect of cancer's adaptive immunity, and the quantification of T-cells in specific can- cer types has been suggested as a potential diagnostic aid. However, this is cur- rently not part of routine diagnostics. To address this challenge, we present a new method called True-T, which employs artificial intelligence-based techniques to quantify T-cells in colorectal cancer (CRC) using immunohistochemistry (IHC) images. True-T analyses the chromogenic tissue hybridization signal of three widely recognized T-cell markers (CD3, CD4, and CD8). Our method employs a pipeline consisting of three stages: T-cell segmentation, density estimation from the segmented mask, and prediction of individual five-year survival rates. In the first stage, we utilize the U-Net method, where a pre-trained ResNet-34 is em- ployed as an encoder to extract clinically relevant T-cell features. The segmenta- tion model is trained and evaluated individually, demonstrating its generalization in detecting the CD3, CD4, and CD8 biomarkers in IHC images. In the second stage, the density of T-cells is estimated using the predicted mask, which serves as a crucial indicator for patient survival statistics in the third stage. This ap- proach was developed and tested in 1041 patients from four reference diagnostic institutions, ensuring broad applicability. The clinical effectiveness of True-T is demonstrated in stages II-IV CRC by offering valuable prognostic information that surpasses previous quantitative gold standards, opening possibilities for po- tential clinical applications. Finally, to evaluate the robustness and broader ap- plicability of our approach without additional training, we assessed the universal accuracy of the CD3 component of the True-T algorithm across 13 distinct solid tumors.
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Affiliation(s)
- Yasmine Makhlouf
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston, Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK
| | - Vivek Kumar Singh
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston, Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK
| | - Stephanie Craig
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston, Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK
| | - Aoife McArdle
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston, Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK
| | - Dominique French
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston, Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK
| | - Maurice B. Loughrey
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston, Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK
- Cellular Pathology, Belfast Health and Social Care Trust, Belfast City Hospital, Lisburn Road, Belfast BT9 7AB, UK
| | - Nicola Oliver
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston, Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK
| | - Juvenal Baena Acevedo
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston, Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK
| | | | - Jacqueline A. James
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston, Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK
- Regional Molecular Diagnostic Service, Belfast Health and Social Care Trust, Belfast BT9 7AE, UK
| | - Perry Maxwell
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston, Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK
| | - Manuel Salto-Tellez
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston, Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK
- Sonrai Analytics, Belfast BT9 7AE, UK
- Regional Molecular Diagnostic Service, Belfast Health and Social Care Trust, Belfast BT9 7AE, UK
- Integrated Pathology Unit, Institute of Cancer Research and Royal Marsden Hospital, London SW7 3RP, UK
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Adiwinata R, Tandarto K, Arifputra J, Waleleng BJ, Gosal F, Rotty L, Winarta J, Waleleng A, Simadibrata P, Simadibrata M. The Impact of Artificial Intelligence in Improving Polyp and Adenoma Detection Rate During Colonoscopy: Systematic-Review and Meta-Analysis. Asian Pac J Cancer Prev 2023; 24:3655-3663. [PMID: 38019222 PMCID: PMC10772777 DOI: 10.31557/apjcp.2023.24.11.3655] [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: 06/25/2023] [Accepted: 11/10/2023] [Indexed: 11/30/2023] Open
Abstract
INTRODUCTION Colonoscopy may detect colorectal polyp and facilitate its removal in order to prevent colorectal cancer. However, substantial miss rate for colorectal adenomas detection still occurred during screening colonoscopy procedure. Nowadays, artificial intelligence (AI) have been employed in trials to improve polyp detection rate (PDR) and adenoma detection rate (ADR). Therefore, we would like to determine the impact of AI in increasing PDR and ADR. METHODS The present study adhered to the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-analyses 2020 (PRISMA 2020) statement. To identify relevant literature, comprehensive searches were conducted on major scientific databases, including Pubmed, EBSCO-host, and Proquest. The search was limited to articles published up to November 30, 2022. Inclusion criteria for the study encompassed full-text accessibility, articles written in the English language, and randomized controlled trials (RCTs) that reported both ADR and PDR values, comparing conventional diagnostic methods with AI-aided approaches. To synthesize the data, we computed the combined pooled odds ratio (OR) using a random-effects model. This model was chosen due to the expectation of considerable heterogeneity among the selected studies. To evaluate potential publication bias, the Begg's funnel diagram was employed. RESULTS A total of 13 studies were included in this study. Colonoscopy with AI had significantly higher PDR compared to without AI (pooled OR 1.46, 95% CI 1.13-1.89, p = 0.003) and higher ADR (pooled OR 1.58, 95% CI 1.37-1.82, p < 0.00001). PDR analysis showed moderate heterogeneity between included studies (p = 0.004; I2=63%). Furthermore, ADR analysis showed moderate heterogeneity (p < 0.007; I2 = 57%). Additionally, the funnels plot of ADR and PDR analysis showed an asymmetry plot and low publication bias. CONCLUSION AI may improve colonoscopy result quality through improving PDR and ADR.
