METHODS:: A literature search was done for articles published between 2002 and 2017 on Medline electronic databases. Of 249 titles identified, 38 fulfilled the inclusion criteria, with 14 articles related to quantifiable imaging parameters (heterogeneity, vascularity, diffusion, cell density, infiltrations, perfusion, and metabolite changes) and 24 articles relevant to molecular biomarkers linked to imaging.
RESULTS:: Genes found to correlate with various imaging phenotypes were EGFR, MGMT, IDH1, VEGF, PDGF, TP53, and Ki-67. EGFR is the most studied gene related to imaging characteristics in the studies reviewed (41.7%), followed by MGMT (20.8%) and IDH1 (16.7%). A summary of the relationship amongst glioma morphology, gene expressions, imaging characteristics, prognosis and therapeutic response are presented.
CONCLUSION:: The use of radiogenomics can provide insights to understanding tumour biology and the underlying molecular pathways. Certain MRI characteristics that show strong correlations with EGFR, MGMT and IDH1 could be used as imaging biomarkers. Knowing the pathways involved in tumour progression and their associated imaging patterns may assist in diagnosis, prognosis and treatment management, while facilitating personalised medicine.
ADVANCES IN KNOWLEDGE:: Radiogenomics can offer clinicians better insight into diagnosis, prognosis, and prediction of therapeutic responses of glioma.
OBJECTIVE: In this work, we aimed to develop a practical, structured approach to identify narratives in public online conversations on social media platforms where concerns or confusion exist or where narratives are gaining traction, thus providing actionable data to help the WHO prioritize its response efforts to address the COVID-19 infodemic.
METHODS: We developed a taxonomy to filter global public conversations in English and French related to COVID-19 on social media into 5 categories with 35 subcategories. The taxonomy and its implementation were validated for retrieval precision and recall, and they were reviewed and adapted as language about the pandemic in online conversations changed over time. The aggregated data for each subcategory were analyzed on a weekly basis by volume, velocity, and presence of questions to detect signals of information voids with potential for confusion or where mis- or disinformation may thrive. A human analyst reviewed and identified potential information voids and sources of confusion, and quantitative data were used to provide insights on emerging narratives, influencers, and public reactions to COVID-19-related topics.
RESULTS: A COVID-19 public health social listening taxonomy was developed, validated, and applied to filter relevant content for more focused analysis. A weekly analysis of public online conversations since March 23, 2020, enabled quantification of shifting interests in public health-related topics concerning the pandemic, and the analysis demonstrated recurring voids of verified health information. This approach therefore focuses on the detection of infodemic signals to generate actionable insights to rapidly inform decision-making for a more targeted and adaptive response, including risk communication.
CONCLUSIONS: This approach has been successfully applied to identify and analyze infodemic signals, particularly information voids, to inform the COVID-19 pandemic response. More broadly, the results have demonstrated the importance of ongoing monitoring and analysis of public online conversations, as information voids frequently recur and narratives shift over time. The approach is being piloted in individual countries and WHO regions to generate localized insights and actions; meanwhile, a pilot of an artificial intelligence-based social listening platform is using this taxonomy to aggregate and compare online conversations across 20 countries. Beyond the COVID-19 pandemic, the taxonomy and methodology may be adapted for fast deployment in future public health events, and they could form the basis of a routine social listening program for health preparedness and response planning.
Objective: The World Health Organization organized the first global infodemiology conference, entirely online, during June and July 2020, with a follow-up process from August to October 2020, to review current multidisciplinary evidence, interventions, and practices that can be applied to the COVID-19 infodemic response. This resulted in the creation of a public health research agenda for managing infodemics.
Methods: As part of the conference, a structured expert judgment synthesis method was used to formulate a public health research agenda. A total of 110 participants represented diverse scientific disciplines from over 35 countries and global public health implementing partners. The conference used a laddered discussion sprint methodology by rotating participant teams, and a managed follow-up process was used to assemble a research agenda based on the discussion and structured expert feedback. This resulted in a five-workstream frame of the research agenda for infodemic management and 166 suggested research questions. The participants then ranked the questions for feasibility and expected public health impact. The expert consensus was summarized in a public health research agenda that included a list of priority research questions.
Results: The public health research agenda for infodemic management has five workstreams: (1) measuring and continuously monitoring the impact of infodemics during health emergencies; (2) detecting signals and understanding the spread and risk of infodemics; (3) responding and deploying interventions that mitigate and protect against infodemics and their harmful effects; (4) evaluating infodemic interventions and strengthening the resilience of individuals and communities to infodemics; and (5) promoting the development, adaptation, and application of interventions and toolkits for infodemic management. Each workstream identifies research questions and highlights 49 high priority research questions.
Conclusions: Public health authorities need to develop, validate, implement, and adapt tools and interventions for managing infodemics in acute public health events in ways that are appropriate for their countries and contexts. Infodemiology provides a scientific foundation to make this possible. This research agenda proposes a structured framework for targeted investment for the scientific community, policy makers, implementing organizations, and other stakeholders to consider.
OBJECTIVES: To report the retinoblastoma stage at diagnosis in patients across the world during a single year, to investigate associations between clinical variables and national income level, and to investigate risk factors for advanced disease at diagnosis.
DESIGN, SETTING, AND PARTICIPANTS: A total of 278 retinoblastoma treatment centers were recruited from June 2017 through December 2018 to participate in a cross-sectional analysis of treatment-naive patients with retinoblastoma who were diagnosed in 2017.
MAIN OUTCOMES AND MEASURES: Age at presentation, proportion of familial history of retinoblastoma, and tumor stage and metastasis.
RESULTS: The cohort included 4351 new patients from 153 countries; the median age at diagnosis was 30.5 (interquartile range, 18.3-45.9) months, and 1976 patients (45.4%) were female. Most patients (n = 3685 [84.7%]) were from low- and middle-income countries (LMICs). Globally, the most common indication for referral was leukocoria (n = 2638 [62.8%]), followed by strabismus (n = 429 [10.2%]) and proptosis (n = 309 [7.4%]). Patients from high-income countries (HICs) were diagnosed at a median age of 14.1 months, with 656 of 666 (98.5%) patients having intraocular retinoblastoma and 2 (0.3%) having metastasis. Patients from low-income countries were diagnosed at a median age of 30.5 months, with 256 of 521 (49.1%) having extraocular retinoblastoma and 94 of 498 (18.9%) having metastasis. Lower national income level was associated with older presentation age, higher proportion of locally advanced disease and distant metastasis, and smaller proportion of familial history of retinoblastoma. Advanced disease at diagnosis was more common in LMICs even after adjusting for age (odds ratio for low-income countries vs upper-middle-income countries and HICs, 17.92 [95% CI, 12.94-24.80], and for lower-middle-income countries vs upper-middle-income countries and HICs, 5.74 [95% CI, 4.30-7.68]).
CONCLUSIONS AND RELEVANCE: This study is estimated to have included more than half of all new retinoblastoma cases worldwide in 2017. Children from LMICs, where the main global retinoblastoma burden lies, presented at an older age with more advanced disease and demonstrated a smaller proportion of familial history of retinoblastoma, likely because many do not reach a childbearing age. Given that retinoblastoma is curable, these data are concerning and mandate intervention at national and international levels. Further studies are needed to investigate factors, other than age at presentation, that may be associated with advanced disease in LMICs.