A Survey on Progressive Visualization.

Currently, growing data sources and long-running algorithms impede user attention and interaction with visual analytics applications. Progressive visualization (PV) and visual analytics (PVA) alleviate this problem by allowing immediate feedback and interaction with large datasets and complex computations, avoiding waiting for complete results by using partial results improving with time. Yet, creating a progressive visualization requires more effort than a regular visualization but also opens up new possibilities, such as steering the computations towards more relevant parts of the data, thus saving computational resources. However, there is currently no comprehensive overview of the design space for progressive visualization systems. We surveyed the related work of PV and derived a new taxonomy for progressive visualizations by systematically categorizing all PV publications that included visualizations with progressive features. Progressive visualizations can be categorized by well-known visualization taxonomies, but we also found that progressive visualizations can be distinguished by the way they manage their data processing, data domain, and visual update. Furthermore, we identified key properties such as uncertainty, steering, visual stability, and real-time processing that are significantly different with progressive applications. We also collected evaluation methodologies reported by the publications and conclude with statistical findings, research gaps, and open challenges. A continuously updated visual browser of the survey data is available at visualsurvey.net/pva.

Decrease of visits and hospital admissions for cancer patients during the COVID-19 pandemic. A systematic review and meta-analysis.

Background: During the COVID-19 pandemic, many nonurgent oncologic services were postponed. The aim of the present study was to estimate the impact of the pandemic on visits and hospital admissions for cancer patients worldwide. Methods: In our systematic review and meta-analysis, databases such as Pubmed, Proquest, and Scopus were searched comprehensively for articles published between January 1, 2020, and December 12, 2021. We included articles reporting data comparing the number of visits and hospital admissions for oncologic patients performed before and during the pandemic. Two pairs of independent reviewers extracted data from the selected studies. The weighted average of the percentage change was calculated and compared between pandemic and pre-pandemic periods. Stratified analysis was performed by geographic area, time interval, and study setting. Findings: We found a mean relative change throughout January-October 2020 of -37.8% (95% CI -42.6; -32.9) and -26.3% (95% CI -31.4; -21.1) compared to pre-pandemic periods for oncologic visits and hospital admission, respectively. The temporal trend showed a U-shaped curve with nadir in April for cancer visits and in May 2020 for hospital admissions. All geographic areas showed a similar pattern and the same was observed when stratifying the studies as clinic-based and population-based. Interpretation: Our results showed a decrease in the number of visits and hospital admission during the January-October 2020 period after the outbreak of the COVID-19 pandemic. The postponement or cancellation of these oncologic services may negatively affect the patient's outcome and the future burden of disease. Supplementary Information: The online version contains supplementary material available at 10.1007/s10389-023-01857-w.

A Visual Analytics Conceptual Framework for Explorable and Steerable Partial Dependence Analysis.

Machine learning techniques are a driving force for research in various fields, from credit card fraud detection to stock analysis. Recently, a growing interest in increasing human involvement has emerged, with the primary goal of improving the interpretability of machine learning models. Among different techniques, Partial Dependence Plots (PDP) represent one of the main model-agnostic approaches for interpreting how the features influence the prediction of a machine learning model. However, its limitations (i.e., visual interpretation, aggregation of heterogeneous effects, inaccuracy, and computability) could complicate or misdirect the analysis. Moreover, the resulting combinatorial space can be challenging to explore both computationally and cognitively when analyzing the effects of more features at the same time. This paper proposes a conceptual framework that enables effective analysis workflows, mitigating state-of-the-art limitations. The proposed framework allows for exploring and refining computed partial dependences, observing incrementally accurate results, and steering the computation of new partial dependences on user-selected subspaces of the combinatorial and intractable space. With this approach, the user can save both computational and cognitive costs, in contrast with the standard monolithic approach that computes all the possible combinations of features on all their domains in batch. The framework is the result of a careful design process involving experts' knowledge during its validation and informed the development of a prototype, W4SP (available at https://aware-diag-sapienza.github.io/W4SP/), that demonstrates its applicability traversing its different paths. A case study shows the advantages of the proposed approach.