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Affiliation(s)
- Randy Adiwinata
- Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Kevin Tandarto
- S.K Lerik Regional Public Hospital, Kupang, East Nusa Tenggara, Indonesia.
| | - Jonathan Arifputra
- Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Bradley Jimmy Waleleng
- Division of Gastroenterology-Hepatology, Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Fandy Gosal
- Division of Gastroenterology-Hepatology, Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Luciana Rotty
- Division of Gastroenterology-Hepatology, Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Jeanne Winarta
- Division of Gastroenterology-Hepatology, Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Andrew Waleleng
- Division of Gastroenterology-Hepatology, Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Paulus Simadibrata
- Department of Internal Medicine, Abdi Waluyo Hospital, Jakarta, Indonesia.
| | - Marcellus Simadibrata
- Division of Gastroenterology, Pancreatobiliary and Digestive Endoscopy, Department of Internal Medicine, Faculty of Medicine, Universitas Indonesia, Cipto Mangunkusumo General Hospital, Jakarta, Indonesia.
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Geaney A, O'Reilly P, Maxwell P, James JA, McArt D, Salto-Tellez M. Translation of tissue-based artificial intelligence into clinical practice: from discovery to adoption. Oncogene 2023; 42:3545-3555. [PMID: 37875656 PMCID: PMC10673711 DOI: 10.1038/s41388-023-02857-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 09/19/2023] [Accepted: 10/02/2023] [Indexed: 10/26/2023]
Abstract
Digital pathology (DP), or the digitization of pathology images, has transformed oncology research and cancer diagnostics. The application of artificial intelligence (AI) and other forms of machine learning (ML) to these images allows for better interpretation of morphology, improved quantitation of biomarkers, introduction of novel concepts to discovery and diagnostics (such as spatial distribution of cellular elements), and the promise of a new paradigm of cancer biomarkers. The application of AI to tissue analysis can take several conceptual approaches, within the domains of language modelling and image analysis, such as Deep Learning Convolutional Neural Networks, Multiple Instance Learning approaches, or the modelling of risk scores and their application to ML. The use of different approaches solves different problems within pathology workflows, including assistive applications for the detection and grading of tumours, quantification of biomarkers, and the delivery of established and new image-based biomarkers for treatment prediction and prognostic purposes. All these AI formats, applied to digital tissue images, are also beginning to transform our approach to clinical trials. In parallel, the novelty of DP/AI devices and the related computational science pipeline introduces new requirements for manufacturers to build into their design, development, regulatory and post-market processes, which may need to be taken into account when using AI applied to tissues in cancer discovery. Finally, DP/AI represents challenge to the way we accredit new diagnostic tools with clinical applicability, the understanding of which will allow cancer patients to have access to a new generation of complex biomarkers.
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Affiliation(s)
- Alice Geaney
- Sonraí Analytics, Whitla Medical Building, 97 Lisburn Rd, Belfast, BT9 7BL, UK
| | - Paul O'Reilly
- Sonraí Analytics, Whitla Medical Building, 97 Lisburn Rd, Belfast, BT9 7BL, UK
- Precision Medicine Centre of Excellence, The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Health Science Building; 97 Lisburn Road, Belfast, BT9 7BL, UK
| | - Perry Maxwell
- Precision Medicine Centre of Excellence, The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Health Science Building; 97 Lisburn Road, Belfast, BT9 7BL, UK
| | - Jacqueline A James
- Precision Medicine Centre of Excellence, The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Health Science Building; 97 Lisburn Road, Belfast, BT9 7BL, UK
- Northern Ireland Biobank, The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, BT9 7AE, UK
| | - Darragh McArt
- Sonraí Analytics, Whitla Medical Building, 97 Lisburn Rd, Belfast, BT9 7BL, UK
- Precision Medicine Centre of Excellence, The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Health Science Building; 97 Lisburn Road, Belfast, BT9 7BL, UK
| | - Manuel Salto-Tellez
- Precision Medicine Centre of Excellence, The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Health Science Building; 97 Lisburn Road, Belfast, BT9 7BL, UK.
