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Wang M, Peng Y, Wang Y, Luo D. Research Trends and Evolution in Radiogenomics (2005-2023): Bibliometric Analysis. Interact J Med Res 2024; 13:e51347. [PMID: 38980713 PMCID: PMC11267093 DOI: 10.2196/51347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 03/10/2024] [Accepted: 05/20/2024] [Indexed: 07/10/2024] Open
Abstract
BACKGROUND Radiogenomics is an emerging technology that integrates genomics and medical image-based radiomics, which is considered a promising approach toward achieving precision medicine. OBJECTIVE The aim of this study was to quantitatively analyze the research status, dynamic trends, and evolutionary trajectory in the radiogenomics field using bibliometric methods. METHODS The relevant literature published up to 2023 was retrieved from the Web of Science Core Collection. Excel was used to analyze the annual publication trend. VOSviewer was used for constructing the keywords co-occurrence network and the collaboration networks among countries and institutions. CiteSpace was used for citation keywords burst analysis and visualizing the references timeline. RESULTS A total of 3237 papers were included and exported in plain-text format. The annual number of publications showed an increasing annual trend. China and the United States have published the most papers in this field, with the highest number of citations in the United States and the highest average number per item in the Netherlands. Keywords burst analysis revealed that several keywords, including "big data," "magnetic resonance spectroscopy," "renal cell carcinoma," "stage," and "temozolomide," experienced a citation burst in recent years. The timeline views demonstrated that the references can be categorized into 8 clusters: lower-grade glioma, lung cancer histology, lung adenocarcinoma, breast cancer, radiation-induced lung injury, epidermal growth factor receptor mutation, late radiotherapy toxicity, and artificial intelligence. CONCLUSIONS The field of radiogenomics is attracting increasing attention from researchers worldwide, with the United States and the Netherlands being the most influential countries. Exploration of artificial intelligence methods based on big data to predict the response of tumors to various treatment methods represents a hot spot research topic in this field at present.
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Affiliation(s)
- Meng Wang
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Yun Peng
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Ya Wang
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Dehong Luo
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
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Zhang Q, Fotaki A, Ghadimi S, Wang Y, Doneva M, Wetzl J, Delfino JG, O'Regan DP, Prieto C, Epstein FH. Improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence-review of evidence and proposition of a roadmap to clinical translation. J Cardiovasc Magn Reson 2024; 26:101051. [PMID: 38909656 PMCID: PMC11331970 DOI: 10.1016/j.jocmr.2024.101051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 06/09/2024] [Accepted: 06/18/2024] [Indexed: 06/25/2024] Open
Abstract
BACKGROUND Cardiovascular magnetic resonance (CMR) is an important imaging modality for the assessment of heart disease; however, limitations of CMR include long exam times and high complexity compared to other cardiac imaging modalities. Recently advancements in artificial intelligence (AI) technology have shown great potential to address many CMR limitations. While the developments are remarkable, translation of AI-based methods into real-world CMR clinical practice remains at a nascent stage and much work lies ahead to realize the full potential of AI for CMR. METHODS Herein we review recent cutting-edge and representative examples demonstrating how AI can advance CMR in areas such as exam planning, accelerated image reconstruction, post-processing, quality control, classification and diagnosis. RESULTS These advances can be applied to speed up and simplify essentially every application including cine, strain, late gadolinium enhancement, parametric mapping, 3D whole heart, flow, perfusion and others. AI is a unique technology based on training models using data. Beyond reviewing the literature, this paper discusses important AI-specific issues in the context of CMR, including (1) properties and characteristics of datasets for training and validation, (2) previously published guidelines for reporting CMR AI research, (3) considerations around clinical deployment, (4) responsibilities of clinicians and the need for multi-disciplinary teams in the development and deployment of AI in CMR, (5) industry considerations, and (6) regulatory perspectives. CONCLUSIONS Understanding and consideration of all these factors will contribute to the effective and ethical deployment of AI to improve clinical CMR.
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Affiliation(s)
- Qiang Zhang
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK; Big Data Institute, University of Oxford, Oxford, UK.
| | - Anastasia Fotaki
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Royal Brompton Hospital, Guy's and St Thomas' NHS Foundation Trust, London, UK.
| | - Sona Ghadimi
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
| | - Yu Wang
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
| | | | - Jens Wetzl
- Siemens Healthineers AG, Erlangen, Germany.
| | - Jana G Delfino
- US Food and Drug Administration, Center for Devices and Radiological Health (CDRH), Office of Science and Engineering Laboratories (OSEL), Silver Spring, MD, USA.
| | - Declan P O'Regan
- MRC Laboratory of Medical Sciences, Imperial College London, London, UK.
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile.
| | - Frederick H Epstein
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
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Rogers MP, Janjua HM, Walczak S, Baker M, Read M, Cios K, Velanovich V, Pietrobon R, Kuo PC. Artificial Intelligence in Surgical Research: Accomplishments and Future Directions. Am J Surg 2024; 230:82-90. [PMID: 37981516 DOI: 10.1016/j.amjsurg.2023.10.045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Accepted: 10/22/2023] [Indexed: 11/21/2023]
Abstract
MINI-ABSTRACT The study introduces various methods of performing conventional ML and their implementation in surgical areas, and the need to move beyond these traditional approaches given the advent of big data. OBJECTIVE Investigate current understanding and future directions of machine learning applications, such as risk stratification, clinical data analytics, and decision support, in surgical practice. SUMMARY BACKGROUND DATA The advent of the electronic health record, near unlimited computing, and open-source computational packages have created an environment for applying artificial intelligence, machine learning, and predictive analytic techniques to healthcare. The "hype" phase has passed, and algorithmic approaches are being developed for surgery patients through all stages of care, involving preoperative, intraoperative, and postoperative components. Surgeons must understand and critically evaluate the strengths and weaknesses of these methodologies. METHODS The current body of AI literature was reviewed, emphasizing on contemporary approaches important in the surgical realm. RESULTS AND CONCLUSIONS The unrealized impacts of AI on clinical surgery and its subspecialties are immense. As this technology continues to pervade surgical literature and clinical applications, knowledge of its inner workings and shortcomings is paramount in determining its appropriate implementation.
