Vascular Spectrum of Imaging Findings in COVID-19: Ischemic, Hemorrhagic, and Thromboembolic Complications

Abstract Ischemic strokes secondary to occlusion of large vessels have been described in patients with COVID-19. Also, venous thrombosis and pulmonary thromboembolism have been related to the disease. Vascular occlusion may be associated with a prothrombotic state due to COVID-19-related coagulopathy and endotheliopathy. Intracranial hemorrhagic lesions can additionally be seen in these patients. The causative mechanism of hemorrhage could be associated with anticoagulant therapy or factors such as coagulopathy and endotheliopathy. We report on cases of ischemic, thrombotic, and hemorrhagic complications in six patients diagnosed with SARS-CoV-2 infection. Chest computed tomography (CT) showed typical SARS-CoV-2 pneumonia findings in all the cases, which were all confirmed by either serology or reverse transcription polymerase chain reaction (RT-PCR) tests.

AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer-aided diagnosis in medical imaging.

Rapid advances in artificial intelligence (AI) and machine learning, and specifically in deep learning (DL) techniques, have enabled broad application of these methods in health care. The promise of the DL approach has spurred further interest in computer-aided diagnosis (CAD) development and applications using both "traditional" machine learning methods and newer DL-based methods. We use the term CAD-AI to refer to this expanded clinical decision support environment that uses traditional and DL-based AI methods. Numerous studies have been published to date on the development of machine learning tools for computer-aided, or AI-assisted, clinical tasks. However, most of these machine learning models are not ready for clinical deployment. It is of paramount importance to ensure that a clinical decision support tool undergoes proper training and rigorous validation of its generalizability and robustness before adoption for patient care in the clinic. To address these important issues, the American Association of Physicists in Medicine (AAPM) Computer-Aided Image Analysis Subcommittee (CADSC) is charged, in part, to develop recommendations on practices and standards for the development and performance assessment of computer-aided decision support systems. The committee has previously published two opinion papers on the evaluation of CAD systems and issues associated with user training and quality assurance of these systems in the clinic. With machine learning techniques continuing to evolve and CAD applications expanding to new stages of the patient care process, the current task group report considers the broader issues common to the development of most, if not all, CAD-AI applications and their translation from the bench to the clinic. The goal is to bring attention to the proper training and validation of machine learning algorithms that may improve their generalizability and reliability and accelerate the adoption of CAD-AI systems for clinical decision support.

New Biosensor for Determination of Neuropilin-1 with Detection by Surface Plasmon Resonance Imaging.

Neuropilin-1 is transmembrane protein with soluble isoforms. It plays a pivotal role in both physiological and pathological processes. NRP-1 is involved in the immune response, formation of neuronal circuits, angiogenesis, survival and migration of cells. The specific SPRI biosensor for the determination of neuropilin-1 was constructed using mouse monoclonal antibody that captures unbound NRP-1 form body fluids. The biosensor exhibits linearity of the analytical signal between 0.01 and 2.5 ng/mL, average precision value 4.7% and recovery between 97% and 104%. The detection limit is 0.011 ng/mL, and the limit of quantification is 0.038 ng/mL. The biosensor was validated by parallel determination of NRP-1 in serum and saliva samples using the ELISA test, with good agreement of the results.

Infection prevention and control in medical imaging surveys: The need to map to guidelines to address systemic issues?

BACKGROUND: Infection prevention and control (IPC) in the medical imaging (MI) setting is recognised as an important factor in providing high-quality patient care and safe working conditions. Surveys are commonly used and have advantages for IPC research. The aim of this study was to identify the core concepts in surveys published in the literature that examined IPC in MI environments. METHODS: A literature review was conducted to identify studies that employed a survey relating to IPC in the MI setting. For each included study, descriptive study information and survey information were extracted. For IPC-specific survey items, directed content analysis was undertaken, using eleven pre-determined codes based on the 'Australian Guidelines for the Prevention and Control of Infection in Healthcare'. Content that related to 'Knowledge', 'Attitudes' and 'Practice' were also identified. RESULTS: A total of 23 studies and 21 unique surveys were included in this review. IPC-specific survey items assessed diverse dimensions of IPC, most commonly relating to 'transmission-based precautions' and 'applying standard and transmission-based precautions during procedures'. 'Practice' and 'Knowledge' related survey items were most frequent, compared to 'Attitudes'. CONCLUSION: MI research using survey methods have focused on the 'entry' points of IPC, rather than systemic IPC matters around policy, education, and stewardship. The concepts of 'Knowledge', 'Attitudes' and 'Practice' are integrated in IPC surveys in the MI context, with a greater focus evident on staff knowledge and practice. Existing topics within IPC surveys in MI are tailored to individual studies and locales, with lack of consistency to national frameworks.

