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Novak A, Hollowday M, Espinosa Morgado AT, Oke J, Shelmerdine S, Woznitza N, Metcalfe D, Costa ML, Wilson S, Kiam JS, Vaz J, Limphaibool N, Ventre J, Jones D, Greenhalgh L, Gleeson F, Welch N, Mistry A, Devic N, Teh J, Ather S. Evaluating the impact of artificial intelligence-assisted image analysis on the diagnostic accuracy of front-line clinicians in detecting fractures on plain X-rays (FRACT-AI): protocol for a prospective observational study. BMJ Open 2024; 14:e086061. [PMID: 39237277 PMCID: PMC11381697 DOI: 10.1136/bmjopen-2024-086061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 08/15/2024] [Indexed: 09/07/2024] Open
Abstract
INTRODUCTION Missed fractures are the most frequent diagnostic error attributed to clinicians in UK emergency departments and a significant cause of patient morbidity. Recently, advances in computer vision have led to artificial intelligence (AI)-enhanced model developments, which can support clinicians in the detection of fractures. Previous research has shown these models to have promising effects on diagnostic performance, but their impact on the diagnostic accuracy of clinicians in the National Health Service (NHS) setting has not yet been fully evaluated. METHODS AND ANALYSIS A dataset of 500 plain radiographs derived from Oxford University Hospitals (OUH) NHS Foundation Trust will be collated to include all bones except the skull, facial bones and cervical spine. The dataset will be split evenly between radiographs showing one or more fractures and those without. The reference ground truth for each image will be established through independent review by two senior musculoskeletal radiologists. A third senior radiologist will resolve disagreements between two primary radiologists. The dataset will be analysed by a commercially available AI tool, BoneView (Gleamer, Paris, France), and its accuracy for detecting fractures will be determined with reference to the ground truth diagnosis. We will undertake a multiple case multiple reader study in which clinicians interpret all images without AI support, then repeat the process with access to AI algorithm output following a 4-week washout. 18 clinicians will be recruited as readers from four hospitals in England, from six distinct clinical groups, each with three levels of seniority (early-stage, mid-stage and later-stage career). Changes in the accuracy, confidence and speed of reporting will be compared with and without AI support. Readers will use a secure web-based DICOM (Digital Imaging and Communications in Medicine) viewer (www.raiqc.com), allowing radiograph viewing and abnormality identification. Pooled analyses will be reported for overall reader performance as well as for subgroups including clinical role, level of seniority, pathological finding and difficulty of image. ETHICS AND DISSEMINATION The study has been approved by the UK Healthcare Research Authority (IRAS 310995, approved on 13 December 2022). The use of anonymised retrospective radiographs has been authorised by OUH NHS Foundation Trust. The results will be presented at relevant conferences and published in a peer-reviewed journal. TRIAL REGISTRATION NUMBERS This study is registered with ISRCTN (ISRCTN19562541) and ClinicalTrials.gov (NCT06130397). The paper reports the results of a substudy of STEDI2 (Simulation Training for Emergency Department Imaging Phase 2).
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Affiliation(s)
- Alex Novak
- Emergency Medicine Research Oxford, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Max Hollowday
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | | | - Jason Oke
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Susan Shelmerdine
- Clinical Radiology, Great Ormond Street Hospital for Children, London, UK
- Radiology, UCL GOSH ICH, London, UK
- NIHR Great Ormond Street Hospital Biomedical Research Centre, London, UK
| | - Nick Woznitza
- Radiology, University College London Hospitals NHS Foundation Trust, London, UK
- Canterbury Christ Church University, Canterbury Christ Church University, Canterbury, UK
| | - David Metcalfe
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Matthew L Costa
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), Oxford Trauma & Emergency Care (OxTEC), University of Oxford, Oxford, UK
| | - Sarah Wilson
- Frimley Health NHS Foundation Trust, Frimley, UK
| | - Jian Shen Kiam
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - James Vaz
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | | | | | | | | | - Fergus Gleeson
- Department of Oncology, University of Oxford, Oxford, UK
| | - Nick Welch
- Patient and Public Involvement Member, Oxford, UK
| | - Alpesh Mistry
- Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
- North West MSK Imaging, Liverpool, UK
| | - Natasa Devic
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - James Teh
- Nuffield Orthopaedic Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Sarim Ather
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
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2
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Nazeri A, Majidpour A, Nazeri A, Kamrani A, Aazh H. Prevalence of Ear-Related Problems in Individuals Recovered From COVID-19. IRANIAN JOURNAL OF OTORHINOLARYNGOLOGY 2024; 36:489-497. [PMID: 38745685 PMCID: PMC11090090 DOI: 10.22038/ijorl.2024.71040.3414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Accepted: 04/23/2024] [Indexed: 05/16/2024]
Abstract
Introduction The aim was to assess prevalence of tinnitus, hyperacusis, hearing and balance problems among patients recovered from COVID-19 infection. Self-reported ear and hearing symptoms were compared in three groups comprising: confirmed COVID-19, possible COVID-19, and non-COVID-19. Materials and Methods 1649 participants completed the survey in this cross-sectional study. The mean age was 34 years and 65% were female. Participants with confirmed and possible COVID-19 were asked if after their infection (compared to the past) they experienced hearing loss, ringing or whistling noises, fullness or blockage in their ears, loudness of the sounds that are normal to other people bother them more (an indication of hyperacusis), dizziness, giddiness, or imbalance. Results Among participants with confirmed COVID-19, 16% reported that compared to the past their hearing has decreased, 21.5% noticed tinnitus, 22.5% aural fullness, 26.1% hyperacusis and 17.3% balance problems. Regression models showed that compared to the non-COVID-19 group, participants with confirmed COVID-19 had odds ratios (ORs) of significantly greater than 1 in predicting presence of self-reported symptoms of hearing loss, tinnitus, aural fullness, hyperacusis and balance problems, OR=1.96 (p=0.001), OR=1.63 (p=0.003), OR=1.8 (p<0.001), OR=2.2 (p<0.001), and OR=2.99 (p<0.001), respectively. Conclusions There seem to be higher prevalence of self-report symptoms of ear-related problems among individuals with confirmed COVID-19 infection compared to a non-COVID-19 group during the pandemic.
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Affiliation(s)
- Ahmadreza Nazeri
- Department of Audiology, School of Rehabilitation, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Amir Majidpour
- Department of Audiology, School of Rehabilitation, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Ali Nazeri
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
| | - Ali Kamrani
- Department of Occupational Therapy, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran.
| | - Hashir Aazh
- Hashir International Specialist Clinics & Research Institute for Misophonia, Tinnitus and Hyperacusis, London, UK.
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Hurvitz N, Ilan Y. The Constrained-Disorder Principle Assists in Overcoming Significant Challenges in Digital Health: Moving from "Nice to Have" to Mandatory Systems. Clin Pract 2023; 13:994-1014. [PMID: 37623270 PMCID: PMC10453547 DOI: 10.3390/clinpract13040089] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 08/16/2023] [Accepted: 08/18/2023] [Indexed: 08/26/2023] Open
Abstract
The success of artificial intelligence depends on whether it can penetrate the boundaries of evidence-based medicine, the lack of policies, and the resistance of medical professionals to its use. The failure of digital health to meet expectations requires rethinking some of the challenges faced. We discuss some of the most significant challenges faced by patients, physicians, payers, pharmaceutical companies, and health systems in the digital world. The goal of healthcare systems is to improve outcomes. Assisting in diagnosing, collecting data, and simplifying processes is a "nice to have" tool, but it is not essential. Many of these systems have yet to be shown to improve outcomes. Current outcome-based expectations and economic constraints make "nice to have," "assists," and "ease processes" insufficient. Complex biological systems are defined by their inherent disorder, bounded by dynamic boundaries, as described by the constrained disorder principle (CDP). It provides a platform for correcting systems' malfunctions by regulating their degree of variability. A CDP-based second-generation artificial intelligence system provides solutions to some challenges digital health faces. Therapeutic interventions are held to improve outcomes with these systems. In addition to improving clinically meaningful endpoints, CDP-based second-generation algorithms ensure patient and physician engagement and reduce the health system's costs.
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Affiliation(s)
| | - Yaron Ilan
- Hadassah Medical Center, Department of Medicine, Faculty of Medicine, Hebrew University, POB 1200, Jerusalem IL91120, Israel;
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Borgheresi A, Agostini A, Pierpaoli L, Bruno A, Valeri T, Danti G, Bicci E, Gabelloni M, De Muzio F, Brunese MC, Bruno F, Palumbo P, Fusco R, Granata V, Gandolfo N, Miele V, Barile A, Giovagnoni A. Tips and Tricks in Thoracic Radiology for Beginners: A Findings-Based Approach. Tomography 2023; 9:1153-1186. [PMID: 37368547 PMCID: PMC10301342 DOI: 10.3390/tomography9030095] [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: 05/05/2023] [Revised: 06/03/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023] Open
Abstract
This review has the purpose of illustrating schematically and comprehensively the key concepts for the beginner who approaches chest radiology for the first time. The approach to thoracic imaging may be challenging for the beginner due to the wide spectrum of diseases, their overlap, and the complexity of radiological findings. The first step consists of the proper assessment of the basic imaging findings. This review is divided into three main districts (mediastinum, pleura, focal and diffuse diseases of the lung parenchyma): the main findings will be discussed in a clinical scenario. Radiological tips and tricks, and relative clinical background, will be provided to orient the beginner toward the differential diagnoses of the main thoracic diseases.
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Affiliation(s)
- Alessandra Borgheresi
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Via Tronto 10/a, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliero Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
| | - Andrea Agostini
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Via Tronto 10/a, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliero Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Luca Pierpaoli
- School of Radiology, University Politecnica delle Marche, Via Tronto 10/a, 60126 Ancona, Italy
| | - Alessandra Bruno
- School of Radiology, University Politecnica delle Marche, Via Tronto 10/a, 60126 Ancona, Italy
| | - Tommaso Valeri
- School of Radiology, University Politecnica delle Marche, Via Tronto 10/a, 60126 Ancona, Italy
| | - Ginevra Danti
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, 50134 Florence, Italy
| | - Eleonora Bicci
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, 50134 Florence, Italy
| | - Michela Gabelloni
- Nuclear Medicine Unit, Department of Translational Research, University of Pisa, 56126 Pisa, Italy
| | - Federica De Muzio
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100 Campobasso, Italy
| | - Maria Chiara Brunese
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100 Campobasso, Italy
| | - Federico Bruno
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
- Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health, Unit 1, 67100 L’Aquila, Italy
| | - Pierpaolo Palumbo
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
- Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health, Unit 1, 67100 L’Aquila, Italy
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Nicoletta Gandolfo
- Diagnostic Imaging Department, Villa Scassi Hospital-ASL 3, 16149 Genoa, Italy
| | - Vittorio Miele
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, 50134 Florence, Italy
| | - Antonio Barile
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100 L’Aquila, Italy
| | - Andrea Giovagnoni
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Via Tronto 10/a, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliero Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
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Panico A, Gatta G, Salvia A, Grezia GD, Fico N, Cuccurullo V. Radiomics in Breast Imaging: Future Development. J Pers Med 2023; 13:jpm13050862. [PMID: 37241032 DOI: 10.3390/jpm13050862] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 05/02/2023] [Accepted: 05/16/2023] [Indexed: 05/28/2023] Open
Abstract
Breast cancer is the most common and most commonly diagnosed non-skin cancer in women. There are several risk factors related to habits and heredity, and screening is essential to reduce the incidence of mortality. Thanks to screening and increased awareness among women, most breast cancers are diagnosed at an early stage, increasing the chances of cure and survival. Regular screening is essential. Mammography is currently the gold standard for breast cancer diagnosis. In mammography, we can encounter problems with the sensitivity of the instrument; in fact, in the case of a high density of glands, the ability to detect small masses is reduced. In fact, in some cases, the lesion may not be particularly evident, it may be hidden, and it is possible to incur false negatives as partial details that may escape the radiologist's eye. The problem is, therefore, substantial, and it makes sense to look for techniques that can increase the quality of diagnosis. In recent years, innovative techniques based on artificial intelligence have been used in this regard, which are able to see where the human eye cannot reach. In this paper, we can see the application of radiomics in mammography.
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Affiliation(s)
- Alessandra Panico
- Radiology Division, Department of Precision Medicine, Università della Campania "Luigi Vanvitelli", 80138 Naples, Italy
| | - Gianluca Gatta
- Radiology Division, Department of Precision Medicine, Università della Campania "Luigi Vanvitelli", 80138 Naples, Italy
| | - Antonio Salvia
- Radiology Division, Department of Precision Medicine, Università della Campania "Luigi Vanvitelli", 80138 Naples, Italy
| | | | - Noemi Fico
- Department of Physics "Ettore Pancini", Università di Napoli Federico II, 80126 Naples, Italy
| | - Vincenzo Cuccurullo
- Nuclear Medicine Unit, Department of Precision Medicine, Università della Campania "Luigi Vanvitelli", 80138 Naples, Italy
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Karbasi Z, Gohari SH, Sabahi A. Bibliometric analysis of the use of artificial intelligence in COVID-19 based on scientific studies. Health Sci Rep 2023; 6:e1244. [PMID: 37152228 PMCID: PMC10158785 DOI: 10.1002/hsr2.1244] [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: 12/02/2022] [Revised: 04/11/2023] [Accepted: 04/16/2023] [Indexed: 05/09/2023] Open
Abstract
Background and Aims One such strategy is citation analysis used by researchers for research planning an article referred to by another article receives a "citation." By using bibliometric analysis, the development of research areas and authors' influence can be investigated. The current study aimed to identify and analyze the characteristics of 100 highly cited articles on the use of artificial intelligence concerning COVID-19. Methods On July 27, 2022, this database was searched using the keywords "artificial intelligence" and "COVID-19" in the topic. After extensive searching, all retrieved articles were sorted by the number of citations, and 100 highly cited articles were included based on the number of citations. The following data were extracted: year of publication, type of study, name of journal, country, number of citations, language, and keywords. Results The average number of citations for 100 highly cited articles was 138.54. The top three cited articles with 745, 596, and 549 citations. The top 100 articles were all in English and were published in 2020 and 2021. China was the most prolific country with 19 articles, followed by the United States with 15 articles and India with 10 articles. Conclusion The current bibliometric analysis demonstrated the significant growth of the use of artificial intelligence for COVID-19. Using these results, research priorities are more clearly defined, and researchers can focus on hot topics.
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Affiliation(s)
- Zahra Karbasi
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
- Department of Health Information Sciences, Faculty of Management and Medical Information SciencesKerman University of Medical SciencesKermanIran
| | - Sadrieh H. Gohari
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
| | - Azam Sabahi
- Department of Health Information Technology, Ferdows School of Health and Allied Medical SciencesBirjand University of Medical SciencesBirjandIran
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Grassi F, Granata V, Fusco R, De Muzio F, Cutolo C, Gabelloni M, Borgheresi A, Danti G, Picone C, Giovagnoni A, Miele V, Gandolfo N, Barile A, Nardone V, Grassi R. Radiation Recall Pneumonitis: The Open Challenge in Differential Diagnosis of Pneumonia Induced by Oncological Treatments. J Clin Med 2023; 12:jcm12041442. [PMID: 36835977 PMCID: PMC9964719 DOI: 10.3390/jcm12041442] [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: 01/08/2023] [Revised: 02/06/2023] [Accepted: 02/07/2023] [Indexed: 02/17/2023] Open
Abstract
The treatment of primary and secondary lung neoplasms now sees the fundamental role of radiotherapy, associated with surgery and systemic therapies. The improvement in survival outcomes has also increased attention to the quality of life, treatment compliance and the management of side effects. The role of imaging is not only limited to recognizing the efficacy of treatment but also to identifying, as soon as possible, the uncommon effects, especially when more treatments, such as chemotherapy, immunotherapy and radiotherapy, are associated. Radiation recall pneumonitis is an uncommon treatment complication that should be correctly characterized, and it is essential to recognize the mechanisms of radiation recall pneumonitis pathogenesis and diagnostic features in order to promptly identify them and adopt the best therapeutic strategy, with the shortest possible withdrawal of the current oncological drug. In this setting, artificial intelligence could have a critical role, although a larger patient data set is required.