- Integrated Pathology Unit, Division of Molecular Pathology, The Institute of Cancer Research London, 15 Cotswold Rd, Sutton, SM2 5NG, UK.
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Iqbal S, Qureshi AN, Alhussein M, Aurangzeb K, Kadry S. A Novel Heteromorphous Convolutional Neural Network for Automated Assessment of Tumors in Colon and Lung Histopathology Images. Biomimetics (Basel) 2023; 8:370. [PMID: 37622975 PMCID: PMC10452605 DOI: 10.3390/biomimetics8040370] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 08/26/2023] Open
Abstract
The automated assessment of tumors in medical image analysis encounters challenges due to the resemblance of colon and lung tumors to non-mitotic nuclei and their heteromorphic characteristics. An accurate assessment of tumor nuclei presence is crucial for determining tumor aggressiveness and grading. This paper proposes a new method called ColonNet, a heteromorphous convolutional neural network (CNN) with a feature grafting methodology categorically configured for analyzing mitotic nuclei in colon and lung histopathology images. The ColonNet model consists of two stages: first, identifying potential mitotic patches within the histopathological imaging areas, and second, categorizing these patches into squamous cell carcinomas, adenocarcinomas (lung), benign (lung), benign (colon), and adenocarcinomas (colon) based on the model's guidelines. We develop and employ our deep CNNs, each capturing distinct structural, textural, and morphological properties of tumor nuclei, to construct the heteromorphous deep CNN. The execution of the proposed ColonNet model is analyzed by its comparison with state-of-the-art CNNs. The results demonstrate that our model surpasses others on the test set, achieving an impressive F1 score of 0.96, sensitivity and specificity of 0.95, and an area under the accuracy curve of 0.95. These outcomes underscore our hybrid model's superior performance, excellent generalization, and accuracy, highlighting its potential as a valuable tool to support pathologists in diagnostic activities.
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Affiliation(s)
- Saeed Iqbal
- Department of Computer Science, Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore 54000, Pakistan;
| | - Adnan N. Qureshi
- Department of Computer Science, Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore 54000, Pakistan;
| | - Musaed Alhussein
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia; (M.A.); (K.A.)
| | - Khursheed Aurangzeb
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia; (M.A.); (K.A.)
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway;
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Arias S, Zhang Y, Zahl P, Hollen S. Autonomous Molecular Structure Imaging with High-Resolution Atomic Force Microscopy for Molecular Mixture Discovery. J Phys Chem A 2023; 127:6116-6122. [PMID: 37462432 DOI: 10.1021/acs.jpca.3c01685] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
Due to its single-molecule sensitivity, high-resolution atomic force microscopy (HR-AFM) has proved to be a valuable and uniquely advantageous tool to study complex molecular mixtures, which hold promise for developing clean energy and achieving environmental sustainability. However, significant challenges remain to achieve the full potential of the sophisticated and time-consuming experiments. Automation combined with machine learning (ML) and artificial intelligence (AI) is key to overcoming these challenges. Here we present Auto-HR-AFM, an AI tool to automatically collect HR-AFM images of petroleum-based mixtures. We trained an instance segmentation model to teach Auto-HR-AFM how to recognize features in HR-AFM images. Auto-HR-AFM then uses that information to optimize the imaging by adjusting the probe-molecule distance for each molecule in the run. Auto-HR-AFM is the initial tool that will lead to fully automated scanning probe microscopy (SPM) experiments, from start to finish. This automation will allow SPM to become a mainstream characterization technique for complex mixtures, an otherwise unattainable target.