Management of patients with advanced prostate cancer-metastatic and/or castration-resistant prostate cancer: Report of the Advanced Prostate Cancer Consensus Conference (APCCC) 2022.

BACKGROUND: Innovations in imaging and molecular characterisation together with novel treatment options have improved outcomes in advanced prostate cancer. However, we still lack high-level evidence in many areas relevant to making management decisions in daily clinical practise. The 2022 Advanced Prostate Cancer Consensus Conference (APCCC 2022) addressed some questions in these areas to supplement guidelines that mostly are based on level 1 evidence. OBJECTIVE: To present the voting results of the APCCC 2022. DESIGN, SETTING, AND PARTICIPANTS: The experts voted on controversial questions where high-level evidence is mostly lacking: locally advanced prostate cancer; biochemical recurrence after local treatment; metastatic hormone-sensitive, non-metastatic, and metastatic castration-resistant prostate cancer; oligometastatic prostate cancer; and managing side effects of hormonal therapy. A panel of 105 international prostate cancer experts voted on the consensus questions. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The panel voted on 198 pre-defined questions, which were developed by 117 voting and non-voting panel members prior to the conference following a modified Delphi process. A total of 116 questions on metastatic and/or castration-resistant prostate cancer are discussed in this manuscript. In 2022, the voting was done by a web-based survey because of COVID-19 restrictions. RESULTS AND LIMITATIONS: The voting reflects the expert opinion of these panellists and did not incorporate a standard literature review or formal meta-analysis. The answer options for the consensus questions received varying degrees of support from panellists, as reflected in this article and the detailed voting results are reported in the supplementary material. We report here on topics in metastatic, hormone-sensitive prostate cancer (mHSPC), non-metastatic, castration-resistant prostate cancer (nmCRPC), metastatic castration-resistant prostate cancer (mCRPC), and oligometastatic and oligoprogressive prostate cancer. CONCLUSIONS: These voting results in four specific areas from a panel of experts in advanced prostate cancer can help clinicians and patients navigate controversial areas of management for which high-level evidence is scant or conflicting and can help research funders and policy makers identify information gaps and consider what areas to explore further. However, diagnostic and treatment decisions always have to be individualised based on patient characteristics, including the extent and location of disease, prior treatment(s), co-morbidities, patient preferences, and treatment recommendations and should also incorporate current and emerging clinical evidence and logistic and economic factors. Enrolment in clinical trials is strongly encouraged. Importantly, APCCC 2022 once again identified important gaps where there is non-consensus and that merit evaluation in specifically designed trials. PATIENT SUMMARY: The Advanced Prostate Cancer Consensus Conference (APCCC) provides a forum to discuss and debate current diagnostic and treatment options for patients with advanced prostate cancer. The conference aims to share the knowledge of international experts in prostate cancer with healthcare providers worldwide. At each APCCC, an expert panel votes on pre-defined questions that target the most clinically relevant areas of advanced prostate cancer treatment for which there are gaps in knowledge. The results of the voting provide a practical guide to help clinicians discuss therapeutic options with patients and their relatives as part of shared and multidisciplinary decision-making. This report focuses on the advanced setting, covering metastatic hormone-sensitive prostate cancer and both non-metastatic and metastatic castration-resistant prostate cancer. TWITTER SUMMARY: Report of the results of APCCC 2022 for the following topics: mHSPC, nmCRPC, mCRPC, and oligometastatic prostate cancer. TAKE-HOME MESSAGE: At APCCC 2022, clinically important questions in the management of advanced prostate cancer management were identified and discussed, and experts voted on pre-defined consensus questions. The report of the results for metastatic and/or castration-resistant prostate cancer is summarised here.

Breast Imaging During a Cyberattack and Global Pandemic: What We Did to Pick Up the Pieces.

Cybersecurity in healthcare is a very real threat with the potential to severely disrupt patient care, place extra burden on an already strained system, and result in significant financial losses for a hospital or healthcare network. In October 2020, on the backdrop of the ongoing COVID-19 pandemic, our institution experienced one of the most significant cyberattacks on a healthcare system to date, lasting for nearly 40 days. By sharing our experience in radiology, and specifically in breast imaging, including the downtime procedures we relied upon and the lessons that we learned emerging from this cyberattack, we hope to help future victims of a healthcare cyberattack successfully weather such an experience.

Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks.