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Affiliation(s)
- Francesca Grassi
- Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, 80127 Naples, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
- Correspondence:
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80015 Naples, Italy
| | - Federica De Muzio
- Diagnostic Imaging Section, Department of Medical and Surgical Sciences & Neurosciences, University of Molise, 86100 Campobasso, Italy
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84084 Salerno, Italy
| | - Michela Gabelloni
- Department of Translational Research, Diagnostic and Interventional Radiology, University of Pisa, 56126 Pisa, Italy
| | - Alessandra Borgheresi
- Department of Clinical, Special and Dental Sciences, University Politecnica Delle Marche, Via Conca 71, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
| | - Ginevra Danti
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Carmine Picone
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Andrea Giovagnoni
- Department of Clinical, Special and Dental Sciences, University Politecnica Delle Marche, Via Conca 71, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
| | - Vittorio Miele
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Nicoletta Gandolfo
- Diagnostic Imaging Department, Villa Scassi Hospital-ASL 3, Corso Scassi 1, 16149 Genoa, Italy
| | - Antonio Barile
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy
| | - Valerio Nardone
- Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, 80127 Naples, Italy
| | - Roberta Grassi
- Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, 80127 Naples, Italy
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Tzeng IS, Hsieh PC, Su WL, Hsieh TH, Chang SC. Artificial Intelligence-Assisted Chest X-ray for the Diagnosis of COVID-19: A Systematic Review and Meta-Analysis. Diagnostics (Basel) 2023; 13:584. [PMID: 36832072 PMCID: PMC9955250 DOI: 10.3390/diagnostics13040584] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 01/25/2023] [Accepted: 02/01/2023] [Indexed: 02/09/2023] Open
Abstract
Because it is an accessible and routine image test, medical personnel commonly use a chest X-ray for COVID-19 infections. Artificial intelligence (AI) is now widely applied to improve the precision of routine image tests. Hence, we investigated the clinical merit of the chest X-ray to detect COVID-19 when assisted by AI. We used PubMed, Cochrane Library, MedRxiv, ArXiv, and Embase to search for relevant research published between 1 January 2020 and 30 May 2022. We collected essays that dissected AI-based measures used for patients diagnosed with COVID-19 and excluded research lacking measurements using relevant parameters (i.e., sensitivity, specificity, and area under curve). Two independent researchers summarized the information, and discords were eliminated by consensus. A random effects model was used to calculate the pooled sensitivities and specificities. The sensitivity of the included research studies was enhanced by eliminating research with possible heterogeneity. A summary receiver operating characteristic curve (SROC) was generated to investigate the diagnostic value for detecting COVID-19 patients. Nine studies were recruited in this analysis, including 39,603 subjects. The pooled sensitivity and specificity were estimated as 0.9472 (p = 0.0338, 95% CI 0.9009-0.9959) and 0.9610 (p < 0.0001, 95% CI 0.9428-0.9795), respectively. The area under the SROC was 0.98 (95% CI 0.94-1.00). The heterogeneity of diagnostic odds ratio was presented in the recruited studies (I2 = 36.212, p = 0.129). The AI-assisted chest X-ray scan for COVID-19 detection offered excellent diagnostic potential and broader application.
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Affiliation(s)
- I-Shiang Tzeng
- Department of Research, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City 23142, Taiwan
| | - Po-Chun Hsieh
- Department of Chinese Medicine, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City 23142, Taiwan
| | - Wen-Lin Su
- Division of Pulmonary Medicine, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City 23142, Taiwan
| | - Tsung-Han Hsieh
- Department of Research, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City 23142, Taiwan
| | - Sheng-Chang Chang
- Department of Medical Imaging, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City 23142, Taiwan
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Ruscitti P, Bruno F, Berardicurti O, Acanfora C, Pavlych V, Palumbo P, Conforti A, Carubbi F, Di Cola I, Di Benedetto P, Cipriani P, Grassi D, Masciocchi C, Iagnocco A, Barile A, Giacomelli R. Response to: 'Correspondence on 'Lung involvement in macrophage activation syndrome and severe COVID-19: results from a cross-sectional study to assess clinical, laboratory and artificial intelligence-radiological differences' by Ruscitti et al' by Chen et al. Ann Rheum Dis 2022; 81:e221. [PMID: 32907802 DOI: 10.1136/annrheumdis-2020-218909] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 08/27/2020] [Indexed: 12/29/2022]
Affiliation(s)
- Piero Ruscitti
- Department of Clinical Sciences and Applied Biotechnology, University of L'Aquila, L'Aquila, Abruzzo, Italy
| | - Federico Bruno
- Department of Clinical Sciences and Applied Biotechnology, University of L'Aquila, L'Aquila, Abruzzo, Italy
| | - Onorina Berardicurti
- Department of Clinical Sciences and Applied Biotechnology, University of L'Aquila, L'Aquila, Abruzzo, Italy
| | - Chiara Acanfora
- Department of Clinical Sciences and Applied Biotechnology, University of L'Aquila, L'Aquila, Abruzzo, Italy
| | - Viktoriya Pavlych
- Department of Clinical Sciences and Applied Biotechnology, University of L'Aquila, L'Aquila, Abruzzo, Italy
| | - Pierpaolo Palumbo
- Department of Clinical Sciences and Applied Biotechnology, University of L'Aquila, L'Aquila, Abruzzo, Italy
| | - Alessandro Conforti
- Department of Clinical Sciences and Applied Biotechnology, University of L'Aquila, L'Aquila, Abruzzo, Italy
| | - Francesco Carubbi
- Department of Medicine, ASL 1 Avezzano Sulmona L'Aquila, L'Aquila, Abruzzo, Italy
| | - Ilenia Di Cola
- Department of Clinical Sciences and Applied Biotechnology, University of L'Aquila, L'Aquila, Abruzzo, Italy
| | - Paola Di Benedetto
- Department of Clinical Sciences and Applied Biotechnology, University of L'Aquila, L'Aquila, Abruzzo, Italy
| | - Paola Cipriani
- Department of Clinical Sciences and Applied Biotechnology, University of L'Aquila, L'Aquila, Abruzzo, Italy
| | - Davide Grassi
- Department of Clinical Medicine Life Health and Environmental Sciences, University of L'Aquila, L'Aquila, Abruzzo, Italy
| | - Carlo Masciocchi
- Department of Clinical Sciences and Applied Biotechnology, University of L'Aquila, L'Aquila, Abruzzo, Italy
| | - Annamaria Iagnocco
- Dipartimento Scienze Cliniche e Biologiche, Università degli Studi di Torino, Torino, Italy
| | - Antonio Barile
- Department of Clinical Sciences and Applied Biotechnology, University of L'Aquila, L'Aquila, Abruzzo, Italy
| | - Roberto Giacomelli
- Department of Clinical Sciences and Applied Biotechnology, University of L'Aquila, L'Aquila, Abruzzo, Italy
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Pellegrino F, Carnevale A, Bisi R, Cavedagna D, Reverberi R, Uccelli L, Leprotti S, Giganti M. Best Practices on Radiology Department Workflow: Tips from the Impact of the COVID-19 Lockdown on an Italian University Hospital. Healthcare (Basel) 2022; 10:healthcare10091771. [PMID: 36141383 PMCID: PMC9498676 DOI: 10.3390/healthcare10091771] [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: 08/24/2022] [Revised: 09/11/2022] [Accepted: 09/12/2022] [Indexed: 12/02/2022] Open
Abstract
Purpose: The workload of the radiology department (RD) of a university hospital in northern Italy dramatically changed during the COVID-19 outbreak. The restrictive measures of the COVID-19 pandemic lockdown influenced the use of radiological services and particularly in the emergency department (ED). Methods: Data on diagnostic services from March 2020 to May 2020 were retrospectively collected and analysed in aggregate form and compared with those of the same timeframe in the previous year. Data were sorted by patient type in the following categories: inpatients, outpatients, and ED patients; the latter divided in “traumatic” and “not traumatic” cases. Results: Compared to 2019, 6449 fewer patients (−32.6%) were assisted in the RD. This decrease was more pronounced for the emergency radiology unit (ERU) (−41%) compared to the general radiology unit (−25.7%). The proportion of investigations performed for trauma appeared to decrease significantly from 14.8% to 12.5% during the COVID-19 emergency (p < 0.001). Similarly, the proportion of assisted traumatic patients decreased from 16.6% to 12.5% (p < 0.001). The number of emergency patients assisted by the RD was significantly reduced from 45% during routine activity to 39.4% in the COVID-19 outbreak (p < 0.001). Conclusion: The COVID-19 outbreak had a tremendous impact on all radiology activities. We documented a drastic reduction in total imaging volume compared to 2019 because of both the pandemic and the lockdown. In this context, investigations performed for trauma showed a substantial decrease.
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Affiliation(s)
- Fabio Pellegrino
- Department of Translational Medicine, University of Ferrara, 44124 Ferrara, Italy
- Correspondence:
| | - Aldo Carnevale
- Department of Translational Medicine, University of Ferrara, 44124 Ferrara, Italy
| | - Riccardo Bisi
- Department of Translational Medicine, University of Ferrara, 44124 Ferrara, Italy
| | - Davide Cavedagna
- Department of Radiology, Sant’Anna University Hospital Ferrara, 44124 Ferrara, Italy
| | - Roberto Reverberi
- Blood Transfusion Service, Azienda Ospedaliera Universitaria di Ferrara, 44124 Ferrara, Italy
| | - Licia Uccelli
- Department of Translational Medicine, University of Ferrara, 44124 Ferrara, Italy
| | - Stefano Leprotti
- Department of Radiology, Sant’Anna University Hospital Ferrara, 44124 Ferrara, Italy
| | - Melchiore Giganti
- Department of Translational Medicine, University of Ferrara, 44124 Ferrara, Italy
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11
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Jena KK, Nayak SR, Bhoi SK, Verma KD, Prakash D, Gupta A. A novel service robot assignment approach for COVID-19 infected patients: a case of medical data driven decision making. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:41995-42021. [PMID: 36090152 PMCID: PMC9440332 DOI: 10.1007/s11042-022-13524-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 09/22/2021] [Accepted: 07/13/2022] [Indexed: 06/15/2023]
Abstract
Coronavirus Disease-19 (COVID-19) is a major concern for the entire world in the current era. Coronavirus is a very dangerous infectious virus that spreads rapidly from person to person. It spreads in exponential manner on a global scale. It affects the doctors, nurse and other COVID-19 warriors those who are actively involved for the treatment of COVID-19 infected (CI) patients. So, it is very much essential to focus on automation and artificial intelligence (AI) in different hospitals for the treatment of such infected patients and all should be very much careful to break the chain of spreading this novel virus. In this paper, a novel patient service robots (PSRs) assignment framework and a priority based (PB) method using fuzzy rule based (FRB) approach is proposed for the assignment of PSRs for CI patients in hospitals in order to provide safety to the COVID-19 warriors as well as to the CI infected patients. This novel approach is mainly focused on lowering the active involvement of COVID-19 warriors for the treatment of high asymptotic COVID-19 infected (HACI) patients for handling this tough situation. In this work, we have focused on HACI and low asymptotic COVID-19 infected (LACI) patients. Higher priority is given to HACI patients as compared to LACI patients to handle this critical situation in order to increase the survival probability of these patients. The proposed method deals with situations that practically arise during the assignment of PSRs for the treatment of such patients. The simulation of the work is carried out using MATLAB R2015b.
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Affiliation(s)
- Kalyan Kumar Jena
- Department of Computer Science and Engineering, PMECParala Maharaja Engineering College, Berhampur, India
| | - Soumya Ranjan Nayak
- PradeshAmity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, India
| | - Sourav Kumar Bhoi
- Department of Computer Science and Engineering, PMECParala Maharaja Engineering College, Berhampur, India
| | - K. D. Verma
- Department of Physics, Shri Varshney (P.G.) College, Aligarh, UP 202001 India
| | - Deo Prakash
- School of Computer Science & Engineering, Shri Mata Vaishno Devi University, Kakryal, Katra, J&K 182320 India
| | - Abhishek Gupta
- School of Computer Science & Engineering, Shri Mata Vaishno Devi University, Kakryal, Katra, J&K 182320 India
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12
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Structured Reporting in Radiological Settings: Pitfalls and Perspectives. J Pers Med 2022; 12:jpm12081344. [PMID: 36013293 PMCID: PMC9409900 DOI: 10.3390/jpm12081344] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 08/08/2022] [Accepted: 08/17/2022] [Indexed: 12/01/2022] Open
Abstract
Objective: The aim of this manuscript is to give an overview of structured reporting in radiological settings. Materials and Method: This article is a narrative review on structured reporting in radiological settings. Particularly, limitations and future perspectives are analyzed. RESULTS: The radiological report is a communication tool for the referring physician and the patients. It was conceived as a free text report (FTR) to allow radiologists to have their own individuality in the description of the radiological findings. However, this form could suffer from content, style, and presentation discrepancies, with a probability of transferring incorrect radiological data. Quality, datafication/quantification, and accessibility represent the three main goals in moving from FTRs to structured reports (SRs). In fact, the quality is related to standardization, which aims to improve communication and clarification. Moreover, a “structured” checklist, which allows all the fundamental items for a particular radiological study to be reported and permits the connection of the radiological data with clinical features, allowing a personalized medicine. With regard to accessibility, since radiological reports can be considered a source of research data, SR allows data mining to obtain new biomarkers and to help the development of new application domains, especially in the field of radiomics. Conclusions: Structured reporting could eliminate radiologist individuality, allowing a standardized approach.
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13
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Granata V, Fusco R, Villanacci A, Magliocchetti S, Urraro F, Tetaj N, Marchioni L, Albarello F, Campioni P, Cristofaro M, Di Stefano F, Fusco N, Petrone A, Schininà V, Grassi F, Girardi E, Ianniello S. Imaging Severity COVID-19 Assessment in Vaccinated and Unvaccinated Patients: Comparison of the Different Variants in a High Volume Italian Reference Center. J Pers Med 2022; 12:955. [PMID: 35743740 PMCID: PMC9224665 DOI: 10.3390/jpm12060955] [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: 04/28/2022] [Revised: 06/03/2022] [Accepted: 06/09/2022] [Indexed: 11/17/2022] Open
Abstract
Purpose: To analyze the vaccine effect by comparing five groups: unvaccinated patients with Alpha variant, unvaccinated patients with Delta variant, vaccinated patients with Delta variant, unvaccinated patients with Omicron variant, and vaccinated patients with Omicron variant, assessing the “gravity” of COVID-19 pulmonary involvement, based on CT findings in critically ill patients admitted to Intensive Care Unit (ICU). Methods: Patients were selected by ICU database considering the period from December 2021 to 23 March 2022, according to the following inclusion criteria: patients with proven Omicron variant COVID-19 infection with known COVID-19 vaccination with at least two doses and with chest Computed Tomography (CT) study during ICU hospitalization. Wee also evaluated the ICU database considering the period from March 2020 to December 2021, to select unvaccinated consecutive patients with Alpha variant, subjected to CT study, consecutive unvaccinated and vaccinated patients with Delta variant, subjected to CT study, and, consecutive unvaccinated patients with Omicron variant, subjected to CT study. CT images were evaluated qualitatively using a severity score scale of 5 levels (none involvement, mild: ≤25% of involvement, moderate: 26−50% of involvement, severe: 51−75% of involvement, and critical involvement: 76−100%) and quantitatively, using the Philips IntelliSpace Portal clinical application CT COPD computer tool. For each patient the lung volumetry was performed identifying the percentage value of aerated residual lung volume. Non-parametric tests for continuous and categorical variables were performed to assess statistically significant differences among groups. Results: The patient study group was composed of 13 vaccinated patients affected by the Omicron variant (Omicron V). As control groups we identified: 20 unvaccinated patients with Alpha variant (Alpha NV); 20 unvaccinated patients with Delta variant (Delta NV); 18 vaccinated patients with Delta variant (Delta V); and 20 unvaccinated patients affected by the Omicron variant (Omicron NV). No differences between the groups under examination were found (p value > 0.05 at Chi square test) in terms of risk factors (age, cardiovascular diseases, diabetes, immunosuppression, chronic kidney, cardiac, pulmonary, neurologic, and liver disease, etc.). A different median value of aerated residual lung volume was observed in the Delta variant groups: median value of aerated residual lung volume was 46.70% in unvaccinated patients compared to 67.10% in vaccinated patients. In addition, in patients with Delta variant every other extracted volume by automatic tool showed a statistically significant difference between vaccinated and unvaccinated group. Statistically significant differences were observed for each extracted volume by automatic tool between unvaccinated patients affected by Alpha variant and vaccinated patients affected by Delta variant of COVID-19. Good statistically significant correlations among volumes extracted by automatic tool for each lung lobe and overall radiological severity score were obtained (ICC range 0.71−0.86). GGO was the main sign of COVID-19 lesions on CT images found in 87 of the 91 (95.6%) patients. No statistically significant differences were observed in CT findings (ground glass opacities (GGO), consolidation or crazy paving sign) among patient groups. Conclusion: In our study, we showed that in critically ill patients no difference were observed in terms of severity of disease or exitus, between unvaccinated and vaccinated patients. The only statistically significant differences were observed, with regard to the severity of COVID-19 pulmonary parenchymal involvement, between unvaccinated patients affected by Alpha variant and vaccinated patients affected by Delta variant, and between unvaccinated patients with Delta variant and vaccinated patients with Delta variant.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Napoli, Italy
| | - Alberta Villanacci
- Diagnostic Imaging of Infectious Diseases, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy; (A.V.); (F.A.); (P.C.); (M.C.); (F.D.S.); (N.F.); (A.P.); (V.S.); (S.I.)