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Affiliation(s)
- Michael P Rogers
- Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL, USA
| | - Haroon M Janjua
- Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL, USA
| | - Steven Walczak
- School of Information & Florida Center for Cybersecurity, University of South Florida, Tampa, FL, USA
| | - Marshall Baker
- Department of Surgery, Loyola University Medical Center, Maywood, IL, USA
| | - Meagan Read
- Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL, USA
| | - Konrad Cios
- Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL, USA
| | - Vic Velanovich
- Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL, USA
| | | | - Paul C Kuo
- Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL, USA.
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Cai Y, Cai YQ, Tang LY, Wang YH, Gong M, Jing TC, Li HJ, Li-Ling J, Hu W, Yin Z, Gong DX, Zhang GW. Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review. BMC Med 2024; 22:56. [PMID: 38317226 PMCID: PMC10845808 DOI: 10.1186/s12916-024-03273-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 01/23/2024] [Indexed: 02/07/2024] Open
Abstract
BACKGROUND A comprehensive overview of artificial intelligence (AI) for cardiovascular disease (CVD) prediction and a screening tool of AI models (AI-Ms) for independent external validation are lacking. This systematic review aims to identify, describe, and appraise AI-Ms of CVD prediction in the general and special populations and develop a new independent validation score (IVS) for AI-Ms replicability evaluation. METHODS PubMed, Web of Science, Embase, and IEEE library were searched up to July 2021. Data extraction and analysis were performed for the populations, distribution, predictors, algorithms, etc. The risk of bias was evaluated with the prediction risk of bias assessment tool (PROBAST). Subsequently, we designed IVS for model replicability evaluation with five steps in five items, including transparency of algorithms, performance of models, feasibility of reproduction, risk of reproduction, and clinical implication, respectively. The review is registered in PROSPERO (No. CRD42021271789). RESULTS In 20,887 screened references, 79 articles (82.5% in 2017-2021) were included, which contained 114 datasets (67 in Europe and North America, but 0 in Africa). We identified 486 AI-Ms, of which the majority were in development (n = 380), but none of them had undergone independent external validation. A total of 66 idiographic algorithms were found; however, 36.4% were used only once and only 39.4% over three times. A large number of different predictors (range 5-52,000, median 21) and large-span sample size (range 80-3,660,000, median 4466) were observed. All models were at high risk of bias according to PROBAST, primarily due to the incorrect use of statistical methods. IVS analysis confirmed only 10 models as "recommended"; however, 281 and 187 were "not recommended" and "warning," respectively. CONCLUSION AI has led the digital revolution in the field of CVD prediction, but is still in the early stage of development as the defects of research design, report, and evaluation systems. The IVS we developed may contribute to independent external validation and the development of this field.
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Affiliation(s)
- Yue Cai
- China Medical University, Shenyang, 110122, China
| | - Yu-Qing Cai
- China Medical University, Shenyang, 110122, China
| | - Li-Ying Tang
- China Medical University, Shenyang, 110122, China
| | - Yi-Han Wang
- China Medical University, Shenyang, 110122, China
| | - Mengchun Gong
- Digital Health China Co. Ltd, Beijing, 100089, China
| | - Tian-Ci Jing
- Smart Hospital Management Department, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Hui-Jun Li
- Shenyang Medical & Film Science and Technology Co. Ltd., Shenyang, 110001, China
- Enduring Medicine Smart Innovation Research Institute, Shenyang, 110001, China
| | - Jesse Li-Ling
- Institute of Genetic Medicine, School of Life Science, State Key Laboratory of Biotherapy, Sichuan University, Chengdu, 610065, China
| | - Wei Hu
- Bayi Orthopedic Hospital, Chengdu, 610017, China
| | - Zhihua Yin
- Department of Epidemiology, School of Public Health, China Medical University, Shenyang, 110122, China.
| | - Da-Xin Gong
- Smart Hospital Management Department, the First Hospital of China Medical University, Shenyang, 110001, China.
- The Internet Hospital Branch of the Chinese Research Hospital Association, Beijing, 100006, China.
| | - Guang-Wei Zhang
- Smart Hospital Management Department, the First Hospital of China Medical University, Shenyang, 110001, China.
- The Internet Hospital Branch of the Chinese Research Hospital Association, Beijing, 100006, China.
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Liang F, Yang X, Peng W, Zhen S, Cao W, Li Q, Xiao Z, Gong M, Wang Y, Gu D. Applications of digital health approaches for cardiometabolic diseases prevention and management in the Western Pacific region. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2024; 43:100817. [PMID: 38456090 PMCID: PMC10920052 DOI: 10.1016/j.lanwpc.2023.100817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 05/04/2023] [Accepted: 05/23/2023] [Indexed: 03/09/2024]
Abstract
Cardiometabolic diseases (CMDs) are the major types of non-communicable diseases, contributing to huge disease burdens in the Western Pacific region (WPR). The use of digital health (dHealth) technologies, such as wearable gadgets, mobile apps, and artificial intelligence (AI), facilitates interventions for CMDs prevention and treatment. Currently, most studies on dHealth and CMDs in WPR were conducted in a few high- and middle-income countries like Australia, China, Japan, the Republic of Korea, and New Zealand. Evidence indicated that dHealth services promoted early prevention by behavior interventions, and AI-based innovation brought automated diagnosis and clinical decision-support. dHealth brought facilitators for the doctor-patient interplay in the effectiveness, experience, and communication skills during healthcare services, with rapidly development during the pandemic of coronavirus disease 2019. In the future, the improvement of dHealth services in WPR needs to gain more policy support, enhance technology innovation and privacy protection, and perform cost-effectiveness research.