Artificial Intelligence (AI) techniques of deep learning have revolutionized the disease diagnosis with their outstanding image classification performance. In spite of the outstanding results, the widespread adoption of these techniques in clinical practice is still taking place at a moderate pace. One of the major hindrance is that a trained Deep Neural Networks (DNN) model provides a prediction, but questions about why and how that prediction was made remain unanswered. This linkage is of utmost importance for the regulated healthcare domain to increase the trust in the automated diagnosis system by the practitioners, patients and other stakeholders. The application of deep learning for medical imaging has to be interpreted with caution due to the health and safety concerns similar to blame attribution in the case of an accident involving autonomous cars. The consequences of both a false positive and false negative cases are far reaching for patients' welfare and cannot be ignored. This is exacerbated by the fact that the state-of-the-art deep learning algorithms comprise of complex interconnected structures, millions of parameters, and a 'black box' nature, offering little understanding of their inner working unlike the traditional machine learning algorithms. Explainable AI (XAI) techniques help to understand model predictions which help develop trust in the system, accelerate the disease diagnosis, and meet adherence to regulatory requirements. This survey provides a comprehensive review of the promising field of XAI for biomedical imaging diagnostics. We also provide a categorization of the XAI techniques, discuss the open challenges, and provide future directions for XAI which would be of interest to clinicians, regulators and model developers.

Advances and opportunities in nanoimaging agents for the diagnosis of inflammatory lung diseases.

The development of rapid, noninvasive diagnostics to detect lung diseases is a great need after the COVID-2019 outbreak. The nanotechnology-based approach has improved imaging and facilitates the early diagnosis of inflammatory lung diseases. The multifunctional properties of nanoprobes enable better spatial-temporal resolution and a high signal-to-noise ratio in imaging. Targeted nanoimaging agents have been used to bind specific tissues in inflammatory lungs for early-stage diagnosis. However, nanobased imaging approaches for inflammatory lung diseases are still in their infancy. This review provides a solution-focused approach to exploring medical imaging technologies and nanoprobes for the detection of inflammatory lung diseases. Prospects for the development of contrast agents for lung disease detection are also discussed.

COVID-19 imaging, where do we go from here? Bibliometric analysis of medical imaging in COVID-19.

OBJECTIVES: We conducted a systematic and comprehensive bibliometric analysis of COVID-19-related medical imaging to determine the current status and indicate possible future directions. METHODS: This research provides an analysis of Web of Science Core Collection (WoSCC) indexed articles on COVID-19 and medical imaging published between 1 January 2020 and 30 June 2022, using the search terms "COVID-19" and medical imaging terms (such as "X-ray" or "CT"). Publications based solely on COVID-19 themes or medical image themes were excluded. CiteSpace was used to identify the predominant topics and generate a visual map of countries, institutions, authors, and keyword networks. RESULTS: The search included 4444 publications. The journal with the most publications was European Radiology, and the most co-cited journal was Radiology. China was the most frequently cited country in terms of co-authorship, with the Huazhong University of Science and Technology being the institution contributing with the highest number of relevant co-authorships. Research trends and leading topics included: assessment of initial COVID-19-related clinical imaging features, differential diagnosis using artificial intelligence (AI) technology and model interpretability, diagnosis systems construction, COVID-19 vaccination, complications, and predicting prognosis. CONCLUSIONS: This bibliometric analysis of COVID-19-related medical imaging helps clarify the current research situation and developmental trends. Subsequent trends in COVID-19 imaging are likely to shift from lung structure to function, from lung tissue to other related organs, and from COVID-19 to the impact of COVID-19 on the diagnosis and treatment of other diseases. Key Points • We conducted a systematic and comprehensive bibliometric analysis of COVID-19-related medical imaging from 1 January 2020 to 30 June 2022. • Research trends and leading topics included assessment of initial COVID-19-related clinical imaging features, differential diagnosis using AI technology and model interpretability, diagnosis systems construction, COVID-19 vaccination, complications, and predicting prognosis. • Future trends in COVID-19-related imaging are likely to involve a shift from lung structure to function, from lung tissue to other related organs, and from COVID-19 to the impact of COVID-19 on the diagnosis and treatment of other diseases.

Knowledge of Medical Imaging Professionals on Healthcare-Associated Infections: A Systematic Review and Meta-Analysis.