| | - Simona Magliocchetti
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80128 Naples, Italy; (S.M.); (F.U.); (F.G.)
| | - Fabrizio Urraro
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80128 Naples, Italy; (S.M.); (F.U.); (F.G.)
| | - Nardi Tetaj
- Intensive Care Unit, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy; (N.T.); (L.M.)
| | - Luisa Marchioni
- Intensive Care Unit, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy; (N.T.); (L.M.)
| | - Fabrizio Albarello
- Diagnostic Imaging of Infectious Diseases, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy; (A.V.); (F.A.); (P.C.); (M.C.); (F.D.S.); (N.F.); (A.P.); (V.S.); (S.I.)
| | - Paolo Campioni
- Diagnostic Imaging of Infectious Diseases, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy; (A.V.); (F.A.); (P.C.); (M.C.); (F.D.S.); (N.F.); (A.P.); (V.S.); (S.I.)
| | - Massimo Cristofaro
- Diagnostic Imaging of Infectious Diseases, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy; (A.V.); (F.A.); (P.C.); (M.C.); (F.D.S.); (N.F.); (A.P.); (V.S.); (S.I.)
| | - Federica Di Stefano
- Diagnostic Imaging of Infectious Diseases, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy; (A.V.); (F.A.); (P.C.); (M.C.); (F.D.S.); (N.F.); (A.P.); (V.S.); (S.I.)
| | - Nicoletta Fusco
- Diagnostic Imaging of Infectious Diseases, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy; (A.V.); (F.A.); (P.C.); (M.C.); (F.D.S.); (N.F.); (A.P.); (V.S.); (S.I.)
| | - Ada Petrone
- Diagnostic Imaging of Infectious Diseases, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy; (A.V.); (F.A.); (P.C.); (M.C.); (F.D.S.); (N.F.); (A.P.); (V.S.); (S.I.)
| | - Vincenzo Schininà
- Diagnostic Imaging of Infectious Diseases, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy; (A.V.); (F.A.); (P.C.); (M.C.); (F.D.S.); (N.F.); (A.P.); (V.S.); (S.I.)
| | - Francesca Grassi
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80128 Naples, Italy; (S.M.); (F.U.); (F.G.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via Della Signora 2, 20122 Milan, Italy
| | - Enrico Girardi
- Department of Epidemiology and Research, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy;
| | - Stefania Ianniello
- Diagnostic Imaging of Infectious Diseases, National Institute for Infectious Diseases Lazzaro Spallanzani IRCCS, 00149 Rome, Italy; (A.V.); (F.A.); (P.C.); (M.C.); (F.D.S.); (N.F.); (A.P.); (V.S.); (S.I.)
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14
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Gülbay M, Baştuğ A, Özkan E, Öztürk BY, Mendi BAR, Bodur H. Evaluation of the models generated from clinical features and deep learning-based segmentations: Can thoracic CT on admission help us to predict hospitalized COVID-19 patients who will require intensive care? BMC Med Imaging 2022; 22:110. [PMID: 35672719 PMCID: PMC9172094 DOI: 10.1186/s12880-022-00833-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Accepted: 05/27/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND The aim of the study was to predict the probability of intensive care unit (ICU) care for inpatient COVID-19 cases using clinical and artificial intelligence segmentation-based volumetric and CT-radiomics parameters on admission. METHODS Twenty-eight clinical/laboratory features, 21 volumetric parameters, and 74 radiomics parameters obtained by deep learning (DL)-based segmentations from CT examinations of 191 severe COVID-19 inpatients admitted between March 2020 and March 2021 were collected. Patients were divided into Group 1 (117 patients discharged from the inpatient service) and Group 2 (74 patients transferred to the ICU), and the differences between the groups were evaluated with the T-test and Mann-Whitney test. The sensitivities and specificities of significantly different parameters were evaluated by ROC analysis. Subsequently, 152 (79.5%) patients were assigned to the training/cross-validation set, and 39 (20.5%) patients were assigned to the test set. Clinical, radiological, and combined logit-fit models were generated by using the Bayesian information criterion from the training set and optimized via tenfold cross-validation. To simultaneously use all of the clinical, volumetric, and radiomics parameters, a random forest model was produced, and this model was trained by using a balanced training set created by adding synthetic data to the existing training/cross-validation set. The results of the models in predicting ICU patients were evaluated with the test set. RESULTS No parameter individually created a reliable classifier. When the test set was evaluated with the final models, the AUC values were 0.736, 0.708, and 0.794, the specificity values were 79.17%, 79.17%, and 87.50%, the sensitivity values were 66.67%, 60%, and 73.33%, and the F1 values were 0.67, 0.62, and 0.76 for the clinical, radiological, and combined logit-fit models, respectively. The random forest model that was trained with the balanced training/cross-validation set was the most successful model, achieving an AUC of 0.837, specificity of 87.50%, sensitivity of 80%, and F1 value of 0.80 in the test set. CONCLUSION By using a machine learning algorithm that was composed of clinical and DL-segmentation-based radiological parameters and that was trained with a balanced data set, COVID-19 patients who may require intensive care could be successfully predicted.
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Affiliation(s)
- Mutlu Gülbay
- Department of Radiology, Ankara City Hospital, Üniversiteler Mahallesi 1604. Cadde No: 9, 06800, Çankaya, Ankara, Turkey.
| | - Aliye Baştuğ
- Department of Infectious Diseases and Clinical Microbiology, University of Health Sciences Turkey, Gülhane Faculty of Medicine, Ankara City Hospital, Ankara, Turkey
| | - Erdem Özkan
- Department of Radiology, Ankara City Hospital, Üniversiteler Mahallesi 1604. Cadde No: 9, 06800, Çankaya, Ankara, Turkey
| | - Büşra Yüce Öztürk
- Department of Clinical Microbiology and Infectious Diseases, Ankara City Hospital, Ankara, Turkey
| | - Bökebatur Ahmet Raşit Mendi
- Department of Radiology, Ankara City Hospital, Üniversiteler Mahallesi 1604. Cadde No: 9, 06800, Çankaya, Ankara, Turkey
| | - Hürrem Bodur
- Department of Infectious Diseases and Clinical Microbiology, University of Health Sciences Turkey, Gülhane Faculty of Medicine, Ankara City Hospital, Ankara, Turkey
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15
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Iskander J, Kelada P, Rashad L, Massoud D, Afdal P, Abdelmassih AF. Advanced Echocardiography Techniques: The Future Stethoscope of Systemic Diseases. Curr Probl Cardiol 2022; 47:100847. [PMID: 33992429 PMCID: PMC9046647 DOI: 10.1016/j.cpcardiol.2021.100847] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 03/22/2021] [Indexed: 01/11/2023]
Abstract
Cardiovascular disease (CVD) has been showing patterns of extensive rise in prevalence in the contemporary era, affecting the quality of life of millions of people and leading the causes of death worldwide. It has been a provocative challenge for modern medicine to diagnose CVD in its crib, owing to its etiological factors being attributed to a large array of systemic diseases, as well as its non-binary hideous nature that gradually leads to functional disability. Novel echocardiography techniques have enabled the cardiac ultrasound to provide a comprehensive analysis of the heart in an objective, feasible, time- and cost-effective manner. Speckle tracking echocardiography, contrast echocardiography, and 3D echocardiography have shown the highest potential for widespread use. The uses of novel modalities have been elaborately demonstrated in this study as a proof of concept that echocardiography has a place in routine general practice with supportive evidence being as recent as its role in the concurrent COVID-19 pandemic. Despite such evidence, many uses remain off-label and unexploited in practice. Generalization of echocardiography at the point of care can become a much-needed turning point in the clinical approach to case management. To actualize such aspirations, we recommend further prospective and interventional studies to examine the effect of implementing advanced techniques at the point of care on the decision-making process and evaluate their effectiveness in prevention of cardiovascular morbidities and mortalities.
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Affiliation(s)
- John Iskander
- Faculty of Medicine, Cairo University, Cairo, Egypt.
| | - Peter Kelada
- Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Lara Rashad
- Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Doaa Massoud
- Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Peter Afdal
- Residency program, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Antoine Fakhry Abdelmassih
- Pediatric Cardiology Unit, Department of Pediatrics, Kasr AlAiny Faculty of Medicine, Cairo University, Cairo, Egypt; Consultant of Pediatric Cardiology, Children Cancer Hospital of Egypt (57357 Hospital), Cairo, Egypt
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16
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Tan T, Das B, Soni R, Fejes M, Yang H, Ranjan S, Szabo DA, Melapudi V, Shriram KS, Agrawal U, Rusko L, Herczeg Z, Darazs B, Tegzes P, Ferenczi L, Mullick R, Avinash G. Multi-modal trained artificial intelligence solution to triage chest X-ray for COVID-19 using pristine ground-truth, versus radiologists. Neurocomputing 2022; 485:36-46. [PMID: 35185296 PMCID: PMC8847079 DOI: 10.1016/j.neucom.2022.02.040] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 12/25/2021] [Accepted: 02/11/2022] [Indexed: 11/05/2022]
Abstract
The front-line imaging modalities computed tomography (CT) and X-ray play important roles for triaging COVID patients. Thoracic CT has been accepted to have higher sensitivity than a chest X-ray for COVID diagnosis. Considering the limited access to resources (both hardware and trained personnel) and issues related to decontamination, CT may not be ideal for triaging suspected subjects. Artificial intelligence (AI) assisted X-ray based application for triaging and monitoring require experienced radiologists to identify COVID patients in a timely manner with the additional ability to delineate and quantify the disease region is seen as a promising solution for widespread clinical use. Our proposed solution differs from existing solutions presented by industry and academic communities. We demonstrate a functional AI model to triage by classifying and segmenting a single chest X-ray image, while the AI model is trained using both X-ray and CT data. We report on how such a multi-modal training process improves the solution compared to single modality (X-ray only) training. The multi-modal solution increases the AUC (area under the receiver operating characteristic curve) from 0.89 to 0.93 for a binary classification between COVID-19 and non-COVID-19 cases. It also positively impacts the Dice coefficient (0.59 to 0.62) for localizing the COVID-19 pathology. To compare the performance of experienced readers to the AI model, a reader study is also conducted. The AI model showed good consistency with respect to radiologists. The DICE score between two radiologists on the COVID group was 0.53 while the AI had a DICE value of 0.52 and 0.55 when compared to the segmentation done by the two radiologists separately. From a classification perspective, the AUCs of two readers was 0.87 and 0.81 while the AUC of the AI is 0.93 based on the reader study dataset. We also conducted a generalization study by comparing our method to the-state-art methods on independent datasets. The results show better performance from the proposed method. Leveraging multi-modal information for the development benefits the single-modal inferencing.
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Affiliation(s)
- Tao Tan
- GE Healthcare, The Netherlands
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17
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Borgheresi A, De Muzio F, Agostini A, Ottaviani L, Bruno A, Granata V, Fusco R, Danti G, Flammia F, Grassi R, Grassi F, Bruno F, Palumbo P, Barile A, Miele V, Giovagnoni A. Lymph Nodes Evaluation in Rectal Cancer: Where Do We Stand and Future Perspective. J Clin Med 2022; 11:2599. [PMID: 35566723 PMCID: PMC9104021 DOI: 10.3390/jcm11092599] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 04/25/2022] [Accepted: 05/03/2022] [Indexed: 12/12/2022] Open
Abstract
The assessment of nodal involvement in patients with rectal cancer (RC) is fundamental in disease management. Magnetic Resonance Imaging (MRI) is routinely used for local and nodal staging of RC by using morphological criteria. The actual dimensional and morphological criteria for nodal assessment present several limitations in terms of sensitivity and specificity. For these reasons, several different techniques, such as Diffusion Weighted Imaging (DWI), Intravoxel Incoherent Motion (IVIM), Diffusion Kurtosis Imaging (DKI), and Dynamic Contrast Enhancement (DCE) in MRI have been introduced but still not fully validated. Positron Emission Tomography (PET)/CT plays a pivotal role in the assessment of LNs; more recently PET/MRI has been introduced. The advantages and limitations of these imaging modalities will be provided in this narrative review. The second part of the review includes experimental techniques, such as iron-oxide particles (SPIO), and dual-energy CT (DECT). Radiomics analysis is an active field of research, and the evidence about LNs in RC will be discussed. The review also discusses the different recommendations between the European and North American guidelines for the evaluation of LNs in RC, from anatomical considerations to structured reporting.
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Affiliation(s)
- Alessandra Borgheresi
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60121 Ancona, Italy; (A.B.); (A.A.); (A.B.); (A.G.)
| | - Federica De Muzio
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy;
| | - Andrea Agostini
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60121 Ancona, Italy; (A.B.); (A.A.); (A.B.); (A.G.)
- Department of Radiological Sciences, University Hospital Ospedali Riuniti, 60126 Ancona, Italy;
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
| | - Letizia Ottaviani
- Department of Radiological Sciences, University Hospital Ospedali Riuniti, 60126 Ancona, Italy;
| | - Alessandra Bruno
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60121 Ancona, Italy; (A.B.); (A.A.); (A.B.); (A.G.)
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale IRCCS di Napoli, 80131 Naples, Italy;
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Napoli, Italy
| | - Ginevra Danti
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134 Florence, Italy;
| | - Federica Flammia
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134 Florence, Italy;
| | - Roberta Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80128 Naples, Italy
| | - Francesca Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80128 Naples, Italy
| | - Federico Bruno
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100 L’Aquila, Italy;
| | - Pierpaolo Palumbo
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Abruzzo Health Unit 1, Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, 67100 L’Aquila, Italy
| | - Antonio Barile
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100 L’Aquila, Italy;
| | - Vittorio Miele
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134 Florence, Italy;
| | - Andrea Giovagnoni
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60121 Ancona, Italy; (A.B.); (A.A.); (A.B.); (A.G.)
- Department of Radiological Sciences, University Hospital Ospedali Riuniti, 60126 Ancona, Italy;
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18
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Jungmann F, Müller L, Hahn F, Weustenfeld M, Dapper AK, Mähringer-Kunz A, Graafen D, Düber C, Schafigh D, Pinto Dos Santos D, Mildenberger P, Kloeckner R. Commercial AI solutions in detecting COVID-19 pneumonia in chest CT: not yet ready for clinical implementation? Eur Radiol 2022; 32:3152-3160. [PMID: 34950973 PMCID: PMC8700707 DOI: 10.1007/s00330-021-08409-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 09/16/2021] [Accepted: 10/08/2021] [Indexed: 11/29/2022]
Abstract
OBJECTIVES In response to the COVID-19 pandemic, many researchers have developed artificial intelligence (AI) tools to differentiate COVID-19 pneumonia from other conditions in chest CT. However, in many cases, performance has not been clinically validated. The aim of this study was to evaluate the performance of commercial AI solutions in differentiating COVID-19 pneumonia from other lung conditions. METHODS Four commercial AI solutions were evaluated on a dual-center clinical dataset consisting of 500 CT studies; COVID-19 pneumonia was microbiologically proven in 50 of these. Sensitivity, specificity, positive and negative predictive values, and AUC were calculated. In a subgroup analysis, the performance of the AI solutions in differentiating COVID-19 pneumonia from other conditions was evaluated in CT studies with ground-glass opacities (GGOs). RESULTS Sensitivity and specificity ranges were 62-96% and 31-80%, respectively. Negative and positive predictive values ranged between 82-99% and 19-25%, respectively. AUC was in the range 0.54-0.79. In CT studies with GGO, sensitivity remained unchanged. However, specificity was lower, and ranged between 15 and 53%. AUC for studies with GGO was in the range 0.54-0.69. CONCLUSIONS This study highlights the variable specificity and low positive predictive value of AI solutions in diagnosing COVID-19 pneumonia in chest CT. However, one solution yielded acceptable values for sensitivity. Thus, with further improvement, commercial AI solutions currently under development have the potential to be integrated as alert tools in clinical routine workflow. Randomized trials are needed to assess the true benefits and also potential harms of the use of AI in image analysis. KEY POINTS • Commercial AI solutions achieved a sensitivity and specificity ranging from 62 to 96% and from 31 to 80%, respectively, in identifying patients suspicious for COVID-19 in a clinical dataset. • Sensitivity remained within the same range, while specificity was even lower in subgroup analysis of CT studies with ground-glass opacities, and interrater agreement between the commercial AI solutions was minimal to nonexistent. • Thus, commercial AI solutions have the potential to be integrated as alert tools for the detection of patients with lung changes suspicious for COVID-19 pneumonia in a clinical routine workflow, if further improvement is made.