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Affiliation(s)
- Steven Arias
- Department of Physics and Astronomy, University of New Hampshire, Durham, New Hampshire 03824, United States
| | - Yunlong Zhang
- ExxonMobil Technology and Engineering Company, Annandale, New Jersey 08801, United States
| | - Percy Zahl
- Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, New York 11973, United States
| | - Shawna Hollen
- Department of Physics and Astronomy, University of New Hampshire, Durham, New Hampshire 03824, United States
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Al-Jabbar M, Alshahrani M, Senan EM, Ahmed IA. Histopathological Analysis for Detecting Lung and Colon Cancer Malignancies Using Hybrid Systems with Fused Features. Bioengineering (Basel) 2023; 10:bioengineering10030383. [PMID: 36978774 PMCID: PMC10045080 DOI: 10.3390/bioengineering10030383] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 03/05/2023] [Accepted: 03/16/2023] [Indexed: 03/30/2023] Open
Abstract
Lung and colon cancer are among humanity's most common and deadly cancers. In 2020, there were 4.19 million people diagnosed with lung and colon cancer, and more than 2.7 million died worldwide. Some people develop lung and colon cancer simultaneously due to smoking which causes lung cancer, leading to an abnormal diet, which also causes colon cancer. There are many techniques for diagnosing lung and colon cancer, most notably the biopsy technique and its analysis in laboratories. Due to the scarcity of health centers and medical staff, especially in developing countries. Moreover, manual diagnosis takes a long time and is subject to differing opinions of doctors. Thus, artificial intelligence techniques solve these challenges. In this study, three strategies were developed, each with two systems for early diagnosis of histological images of the LC25000 dataset. Histological images have been improved, and the contrast of affected areas has been increased. The GoogLeNet and VGG-19 models of all systems produced high dimensional features, so redundant and unnecessary features were removed to reduce high dimensionality and retain essential features by the PCA method. The first strategy for diagnosing the histological images of the LC25000 dataset by ANN uses crucial features of GoogLeNet and VGG-19 models separately. The second strategy uses ANN with the combined features of GoogLeNet and VGG-19. One system reduced dimensions and combined, while the other combined high features and then reduced high dimensions. The third strategy uses ANN with fusion features of CNN models (GoogLeNet and VGG-19) and handcrafted features. With the fusion features of VGG-19 and handcrafted features, the ANN reached a sensitivity of 99.85%, a precision of 100%, an accuracy of 99.64%, a specificity of 100%, and an AUC of 99.86%.
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Affiliation(s)
- Mohammed Al-Jabbar
- Computer Department, Applied College, Najran University, Najran 66462, Saudi Arabia
| | - Mohammed Alshahrani
- Computer Department, Applied College, Najran University, Najran 66462, Saudi Arabia
| | - Ebrahim Mohammed Senan
- Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Alrazi University, Sana'a, Yemen
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Singh VK, Sarker MMK, Makhlouf Y, Craig SG, Humphries MP, Loughrey MB, James JA, Salto-Tellez M, O'Reilly P, Maxwell P. ICOSeg: Real-Time ICOS Protein Expression Segmentation from Immunohistochemistry Slides Using a Lightweight Conv-Transformer Network. Cancers (Basel) 2022; 14:3910. [PMID: 36010903 DOI: 10.3390/cancers14163910] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 08/10/2022] [Accepted: 08/11/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Inducible T-cell COStimulator (ICOS) is a biomarker of interest in checkpoint inhibitor therapy, and as a means of assessing T-cell regulation as part of a complex process of adaptive immunity. The aim of our study is to segment the ICOS positive cells using a lightweight deep-learning segmentation network. We aim to assess the potential of a convolutional neural network and transformer together that permits the capture of relevant features from immunohistochemistry images. The proposed study achieved remarkable results compared to the existing biomedical segmentation methods on our in-house dataset and surpassed our previous analysis by only utilizing the Efficient-UNet network. Abstract In this article, we propose ICOSeg, a lightweight deep learning model that accurately segments the immune-checkpoint biomarker, Inducible T-cell COStimulator (ICOS) protein in colon cancer from immunohistochemistry (IHC) slide patches. The proposed model relies on the MobileViT network that includes two main components: convolutional neural network (CNN) layers for extracting spatial features; and a transformer block for capturing a global feature representation from IHC patch images. The ICOSeg uses an encoder and decoder sub-network. The encoder extracts the positive cell’s salient features (i.e., shape, texture, intensity, and margin), and the decoder reconstructs important features into segmentation maps. To improve the model generalization capabilities, we adopted a channel attention mechanism that added to the bottleneck of the encoder layer. This approach highlighted the most relevant cell structures by discriminating between the targeted cell and background tissues. We performed extensive experiments on our in-house dataset. The experimental results confirm that the proposed model achieves more significant results against state-of-the-art methods, together with an 8× reduction in parameters.