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Affiliation(s)
- Fengchao Liang
- School of Public Health and Emergency Management, Southern University of Science and Technology, 1088 Xueyuan Avenue, Shenzhen 518055, People's Republic of China
| | - Xueli Yang
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin Medical University, 22 Qixiangtai Rd, Tianjin 300070, People's Republic of China
- Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, 22 Qixiangtai Rd, Tianjin 300070, People's Republic of China
| | - Wen Peng
- Nutrition and Health Promotion Center, Department of Public Health, Medical College, Qinghai University, 251 Ningda Road, Xining City 810016, People's Republic of China
- Qinghai Provincial Key Laboratory of Prevention and Control of Glucolipid Metabolic Diseases with Traditional Chinese Medicine, Xining 810008, People's Republic of China
| | - Shihan Zhen
- School of Public Health and Emergency Management, Southern University of Science and Technology, 1088 Xueyuan Avenue, Shenzhen 518055, People's Republic of China
| | - Wenzhe Cao
- School of Public Health and Emergency Management, Southern University of Science and Technology, 1088 Xueyuan Avenue, Shenzhen 518055, People's Republic of China
| | - Qian Li
- School of Public Health and Emergency Management, Southern University of Science and Technology, 1088 Xueyuan Avenue, Shenzhen 518055, People's Republic of China
| | - Zhiyi Xiao
- School of Public Health and Emergency Management, Southern University of Science and Technology, 1088 Xueyuan Avenue, Shenzhen 518055, People's Republic of China
| | - Mengchun Gong
- Institute of Health Management, Southern Medical University, No. 1023-1063, Shatai South Road, Guangzhou 510515, People's Republic of China
| | - Youfa Wang
- The First Affiliated Hospital of Xi'an Jiaotong University Public Health Institute, Global Health Institute, School of Public Health, International Obesity and Metabolic Disease Research Center, Xi'an Jiaotong University, Xi'an 710061, People's Republic of China
| | - Dongfeng Gu
- School of Public Health and Emergency Management, Southern University of Science and Technology, 1088 Xueyuan Avenue, Shenzhen 518055, People's Republic of China
- School of Medicine, Southern University of Science and Technology, 1088 Xueyuan Avenue, Shenzhen 518055, People's Republic of China
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Kolasa K, Admassu B, Hołownia-Voloskova M, Kędzior KJ, Poirrier JE, Perni S. Systematic reviews of machine learning in healthcare: a literature review. Expert Rev Pharmacoecon Outcomes Res 2024; 24:63-115. [PMID: 37955147 DOI: 10.1080/14737167.2023.2279107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 10/31/2023] [Indexed: 11/14/2023]
Abstract
INTRODUCTION The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery. METHODS A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted. RESULTS In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively). EXPERT OPINION The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.
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Affiliation(s)
- Katarzyna Kolasa
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
| | - Bisrat Admassu
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
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Lu H, Huang L, Xie Y, Zhou Z, Cui H, Jing S, Yang Z, Zhu D, Wang S, Bao D, Liang G, Cai Z, Chen H, He W. Prediction of fractional flow reserve with enhanced ant lion optimized support vector machine. Heliyon 2023; 9:e18832. [PMID: 37588610 PMCID: PMC10425907 DOI: 10.1016/j.heliyon.2023.e18832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 07/13/2023] [Accepted: 07/31/2023] [Indexed: 08/18/2023] Open
Abstract
The evaluation of coronary morphology provides important guidance for the treatment of coronary heart disease (CHD). A chaotic Gaussian mutation antlion optimizer algorithm (CGALO) is proposed in the paper, and it is combined with SVM to construct a classification prediction model for Fractional flow reserve (FFR). To overcome the limitations of the original antlion optimizer (ALO) algorithm, the chaotic Gaussian mutation strategy is introduced, which leads to an improvement in its convergence speed and accuracy. To evaluate the proposed algorithm's performance, comparative experiments were conducted on 23 benchmark functions alongside 12 other cutting-edge optimization algorithms. The experimental outcomes demonstrate that the proposed algorithm achieves superior convergence accuracy and speed compared to the alternative comparison algorithms. Additionally, it is combined with SVM and FS to construct a hierarchical FFR classification model, which is utilized to make effective predictions for 84 patients at the affiliated hospital of medical school, Ningbo university. The experimental results demonstrate that the proposed model achieves an average accuracy of 92%. Moreover, it concludes that smoking history, number of lesion vessels, lesion location, diffuse lesions and ST segment changes, and other factors are the most critical indicators for FFR. Therefore, the model that has been established is a new FFR intelligent classification prediction technology that can effectively assist doctors in making corresponding decisions and evaluation plans.