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Affiliation(s)
- Florian Jungmann
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckst. 1, 55131, Mainz, Germany.
| | - Lukas Müller
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckst. 1, 55131, Mainz, Germany
| | - Felix Hahn
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckst. 1, 55131, Mainz, Germany
| | - Maximilian Weustenfeld
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckst. 1, 55131, Mainz, Germany
| | - Ann-Kathrin Dapper
- Department of Neuroradiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Aline Mähringer-Kunz
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckst. 1, 55131, Mainz, Germany
| | - Dirk Graafen
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckst. 1, 55131, Mainz, Germany
| | - Christoph Düber
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckst. 1, 55131, Mainz, Germany
| | - Darius Schafigh
- Department of Radiology, University Hospital of Cologne, Cologne, Germany
| | | | - Peter Mildenberger
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckst. 1, 55131, Mainz, Germany
| | - Roman Kloeckner
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckst. 1, 55131, Mainz, Germany
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19
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Negrini D, Danese E, Henry BM, Lippi G, Montagnana M. Artificial intelligence at the time of COVID-19: who does the lion's share? Clin Chem Lab Med 2022; 60:1881-1886. [PMID: 35470639 DOI: 10.1515/cclm-2022-0306] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 04/13/2022] [Indexed: 01/06/2023]
Abstract
OBJECTIVES The development and use of artificial intelligence (AI) methodologies, especially machine learning (ML) and deep learning (DL), have been considerably fostered during the ongoing coronavirus disease 2019 (COVID-19) pandemic. Several models and algorithms have been developed and applied for both identifying COVID-19 cases and for assessing and predicting the risk of developing unfavourable outcomes. Our aim was to summarize how AI is being currently applied to COVID-19. METHODS We conducted a PubMed search using as query MeSH major terms "Artificial Intelligence" AND "COVID-19", searching for articles published until December 31, 2021, which explored the possible role of AI in COVID-19. The dataset origin (internal dataset or public datasets available online) and data used for training and testing the proposed ML/DL model(s) were retrieved. RESULTS Our analysis finally identified 292 articles in PubMed. These studies displayed large heterogeneity in terms of imaging test, laboratory parameters and clinical-demographic data included. Most models were based on imaging data, in particular CT scans or chest X-rays images. C-Reactive protein, leukocyte count, creatinine, lactate dehydrogenase, lymphocytes and platelets counts were found to be the laboratory biomarkers most frequently included in COVID-19 related AI models. CONCLUSIONS The lion's share of AI applied to COVID-19 seems to be played by diagnostic imaging. However, AI in laboratory medicine is also gaining momentum, especially with digital tools characterized by low cost and widespread applicability.
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Affiliation(s)
- Davide Negrini
- Section of Clinical Biochemistry and School of Medicine, University Hospital of Verona, Verona, Italy
| | - Elisa Danese
- Section of Clinical Biochemistry and School of Medicine, University Hospital of Verona, Verona, Italy
| | - Brandon M Henry
- Clinical Laboratory, Division of Nephrology and Hypertension, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Giuseppe Lippi
- Section of Clinical Biochemistry and School of Medicine, University Hospital of Verona, Verona, Italy
| | - Martina Montagnana
- Section of Clinical Biochemistry and School of Medicine, University Hospital of Verona, Verona, Italy
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20
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Mortality Prediction of COVID-19 Patients Using Radiomic and Neural Network Features Extracted from a Wide Chest X-ray Sample Size: A Robust Approach for Different Medical Imbalanced Scenarios. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12083903] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Aim: The aim of this study was to develop robust prognostic models for mortality prediction of COVID-19 patients, applicable to different sets of real scenarios, using radiomic and neural network features extracted from chest X-rays (CXRs) with a certified and commercially available software. Methods: 1816 patients from 5 different hospitals in the Province of Reggio Emilia were included in the study. Overall, 201 radiomic features and 16 neural network features were extracted from each COVID-19 patient’s radiography. The initial dataset was balanced to train the classifiers with the same number of dead and survived patients, randomly selected. The pipeline had three main parts: balancing procedure; three-step feature selection; and mortality prediction with radiomic features through three machine learning (ML) classification models: AdaBoost (ADA), Quadratic Discriminant Analysis (QDA) and Random Forest (RF). Five evaluation metrics were computed on the test samples. The performance for death prediction was validated on both a balanced dataset (Case 1) and an imbalanced dataset (Case 2). Results: accuracy (ACC), area under the ROC-curve (AUC) and sensitivity (SENS) for the best classifier were, respectively, 0.72 ± 0.01, 0.82 ± 0.02 and 0.84 ± 0.04 for Case 1 and 0.70 ± 0.04, 0.79 ± 0.03 and 0.76 ± 0.06 for Case 2. These results show that the prediction of COVID-19 mortality is robust in a different set of scenarios. Conclusions: Our large and varied dataset made it possible to train ML algorithms to predict COVID-19 mortality using radiomic and neural network features of CXRs.
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21
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Fusco R, Simonetti I, Ianniello S, Villanacci A, Grassi F, Dell’Aversana F, Grassi R, Cozzi D, Bicci E, Palumbo P, Borgheresi A, Giovagnoni A, Miele V, Barile A, Granata V. Pulmonary Lymphangitis Poses a Major Challenge for Radiologists in an Oncological Setting during the COVID-19 Pandemic. J Pers Med 2022; 12:624. [PMID: 35455740 PMCID: PMC9024504 DOI: 10.3390/jpm12040624] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 03/29/2022] [Accepted: 03/31/2022] [Indexed: 12/17/2022] Open
Abstract
Due to the increasing number of COVID-19-infected and vaccinated individuals, radiologists continue to see patients with COVID-19 pneumonitis and recall pneumonitis, which could result in additional workups and false-positive results. Moreover, cancer patients undergoing immunotherapy may show therapy-related pneumonitis during imaging management. This is otherwise known as immune checkpoint inhibitor-related pneumonitis. Following on from this background, radiologists should seek to know their patients' COVID-19 infection and vaccination history. Knowing the imaging features related to COVID-19 infection and vaccination is critical to avoiding misleading results and alarmism in patients and clinicians.
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Affiliation(s)
- Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Napoli, Italy;
| | - Igino Simonetti
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
| | - Stefania Ianniello
- Diagnostica per Immagini nelle Malattie Infettive INMI Spallanzani IRCCS, 00161 Rome, Italy; (S.I.); (A.V.)
| | - Alberta Villanacci
- Diagnostica per Immagini nelle Malattie Infettive INMI Spallanzani IRCCS, 00161 Rome, Italy; (S.I.); (A.V.)
| | - Francesca Grassi
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80127 Naples, Italy; (F.G.); (F.D.); (R.G.)
| | - Federica Dell’Aversana
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80127 Naples, Italy; (F.G.); (F.D.); (R.G.)
| | - Roberta Grassi
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80127 Naples, Italy; (F.G.); (F.D.); (R.G.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy; (D.C.); (E.B.); (A.B.); (A.G.); (V.M.)
| | - Diletta Cozzi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy; (D.C.); (E.B.); (A.B.); (A.G.); (V.M.)
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, 50134 Florence, Italy
| | - Eleonora Bicci
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy; (D.C.); (E.B.); (A.B.); (A.G.); (V.M.)
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, 50134 Florence, Italy
| | - Pierpaolo Palumbo
- Abruzzo Health Unit 1, Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, 67100 L’Aquila, Italy;
| | - Alessandra Borgheresi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy; (D.C.); (E.B.); (A.B.); (A.G.); (V.M.)
- Department of Clinical, Special and Dental Sciences, Marche Polytechnic University, 60126 Ancona, Italy
| | - Andrea Giovagnoni
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy; (D.C.); (E.B.); (A.B.); (A.G.); (V.M.)
- Department of Clinical, Special and Dental Sciences, Marche Polytechnic University, 60126 Ancona, Italy
| | - Vittorio Miele
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy; (D.C.); (E.B.); (A.B.); (A.G.); (V.M.)
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, 50134 Florence, Italy
| | - Antonio Barile
- Department of Applied Clinical Science and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy;
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
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22
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Systemic Emergencies in COVID-19 Patient: A Pictorial Review. Tomography 2022; 8:1041-1051. [PMID: 35448718 PMCID: PMC9031887 DOI: 10.3390/tomography8020084] [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: 02/23/2022] [Revised: 03/21/2022] [Accepted: 03/30/2022] [Indexed: 11/25/2022] Open
Abstract
Since the first report of the outbreak in Wuhan, China in December 2019, as of 1 September 2021, the World Health Organization has confirmed more than 239 million cases of the novel coronavirus (SARS-CoV-2) infectious disease named coronavirus disease 2019 (COVID-19), with more than 4.5 million deaths. Although SARS-CoV-2 mainly involves the respiratory tract, it is considered to be a systemic disease. Imaging plays a pivotal role in the diagnosis of all manifestations of COVID-19 disease, as well as its related complications. The figure of the radiologist is fundamental in the management and treatment of the patient. The authors try to provide a systematic approach based on an imaging review of major multi-organ manifestations of this infection.
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23
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Tagliafico AS, Campi C, Bianca B, Bortolotto C, Buccicardi D, Francesca C, Prost R, Rengo M, Faggioni L. Blockchain in radiology research and clinical practice: current trends and future directions. LA RADIOLOGIA MEDICA 2022; 127:391-397. [PMID: 35194720 PMCID: PMC8863512 DOI: 10.1007/s11547-022-01460-1] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 01/21/2022] [Indexed: 12/31/2022]
Abstract
Blockchain usage in healthcare, in radiology, in particular, is at its very early infancy. Only a few research applications have been tested, however, blockchain technology is widely known outside healthcare and widely adopted, especially in Finance, since 2009 at least. Learning by history, radiology is a potential ideal scenario to apply this technology. Blockchain could have the potential to increase radiological data value in both clinical and research settings for the patient digital record, radiological reports, privacy control, quantitative image analysis, cybersecurity, radiomics and artificial intelligence.Up-to-date experiences using blockchain in radiology are still limited, but radiologists should be aware of the emergence of this technology and follow its next developments. We present here the potentials of some applications of blockchain in radiology.
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Affiliation(s)
- Alberto Stefano Tagliafico
- IRCCS Ospedale Policlinico San Martino, Genova, Genoa, Italy
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Cristina Campi
- Dipartimento Di Matematica, Università Di Genova, via Dodecaneso 35, 16146 Genova, Italy
| | - Bignotti Bianca
- IRCCS Ospedale Policlinico San Martino, Genova, Genoa, Italy
| | - Chandra Bortolotto
- Dipartimento Di Radiologia, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | | | - Coppola Francesca
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria Di Bologna, Bologna, Italy
| | - Roberto Prost
- Azienda Ospedaliera Brotzu, Cagliari, Sardegna Italy
| | - Marco Rengo
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome - I.C.O.T. Hospital, Via Franco Faggiana, 1668, 04100 Latina, Italy
| | - Lorenzo Faggioni
- Diagnostic and Interventional Radiology, University Hospital of Pisa, Via Paradisa 2, 56100 Pisa, Italy
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24
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Fusco R, Granata V, Grazzini G, Pradella S, Borgheresi A, Bruno A, Palumbo P, Bruno F, Grassi R, Giovagnoni A, Grassi R, Miele V, Barile A. Radiomics in medical imaging: pitfalls and challenges in clinical management. Jpn J Radiol 2022; 40:919-929. [PMID: 35344132 DOI: 10.1007/s11604-022-01271-4] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 03/14/2022] [Indexed: 12/21/2022]
Abstract
BACKGROUND Radiomics and radiogenomics are two words that recur often in language of radiologists, nuclear doctors and medical physicists especially in oncology field. Radiomics is the technique of medical images analysis to extract quantitative data that are not detected by human eye. METHODS This article is a narrative review on Radiomics in Medical Imaging. In particular, the review exposes the process, the limitations related to radiomics, and future prospects are discussed. RESULTS Several studies showed that radiomics is very promising. However, there were some critical issues: poor standardization and generalization of radiomics results, data-quality control, repeatability, reproducibility, database balancing and issues related to model overfitting. CONCLUSIONS Radiomics procedure should made considered all pitfalls and challenges to obtain robust and reproducible results that could be generalized in other patients cohort.
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Affiliation(s)
| | - Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli", Naples, Italy.
| | - Giulia Grazzini
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy
| | - Silvia Pradella
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy
| | - Alessandra Borgheresi
- Department of Clinical Special and Dental Sciences, School of Radiology, University Politecnica delle Marche, Ancona, Italy
| | - Alessandra Bruno
- Department of Clinical Special and Dental Sciences, School of Radiology, University Politecnica delle Marche, Ancona, Italy
| | - Pierpaolo Palumbo
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy.,Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, 67100, L'Aquila, Italy
| | - Federico Bruno
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy.,Department of Applied Clinical Sciences and Biotechnology, University of L'Aquila, 67100, L'Aquila, Italy
| | - Roberta Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy.,Division of Radiology, "Università Degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Andrea Giovagnoni
- Department of Clinical Special and Dental Sciences, School of Radiology, University Politecnica delle Marche, Ancona, Italy
| | - Roberto Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy.,Division of Radiology, "Università Degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Vittorio Miele
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy
| | - Antonio Barile
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy.,Department of Applied Clinical Sciences and Biotechnology, University of L'Aquila, 67100, L'Aquila, Italy
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25
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Granata V, Fusco R, Vallone P, Setola SV, Picone C, Grassi F, Patrone R, Belli A, Izzo F, Petrillo A. Not only lymphadenopathy: case of chest lymphangitis assessed with MRI after COVID 19 vaccine. Infect Agent Cancer 2022; 17:8. [PMID: 35300727 PMCID: PMC8929244 DOI: 10.1186/s13027-022-00419-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 02/16/2022] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND To date, no paper reports cases of lymphangitis after COVID 19 vaccination. We present a case of lymphangitis after vaccination from COVID 19, in a patient with colorectal liver metastases. METHODS We described the case of a 56-year-old woman with history of a surgical resection of colorectal cancer and liver metastases, without any kind of drug therapy for about a month. In addition, a recent administration (2 days ago) of Spikevax (mRNA-1273, Moderna vaccine), as a booster dose, on the right arm was reported. RESULTS The magnetic resonance (MR) examination showed the effects of the previous surgical resection and five new hepatic metastases, located in the VIII, VI, V, IV and II hepatic segments. As an accessory finding the presence of lymphadenopathy in the axillary area and lymphangitis of the right breast and chest were identified. The computed tomography scan performed a week earlier, and re-evaluated in light of the MR data, did not identify the presence of lymphadenopathy in the axillary area and lymphangitis signs. CONCLUSIONS Lymphangitis could occur after COVID 19 vaccine and it is important to know this data to avoid alarmism in patients and clinicians and economic waste linked to the execution of various radiological investigations for the search for a tumour that probably does not exist. TRIAL REGISTRATION Not applicable.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy.