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Makhlouf Y, Salto-tellez M, James J, O’reilly P, Maxwell P. General Roadmap and Core Steps for the Development of AI Tools in Digital Pathology. Diagnostics (Basel) 2022; 12:1272. [PMID: 35626427 PMCID: PMC9141041 DOI: 10.3390/diagnostics12051272] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 05/16/2022] [Accepted: 05/18/2022] [Indexed: 11/17/2022] Open
Abstract
Integrating Artificial Intelligence (AI) tools in the tissue diagnostic workflow will benefit the pathologist and, ultimately, the patient. The generation of such AI tools has two parallel yet interconnected processes, namely the definition of the pathologist’s task to be delivered in silico, and the software development requirements. In this review paper, we demystify this process, from a viewpoint that joins experienced pathologists and data scientists, by proposing a general pathway and describing the core steps to build an AI digital pathology tool. In doing so, we highlight the importance of the collaboration between AI scientists and pathologists, from the initial formulation of the hypothesis to the final, ready-to-use product.
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Heinz CN, Echle A, Foersch S, Bychkov A, Kather JN. The future of artificial intelligence in digital pathology - results of a survey across stakeholder groups. Histopathology 2022; 80:1121-1127. [PMID: 35373378 DOI: 10.1111/his.14659] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 03/17/2022] [Accepted: 04/02/2022] [Indexed: 11/30/2022]
Abstract
AIMS Artificial intelligence (AI) provides a powerful tool to extract information from digitized histopathology whole slide images. In the last five years, academic and commercial actors have developed new technical solutions for a diverse set of tasks, including tissue segmentation, cell detection, mutation prediction, prognostication and prediction of treatment response. In the light of limited overall resources, it is presently unclear for researchers, practitioners and policymakers which of these topics are stable enough for clinical use in the near future and which topics are still experimental, but worth investing time and effort into. METHODS To identify potentially promising applications of AI in pathology, we performed an anonymous online survey of 75 computational pathology domain experts from academia and industry. Participants enrolled in 2021 were queried about their subjective opinion on promising and appealing sub-fields of computational pathology with a focus on solid tumors. RESULTS The results of this survey indicate that the prediction of treatment response directly from routine pathology slides is regarded as the most promising future application. This item was ranked highest in the overall analysis and in sub-groups by age and professional background. Furthermore, prediction of genetic alterations, gene expression and survival directly from routine pathology images scored consistently high across subgroups. CONCLUSIONS Together, these data demonstrate a possible direction for the development of computational pathology systems in clinical, academic and industrial research in the near future.
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Affiliation(s)
- Céline N Heinz
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
| | - Amelie Echle
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
| | - Sebastian Foersch
- Department of Pathology, University Medical Center Mainz, Mainz, Germany
| | - Andrey Bychkov
- Department of Pathology, Kameda Medical Center, Kamogawa, Chiba, Japan
| | - Jakob Nikolas Kather
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.,Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.,Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
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Wu G, He M, Ren K, Ma H, Xue Q. Inducible Co-Stimulator ICOS Expression Correlates with Immune Cell Infiltration and Can Predict Prognosis in Lung Adenocarcinoma. Int J Gen Med 2022; 15:3739-3751. [PMID: 35418779 PMCID: PMC8995865 DOI: 10.2147/ijgm.s349441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 03/09/2022] [Indexed: 11/23/2022] Open
Abstract
Background Inducible co-stimulator (ICOS) is a cell-enhanced co-stimulatory receptor that has shown great potential in the regulation of innate and adaptive immunity. However, the role of ICOS in lung adenocarcinoma (LUAD) remains unclear. Methods We used data from the Cancer Genome Atlas(TCGA) database to identify the expression and prognostic role of ICOS in LUAD. The results were validated using Gene Expression Omnibus(GEO) and Kaplan-Meier plotter databases. A model with predictive performance for overall survival of LUAD patients was constructed using fitted ICOS expression and other clinical parameters. We explored the biological function of ICOS. Subsequently, we further analysed and validated the effect of ICOS expression on tumour immune microenvironment (TIME) and survival. Finally, the CellMiner database was used to determine the relationship between ICOS expression and drug sensitivity. Results ICOS expression is significantly associated with poor prognosis in multiple cancers, especially LUAD, and is a good predictor of overall survival in LUAD patients. The biological function is to promote autoimmunity and inhibit cell proliferation. ICOS-related survival prediction model developed to more accurately predict 1-, 3- and 5-year survival probabilities for LUAD patients. In addition, we can use the expression of ICOS to effectively assess patient malignancy, prognosis, TIME status and clinical combination of drugs. Conclusion Our results suggest that ICOS is correlated with prognosis and immune infiltrating levels in LUAD. Higher ICOS expression predicts better TIME. This study provides a novel strategy for the development of immunotherapeutic and prognostic markers in LUAD.