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Affiliation(s)
- Haoxuan Lu
- Department of Cardiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China
| | - Li Huang
- Department of Emergency, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China
| | - Yanqing Xie
- Department of Cardiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China
| | - Zhong Zhou
- Department of Cardiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China
| | - Hanbin Cui
- Department of Cardiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China
| | - Sheng Jing
- Department of Cardiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China
| | - Zhuo Yang
- Department of Cardiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China
| | - Decai Zhu
- Department of Cardiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China
| | - Shiqi Wang
- Department of Cardiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China
| | - Donggang Bao
- Department of Cardiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China
| | - Guoxi Liang
- Department of Information Technology, Wenzhou Polytechnic, Wenzhou, 325035, China
| | - Zhennao Cai
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China
| | - Huiling Chen
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China
| | - Wenming He
- Department of Cardiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China
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Palmieri V, Montisci A, Vietri MT, Colombo PC, Sala S, Maiello C, Coscioni E, Donatelli F, Napoli C. Artificial intelligence, big data and heart transplantation: Actualities. Int J Med Inform 2023; 176:105110. [PMID: 37285695 DOI: 10.1016/j.ijmedinf.2023.105110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 05/21/2023] [Accepted: 05/22/2023] [Indexed: 06/09/2023]
Abstract
BACKGROUND As diagnostic and prognostic models developed by traditional statistics perform poorly in real-world, artificial intelligence (AI) and Big Data (BD) may improve the supply chain of heart transplantation (HTx), allocation opportunities, correct treatments, and finally optimize HTx outcome. We explored available studies, and discussed opportunities and limits of medical application of AI to the field of HTx. METHOD A systematic overview of studies published up to December 31st, 2022, in English on peer-revied journals, have been identified through PUBMED-MEDLINE-WEB of Science, referring to HTx, AI, BD. Studies were grouped in 4 domains based on main studies' objectives and results: etiology, diagnosis, prognosis, treatment. A systematic attempt was made to evaluate studies by the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD). RESULTS Among the 27 publications selected, none used AI applied to BD. Of the selected studies, 4 fell in the domain of etiology, 6 in the domain of diagnosis, 3 in the domain of treatment, and 17 in that of prognosis, as AI was most frequently used for algorithmic prediction and discrimination of survival, but in retrospective cohorts and registries. AI-based algorithms appeared superior to probabilistic functions to predict patterns, but external validation was rarely employed. Indeed, based on PROBAST, selected studies showed, to some extent, significant risk of bias (especially in the domain of predictors and analysis). In addition, as example of applicability in the real-world, a free-use prediction algorithm developed through AI failed to predict 1-year mortality post-HTx in cases from our center. CONCLUSIONS While AI-based prognostic and diagnostic functions performed better than those developed by traditional statistics, risk of bias, lack of external validation, and relatively poor applicability, may affect AI-based tools. More unbiased research with high quality BD meant for AI, transparency and external validations, are needed to have medical AI as a systematic aid to clinical decision making in HTx.
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Affiliation(s)
- Vittorio Palmieri
- Azienda Ospedaliera dei Colli Monaldi-Cotugno-CTO, Department of Cardiac Surgery and Transplantation, Naples, Italy.
| | - Andrea Montisci
- Division of Cardiothoracic Intensive Care, Cardiothoracic Department, ASST Spedali Civili, Brescia, Italy
| | - Maria Teresa Vietri
- Department of Precision Medicine, "Luigi Vanvitelli" University of Campania School of Medicine, Naples, Italy
| | - Paolo C Colombo
- Milstein Division of Cardiology, Department of Medicine, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
| | - Silvia Sala
- Chair of Anesthesia and Intensive Care, University of Brescia, Brescia, Italy
| | - Ciro Maiello
- Azienda Ospedaliera dei Colli Monaldi-Cotugno-CTO, Department of Cardiac Surgery and Transplantation, Naples, Italy
| | - Enrico Coscioni
- Department of Cardiac Surgery, AOU San Giovanni di Dio e Ruggi d'Aragona, Salerno, Italy
| | - Francesco Donatelli
- Department of Cardiac Surgery, Istituto Clinico Sant'Ambrogio, Milan, Italy; Chair of Cardiac Surgery, University of Milan, Milan, Italy
| | - Claudio Napoli
- Department of Advanced Medical and Surgical Sciences (DAMSS), "Luigi Vanvitelli" University of Campania School of Medicine, Naples, Italy
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He B, Kwan AC, Cho JH, Yuan N, Pollick C, Shiota T, Ebinger J, Bello NA, Wei J, Josan K, Duffy G, Jujjavarapu M, Siegel R, Cheng S, Zou JY, Ouyang D. Blinded, randomized trial of sonographer versus AI cardiac function assessment. Nature 2023; 616:520-524. [PMID: 37020027 PMCID: PMC10115627 DOI: 10.1038/s41586-023-05947-3] [Citation(s) in RCA: 66] [Impact Index Per Article: 66.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 03/13/2023] [Indexed: 04/07/2023]
Abstract
Artificial intelligence (AI) has been developed for echocardiography1-3, although it has not yet been tested with blinding and randomization. Here we designed a blinded, randomized non-inferiority clinical trial (ClinicalTrials.gov ID: NCT05140642; no outside funding) of AI versus sonographer initial assessment of left ventricular ejection fraction (LVEF) to evaluate the impact of AI in the interpretation workflow. The primary end point was the change in the LVEF between initial AI or sonographer assessment and final cardiologist assessment, evaluated by the proportion of studies with substantial change (more than 5% change). From 3,769 echocardiographic studies screened, 274 studies were excluded owing to poor image quality. The proportion of studies substantially changed was 16.8% in the AI group and 27.2% in the sonographer group (difference of -10.4%, 95% confidence interval: -13.2% to -7.7%, P < 0.001 for non-inferiority, P < 0.001 for superiority). The mean absolute difference between final cardiologist assessment and independent previous cardiologist assessment was 6.29% in the AI group and 7.23% in the sonographer group (difference of -0.96%, 95% confidence interval: -1.34% to -0.54%, P < 0.001 for superiority). The AI-guided workflow saved time for both sonographers and cardiologists, and cardiologists were not able to distinguish between the initial assessments by AI versus the sonographer (blinding index of 0.088). For patients undergoing echocardiographic quantification of cardiac function, initial assessment of LVEF by AI was non-inferior to assessment by sonographers.