| | | | - Paolo Vallone
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
| | - Sergio Venanzio Setola
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
| | - Carmine Picone
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
| | - Francesca Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122, Milan, Italy.,Division of Radiology, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Renato Patrone
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
| | - Andrea Belli
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
| | - Francesco Izzo
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
| | - Antonella Petrillo
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
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26
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Granata V, Fusco R, Setola SV, Simonetti I, Cozzi D, Grazzini G, Grassi F, Belli A, Miele V, Izzo F, Petrillo A. An update on radiomics techniques in primary liver cancers. Infect Agent Cancer 2022; 17:6. [PMID: 35246207 PMCID: PMC8897888 DOI: 10.1186/s13027-022-00422-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Accepted: 02/28/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Radiomics is a progressing field of research that deals with the extraction of quantitative metrics from medical images. Radiomic features detention indirectly tissue features such as heterogeneity and shape and can, alone or in combination with demographic, histological, genomic, or proteomic data, be used for decision support system in clinical setting. METHODS This article is a narrative review on Radiomics in Primary Liver Cancers. Particularly, limitations and future perspectives are discussed. RESULTS In oncology, assessment of tissue heterogeneity is of particular interest: genomic analysis have demonstrated that the degree of tumour heterogeneity is a prognostic determinant of survival and an obstacle to cancer control. Therefore, that Radiomics could support cancer detection, diagnosis, evaluation of prognosis and response to treatment, so as could supervise disease status in hepatocellular carcinoma (HCC) and Intrahepatic Cholangiocarcinoma (ICC) patients. Radiomic analysis is a convenient radiological image analysis technique used to support clinical decisions as it is able to provide prognostic and / or predictive biomarkers that allow a fast, objective and repeatable tool for disease monitoring. CONCLUSIONS Although several studies have shown that this analysis is very promising, there is little standardization and generalization of the results, which limits the translation of this method into the clinical context. The limitations are mainly related to the evaluation of data quality, repeatability, reproducibility, overfitting of the model. TRIAL REGISTRATION Not applicable.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Via Mariano Semmola 80131, Naples, Italy.
| | | | - Sergio Venazio Setola
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Via Mariano Semmola 80131, Naples, Italy
| | - Igino Simonetti
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Via Mariano Semmola 80131, Naples, Italy
| | - Diletta Cozzi
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via Della Signora 2, 20122, Milan, Italy
| | - Giulia Grazzini
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via Della Signora 2, 20122, Milan, Italy
| | - Francesca Grassi
- Division of Radiology, "Università Degli Studi Della Campania Luigi Vanvitelli", Naples, Italy
| | - Andrea Belli
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", 80131, Naples, Italy
| | - Vittorio Miele
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via Della Signora 2, 20122, Milan, Italy
| | - Francesco Izzo
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", 80131, Naples, Italy
| | - Antonella Petrillo
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Via Mariano Semmola 80131, Naples, Italy
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Liu J, Wang Y, He G, Wang X, Sun M. Quantitative CT comparison between COVID-19 and mycoplasma pneumonia suspected as COVID-19: a longitudinal study. BMC Med Imaging 2022; 22:21. [PMID: 35125096 PMCID: PMC8818096 DOI: 10.1186/s12880-022-00750-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 02/01/2022] [Indexed: 12/21/2022] Open
Abstract
OBJECTIVE The purpose of this study was to compare imaging features between COVID-19 and mycoplasma pneumonia (MP). MATERIALS AND METHODS The data of patients with mild COVID-19 and MP who underwent chest computed tomography (CT) examination from February 1, 2020 to April 17, 2020 were retrospectively analyzed. The Pneumonia-CT-LKM-PP model based on a deep learning algorithm was used to automatically quantify the number, volume, and involved lobes of pulmonary lesions, and longitudinal changes in quantitative parameters were assessed in three CT follow-ups. RESULTS A total of 10 patients with mild COVID-19 and 13 patients with MP were included in this study. There was no difference in lymphocyte counts at baseline between the two groups (1.43 ± 0.45 vs. 1.44 ± 0.50, p = 0.279). C-reactive protein levels were significantly higher in MP group than in COVID-19 group (p < 0.05). The number, volume, and involved lobes of pulmonary lesions reached a peak in 7-14 days in the COVID-19 group, but there was no peak or declining trend over time in the MP group (p < 0.05). CONCLUSION Based on the longitudinal changes of quantitative CT, pulmonary lesions peaked at 7-14 days in patients with COVID-19, and this may be useful to distinguish COVID-19 from MP and evaluate curative effects and prognosis.
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Affiliation(s)
- Junzhong Liu
- Department of Radiology, Weifang No. 2 People's Hospital, The Second Affiliated Hospital of Weifang Medical University, Weifang City, Shandong Province, China.
- Department of Medical Imaging, Weifang No. 2 People's Hospital, The Second Affiliated Hospital of Weifang Medical University, 7 Yuanxiao Street, Weifang City, 261041, Shandong Province, People's Republic of China.
| | - Yuzhen Wang
- Department of Radiology, Weifang No. 2 People's Hospital, The Second Affiliated Hospital of Weifang Medical University, Weifang City, Shandong Province, China
| | - Guanghui He
- Department of Interventional Radiology, Weifang No. 2 People's Hospital, The Second Affiliated Hospital of Weifang Medical University, Weifang City, Shandong Province, China
| | - Xinhua Wang
- Department of Radiology, Weifang No. 2 People's Hospital, The Second Affiliated Hospital of Weifang Medical University, Weifang City, Shandong Province, China
| | - Minfeng Sun
- Department of Radiology, Weifang No. 2 People's Hospital, The Second Affiliated Hospital of Weifang Medical University, Weifang City, Shandong Province, China
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Ishac D, Matta S, Bin S, Aziz H, Karam E, Abche A, Nassar G. Objective Assessment of Covid-19 Severity Affecting the Vocal and Respiratory System Using a Wearable, Autonomous Sound Collar. Cell Mol Bioeng 2022; 15:67-86. [PMID: 34777597 PMCID: PMC8570400 DOI: 10.1007/s12195-021-00712-w] [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: 03/04/2021] [Accepted: 10/11/2021] [Indexed: 11/13/2022] Open
Abstract
INTRODUCTION Since the outbreak began in January 2020, Covid-19 has affected more than 161 million people worldwide and resulted in about 3.3 million deaths. Despite efforts to detect human infection with the virus as early as possible, the confirmatory test still requires the analysis of sputum or blood with estimated results available within approximately 30 minutes; this may potentially be followed by clinical referral if the patient shows signs of aggravated pneumonia. This work aims to implement a soft collar as a sound device dedicated to the objective evaluation of the pathophysiological state resulting from dysphonia of laryngeal origin or respiratory failure of inflammatory origin, in particular caused by Covid-19. METHODS In this study, we exploit the vibrations of waves generated by the vocal and respiratory system of 30 people. A biocompatible acoustic sensor embedded in a soft collar around the neck collects these waves. The collar is also equipped with thermal sensors and a cross-data analysis module in both the temporal and frequency domains (STFT). The optimal coupling conditions and the electrical and dimensional characteristics of the sensors were defined based on a mathematical approach using a matrix formalism. RESULTS The characteristics of the signals in the time domain combined with the quantities obtained from the STFT offer multidimensional information and a decision support tool for determining a pathophysiological state representative of the symptoms explored. The device, tested on 30 people, was able to differentiate patients with mild symptoms from those who had developed acute signs of respiratory failure on a severity scale of 1 to 10. CONCLUSION With the health constraints imposed by the effects of Covid-19, the heavy organization to be implemented resulting from the flow of diagnostics, tests and clinical management, it was urgent to develop innovative and safe biomedical technologies. This passive listening technique will contribute to the non-invasive assessment and dynamic observation of lesions. Moreover, it merits further examination to provide support for medical operators to improve clinical management. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s12195-021-00712-w.
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Affiliation(s)
- D. Ishac
- Electrical Engineering Department, University of Balamand (UOB), Balamand, Lebanon
| | - S. Matta
- Electrical Engineering Department, University of Balamand (UOB), Balamand, Lebanon
| | - S. Bin
- College of Physics, University of Qingdao, Qingdao, China
| | - H. Aziz
- Department of Pulmonary Pathology, Sahlgrenska University Hospital, Göteborg, Sweden
| | - E. Karam
- Electrical Engineering Department, University of Balamand (UOB), Balamand, Lebanon
| | - A. Abche
- Electrical Engineering Department, University of Balamand (UOB), Balamand, Lebanon
| | - G. Nassar
- IEMN – CNRS UMR 8520-INSA (HdF)-Lille academic, Lille, France
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29
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Granata V, Grassi R, Fusco R, Setola SV, Belli A, Ottaiano A, Nasti G, La Porta M, Danti G, Cappabianca S, Cutolo C, Petrillo A, Izzo F. Intrahepatic cholangiocarcinoma and its differential diagnosis at MRI: how radiologist should assess MR features. Radiol Med 2021; 126:1584-1600. [PMID: 34843029 DOI: 10.1007/s11547-021-01428-7] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 11/02/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND Intrahepatic cholangiocarcinoma (ICC) is the second most common type of primary hepatic malignancy. Aim of this work is to analyse the features of ICC and its differential diagnosis at MRI, assessing two categories intraparenchymal and peribiliary lesions. METHODS The study population included 88 patients with histological diagnosis of ICCs: 61 with mass-forming type, 23 with periductal-infiltrating tumours and 4 with intraductal-growing type. As a control study groups, we identified: 86 consecutive patients with liver colorectal intrahepatic metastases (mCRC) (groups A); 35 consecutive patients with peribiliary metastases (groups B); 62 consecutive patients (groups C) with hepatocellular carcinoma (HCC); 18 consecutive patients (groups D) with combined hepatocellular cholangiocarcinoma (cHCC-CCA); and 26 consecutive patients (groups E) with hepatic hemangioma. For all lesions, magnetic resonance (MR) features were assessed according to Liver Imaging Reporting and Data System (LI-RADS) version 2018. The liver-specific gadolinium ethoxybenzyl dimeglumine-EOB (Primovist, Bayer Schering Pharma, Germany), was employed. Chi-square test was employed to analyse differences in percentage values of categorical variable, while the nonparametric Kruskal-Wallis test was used to test for statistically significant differences between the median values of the continuous variables. However, false discovery rate adjustment according to Benjamin and Hochberg for multiple testing was considered. RESULTS T1- and T2-weighted signal intensity (SI), restricted diffusion, transitional phase (TP) and hepatobiliary phase (HP) aspects allowed the differentiation between study group (mass-forming ICCs) and each other control group (A, C, D, E) with statistical significance, while arterial phase (AP) appearance allowed the differentiation between study group and the control groups C and D with statistical significance and PP appearance allowed the differentiation between study group and the control groups A, C and D with statistical significance. Instead, no MR feature allowed the differentiation between study group (periductal-infiltrating type) and control group B. CONCLUSION T1 and T2 W SI, restricted diffusion, TP and HP appearance allowed the differentiation between mass-forming ICCs and mimickers with statistical significance, while AP appearance allowed the differentiation between study group and the control groups C and D with statistical significance and PP appearance allowed the differentiation between study group and the control groups A, C and D.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli", Naples, Italy
| | - Roberta Grassi
- Division of Radiology, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | | | - Sergio Venanzio Setola
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli", Naples, Italy
| | - Andrea Belli
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli", Naples, Italy
| | - Alessandro Ottaiano
- Abdominal Oncology Division, "Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli", Naples, Italy
| | - Guglielmo Nasti
- Abdominal Oncology Division, "Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli", Naples, Italy
| | | | - Ginevra Danti
- Division of Radiodiagnostic, "Azienda Ospedaliero-Universitaria Careggi", Firenze, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy
| | - Salvatore Cappabianca
- Division of Radiology, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Salerno, Italy
| | - Antonella Petrillo
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli", Naples, Italy
| | - Francesco Izzo
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli", Naples, Italy
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Udriștoiu AL, Ghenea AE, Udriștoiu Ș, Neaga M, Zlatian OM, Vasile CM, Popescu M, Țieranu EN, Salan AI, Turcu AA, Nicolosu D, Calina D, Cioboata R. COVID-19 and Artificial Intelligence: An Approach to Forecast the Severity of Diagnosis. Life (Basel) 2021; 11:1281. [PMID: 34833156 PMCID: PMC8617902 DOI: 10.3390/life11111281] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 10/27/2021] [Accepted: 11/18/2021] [Indexed: 12/27/2022] Open
Abstract
(1) Background: The new SARS-COV-2 pandemic overwhelmed intensive care units, clinicians, and radiologists, so the development of methods to forecast the diagnosis' severity became a necessity and a helpful tool. (2) Methods: In this paper, we proposed an artificial intelligence-based multimodal approach to forecast the future diagnosis' severity of patients with laboratory-confirmed cases of SARS-CoV-2 infection. At hospital admission, we collected 46 clinical and biological variables with chest X-ray scans from 475 COVID-19 positively tested patients. An ensemble of machine learning algorithms (AI-Score) was developed to predict the future severity score as mild, moderate, and severe for COVID-19-infected patients. Additionally, a deep learning module (CXR-Score) was developed to automatically classify the chest X-ray images and integrate them into AI-Score. (3) Results: The AI-Score predicted the COVID-19 diagnosis' severity on the testing/control dataset (95 patients) with an average accuracy of 98.59%, average specificity of 98.97%, and average sensitivity of 97.93%. The CXR-Score module graded the severity of chest X-ray images with an average accuracy of 99.08% on the testing/control dataset (95 chest X-ray images). (4) Conclusions: Our study demonstrated that the deep learning methods based on the integration of clinical and biological data with chest X-ray images accurately predicted the COVID-19 severity score of positive-tested patients.
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Affiliation(s)
- Anca Loredana Udriștoiu
- Faculty of Automation, Computers and Electronics, University of Craiova, 200776 Craiova, Romania; (A.L.U.); (Ș.U.); (M.N.)
| | - Alice Elena Ghenea
- Department of Bacteriology-Virology-Parasitology, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania;
| | - Ștefan Udriștoiu
- Faculty of Automation, Computers and Electronics, University of Craiova, 200776 Craiova, Romania; (A.L.U.); (Ș.U.); (M.N.)
| | - Manuela Neaga
- Faculty of Automation, Computers and Electronics, University of Craiova, 200776 Craiova, Romania; (A.L.U.); (Ș.U.); (M.N.)
| | - Ovidiu Mircea Zlatian
- Department of Bacteriology-Virology-Parasitology, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania;
| | - Corina Maria Vasile
- PhD School Department, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania;
| | - Mihaela Popescu
- Department of Endocrinology, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania;
| | - Eugen Nicolae Țieranu
- Department of Cardiology, University of Medicine and Pharmacy of Craiova, 200642 Craiova, Romania;
| | - Alex-Ioan Salan
- Department of Oral and Maxillofacial Surgery, University of Medicine and Pharmacy Craiova, 200349 Craiova, Romania;
| | - Adina Andreea Turcu
- Infectious Disease Department, Victor Babes University Hospital Craiova, 200515 Craiova, Romania;
| | - Dragos Nicolosu
- Pneumology Department, Victor Babes University Hospital Craiova, 200515 Craiova, Romania;
| | - Daniela Calina
- Department of Clinical Pharmacy, University of Pharmacy and Medicine Craiova, 200349 Craiova, Romania
| | - Ramona Cioboata
- Department of Pneumology, University of Pharmacy and Medicine Craiova, 200349 Craiova, Romania;
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Palumbo P, Palumbo MM, Bruno F, Picchi G, Iacopino A, Acanfora C, Sgalambro F, Arrigoni F, Ciccullo A, Cosimini B, Splendiani A, Barile A, Masedu F, Grimaldi A, Di Cesare E, Masciocchi C. Automated Quantitative Lung CT Improves Prognostication in Non-ICU COVID-19 Patients beyond Conventional Biomarkers of Disease. Diagnostics (Basel) 2021; 11:2125. [PMID: 34829472 PMCID: PMC8624922 DOI: 10.3390/diagnostics11112125] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 11/08/2021] [Accepted: 11/12/2021] [Indexed: 12/22/2022] Open
Abstract
(1) Background: COVID-19 continues to represent a worrying pandemic. Despite the high percentage of non-severe illness, a wide clinical variability is often reported in real-world practice. Accurate predictors of disease aggressiveness, however, are still lacking. The purpose of our study was to evaluate the impact of quantitative analysis of lung computed tomography (CT) on non-intensive care unit (ICU) COVID-19 patients' prognostication; (2) Methods: Our historical prospective study included fifty-five COVID-19 patients consecutively submitted to unenhanced lung CT. Primary outcomes were recorded during hospitalization, including composite ICU admission for the need of mechanical ventilation and/or death occurrence. CT examinations were retrospectively evaluated to automatically calculate differently aerated lung tissues (i.e., overinflated, well-aerated, poorly aerated, and non-aerated tissue). Scores based on the percentage of lung weight and volume were also calculated; (3) Results: Patients who reported disease progression showed lower total lung volume. Inflammatory indices correlated with indices of respiratory failure and high-density areas. Moreover, non-aerated and poorly aerated lung tissue resulted significantly higher in patients with disease progression. Notably, non-aerated lung tissue was independently associated with disease progression (HR: 1.02; p-value: 0.046). When different predictive models including clinical, laboratoristic, and CT findings were analyzed, the best predictive validity was reached by the model that included non-aerated tissue (C-index: 0.97; p-value: 0.0001); (4) Conclusions: Quantitative lung CT offers wide advantages in COVID-19 disease stratification. Non-aerated lung tissue is more likely to occur with severe inflammation status, turning out to be a strong predictor for disease aggressiveness; therefore, it should be included in the predictive model of COVID-19 patients.