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Affiliation(s)
- Gujie Wu
- Medical School of Nantong University, Nantong, People’s Republic of China
| | - Min He
- Medical School of Nantong University, Nantong, People’s Republic of China
| | - Kuan Ren
- Medical School of Nantong University, Nantong, People’s Republic of China
| | - Huiyun Ma
- Medical School of Nantong University, Nantong, People’s Republic of China
| | - Qun Xue
- Cardiothoracic Surgery Department, Affiliated Hospital of Nantong University, Nantong, People’s Republic of China
- Correspondence: Qun Xue, Email
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Davri A, Birbas E, Kanavos T, Ntritsos G, Giannakeas N, Tzallas AT, Batistatou A. Deep Learning on Histopathological Images for Colorectal Cancer Diagnosis: A Systematic Review. Diagnostics (Basel) 2022; 12:837. [PMID: 35453885 DOI: 10.3390/diagnostics12040837] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 03/22/2022] [Accepted: 03/25/2022] [Indexed: 02/04/2023] Open
Abstract
Colorectal cancer (CRC) is the second most common cancer in women and the third most common in men, with an increasing incidence. Pathology diagnosis complemented with prognostic and predictive biomarker information is the first step for personalized treatment. The increased diagnostic load in the pathology laboratory, combined with the reported intra- and inter-variability in the assessment of biomarkers, has prompted the quest for reliable machine-based methods to be incorporated into the routine practice. Recently, Artificial Intelligence (AI) has made significant progress in the medical field, showing potential for clinical applications. Herein, we aim to systematically review the current research on AI in CRC image analysis. In histopathology, algorithms based on Deep Learning (DL) have the potential to assist in diagnosis, predict clinically relevant molecular phenotypes and microsatellite instability, identify histological features related to prognosis and correlated to metastasis, and assess the specific components of the tumor microenvironment.
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Suri JS, Agarwal S, Carriero A, Paschè A, Danna PSC, Columbu M, Saba L, Viskovic K, Mehmedović A, Agarwal S, Gupta L, Faa G, Singh IM, Turk M, Chadha PS, Johri AM, Khanna NN, Mavrogeni S, Laird JR, Pareek G, Miner M, Sobel DW, Balestrieri A, Sfikakis PP, Tsoulfas G, Protogerou A, Misra DP, Agarwal V, Kitas GD, Teji JS, Al-Maini M, Dhanjil SK, Nicolaides A, Sharma A, Rathore V, Fatemi M, Alizad A, Krishnan PR, Nagy F, Ruzsa Z, Gupta A, Naidu S, Paraskevas KI, Kalra MK. COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts. Diagnostics (Basel) 2021; 11:diagnostics11122367. [PMID: 34943603 PMCID: PMC8699928 DOI: 10.3390/diagnostics11122367] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 11/29/2021] [Accepted: 12/13/2021] [Indexed: 02/07/2023] Open
Abstract
(1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for COVID lung severity diagnosis. Earlier proposed approaches during 2020–2021 were semiautomated or automated but not accurate, user-friendly, and industry-standard benchmarked. The proposed study compared the COVID Lung Image Analysis System, COVLIAS 1.0 (GBTI, Inc., and AtheroPointTM, Roseville, CA, USA, referred to as COVLIAS), against MedSeg, a web-based Artificial Intelligence (AI) segmentation tool, where COVLIAS uses hybrid deep learning (HDL) models for CT lung segmentation. (2) Materials and Methods: The proposed study used 5000 ITALIAN COVID-19 positive CT lung images collected from 72 patients (experimental data) that confirmed the reverse transcription-polymerase chain reaction (RT-PCR) test. Two hybrid AI models from the COVLIAS system, namely, VGG-SegNet (HDL 1) and ResNet-SegNet (HDL 2), were used to segment the CT lungs. As part of the results, we compared both COVLIAS and MedSeg against two manual delineations (MD 1 and MD 2) using (i) Bland–Altman plots, (ii) Correlation coefficient (CC) plots, (iii) Receiver operating characteristic curve, and (iv) Figure of Merit and (v) visual overlays. A cohort of 500 CROATIA COVID-19 positive CT lung images (validation data) was used. A previously trained COVLIAS model was directly applied to the validation data (as part of Unseen-AI) to segment the CT lungs and compare them against MedSeg. (3) Result: For the experimental data, the four CCs between COVLIAS (HDL 1) vs. MD 1, COVLIAS (HDL 1) vs. MD 2, COVLIAS (HDL 2) vs. MD 1, and COVLIAS (HDL 2) vs. MD 2 were 0.96, 0.96, 0.96, and 0.96, respectively. The mean value of the COVLIAS system for the above four readings was 0.96. CC between MedSeg vs. MD 1 and MedSeg vs. MD 2 was 0.98 and 0.98, respectively. Both had a mean value of 0.98. On the validation data, the CC between COVLIAS (HDL 1) vs. MedSeg and COVLIAS (HDL 2) vs. MedSeg was 0.98 and 0.99, respectively. For the experimental data, the difference between the mean values for COVLIAS and MedSeg showed a difference of <2.5%, meeting the standard of equivalence. The average running times for COVLIAS and MedSeg on a single lung CT slice were ~4 s and ~10 s, respectively. (4) Conclusions: The performances of COVLIAS and MedSeg were similar. However, COVLIAS showed improved computing time over MedSeg.