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Affiliation(s)
- Bryan He
- Department of Computer Science, Stanford University, Palo Alto, CA, USA
| | - Alan C Kwan
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jae Hyung Cho
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Neal Yuan
- Department of Medicine, Division of Cardiology, San Francisco VA, UCSF, San Francisco, CA, USA
| | - Charles Pollick
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Takahiro Shiota
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Joseph Ebinger
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Natalie A Bello
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Janet Wei
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Kiranbir Josan
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Grant Duffy
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Melvin Jujjavarapu
- Enterprise Information Services, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Robert Siegel
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
| | - James Y Zou
- Department of Computer Science, Stanford University, Palo Alto, CA, USA.
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, USA.
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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10
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Kim JY, Jelinek J, Lee YH, Kim DH, Kang K, Ryu SH, Moon HR, Cho K, Rha SH, Cha JK, Issa JPJ, Kim J. Hypomethylation in MTNR1B: a novel epigenetic marker for atherosclerosis profiling using stenosis radiophenotype and blood inflammatory cells. Clin Epigenetics 2023; 15:11. [PMID: 36658621 PMCID: PMC9854223 DOI: 10.1186/s13148-023-01423-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 01/06/2023] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Changes in gene-specific promoter methylation may result from aging and environmental influences. Atherosclerosis is associated with aging and environmental effects. Thus, promoter methylation profiling may be used as an epigenetic tool to evaluate the impact of aging and the environment on atherosclerosis development. However, gene-specific methylation changes are currently inadequate epigenetic markers for predicting atherosclerosis and cardiovascular disease pathogenesis. RESULTS We profiled and validated changes in gene-specific promoter methylation associated with atherosclerosis using stenosis radiophenotypes of cranial vessels and blood inflammatory cells rather than direct sampling of atherosclerotic plaques. First, we profiled gene-specific promoter methylation changes using digital restriction enzyme analysis of methylation (DREAM) sequencing in peripheral blood mononuclear cells from eight samples each of cranial vessels with and without severe-stenosis radiophenotypes. Using DREAM sequencing profiling, 11 tags were detected in the promoter regions of the ACVR1C, ADCK5, EFNA2, ENOSF1, GLS2, KNDC1, MTNR1B, PACSIN3, PAX8-AS1, TLDC1, and ZNF7 genes. Using methylation evaluation, we found that EFNA2, ENOSF1, GLS2, KNDC1, MTNR1B, PAX8-AS1, and TLDC1 showed > 5% promoter methylation in non-plaque intima, atherosclerotic vascular tissues, and buffy coats. Using logistic regression analysis, we identified hypomethylation of MTNR1B as an independent variable for the stenosis radiophenotype prediction model by combining it with traditional atherosclerosis risk factors including age, hypertension history, and increases in creatinine, lipoprotein (a), and homocysteine. We performed fivefold cross-validation of the prediction model using 384 patients with ischemic stroke (50 [13%] no-stenosis and 334 [87%] > 1 stenosis radiophenotype). For the cross-validation, the training dataset included 70% of the dataset. The prediction model showed an accuracy of 0.887, specificity to predict stenosis radiophenotype of 0.940, sensitivity to predict no-stenosis radiophenotype of 0.533, and area under receiver operating characteristic curve of 0.877 to predict stenosis radiophenotype from the test dataset including 30% of the dataset. CONCLUSIONS We identified and validated MTNR1B hypomethylation as an epigenetic marker to predict cranial vessel atherosclerosis using stenosis radiophenotypes and blood inflammatory cells rather than direct atherosclerotic plaque sampling.
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Affiliation(s)
- Jee Yeon Kim
- grid.254230.20000 0001 0722 6377Department of Neurology and Neuroepigenetics Laboratory, College of Medicine and Hospital, Chungnam National University, 282 Moonhwaro, Joongku, Daejeon, 35015 South Korea
| | - Jaroslav Jelinek
- grid.282012.b0000 0004 0627 5048Coriell Institute for Medical Research, Camden, NJ USA
| | - Young Ho Lee
- grid.254230.20000 0001 0722 6377Department of Anatomy, College of Medicine, Chungnam National University, Daejeon, South Korea
| | - Dae Hyun Kim
- grid.412048.b0000 0004 0647 1081Department of Neurology, Dong-A University Hospital, Busan, South Korea
| | - Keunsoo Kang
- grid.411982.70000 0001 0705 4288Department of Microbiology, College of Science and Technology, Dankook University, Cheonan, South Korea
| | - Su Hyun Ryu
- grid.254230.20000 0001 0722 6377Department of Neurology and Neuroepigenetics Laboratory, College of Medicine and Hospital, Chungnam National University, 282 Moonhwaro, Joongku, Daejeon, 35015 South Korea
| | - Hye Rin Moon
- grid.254230.20000 0001 0722 6377Department of Neurology and Neuroepigenetics Laboratory, College of Medicine and Hospital, Chungnam National University, 282 Moonhwaro, Joongku, Daejeon, 35015 South Korea
| | - Kwangjo Cho
- grid.412048.b0000 0004 0647 1081Department of Thoracic and Cardiovascular Surgery, Dong-A University Hospital, Busan, South Korea
| | - Seo Hee Rha
- grid.412048.b0000 0004 0647 1081Department of Pathology, Dong-A University Hospital, Busan, South Korea
| | - Jae Kwan Cha
- grid.254230.20000 0001 0722 6377Department of Anatomy, College of Medicine, Chungnam National University, Daejeon, South Korea
| | - Jean-Pierre J. Issa
- grid.282012.b0000 0004 0627 5048Coriell Institute for Medical Research, Camden, NJ USA
| | - Jei Kim