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Affiliation(s)
- Pierpaolo Palumbo
- Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, Via Saragat, Località Campo di Pile, 67100 L’Aquila, Italy;
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy;
| | - Maria Michela Palumbo
- Department of Anesthesiology and Intensive Care Medicine, Fondazione Policlinico Universitario A. Gemelli IRCCS, Catholic University of The Sacred Heart, 00168 Rome, Italy;
| | - Federico Bruno
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy;
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy; (A.I.); (C.A.); (F.S.); (A.S.); (F.M.); (C.M.)
| | - Giovanna Picchi
- Infectious Disease Unit, San Salvatore Hospital, Via Lorenzo Natali, 1-Località Coppito, 67100 L’Aquila, Italy; (G.P.); (A.C.); (A.G.)
| | - Antonio Iacopino
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy; (A.I.); (C.A.); (F.S.); (A.S.); (F.M.); (C.M.)
| | - Chiara Acanfora
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy; (A.I.); (C.A.); (F.S.); (A.S.); (F.M.); (C.M.)
| | - Ferruccio Sgalambro
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy; (A.I.); (C.A.); (F.S.); (A.S.); (F.M.); (C.M.)
| | - Francesco Arrigoni
- Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, Via Saragat, Località Campo di Pile, 67100 L’Aquila, Italy;
| | - Arturo Ciccullo
- Infectious Disease Unit, San Salvatore Hospital, Via Lorenzo Natali, 1-Località Coppito, 67100 L’Aquila, Italy; (G.P.); (A.C.); (A.G.)
| | - Benedetta Cosimini
- Department of Life, Health and Environmental Sciences, University of L’Aquila, Piazzale Salvatore Tommasi 1, 67100 L’Aquila, Italy; (B.C.); (E.D.C.)
| | - Alessandra Splendiani
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy; (A.I.); (C.A.); (F.S.); (A.S.); (F.M.); (C.M.)
| | - Antonio Barile
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy; (A.I.); (C.A.); (F.S.); (A.S.); (F.M.); (C.M.)
| | - Francesco Masedu
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy; (A.I.); (C.A.); (F.S.); (A.S.); (F.M.); (C.M.)
| | - Alessandro Grimaldi
- Infectious Disease Unit, San Salvatore Hospital, Via Lorenzo Natali, 1-Località Coppito, 67100 L’Aquila, Italy; (G.P.); (A.C.); (A.G.)
| | - Ernesto Di Cesare
- Department of Life, Health and Environmental Sciences, University of L’Aquila, Piazzale Salvatore Tommasi 1, 67100 L’Aquila, Italy; (B.C.); (E.D.C.)
| | - Carlo Masciocchi
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy; (A.I.); (C.A.); (F.S.); (A.S.); (F.M.); (C.M.)
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Quantitative Analysis of Residual COVID-19 Lung CT Features: Consistency among Two Commercial Software. J Pers Med 2021; 11:jpm11111103. [PMID: 34834455 PMCID: PMC8623042 DOI: 10.3390/jpm11111103] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 10/21/2021] [Accepted: 10/25/2021] [Indexed: 12/16/2022] Open
Abstract
Objective: To investigate two commercial software and their efficacy in the assessment of chest CT sequelae in patients affected by COVID-19 pneumonia, comparing the consistency of tools. Materials and Methods: Included in the study group were 120 COVID-19 patients (56 women and 104 men; 61 years of median age; range: 21–93 years) who underwent chest CT examinations at discharge between 5 March 2020 and 15 March 2021 and again at a follow-up time (3 months; range 30–237 days). A qualitative assessment by expert radiologists in the infectious disease field (experience of at least 5 years) was performed, and a quantitative evaluation using thoracic VCAR software (GE Healthcare, Chicago, Illinois, United States) and a pneumonia module of ANKE ASG-340 CT workstation (HTS Med & Anke, Naples, Italy) was performed. The qualitative evaluation included the presence of ground glass opacities (GGOs) consolidation, interlobular septal thickening, fibrotic-like changes (reticular pattern and/or honeycombing), bronchiectasis, air bronchogram, bronchial wall thickening, pulmonary nodules surrounded by GGOs, pleural and pericardial effusion, lymphadenopathy, and emphysema. A quantitative evaluation included the measurements of GGOs, consolidations, emphysema, residual healthy parenchyma, and total lung volumes for the right and left lung. A chi-square test and non-parametric test were utilized to verify the differences between groups. Correlation coefficients were used to analyze the correlation and variability among quantitative measurements by different computer tools. A receiver operating characteristic (ROC) analysis was performed. Results: The correlation coefficients showed great variability among the quantitative measurements by different tools when calculated on baseline CT scans and considering all patients. Instead, a good correlation (≥0.6) was obtained for the quantitative GGO, as well as the consolidation volumes obtained by two tools when calculated on baseline CT scans, considering the control group. An excellent correlation (≥0.75) was obtained for the quantitative residual healthy lung parenchyma volume, GGO, consolidation volumes obtained by two tools when calculated on follow-up CT scans, and for residual healthy lung parenchyma and GGO quantification when the percentage change of these volumes were calculated between a baseline and follow-up scan. The highest value of accuracy to identify patients with RT-PCR positive compared to the control group was obtained by a GGO total volume quantification by thoracic VCAR (accuracy = 0.75). Conclusions: Computer aided quantification could be an easy and feasible way to assess chest CT sequelae due to COVID-19 pneumonia; however, a great variability among measurements provided by different tools should be considered.
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Churruca M, Martínez-Besteiro E, Couñago F, Landete P. COVID-19 pneumonia: A review of typical radiological characteristics. World J Radiol 2021; 13:327-343. [PMID: 34786188 PMCID: PMC8567439 DOI: 10.4329/wjr.v13.i10.327] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 03/08/2021] [Accepted: 09/14/2021] [Indexed: 02/06/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) was first discovered after unusual cases of severe pneumonia emerged by the end of 2019 in Wuhan (China) and was declared a global public health emergency by the World Health Organization in January 2020. The new pathogen responsible for the infection, genetically similar to the beta-coronavirus family, is known as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), and the current gold standard diagnostic tool for its detection in respiratory samples is the reverse transcription-polymerase chain reaction test. Imaging findings on COVID-19 have been widely described in studies published throughout last year, 2020. In general, ground-glass opacities and consolidations, with a bilateral and peripheral distribution, are the most typical patterns found in COVID-19 pneumonia. Even though much of the literature focuses on chest computed tomography (CT) and X-ray imaging and their findings, other imaging modalities have also been useful in the assessment of COVID-19 patients. Lung ultrasonography is an emerging technique with a high sensitivity, and thus useful in the initial evaluation of SARS-CoV-2 infection. In addition, combined positron emission tomography-CT enables the identification of affected areas and follow-up treatment responses. This review intends to clarify the role of the imaging modalities available and identify the most common radiological manifestations of COVID-19.
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Affiliation(s)
- María Churruca
- Pulmonology Department, Hospital Universitario de La Princesa, Madrid 28006, Spain
| | | | - Felipe Couñago
- Department of Radiation Oncology, Hospital Universitario Quirónsalud Madrid, Madrid 28223, Spain
- Department of Radiation Oncology, Hospital La Luz, Madrid 28003, Spain
- Clinical Department, Faculty of Biomedicine,Universidad Europea de Madrid, Madrid 28670, Spain
| | - Pedro Landete
- Department of Pneumology, Hospital Universitario de la Princesa, Universidad Autónoma de Madrid, Madrid 28006, Spain
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Wang L, Zhang Y, Wang D, Tong X, Liu T, Zhang S, Huang J, Zhang L, Chen L, Fan H, Clarke M. Artificial Intelligence for COVID-19: A Systematic Review. Front Med (Lausanne) 2021; 8:704256. [PMID: 34660623 PMCID: PMC8514781 DOI: 10.3389/fmed.2021.704256] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Accepted: 08/09/2021] [Indexed: 02/05/2023] Open
Abstract
Background: Recently, Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome virus 2 (SARS-CoV-2), has affected more than 200 countries and lead to enormous losses. This study systematically reviews the application of Artificial Intelligence (AI) techniques in COVID-19, especially for diagnosis, estimation of epidemic trends, prognosis, and exploration of effective and safe drugs and vaccines; and discusses the potential limitations. Methods: We report this systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched PubMed, Embase and the Cochrane Library from inception to 19 September 2020 for published studies of AI applications in COVID-19. We used PROBAST (prediction model risk of bias assessment tool) to assess the quality of literature related to the diagnosis and prognosis of COVID-19. We registered the protocol (PROSPERO CRD42020211555). Results: We included 78 studies: 46 articles discussed AI-assisted diagnosis for COVID-19 with total accuracy of 70.00 to 99.92%, sensitivity of 73.00 to 100.00%, specificity of 25 to 100.00%, and area under the curve of 0.732 to 1.000. Fourteen articles evaluated prognosis based on clinical characteristics at hospital admission, such as clinical, laboratory and radiological characteristics, reaching accuracy of 74.4 to 95.20%, sensitivity of 72.8 to 98.00%, specificity of 55 to 96.87% and AUC of 0.66 to 0.997 in predicting critical COVID-19. Nine articles used AI models to predict the epidemic of the COVID-19, such as epidemic peak, infection rate, number of infected cases, transmission laws, and development trend. Eight articles used AI to explore potential effective drugs, primarily through drug repurposing and drug development. Finally, 1 article predicted vaccine targets that have the potential to develop COVID-19 vaccines. Conclusions: In this review, we have shown that AI achieved high performance in diagnosis, prognosis evaluation, epidemic prediction and drug discovery for COVID-19. AI has the potential to enhance significantly existing medical and healthcare system efficiency during the COVID-19 pandemic.
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Affiliation(s)
- Lian Wang
- Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Yonggang Zhang
- Department of Periodical Press and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China.,Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, Chengdu, China
| | - Dongguang Wang
- Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Xiang Tong
- Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Tao Liu
- Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Shijie Zhang
- Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Jizhen Huang
- Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Li Zhang
- Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Lingmin Chen
- Department of Anesthesiology and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University and The Research Units of West China, Chinese Academy of Medical Sciences, Chengdu, China
| | - Hong Fan
- Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China
| | - Mike Clarke
- Northern Ireland Methodology Hub, Queen's University Belfast, Belfast, United Kingdom
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Gashi A, Kubik-Huch RA, Chatzaraki V, Potempa A, Rauch F, Grbic S, Wiggli B, Friedl A, Niemann T. Detection and characterization of COVID-19 findings in chest CT: Feasibility and applicability of an AI-based software tool. Medicine (Baltimore) 2021; 100:e27478. [PMID: 34731126 PMCID: PMC8519217 DOI: 10.1097/md.0000000000027478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 04/19/2021] [Accepted: 09/17/2021] [Indexed: 01/05/2023] Open
Abstract
ABSTRACT The COVID-19 pandemic has challenged institutions' diagnostic processes worldwide. The aim of this study was to assess the feasibility of an artificial intelligence (AI)-based software tool that automatically evaluates chest computed tomography for findings of suspected COVID-19.Two groups were retrospectively evaluated for COVID-19-associated ground glass opacities of the lungs (group A: real-time polymerase chain reaction positive COVID patients, n = 108; group B: asymptomatic pre-operative group, n = 88). The performance of an AI-based software assessment tool for detection of COVID-associated abnormalities was compared with human evaluation based on COVID-19 reporting and data system (CO-RADS) scores performed by 3 readers.All evaluated variables of the AI-based assessment showed significant differences between the 2 groups (P < .01). The inter-reader reliability of CO-RADS scoring was 0.87. The CO-RADS scores were substantially higher in group A (mean 4.28) than group B (mean 1.50). The difference between CO-RADS scoring and AI assessment was statistically significant for all variables but showed good correlation with the clinical context of the CO-RADS score. AI allowed to predict COVID positive cases with an accuracy of 0.94.The evaluated AI-based algorithm detects COVID-19-associated findings with high sensitivity and may support radiologic workflows during the pandemic.
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Affiliation(s)
- Andi Gashi
- Department of Health Sciences and Technology, Swiss Federal Institute of Technology, ETH Zurich, 101 Rämistrasse, Zurich, Switzerland
| | - Rahel A. Kubik-Huch
- Department of Radiology, Kantonsspital Baden, 1 Im Ergel, Baden, Switzerland
| | - Vasiliki Chatzaraki
- Department of Radiology, Kantonsspital Baden, 1 Im Ergel, Baden, Switzerland
| | - Anna Potempa
- Department of Radiology, Kantonsspital Baden, 1 Im Ergel, Baden, Switzerland
| | - Franziska Rauch
- Siemens Healthcare GmbH, 3 Siemensstrasse, Forchheim, Germany
| | - Sasa Grbic
- Siemens Healthcare GmbH, 3 Siemensstrasse, Forchheim, Germany
| | - Benedikt Wiggli
- Department of Infectious Diseases, Kantonsspital Baden, 1 Im Ergel, Baden, Switzerland
| | - Andrée Friedl
- Department of Infectious Diseases, Kantonsspital Baden, 1 Im Ergel, Baden, Switzerland
| | - Tilo Niemann
- Department of Radiology, Kantonsspital Baden, 1 Im Ergel, Baden, Switzerland
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36
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Fusco R, Grassi R, Granata V, Setola SV, Grassi F, Cozzi D, Pecori B, Izzo F, Petrillo A. Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment. J Pers Med 2021; 11:993. [PMID: 34683133 PMCID: PMC8540782 DOI: 10.3390/jpm11100993] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 09/22/2021] [Accepted: 09/28/2021] [Indexed: 12/26/2022] Open
Abstract
OBJECTIVE To report an overview and update on Artificial Intelligence (AI) and COVID-19 using chest Computed Tomography (CT) scan and chest X-ray images (CXR). Machine Learning and Deep Learning Approaches for Diagnosis and Treatment were identified. METHODS Several electronic datasets were analyzed. The search covered the years from January 2019 to June 2021. The inclusion criteria were studied evaluating the use of AI methods in COVID-19 disease reporting performance results in terms of accuracy or precision or area under Receiver Operating Characteristic (ROC) curve (AUC). RESULTS Twenty-two studies met the inclusion criteria: 13 papers were based on AI in CXR and 10 based on AI in CT. The summarized mean value of the accuracy and precision of CXR in COVID-19 disease were 93.7% ± 10.0% of standard deviation (range 68.4-99.9%) and 95.7% ± 7.1% of standard deviation (range 83.0-100.0%), respectively. The summarized mean value of the accuracy and specificity of CT in COVID-19 disease were 89.1% ± 7.3% of standard deviation (range 78.0-99.9%) and 94.5 ± 6.4% of standard deviation (range 86.0-100.0%), respectively. No statistically significant difference in summarized accuracy mean value between CXR and CT was observed using the Chi square test (p value > 0.05). CONCLUSIONS Summarized accuracy of the selected papers is high but there was an important variability; however, less in CT studies compared to CXR studies. Nonetheless, AI approaches could be used in the identification of disease clusters, monitoring of cases, prediction of the future outbreaks, mortality risk, COVID-19 diagnosis, and disease management.
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Affiliation(s)
- Roberta Fusco
- IGEA SpA Medical Division—Oncology, Via Casarea 65, Casalnuovo di Napoli, 80013 Naples, Italy;
| | - Roberta Grassi
- Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, 80138 Naples, Italy; (R.G.); (F.G.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy; (S.V.S.); (A.P.)
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy; (S.V.S.); (A.P.)
| | - Francesca Grassi
- Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, 80138 Naples, Italy; (R.G.); (F.G.)
| | - Diletta Cozzi
- Division of Radiology, Azienda Ospedaliera Universitaria Careggi, 50134 Florence, Italy;
| | - Biagio Pecori
- Division of Radiotherapy and Innovative Technologies, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
| | - Francesco Izzo
- Division of Hepatobiliary Surgery, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy; (S.V.S.); (A.P.)