Healthcare-associated infections (HCAIs) are a significant concern for both healthcare professionals and patients. With recent advances in imaging modalities, there is an increase in patients visiting the radiology department for diagnosis and therapeutic examination. The equipment used for the investigator is contaminated, which may result in HCAIs to the patients and healthcare professionals. Medical imaging professionals (MIPs) should have adequate knowledge to overcome the spread of infection in the radiology department. This systematic review aimed to examine the literature on the knowledge and precaution standard of MIPs on HCIAs. This study was performed with a relative keyword using PRISMA guidelines. The articles were retrieved from 2000 to 2022 using Scopus, PubMed, and ProQuest databases. The NICE public health guidance manual was used to assess the quality of the full-length article. The search yielded 262 articles, of which Scopus published 13 articles, PubMed published 179 articles, and ProQuest published 55 articles. In the present review, out of 262 articles, only 5 fulfilled the criteria that reported MIPs' knowledge of Jordan, Egypt, Sri Lanka, France, and Malawi populations. The present review reported that MIPs have moderate knowledge and precautionary standards regarding HCIAs in the radiology department. However, due to the limited studies published in the literature, the current review limits the application of the outcome in the vast MIPs population. This review recommended further studies to be conducted among the MIPs worldwide to know the actual knowledge and precaution standards regarding HCIAs.

Ferritin - a multifaceted protein scaffold for biotherapeutics.

The ferritin nanocage is an endogenous protein that exists in almost all mammals. Its hollow spherical structure that naturally stores iron ions has been diversely exploited by researchers in biotherapeutics. Ferritin has excellent biosafety profiles, and the nanosized particles exhibit rapid dispersion and controlled/sustained release pharmacokinetics. Moreover, the large surface-to-volume ratio and the disassembly/reassembly behavior of the 24 monomer subunits into a sphere allow diverse modifications by chemical and genetic methods on the surface and inner cage of ferritin. Here, we critically review ferritin and its applications. We (i) introduce the application of ferritin in drug delivery; (ii) present an overview of the use of ferritin in imaging and diagnosis for biomedical purposes; (iii) discuss ferritin-based vaccines; and (iv) review ferritin-based agents currently in clinical trials. Although there are no currently approved drugs based on ferritin, this multifunctional protein scaffold shows immense potential in drug development in diverse categories, and ferritin-based drugs have recently entered phase I clinical trials. This golden shortlist of recent developments will be of immediate benefit and interest to researchers studying ferritin and other protein-based biotherapeutics.

Medical imaging in problem-based learning and impact on the students: A cross-sectional study.

OBJECTIVE: To investigate the medical students' performance with and perception towards different multimedia medical imaging tools. METHODS: The cross-sectional study was conducted at the College of Medicine, Qassim University, Saudi Arabia, from 2019 to 2020, and comprised third year undergraduate medical students during the academic year 2019-2020. The students were divided into tow groups. Those receiving multimedia-enhanced problem-based learning sessions were in intervention group A, while those receiving traditional problem-based learning sessions were in control group B. Scores of the students in the formative assessment at the end of the sessions were compared between the groups. Students' satisfaction survey was also conducted online and analysed. Data was analysed using SPSS 21. RESULTS: Of the 130 medical students, 75(57.7%) were males and 55(42.3%) were females. A significant increase in the mean scores was observed for both male and female students in group A compared to those in group B (p<0.05). The perception survey was filled up by 100(77%) students, and open-ended comments were obtained from 88(88%) of them. Overall, 69(74%) subjects expressed satisfaction with the multimedia-enhanced problem-based learning sessions. CONCLUSIONS: Radiological and pathological images enhanced the students' understanding, interaction and critical thinking during problem-based learning sessions.

Exploring Radiologists' Burnout in the COVID-19 Era: A Narrative Review.

Since its beginning in March 2020, the COVID-19 pandemic has claimed an exceptionally high number of victims and brought significant disruption to the personal and professional lives of millions of people worldwide. Among medical specialists, radiologists have found themselves at the forefront of the crisis due to the pivotal role of imaging in the diagnostic and interventional management of COVID-19 pneumonia and its complications. Because of the disruptive changes related to the COVID-19 outbreak, a proportion of radiologists have faced burnout to several degrees, resulting in detrimental effects on their working activities and overall wellbeing. This paper aims to provide an overview of the literature exploring the issue of radiologists' burnout in the COVID-19 era.

Institutional Strategies to Maintain and Grow Imaging Research During the COVID-19 Pandemic.

Understanding imaging research experiences, challenges, and strategies for academic radiology departments during and after COVID-19 is critical to prepare for future disruptive events. We summarize key insights and programmatic initiatives at major academic hospitals across the world, based on literature review and meetings of the Radiological Society of North America Vice Chairs of Research (RSNA VCR) group. Through expert discussion and case studies, we provide suggested guidelines to maintain and grow radiology research in the postpandemic era.