- grid.254230.20000 0001 0722 6377Department of Neurology and Neuroepigenetics Laboratory, College of Medicine and Hospital, Chungnam National University, 282 Moonhwaro, Joongku, Daejeon, 35015 South Korea ,grid.411665.10000 0004 0647 2279Daejeon-Chungnam Regional Cerebrovascular Center, Chungnam National University Hospital, Daejeon, South Korea
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11
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Montisci A, Palmieri V, Vietri MT, Sala S, Maiello C, Donatelli F, Napoli C. Big Data in cardiac surgery: real world and perspectives. J Cardiothorac Surg 2022; 17:277. [PMID: 36309702 PMCID: PMC9617748 DOI: 10.1186/s13019-022-02025-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 10/14/2022] [Indexed: 11/10/2022] Open
Abstract
Big Data, and the derived analysis techniques, such as artificial intelligence and machine learning, have been considered a revolution in the modern practice of medicine. Big Data comes from multiple sources, encompassing electronic health records, clinical studies, imaging data, registries, administrative databases, patient-reported outcomes and OMICS profiles. The main objective of such analyses is to unveil hidden associations and patterns. In cardiac surgery, the main targets for the use of Big Data are the construction of predictive models to recognize patterns or associations better representing the individual risk or prognosis compared to classical surgical risk scores. The results of these studies contributed to kindle the interest for personalized medicine and contributed to recognize the limitations of randomized controlled trials in representing the real world. However, the main sources of evidence for guidelines and recommendations remain RCTs and meta-analysis. The extent of the revolution of Big Data and new analytical models in cardiac surgery is yet to be determined.
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12
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Sahu M, Gupta R, Ambasta RK, Kumar P. Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2022; 190:57-100. [PMID: 36008002 DOI: 10.1016/bs.pmbts.2022.03.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
The integration of artificial intelligence in precision medicine has revolutionized healthcare delivery. Precision medicine identifies the phenotype of particular patients with less-common responses to treatment. Recent studies have demonstrated that translational research exploring the convergence between artificial intelligence and precision medicine will help solve the most difficult challenges facing precision medicine. Here, we discuss different aspects of artificial intelligence in precision medicine that improve healthcare delivery. First, we discuss how artificial intelligence changes the landscape of precision medicine and the evolution of artificial intelligence in precision medicine. Second, we highlight the synergies between artificial intelligence and precision medicine and promises of artificial intelligence and precision medicine in healthcare delivery. Third, we briefly explain the promise of big data analytics and the integration of nanomaterials in precision medicine. Last, we highlight the challenges and opportunities of artificial intelligence in precision medicine.
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Affiliation(s)
- Mehar Sahu
- Molecular Neuroscience and Functional Genomics Laboratory, Delhi Technological University (Formerly Delhi College of Engineering), Shahbad Daulatpur, Delhi, India
| | - Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Delhi Technological University (Formerly Delhi College of Engineering), Shahbad Daulatpur, Delhi, India
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Delhi Technological University (Formerly Delhi College of Engineering), Shahbad Daulatpur, Delhi, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Delhi Technological University (Formerly Delhi College of Engineering), Shahbad Daulatpur, Delhi, India.
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13
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Schiano C, Balbi C, Burrello J, Ruocco A, Infante T, Fiorito C, Panella S, Barile L, Mauro C, Vassalli G, Napoli C. De novo DNA methylation induced by circulating extracellular vesicles from acute coronary syndrome patients. Atherosclerosis 2022; 354:41-52. [PMID: 35830762 DOI: 10.1016/j.atherosclerosis.2022.06.1026] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 06/25/2022] [Accepted: 06/29/2022] [Indexed: 11/02/2022]
Abstract
BACKGROUND AND AIMS DNA methylation is associated with gene silencing, but its clinical role in cardiovascular diseases (CVDs) remains to be elucidated. We hypothesized that extracellular vesicles (EVs) may carry epigenetic changes, showing themselves as a potentially valuable non-invasive diagnostic liquid biopsy. We isolated and characterized circulating EVs of acute coronary syndrome (ACS) patients and assessed their role on DNA methylation in epigenetic modifications. METHODS EVs were recovered from plasma of 19 ACS patients and 50 healthy subjects (HS). Flow cytometry, qRT-PCR, and Western blot (WB) were performed to evaluate both intra-vesicular and intra-cellular signals. ShinyGO, PANTHER, and STRING tools were used to perform GO and PPI network analyses. RESULTS ACS-derived EVs showed increased levels of DNA methyltransferases (DNMTs) (p<0.001) and Ten-eleven translocation (TET) genes reduction. Specifically, de novo methylation transcripts, as DNMT3A and DNMT3B, were significantly increased in plasma ACS-EVs. DNA methylation analysis on PBMCs from healthy donors treated with HS- and ACS-derived EVs showed an important role of DNMTs carried by EVs. PPI network analysis evidenced that ACS-EVs induced changes in PBMC methylome. In the most enriched subnetwork, the hub gene SRC was connected to NOTCH1, FOXO3, CDC42, IKBKG, RXRA, DGKG, BAIAP2 genes that were showed to have many molecular effects on various cell types into onset of several CVDs. Modulation in gene expression after ACS-EVs treatment was confirmed for SRC, NOTCH1, FOXO3, RXRA, DGKG and BAIAP2 (p<0.05). CONCLUSIONS Our data showed an important role for ACS-derived EVs in gene expression modulation through de novo DNA methylation signals, and modulating signalling pathways in target cells.