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Affiliation(s)
- Jasjit S. Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
- Advanced Knowledge Engineering Centre, Global Biomedical Technologies, Inc., Roseville, CA 95661, USA; (S.A.); (S.A.); (L.G.)
- Correspondence: ; Tel.: +1-(916)-749-5628
| | - Sushant Agarwal
- Advanced Knowledge Engineering Centre, Global Biomedical Technologies, Inc., Roseville, CA 95661, USA; (S.A.); (S.A.); (L.G.)
- Department of Computer Science Engineering, Pranveer Singh Institute of Technology, Kanpur 209305, India
| | - Alessandro Carriero
- Department of Radiology, “Maggiore della Carità” Hospital, University of Piemonte Orientale (UPO), 28100 Novara, Italy;
| | - Alessio Paschè
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (A.P.); (P.S.C.D.); (M.C.); (L.S.); (A.B.)
| | - Pietro S. C. Danna
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (A.P.); (P.S.C.D.); (M.C.); (L.S.); (A.B.)
| | - Marta Columbu
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (A.P.); (P.S.C.D.); (M.C.); (L.S.); (A.B.)
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (A.P.); (P.S.C.D.); (M.C.); (L.S.); (A.B.)
| | - Klaudija Viskovic
- Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, 10 000 Zagreb, Croatia; (K.V.); (A.M.)
| | - Armin Mehmedović
- Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, 10 000 Zagreb, Croatia; (K.V.); (A.M.)
| | - Samriddhi Agarwal
- Advanced Knowledge Engineering Centre, Global Biomedical Technologies, Inc., Roseville, CA 95661, USA; (S.A.); (S.A.); (L.G.)
- Department of Computer Science Engineering, Pranveer Singh Institute of Technology, Kanpur 209305, India
| | - Lakshya Gupta
- Advanced Knowledge Engineering Centre, Global Biomedical Technologies, Inc., Roseville, CA 95661, USA; (S.A.); (S.A.); (L.G.)
| | - Gavino Faa
- Department of Pathology, AOU of Cagliari, 09124 Cagliari, Italy;
| | - Inder M. Singh
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
| | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753 Delmenhorst, Germany;
| | - Paramjit S. Chadha
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
| | - Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada;
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India;
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, 17674 Athens, Greece;
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA 94574, USA;
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA; (G.P.); (D.W.S.)
| | - Martin Miner
- Men’s Health Center, Miriam Hospital, Providence, RI 02906, USA;
| | - David W. Sobel
- Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA; (G.P.); (D.W.S.)
| | - Antonella Balestrieri
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (A.P.); (P.S.C.D.); (M.C.); (L.S.); (A.B.)
| | - Petros P. Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, 15772 Athens, Greece;
| | - George Tsoulfas
- Department of Surgery, Aristoteleion University of Thessaloniki, 54124 Thessaloniki, Greece;
| | - Athanasios Protogerou
- Cardiovascular Prevention and Research Unit, Department of Pathophysiology, National & Kapodistrian University of Athens, 15772 Athens, Greece;
| | - Durga Prasanna Misra
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India; (D.P.M.); (V.A.)
| | - Vikas Agarwal
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India; (D.P.M.); (V.A.)
| | - George D. Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK;
- Arthritis Research UK Epidemiology Unit, Manchester University, Manchester M13 9PL, UK
| | - Jagjit S. Teji
- Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA;
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON L4Z 4C4, Canada;
| | | | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, Nicosia 2408, Cyprus;
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22904, USA;
| | - Vijay Rathore
- AtheroPoint LLC, Roseville, CA 95611, USA; (S.K.D.); (V.R.)