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Ardakani AA, Kwee RM, Mirza-Aghazadeh-Attari M, Castro HM, Kuzan TY, Altintoprak KM, Besutti G, Monelli F, Faeghi F, Acharya UR, Mohammadi A. A practical artificial intelligence system to diagnose COVID-19 using computed tomography: A multinational external validation study. Pattern Recognit Lett 2021; 152:42-49. [PMID: 34580550 PMCID: PMC8457921 DOI: 10.1016/j.patrec.2021.09.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 08/14/2021] [Accepted: 09/16/2021] [Indexed: 01/27/2023]
Abstract
Computed tomography has gained an important role in the early diagnosis of COVID-19 pneumonia. However, the ever-increasing number of patients has overwhelmed radiology departments and has caused a reduction in quality of services. Artificial intelligence (AI) systems are the remedy to the current situation. However, the lack of application in real-world conditions has limited their consideration in clinical settings. This study validated a clinical AI system, COVIDiag, to aid radiologists in accurate and rapid evaluation of COVID-19 cases. 50 COVID-19 and 50 non-COVID-19 pneumonia cases were included from each of five centers: Argentina, Turkey, Iran, Netherlands, and Italy. The Dutch database included only 50 COVID-19 cases. The performance parameters namely sensitivity, specificity, accuracy, and area under the ROC curve (AUC) were computed for each database using COVIDiag model. The most common pattern of involvement among COVID-19 cases in all databases were bilateral involvement of upper and lower lobes with ground-glass opacities. The best sensitivity of 92.0% was recorded for the Italian database. The system achieved an AUC of 0.983, 0.914, 0.910, and 0.882 for Argentina, Turkey, Iran, and Italy, respectively. The model obtained a sensitivity of 86.0% for the Dutch database. COVIDiag model could diagnose COVID-19 pneumonia in all of cohorts with AUC of 0.921 (sensitivity, specificity, and accuracy of 88.8%, 87.0%, and 88.0%, respectively). Our study confirmed the accuracy of our proposed AI model (COVIDiag) in the diagnosis of COVID-19 cases. Furthermore, the system demonstrated consistent optimal diagnostic performance on multinational databases, which is critical to determine the generalizability and objectivity of the proposed COVIDiag model. Our results are significant as they provide real-world evidence regarding the applicability of AI systems in clinical medicine.
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Affiliation(s)
- Ali Abbasian Ardakani
- Radiology Technology Department, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Robert M Kwee
- Department of Radiology, Zuyderland Medical Center, Heerlen/Sittard-Geleen, the Netherlands
| | | | | | - Taha Yusuf Kuzan
- Department of Radiology, Sancaktepe Sehit Prof. Dr. Ilhan Varank Training and Research Hospital, Istanbul, Turkey
| | - Kübra Murzoğlu Altintoprak
- Department of Radiology, Sancaktepe Sehit Prof. Dr. Ilhan Varank Training and Research Hospital, Istanbul, Turkey
| | - Giulia Besutti
- Radiology Department, Azienda USL - IRCCS di Reggio Emilia, Reggio Emilia, Italy.,Clinical and Experimental Medicine PhD program, University of Modena and Reggio Emilia, Modena, Italy
| | - Filippo Monelli
- Radiology Department, Azienda USL - IRCCS di Reggio Emilia, Reggio Emilia, Italy.,Clinical and Experimental Medicine PhD program, University of Modena and Reggio Emilia, Modena, Italy
| | - Fariborz Faeghi
- Radiology Technology Department, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - U Rajendra Acharya
- Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore.,Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore.,Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
| | - Afshin Mohammadi
- Department of Radiology, Faculty of Medicine, Urmia University of Medical Science, Urmia, Iran
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Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review. Diagnostics (Basel) 2021; 11:diagnostics11101796. [PMID: 34679494 PMCID: PMC8534713 DOI: 10.3390/diagnostics11101796] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 09/15/2021] [Accepted: 09/23/2021] [Indexed: 12/12/2022] Open
Abstract
The evaluation of the efficacy of different therapies is of paramount importance for the patients and the clinicians in oncology, and it is usually possible by performing imaging investigations that are interpreted, taking in consideration different response evaluation criteria. In the last decade, texture analysis (TA) has been developed in order to help the radiologist to quantify and identify parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye, that can be correlated with different endpoints, including cancer prognosis. The aim of this work is to analyze the impact of texture in the prediction of response and in prognosis stratification in oncology, taking into consideration different pathologies (lung cancer, breast cancer, gastric cancer, hepatic cancer, rectal cancer). Key references were derived from a PubMed query. Hand searching and clinicaltrials.gov were also used. This paper contains a narrative report and a critical discussion of radiomics approaches related to cancer prognosis in different fields of diseases.
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Imaging of Pancreatic Neuroendocrine Neoplasms. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18178895. [PMID: 34501485 PMCID: PMC8430610 DOI: 10.3390/ijerph18178895] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 08/16/2021] [Accepted: 08/22/2021] [Indexed: 12/25/2022]
Abstract
Pancreatic neuroendocrine neoplasms (panNENs) represent the second most common pancreatic tumors. They are a heterogeneous group of neoplasms with varying clinical expression and biological behavior, from indolent to aggressive ones. PanNENs can be functioning or non-functioning in accordance with their ability or not to produce metabolically active hormones. They are histopathologically classified according to the 2017 World Health Organization (WHO) classification system. Although the final diagnosis of neuroendocrine tumor relies on histologic examination of biopsy or surgical specimens, both morphologic and functional imaging are crucial for patient care. Morphologic imaging with ultrasonography (US), computed tomography (CT) and magnetic resonance imaging (MRI) is used for initial evaluation and staging of disease, as well as surveillance and therapy monitoring. Functional imaging techniques with somatostatin receptor scintigraphy (SRS) and positron emission tomography (PET) are used for functional and metabolic assessment that is helpful for therapy management and post-therapeutic re-staging. This article reviews the morphological and functional imaging modalities now available and the imaging features of panNENs. Finally, future imaging challenges, such as radiomics analysis, are illustrated.
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Caruso D, Pucciarelli F, Zerunian M, Ganeshan B, De Santis D, Polici M, Rucci C, Polidori T, Guido G, Bracci B, Benvenga A, Barbato L, Laghi A. Chest CT texture-based radiomics analysis in differentiating COVID-19 from other interstitial pneumonia. Radiol Med 2021; 126:1415-1424. [PMID: 34347270 PMCID: PMC8335460 DOI: 10.1007/s11547-021-01402-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 07/12/2021] [Indexed: 02/06/2023]
Abstract
PURPOSE To evaluate the potential role of texture-based radiomics analysis in differentiating Coronavirus Disease-19 (COVID-19) pneumonia from pneumonia of other etiology on Chest CT. MATERIALS AND METHODS One hundred and twenty consecutive patients admitted to Emergency Department, from March 8, 2020, to April 25, 2020, with suspicious of COVID-19 that underwent Chest CT, were retrospectively analyzed. All patients presented CT findings indicative for interstitial pneumonia. Sixty patients with positive COVID-19 real-time reverse transcription polymerase chain reaction (RT-PCR) and 60 patients with negative COVID-19 RT-PCR were enrolled. CT texture analysis (CTTA) was manually performed using dedicated software by two radiologists in consensus and textural features on filtered and unfiltered images were extracted as follows: mean intensity, standard deviation (SD), entropy, mean of positive pixels (MPP), skewness, and kurtosis. Nonparametric Mann-Whitney test assessed CTTA ability to differentiate positive from negative COVID-19 patients. Diagnostic criteria were obtained from receiver operating characteristic (ROC) curves. RESULTS Unfiltered CTTA showed lower values of mean intensity, MPP, and kurtosis in COVID-19 positive patients compared to negative patients (p = 0.041, 0.004, and 0.002, respectively). On filtered images, fine and medium texture scales were significant differentiators; fine texture scale being most significant where COVID-19 positive patients had lower SD (p = 0.004) and MPP (p = 0.004) compared to COVID-19 negative patients. A combination of the significant texture features could identify the patients with positive COVID-19 from negative COVID-19 with a sensitivity of 60% and specificity of 80% (p = 0.001). CONCLUSIONS Preliminary evaluation suggests potential role of CTTA in distinguishing COVID-19 pneumonia from other interstitial pneumonia on Chest CT.
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Affiliation(s)
- Damiano Caruso
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Francesco Pucciarelli
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Marta Zerunian
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Balaji Ganeshan
- Institute of Nuclear Medicine, University College London Hospitals NHS Trust, London, UK
| | - Domenico De Santis
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Michela Polici
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Carlotta Rucci
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Tiziano Polidori
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Gisella Guido
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Benedetta Bracci
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Antonella Benvenga
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Luca Barbato
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Andrea Laghi
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy.
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Laino ME, Ammirabile A, Posa A, Cancian P, Shalaby S, Savevski V, Neri E. The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review. Diagnostics (Basel) 2021; 11:1317. [PMID: 34441252 PMCID: PMC8394327 DOI: 10.3390/diagnostics11081317] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 07/02/2021] [Accepted: 07/09/2021] [Indexed: 12/23/2022] Open
Abstract
Diagnostic imaging is regarded as fundamental in the clinical work-up of patients with a suspected or confirmed COVID-19 infection. Recent progress has been made in diagnostic imaging with the integration of artificial intelligence (AI) and machine learning (ML) algorisms leading to an increase in the accuracy of exam interpretation and to the extraction of prognostic information useful in the decision-making process. Considering the ever expanding imaging data generated amid this pandemic, COVID-19 has catalyzed the rapid expansion in the application of AI to combat disease. In this context, many recent studies have explored the role of AI in each of the presumed applications for COVID-19 infection chest imaging, suggesting that implementing AI applications for chest imaging can be a great asset for fast and precise disease screening, identification and characterization. However, various biases should be overcome in the development of further ML-based algorithms to give them sufficient robustness and reproducibility for their integration into clinical practice. As a result, in this literature review, we will focus on the application of AI in chest imaging, in particular, deep learning, radiomics and advanced imaging as quantitative CT.
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Affiliation(s)
- Maria Elena Laino
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy; (P.C.); (V.S.)
| | - Angela Ammirabile
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy;
- Department of Radiology, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Alessandro Posa
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Fondazione Policlinico Universitario Agostino Gemelli—IRCCS, 00168 Rome, Italy;
| | - Pierandrea Cancian
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy; (P.C.); (V.S.)
| | - Sherif Shalaby
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Via Roma 67, 56126 Pisa, Italy; (S.S.); (E.N.)
| | - Victor Savevski
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy; (P.C.); (V.S.)
| | - Emanuele Neri
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Via Roma 67, 56126 Pisa, Italy; (S.S.); (E.N.)
- Italian Society of Medical and Interventional Radiology, SIRM Foundation, Via della Signora 2, 20122 Milano, Italy
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Leite ML, de Loiola Costa LS, Cunha VA, Kreniski V, de Oliveira Braga Filho M, da Cunha NB, Costa FF. Artificial intelligence and the future of life sciences. Drug Discov Today 2021; 26:2515-2526. [PMID: 34245910 DOI: 10.1016/j.drudis.2021.07.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 05/12/2021] [Accepted: 07/01/2021] [Indexed: 12/23/2022]
Abstract
Over the past few decades, the number of health and 'omics-related data' generated and stored has grown exponentially. Patient information can be collected in real time and explored using various artificial intelligence (AI) tools in clinical trials; mobile devices can also be used to improve aspects of both the diagnosis and treatment of diseases. In addition, AI can be used in the development of new drugs or for drug repurposing, in faster diagnosis and more efficient treatment for various diseases, as well as to identify data-driven hypotheses for scientists. In this review, we discuss how AI is starting to revolutionize the life sciences sector.
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Affiliation(s)
- Michel L Leite
- Genomic Sciences and Biotechnology Program, Universidade Católica de Brasília SGAN 916 Modulo B, Bloco C, 70.790-160, Brasília, DF, Brazil; Department of Molecular Biology, Biological Sciences Institute, University of Brasília, Campus Darcy Ribeiro, Block K, 70.790-900, Brasilia, Federal District, Brazil
| | - Lorena S de Loiola Costa
- Genomic Sciences and Biotechnology Program, Universidade Católica de Brasília SGAN 916 Modulo B, Bloco C, 70.790-160, Brasília, DF, Brazil
| | - Victor A Cunha
- Genomic Sciences and Biotechnology Program, Universidade Católica de Brasília SGAN 916 Modulo B, Bloco C, 70.790-160, Brasília, DF, Brazil
| | - Victor Kreniski
- Apple Developer Academy, Universidade Católica de Brasília, Brasilia, Brazil
| | | | - Nicolau B da Cunha
- Genomic Sciences and Biotechnology Program, Universidade Católica de Brasília SGAN 916 Modulo B, Bloco C, 70.790-160, Brasília, DF, Brazil
| | - Fabricio F Costa
- Genomic Sciences and Biotechnology Program, Universidade Católica de Brasília SGAN 916 Modulo B, Bloco C, 70.790-160, Brasília, DF, Brazil; Apple Developer Academy, Universidade Católica de Brasília, Brasilia, Brazil; Cancer Biology and Epigenomics Program, Ann & Robert H Lurie Children's Hospital of Chicago Research Center and Northwestern University's Feinberg School of Medicine, 2430 N. Halsted St, Box 220, Chicago, IL 60614, USA; MATTER Chicago, 222 W. Merchandise Mart Plaza, Suite 12th Floor, Chicago, IL 60654, USA; Genomic Enterprise, San Diego, CA 92008, USA; Genomic Enterprise, New York, NY 11581, USA.
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Evolution of CT Findings and Lung Residue in Patients with COVID-19 Pneumonia: Quantitative Analysis of the Disease with a Computer Automatic Tool. J Pers Med 2021; 11:jpm11070641. [PMID: 34357108 PMCID: PMC8305822 DOI: 10.3390/jpm11070641] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 06/20/2021] [Accepted: 07/03/2021] [Indexed: 02/06/2023] Open
Abstract
Purpose: the purpose of this study was to assess the evolution of computed tomography (CT) findings and lung residue in patients with COVID-19 pneumonia, via quantified evaluation of the disease, using a computer aided tool. Materials and methods: we retrospectively evaluated 341 CT examinations of 140 patients (68 years of median age) infected with COVID-19 (confirmed by real-time reverse transcriptase polymerase chain reaction (RT-PCR)), who were hospitalized, and who received clinical and CT examinations. All CTs were evaluated by two expert radiologists, in consensus, at the same reading session, using a computer-aided tool for quantification of the pulmonary disease. The parameters obtained using the computer tool included the healthy residual parenchyma, ground glass opacity, consolidation, and total lung volume. Results: statistically significant differences (p value ≤ 0.05) were found among quantified volumes of healthy residual parenchyma, ground glass opacity (GGO), consolidation, and total lung volume, considering different clinical conditions (stable, improved, and worsened). Statistically significant differences were found among quantified volumes for healthy residual parenchyma, GGO, and consolidation (p value ≤ 0.05) between dead patients and discharged patients. CT was not performed on cadavers; the death was an outcome, which was retrospectively included to differentiate findings of patients who survived vs. patients who died during hospitalization. Among discharged patients, complete disease resolutions on CT scans were observed in 62/129 patients with lung disease involvement ≤5%; lung disease involvement from 5% to 15% was found in 40/129 patients, while 27/129 patients had lung disease involvement between 16 and 30%. Moreover, 8–21 days (after hospital admission) was an “advanced period” with the most severe lung disease involvement. After the extent of involvement started to decrease—particularly after 21 days—the absorption was more obvious. Conclusions: a complete disease resolution on chest CT scans was observed in 48.1% of discharged patients using a computer-aided tool to quantify the GGO and consolidation volumes; after 16 days of hospital admission, the abnormalities identified by chest CT began to improve; in particular, the absorption was more obvious after 21 days.
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Borghesi A, Sverzellati N, Polverosi R, Balbi M, Baratella E, Busso M, Calandriello L, Cortese G, Farchione A, Iezzi R, Palmucci S, Pulzato I, Rampinelli C, Romei C, Valentini A, Grassi R, Larici AR. Impact of the COVID-19 pandemic on the selection of chest imaging modalities and reporting systems: a survey of Italian radiologists. Radiol Med 2021; 126:1258-1272. [PMID: 34196908 PMCID: PMC8245660 DOI: 10.1007/s11547-021-01385-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Accepted: 06/15/2021] [Indexed: 12/26/2022]
Abstract
PURPOSE Chest imaging modalities play a key role for the management of patient with coronavirus disease (COVID-19). Unfortunately, there is no consensus on the optimal chest imaging approach in the evaluation of patients with COVID-19 pneumonia, and radiology departments tend to use different approaches. Thus, the main objective of this survey was to assess how chest imaging modalities have been used during the different phases of the first COVID-19 wave in Italy, and which diagnostic technique and reporting system would have been preferred based on the experience gained during the pandemic. MATERIAL AND METHODS The questionnaire of the survey consisted of 26 questions. The link to participate in the survey was sent to all members of the Italian Society of Medical and Interventional Radiology (SIRM). RESULTS The survey gathered responses from 716 SIRM members. The most notable result was that the most used and preferred chest imaging modality to assess/exclude/monitor COVID-19 pneumonia during the different phases of the first COVID-19 wave was computed tomography (51.8% to 77.1% of participants). Additionally, while the narrative report was the most used reporting system (55.6% of respondents), one-third of participants would have preferred to utilize structured reporting systems. CONCLUSION This survey shows that the participants' responses did not properly align with the imaging guidelines for managing COVID-19 that have been made by several scientific, including SIRM. Therefore, there is a need for continuing education to keep radiologists up to date and aware of the advantages and limitations of the chest imaging modalities and reporting systems.