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Affiliation(s)
- Concetta Schiano
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania Luigi Vanvitelli, Naples, Italy; Cellular and Molecular Cardiology lab Istituto Cardiocentro Ticino-EOC, Lugano, Switzerland; Laboratories for Translation Research, EOC, Bellinzona, Switzerland.
| | - Carolina Balbi
- Cellular and Molecular Cardiology lab Istituto Cardiocentro Ticino-EOC, Lugano, Switzerland; Laboratories for Translation Research, EOC, Bellinzona, Switzerland; Center for Molecular Cardiology, Zurich, Switzerland
| | - Jacopo Burrello
- Laboratories for Translation Research, EOC, Bellinzona, Switzerland; Cardiovascular Theranostics, Istituto Cardiocentro Ticino-EOC, Lugano, Switzerland
| | - Antonio Ruocco
- Unit of Cardiovascular Diseases and Arrhythmias, Antonio Cardarelli Hospital, Naples, Italy
| | - Teresa Infante
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania Luigi Vanvitelli, Naples, Italy
| | | | - Stefano Panella
- Laboratories for Translation Research, EOC, Bellinzona, Switzerland; Cardiovascular Theranostics, Istituto Cardiocentro Ticino-EOC, Lugano, Switzerland
| | - Lucio Barile
- Laboratories for Translation Research, EOC, Bellinzona, Switzerland; Cardiovascular Theranostics, Istituto Cardiocentro Ticino-EOC, Lugano, Switzerland
| | - Ciro Mauro
- Unit of Cardiovascular Diseases and Arrhythmias, Antonio Cardarelli Hospital, Naples, Italy
| | - Giuseppe Vassalli
- Cellular and Molecular Cardiology lab Istituto Cardiocentro Ticino-EOC, Lugano, Switzerland; Laboratories for Translation Research, EOC, Bellinzona, Switzerland; Center for Molecular Cardiology, Zurich, Switzerland
| | - Claudio Napoli
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania Luigi Vanvitelli, Naples, Italy; Division of Clinical Immunology, Immunohematology, Transfusion Medicine and Transplant Immunology (SIMT), Regional Reference Laboratory of Transplant Immunology (LIT), Azienda Universitaria Policlinico (AOU), Naples, Italy
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14
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Piayda K, Hellhammer K, Veulemans V, Afzal S, Klein K, Berisha N, Leuders P, Erkens R, Kirchner J, Heidari H, Kelm M, Antoch G, Zeus T, Quast C. Multi-Slice Computed Tomography Analysis in Patients Undergoing Transcatheter Aortic Valve Replacement – Impact of Workflows on Measurement of Virtual Aortic Annulus and Valve Size. Front Cardiovasc Med 2022; 9:877511. [PMID: 35800172 PMCID: PMC9253507 DOI: 10.3389/fcvm.2022.877511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 05/18/2022] [Indexed: 11/13/2022] Open
Abstract
Exact and reliable measurements of anatomical dimensions in pre-procedural multi-slice computed tomography (MSCT) scans are crucial for optimal valve sizing and clinical results of transcatheter aortic valve replacement (TAVR). This study aimed to investigate interrater reliability between routinely used workflows for pre-procedural analysis. MSCT scans of 329 patients scheduled for TAVR were analyzed using both a 3mensio and SECTRA IDS7 platform. The results were retrospectively compared using the intraclass correlation coefficient, revealing excellent correlation in the analysis of simple diameters and poor correlation in the assessment of more complex structures with impact on calculated valve size.
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Affiliation(s)
- Kerstin Piayda
- Department of Cardiology, Pulmonology and Vascular Medicine, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Katharina Hellhammer
- Department of Cardiology, Pulmonology and Vascular Medicine, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Verena Veulemans
- Department of Cardiology, Pulmonology and Vascular Medicine, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Shazia Afzal
- Department of Cardiology, Pulmonology and Vascular Medicine, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Kathrin Klein
- Department of Cardiology, Pulmonology and Vascular Medicine, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Nora Berisha
- Department of Cardiology, Pulmonology and Vascular Medicine, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Pia Leuders
- Department of Cardiology, Pulmonology and Vascular Medicine, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Ralf Erkens
- Department of Cardiology, Pulmonology and Vascular Medicine, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Julian Kirchner
- Department of Diagnostic and Interventional Radiology, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Houtan Heidari
- Department of Cardiology, Pulmonology and Vascular Medicine, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Malte Kelm
- Department of Cardiology, Pulmonology and Vascular Medicine, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
- CARID (Cardiovascular Research Institute Düsseldorf), Düsseldorf, Germany
| | - Gerald Antoch
- Department of Diagnostic and Interventional Radiology, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Tobias Zeus
- Department of Cardiology, Pulmonology and Vascular Medicine, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Christine Quast
- Department of Cardiology, Pulmonology and Vascular Medicine, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
- *Correspondence: Christine Quast
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15
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Mirón Mombiela R, Arildskov AR, Bruun FJ, Hasselbalch LH, Holst KB, Rasmussen SH, Borrás C. What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies. Int J Mol Sci 2022; 23:6504. [PMID: 35742947 PMCID: PMC9224495 DOI: 10.3390/ijms23126504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/07/2022] [Accepted: 06/08/2022] [Indexed: 02/04/2023] Open
Abstract
(1) Background: Radiogenomics is motivated by the concept that biomedical images contain information that reflects underlying pathophysiology. This review focused on papers that used genetics to validate their radiomics models and outcomes and assess their contribution to this emerging field. (2) Methods: All original research with the words radiomics and genomics in English and performed in humans up to 31 January 2022, were identified on Medline and Embase. The quality of the studies was assessed with Radiomic Quality Score (RQS) and the Cochrane recommendation for diagnostic accuracy study Quality Assessment 2. (3) Results: 45 studies were included in our systematic review, and more than 50% were published in the last two years. The studies had a mean RQS of 12, and the studied tumors were very diverse. Up to 83% investigated the prognosis as the main outcome, with the rest focusing on response to treatment and risk assessment. Most applied either transcriptomics (54%) and/or genetics (35%) for genetic validation. (4) Conclusions: There is enough evidence to state that new science has emerged, focusing on establishing an association between radiological features and genomic/molecular expression to explain underlying disease mechanisms and enhance prognostic, risk assessment, and treatment response radiomics models in cancer patients.