| | - Mostafa Fatemi
- Department of Physiology & Biomedical Engg., Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA;
| | - Azra Alizad
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA;
| | | | - Ferenc Nagy
- Internal Medicine Department, University of Szeged, 6725 Szeged, Hungary;
| | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, 6725 Szeged, Hungary;
| | - Archna Gupta
- Radiology Department, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India;
| | - Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN 55812, USA;
| | | | - Mannudeep K. Kalra
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA;
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Suri JS, Agarwal S, Elavarthi P, Pathak R, Ketireddy V, Columbu M, Saba L, Gupta SK, Faa G, Singh IM, Turk M, Chadha PS, Johri AM, Khanna NN, Viskovic K, Mavrogeni S, Laird JR, Pareek G, Miner M, Sobel DW, Balestrieri A, Sfikakis PP, Tsoulfas G, Protogerou A, Misra DP, Agarwal V, Kitas GD, Teji JS, Al-Maini M, Dhanjil SK, Nicolaides A, Sharma A, Rathore V, Fatemi M, Alizad A, Krishnan PR, Ferenc N, Ruzsa Z, Gupta A, Naidu S, Kalra MK. Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography. Diagnostics (Basel) 2021; 11:2025. [PMID: 34829372 PMCID: PMC8625039 DOI: 10.3390/diagnostics11112025] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/26/2021] [Accepted: 10/27/2021] [Indexed: 02/05/2023] Open
Abstract
Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.
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Affiliation(s)
- Jasjit S. Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA; (S.A.); (P.E.)
| | - Sushant Agarwal
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA; (S.A.); (P.E.)
- Department of Computer Science Engineering, PSIT, Kanpur 209305, India
| | - Pranav Elavarthi
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA; (S.A.); (P.E.)
- Thomas Jefferson High School for Science and Technology, Alexandria, VA 22312, USA
| | - Rajesh Pathak
- Department of Computer Science Engineering, Rawatpura Sarkar University, Raipur 492001, India;
| | | | - Marta Columbu
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 10015 Cagliari, Italy; (M.C.); (L.S.); (A.B.)
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 10015 Cagliari, Italy; (M.C.); (L.S.); (A.B.)
| | - Suneet K. Gupta
- Department of Computer Science, Bennett University, Noida 201310, India;
| | - Gavino Faa
- Department of Pathology, Azienda Ospedaliero Universitaria (A.O.U.), 10015 Cagliari, Italy;
| | - Inder M. Singh
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
| | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753 Delmenhorst, Germany;
| | - Paramjit S. Chadha
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
| | - Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada;
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India;
| | | | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, 10558 Athens, Greece;
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA;
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA; (G.P.); (D.W.S.)
| | - Martin Miner
- Men’s Health Center, Miriam Hospital, Providence, RI 02906, USA;
| | - David W. Sobel
- Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA; (G.P.); (D.W.S.)
| | - Antonella Balestrieri
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 10015 Cagliari, Italy; (M.C.); (L.S.); (A.B.)
| | - Petros P. Sfikakis
- Rheumatology Unit, National & Kapodistrian University of Athens, 10679 Athens, Greece;
| | - George Tsoulfas
- Aristoteleion University of Thessaloniki, 54636 Thessaloniki, Greece;
| | | | - Durga Prasanna Misra
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India; (D.P.M.); (V.A.)
| | - Vikas Agarwal
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India; (D.P.M.); (V.A.)
| | - George D. Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK;
- Arthritis Research UK Epidemiology Unit, Manchester University, Manchester M13 9PT, UK
| | - Jagjit S. Teji
- Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA;
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON L4Z 4C4, Canada;
| | | | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, University of Nicosia Medical School, Nicosia 2368, Cyprus;
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22904, USA;
| | - Vijay Rathore
- AtheroPoint LLC, Roseville, CA 95611, USA; (S.K.D.); (V.R.)
| | - Mostafa Fatemi
- Department of Physiology & Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA;
| | - Azra Alizad
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA;
| | | | - Nagy Ferenc
- Internal Medicine Department, University of Szeged, 6725 Szeged, Hungary;
| | - Zoltan Ruzsa
- Zoltan Invasive Cardiology Division, University of Szeged, 6725 Szeged, Hungary;
| | - Archna Gupta
- Radiology Department, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India;
| | - Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN 55812, USA;
| | - Mannudeep K. Kalra
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA;
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