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Affiliation(s)
- Andrea Borghesi
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, ASST Spedali Civili of Brescia, Piazzale Spedali Civili, 1, 25123, Brescia, Italy.
| | - Nicola Sverzellati
- Radiological Sciences, Department of Medicine and Surgery, University Hospital of Parma, Parma, Italy
| | | | - Maurizio Balbi
- Radiological Sciences, Department of Medicine and Surgery, University Hospital of Parma, Parma, Italy
| | - Elisa Baratella
- Department of Radiology, Cattinara Hospital, University of Trieste, Trieste, Italy
| | - Marco Busso
- Department of Radiology, Department of Oncology, San Luigi Gonzaga University Hospital, University of Turin, Turin, Italy
| | - Lucio Calandriello
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Roma, Italy
| | - Giancarlo Cortese
- Department of Radiology, Maria Vittoria Hospital, ASL Città Di Torino, Turin, Italy
| | - Alessandra Farchione
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Roma, Italy
| | - Roberto Iezzi
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Roma, Italy.,Department of Radiological and Hematological Sciences, Section of Radiology, Università Cattolica del Sacro Cuore, Roma, Italy
| | - Stefano Palmucci
- Department of Medical Surgical Sciences and Advanced Technologies G.F. Ingrassia- Radiology I Unit, University Hospital Policlinico G. Rodolico-San Marco, University of Catania, Catania, Italy
| | - Ilaria Pulzato
- Department of Radiology, San Martino Hospital, University of Genoa, Genoa, Italy
| | - Cristiano Rampinelli
- Department of Medical Imaging and Radiation Sciences, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Chiara Romei
- Department of Diagnostic and Imaging, University Hospital of Pisa, Pisa, Italy
| | - Adele Valentini
- Department of Radiology, San Matteo Polyclinic Foundation IRCCS, Pavia, Italy
| | - Roberto Grassi
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
| | - Anna Rita Larici
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Roma, Italy.,Department of Radiological and Hematological Sciences, Section of Radiology, Università Cattolica del Sacro Cuore, Roma, Italy
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Role of Chest Imaging in Viral Lung Diseases. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18126434. [PMID: 34198575 PMCID: PMC8296238 DOI: 10.3390/ijerph18126434] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 06/11/2021] [Accepted: 06/12/2021] [Indexed: 12/24/2022]
Abstract
The infection caused by novel beta-coronavirus (SARS-CoV-2) was officially declared a pandemic by the World Health Organization in March 2020. However, in the last 20 years, this has not been the only viral infection to cause respiratory tract infections leading to hundreds of thousands of deaths worldwide, referring in particular to severe acute respiratory syndrome (SARS), influenza H1N1 and Middle East respiratory syndrome (MERS). Although in this pandemic period SARS-CoV-2 infection should be the first diagnosis to exclude, many other viruses can cause pulmonary manifestations and have to be recognized. Through the description of the main radiological patterns, radiologists can suggest the diagnosis of viral pneumonia, also combining information from clinical and laboratory data.
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Rorat M, Jurek T, Simon K, Guziński M. Value of quantitative analysis in lung computed tomography in patients severely ill with COVID-19. PLoS One 2021; 16:e0251946. [PMID: 34015025 PMCID: PMC8136668 DOI: 10.1371/journal.pone.0251946] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Accepted: 05/05/2021] [Indexed: 12/11/2022] Open
Abstract
Introduction Quantitative computed tomography (QCT) is used to objectively assess the degree of parenchymal impairment in COVID-19 pneumonia. Materials and methods Retrospective study on 61 COVID-19 patients (severe and non-severe; 33 men, age 63+/-15 years) who underwent a CT scan due to tachypnea, dyspnoea or desaturation. QCT was performed using VCAR software. Patients’ clinical data was collected, including laboratory results and oxygenation support. The optimal cut-off point for CT parameters for predicting death and respiratory support was performed by maximizing the Youden Index in a receiver operating characteristic (ROC) curve analysis. Results The analysis revealed significantly greater progression of changes: ground-glass opacities (GGO) (31,42% v 13,89%, p<0.001), consolidation (11,85% v 3,32%, p<0.001) in patients with severe disease compared to non-severe disease. Five lobes were involved in all patients with severe disease. In non-severe patients, a positive correlation was found between severity of GGO, consolidation and emphysema and sex, tachypnea, chest x-ray (CXR) score on admission and laboratory parameters: CRP, D-dimer, ALT, lymphocyte count and lymphocyte/neutrophil ratio. In the group of severe patients, a correlation was found between sex, creatinine level and death. ROC analysis on death prediction was used to establish the cut-off point for GGO at 24.3% (AUC 0.8878, 95% CI 0.7889–0.9866; sensitivity 91.7%, specificity 75.5%), 5.6% for consolidation (AUC 0.7466, 95% CI 0.6009–0.8923; sensitivity 83.3%, specificity 59.2%), and 37.8% for total (GGO+consolidation) (AUC 0.8622, 95% CI 0.7525–0.972; sensitivity 75%, specificity 83.7%). The cut-off point for predicting respiratory support was established for GGO at 18.7% (AUC 0.7611, 95% CI 0.6268–0.8954; sensitivity 87.5%, specificity 64.4%), consolidation at 3.88% (AUC 0.7438, 95% CI 0.6146–0.8729; sensitivity 100%, specificity 46.7%), and total at 23.5% (AUC 0.7931, 95% CI 0.673–0.9131; sensitivity 93.8%, specificity 57.8%). Conclusion QCT is a good diagnostic tool which facilitates decision-making regarding intensification of oxygen support and transfer to an intensive care unit in patients severely ill with COVID-19 pneumonia. QCT can make an independent and simple screening tool to assess the risk of death, regardless of clinical symptoms. Usefulness of QCT to predict the risk of death is higher than to assess the indications for respiratory support.
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Affiliation(s)
- Marta Rorat
- Department of Forensic Medicine, Wroclaw Medical University, Wroclaw, Poland
| | - Tomasz Jurek
- Department of Forensic Medicine, Wroclaw Medical University, Wroclaw, Poland
- * E-mail:
| | - Krzysztof Simon
- Department of Infectious Diseases and Hepatology, Wroclaw Medical University, Wroclaw, Poland
| | - Maciej Guziński
- Department of General Radiology, Interventional Radiology and Neuroradiology, Wroclaw Medical University, Wroclaw, Poland
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Moezzi M, Shirbandi K, Shahvandi HK, Arjmand B, Rahim F. The diagnostic accuracy of Artificial Intelligence-Assisted CT imaging in COVID-19 disease: A systematic review and meta-analysis. INFORMATICS IN MEDICINE UNLOCKED 2021; 24:100591. [PMID: 33977119 PMCID: PMC8099790 DOI: 10.1016/j.imu.2021.100591] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 04/17/2021] [Accepted: 04/29/2021] [Indexed: 01/08/2023] Open
Abstract
Artificial intelligence (AI) systems have become critical in support of decision-making. This systematic review summarizes all the data currently available on the AI-assisted CT-Scan prediction accuracy for COVID-19. The ISI Web of Science, Cochrane Library, PubMed, Scopus, CINAHL, Science Direct, PROSPERO, and EMBASE were systematically searched. We used the revised Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool to assess all included studies' quality and potential bias. A hierarchical receiver-operating characteristic summary (HSROC) curve and a summary receiver operating characteristic (SROC) curve have been implemented. The area under the curve (AUC) was computed to determine the diagnostic accuracy. Finally, 36 studies (a total of 39,246 image data) were selected for inclusion into the final meta-analysis. The pooled sensitivity for AI was 0.90 (95% CI, 0.90–0.91), specificity was 0.91 (95% CI, 0.90–0.92) and the AUC was 0.96 (95% CI, 0.91–0.98). For deep learning (DL) method, the pooled sensitivity was 0.90 (95% CI, 0.90–0.91), specificity was 0.88 (95% CI, 0.87–0.88) and the AUC was 0.96 (95% CI, 0.93–0.97). In case of machine learning (ML), the pooled sensitivity was 0.90 (95% CI, 0.90–0.91), specificity was 0.95 (95% CI, 0.94–0.95) and the AUC was 0.97 (95% CI, 0.96–0.99). AI in COVID-19 patients is useful in identifying symptoms of lung involvement. More prospective real-time trials are required to confirm AI's role for high and quick COVID-19 diagnosis due to the possible selection bias and retrospective existence of currently available studies.
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Affiliation(s)
- Meisam Moezzi
- Department of Emergency Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Kiarash Shirbandi
- International Affairs Department (IAD), Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Hassan Kiani Shahvandi
- Allied Health Science, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Babak Arjmand
- Research Assistant Professor of Applied Cellular Sciences (By Research), Cellular and Molecular Institute, Endocrinology and Metabolism Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Fakher Rahim
- Health Research Institute, Thalassemia and Hemoglobinopathies Research Centre, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
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Maio F, Tari DU, Granata V, Fusco R, Grassi R, Petrillo A, Pinto F. Breast Cancer Screening during COVID-19 Emergency: Patients and Department Management in a Local Experience. J Pers Med 2021; 11:380. [PMID: 34066425 PMCID: PMC8148132 DOI: 10.3390/jpm11050380] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 04/30/2021] [Accepted: 05/04/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND During the COVID-19 public health emergency, our breast cancer screening activities have been interrupted. In June 2020, they resumed, calling for mandatory safe procedures to properly manage patients and staff. METHODS A protocol supporting medical activities in breast cancer screening was created, based on six relevant articles published in the literature and in the following National and International guidelines for COVID-19 prevention. The patient population, consisting of both screening and breast ambulatory patients, was classified into one of four categories: 1. Non-COVID-19 patient; 2. Confirmed COVID-19 in an asymptomatic screening patient; 3. suspected COVID-19 in symptomatic or confirmed breast cancer; 4. Confirmed COVID-19 in symptomatic or confirmed breast cancer. The day before the radiological exam, patients are screened for COVID-19 infection through a telephone questionnaire. At a subsequent in person appointment, the body temperature is checked and depending on the clinical scenario at stake, the scenario-specific procedures for medical and paramedical staff are adopted. RESULTS In total, 203 mammograms, 76 breast ultrasound exams, 4 core needle biopsies, and 6 vacuum-assisted breast biopsies were performed in one month. Neither medical nor paramedical staff were infected on any of these occasions. CONCLUSION Our department organization model can represent a case of implementation of National and International guidelines applied in a breast cancer screening program, assisting hospital personnel into COVID-19 infection prevention.
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Affiliation(s)
- Francesca Maio
- Department of Radiology, Marcianise Hospital, Caserta Local Health Authority, Viale Sossietta Scialla, 81025 Marcianise, Italy; (F.M.); (F.P.)
| | - Daniele Ugo Tari
- Department of Breast Radiology, Caserta Local Health Authority Dictrict 12, Viale Paul Harris 79, 81100 Caserta, Italy;
| | - Vincenza Granata
- Department of Radiology, Istituto Nazionale Tumori IRCCS Fondazione G.Pascale di Napoli, Via Mariano Semmola 53, 80131 Naples, Italy; (V.G.); (R.F.)
| | - Roberta Fusco
- Department of Radiology, Istituto Nazionale Tumori IRCCS Fondazione G.Pascale di Napoli, Via Mariano Semmola 53, 80131 Naples, Italy; (V.G.); (R.F.)
| | - Roberta Grassi
- Department of Radiology, Università degli Studi della Campania “Luigi Vanvitelli”, Piazza Miraglia, 80138 Naples, Italy;
| | - Antonella Petrillo
- Department of Radiology, Istituto Nazionale Tumori IRCCS Fondazione G.Pascale di Napoli, Via Mariano Semmola 53, 80131 Naples, Italy; (V.G.); (R.F.)
| | - Fabio Pinto
- Department of Radiology, Marcianise Hospital, Caserta Local Health Authority, Viale Sossietta Scialla, 81025 Marcianise, Italy; (F.M.); (F.P.)
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Moroni C, Cozzi D, Albanesi M, Cavigli E, Bindi A, Luvarà S, Busoni S, Mazzoni LN, Grifoni S, Nazerian P, Miele V. Chest X-ray in the emergency department during COVID-19 pandemic descending phase in Italy: correlation with patients' outcome. LA RADIOLOGIA MEDICA 2021; 126:661-668. [PMID: 33394364 PMCID: PMC7780606 DOI: 10.1007/s11547-020-01327-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Accepted: 12/14/2020] [Indexed: 01/16/2023]
Abstract
PURPOSE The aims of our study are: (1) to estimate admission chest X-ray (CXR) accuracy during the descending phase of pandemic; (2) to identify specific CXR findings strictly associated with COVID-19 infection; and (3) to correlate lung involvement of admission CXR with patients' outcome. MATERIALS AND METHODS We prospectively evaluated the admission CXR of 327 patients accessed to our institute during the Italian pandemic descending phase (April 2020). For each CXR were searched ground glass opacification (GGO), consolidation (CO), reticular-nodular opacities (RNO), nodules, excavations, pneumothorax, pleural effusion, vascular congestion and cardiac enlargement. For lung alterations was defined the predominance (upper or basal, focal or diffuse, central or peripheric, etc.). Then radiologists assessed whether CXRs were suggestive or not for COVID-19 infection. For COVID-19 patients, a prognostic score was applied and correlated with the patients' outcome. RESULTS CXR showed 83% of specificity and 60% of sensitivity. GGO, CO, RNO and a peripheric, diffuse and basal prevalence showed good correlation with COVID-19 diagnosis. A logistic regression analysis pointed out GGO and a basal or diffuse distribution as independent predictors of COVID-19 diagnosis. The prognostic score showed good correlation with the patients' outcome. CONCLUSION In our study, admission CXR showed a fair specificity and a good correlation with patients' outcome. GGO and others CXR findings showed a good correlation with COVID-19 diagnosis; besides GGO a diffuse or bibasal distribution resulted in independent variables highly suggestive for COVID-19 infection thus enabling radiologists to signal to clinicians radiologically suspect patients during the pandemic descending phase.
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Affiliation(s)
- Chiara Moroni
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Diletta Cozzi
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy.
| | - Marco Albanesi
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
- Department of Clinical and Experimental Medicine, Institute of Diagnostic Imaging 2, University of Sassari, Sassari, Italy
| | - Edoardo Cavigli
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Alessandra Bindi
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Silvia Luvarà
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Simone Busoni
- Medical Physics Department, Careggi University Hospital, Florence, Italy
| | - Lorenzo Nicola Mazzoni
- Medical Physics Department, Careggi University Hospital, Florence, Italy
- Medical Physics Unit, AUSL Toscana Centro, Pistoia-Prato, Italy
| | - Stefano Grifoni
- Department of Emergency Medicine, Careggi University Hospital, Florence, Italy
| | - Peiman Nazerian
- Department of Emergency Medicine, Careggi University Hospital, Florence, Italy
| | - Vittorio Miele
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
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Mallio CA, Quattrocchi CC, Beomonte Zobel B, Parizel PM. Artificial intelligence, chest radiographs, and radiology trainees: a powerful combination to enhance the future of radiologists? Quant Imaging Med Surg 2021; 11:2204-2207. [PMID: 33937001 PMCID: PMC8047344 DOI: 10.21037/qims-20-1306] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 12/07/2020] [Indexed: 11/06/2022]
Affiliation(s)
- Carlo A. Mallio
- Departmental Faculty of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Carlo C. Quattrocchi
- Departmental Faculty of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Bruno Beomonte Zobel
- Departmental Faculty of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Paul M. Parizel
- Department of Radiology, Royal Perth Hospital and University of Western Australia Medical School, Perth, WA, Australia
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