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Affiliation(s)
- Rebeca Mirón Mombiela
- Radiology Derpartment, Herlev og Gentofte Hospital, Borgmester Ib Juuls Vej 17, Opgang 4, 4.Etage, E2, 2730 Herlev, Denmark; (A.R.A.); (F.J.B.); (L.H.H.); (K.B.H.); (S.H.R.)
| | - Anne Rix Arildskov
- Radiology Derpartment, Herlev og Gentofte Hospital, Borgmester Ib Juuls Vej 17, Opgang 4, 4.Etage, E2, 2730 Herlev, Denmark; (A.R.A.); (F.J.B.); (L.H.H.); (K.B.H.); (S.H.R.)
| | - Frederik Jager Bruun
- Radiology Derpartment, Herlev og Gentofte Hospital, Borgmester Ib Juuls Vej 17, Opgang 4, 4.Etage, E2, 2730 Herlev, Denmark; (A.R.A.); (F.J.B.); (L.H.H.); (K.B.H.); (S.H.R.)
| | - Lotte Harries Hasselbalch
- Radiology Derpartment, Herlev og Gentofte Hospital, Borgmester Ib Juuls Vej 17, Opgang 4, 4.Etage, E2, 2730 Herlev, Denmark; (A.R.A.); (F.J.B.); (L.H.H.); (K.B.H.); (S.H.R.)
| | - Kristine Bærentz Holst
- Radiology Derpartment, Herlev og Gentofte Hospital, Borgmester Ib Juuls Vej 17, Opgang 4, 4.Etage, E2, 2730 Herlev, Denmark; (A.R.A.); (F.J.B.); (L.H.H.); (K.B.H.); (S.H.R.)
| | - Sine Hvid Rasmussen
- Radiology Derpartment, Herlev og Gentofte Hospital, Borgmester Ib Juuls Vej 17, Opgang 4, 4.Etage, E2, 2730 Herlev, Denmark; (A.R.A.); (F.J.B.); (L.H.H.); (K.B.H.); (S.H.R.)
| | - Consuelo Borrás
- Freshage Research Group, Department of Physiology, Faculty of Medicine, University of Valencia, 46010 Valencia, Spain
- CIBERFES, Institute of Health Research-INCLIVA, 46010 Valencia, Spain
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16
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Dai H, Younis A, Kong JD, Puce L, Jabbour G, Yuan H, Bragazzi NL. Big Data in Cardiology: State-of-Art and Future Prospects. Front Cardiovasc Med 2022; 9:844296. [PMID: 35433868 PMCID: PMC9010556 DOI: 10.3389/fcvm.2022.844296] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 02/24/2022] [Indexed: 11/23/2022] Open
Abstract
Cardiological disorders contribute to a significant portion of the global burden of disease. Cardiology can benefit from Big Data, which are generated and released by different sources and channels, like epidemiological surveys, national registries, electronic clinical records, claims-based databases (epidemiological Big Data), wet-lab, and next-generation sequencing (molecular Big Data), smartphones, smartwatches, and other mobile devices, sensors and wearable technologies, imaging techniques (computational Big Data), non-conventional data streams such as social networks, and web queries (digital Big Data), among others. Big Data is increasingly having a more and more relevant role, being highly ubiquitous and pervasive in contemporary society and paving the way for new, unprecedented perspectives in biomedicine, including cardiology. Big Data can be a real paradigm shift that revolutionizes cardiological practice and clinical research. However, some methodological issues should be properly addressed (like recording and association biases) and some ethical issues should be considered (such as privacy). Therefore, further research in the field is warranted.
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Affiliation(s)
- Haijiang Dai
- Department of Cardiology, The Third Xiangya Hospital, Central South University, Changsha, China
- Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON, Canada
| | - Arwa Younis
- Clinical Cardiovascular Research Center, University of Rochester Medical Center, Rochester, New York, NY, United States
| | - Jude Dzevela Kong
- Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON, Canada
| | - Luca Puce
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Georges Jabbour
- Physical Education Department, College of Education, Qatar University, Doha, Qatar
| | - Hong Yuan
- Department of Cardiology, The Third Xiangya Hospital, Central South University, Changsha, China
- Hong Yuan
| | - Nicola Luigi Bragazzi
- Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON, Canada
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- Postgraduate School of Public Health, Department of Health Sciences, University of Genoa, Genoa, Italy
- Section of Musculoskeletal Disease, Leeds Institute of Molecular Medicine, NIHR Leeds Musculoskeletal Biomedical Research Unit, University of Leeds, Chapel Allerton Hospital, Leeds, United Kingdom
- *Correspondence: Nicola Luigi Bragazzi
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