Decrease of cancer diagnosis during COVID-19 pandemic: a systematic review and meta-analysis.

Many health services, including cancer care, have been affected by the COVID-19 epidemic. This study aimed at providing a systematic review of the impact of the epidemic on cancer diagnostic tests and diagnosis worldwide. In our systematic review and meta-analysis, databases such as Pubmed, Proquest and Scopus were searched comprehensively for articles published between January 1st, 2020 and December 12th, 2021. Observational studies and articles that reported data from single clinics and population registries comparing the number of cancer diagnostic tests and/or diagnosis performed before and during the pandemic, were included. Two pairs of independent reviewers extracted data from the selected studies. The weighted average of the percentage variation was calculated and compared between pandemic and pre-pandemic periods. Stratified analysis was performed by geographic area, time interval and study setting. The review was registered on PROSPERO (ID: CRD42022314314). The review comprised 61 articles, whose results referred to the period January-October 2020. We found an overall decrease of - 37.3% for diagnostic tests and - 27.0% for cancer diagnosis during the pandemic. For both outcomes we identified a U-shaped temporal trend, with an almost complete recovery for the number of cancer diagnosis after May 2020. We also analyzed differences by geographic area and screening setting. We provided a summary estimate of the decrease in cancer diagnosis and diagnostic tests, during the first phase of the COVID-19 pandemic. The delay in cancer diagnosis could lead to an increase in the number of avoidable cancer deaths. Further research is needed to assess the impact of the pandemic measures on cancer treatment and mortality.

The role of chance in cancer causation.

In the last years, the discussion about the role of chance in the causation of cancer has generated much scientific and public debate. The concept that chance, or "bad luck", as responsible for a majority of the variation of cancer incidence, may be misleading, possibly causing an underestimation of the role played by known risk factors. In this commentary we discuss how host and external factors interact with chance in cancer causation in different ways, and provide examples of situations where chance appears to play only a minor role on cancer onset.

Global Association of COVID-19 Pandemic Measures with Cancer Treatment: A Systematic Review and Meta-Analysis.

IMPORTANCE: The COVID-19 pandemic has put a serious strain on health services, including cancer treatment. OBJECTIVE: This study aimed to investigate the changes in cancer treatment worldwide during the first phase of the SARS-CoV-2 outbreak. DATA SOURCES: Pubmed, Proquest, and Scopus databases were searched comprehensively for articles published between 1 January 2020 and 12 December 2021, in order to perform a systematic review and meta-analysis conducted following the PRISMA statement. STUDY SELECTION: Studies and articles that reported data on the number of or variation in cancer treatments between the pandemic and pre-pandemic periods, comprising oncological surgery, radiotherapy, and systemic therapies, were included. DATA EXTRACTION AND SYNTHESIS: Data were extracted from two pairs of independent reviewers. The weighted average of the percentage variation was calculated between the two periods to assess the change in the number of cancer treatments performed during the pandemic. Stratified analyses were performed by type of treatment, geographic area, time period, study setting, and type of cancer. RESULTS: Among the 47 articles retained, we found an overall reduction of -18.7% (95% CI, -24.1 to -13.3) in the total number of cancer treatments administered during the COVID-19 pandemic compared to the previous periods. Surgical treatment had a larger decrease compared to medical treatment (-33.9% versus -12.6%). For all three types of treatments, we identified a U-shaped temporal trend during the entire period January-October 2020. Significant decreases were also identified for different types of cancer, in particular for skin cancer (-34.7% [95% CI, -46.8 to -22.5]) and for all geographic areas, in particular, Asia (-42.1% [95% CI, -49.6 to -34.7]). CONCLUSIONS AND RELEVANCE: The interruption, delay, and modifications to cancer treatment due to the COVID-19 pandemic are expected to alter the quality of care and patient outcomes.

Global Association of COVID-19 Pandemic Measures With Cancer Screening: A Systematic Review and Meta-analysis.

Importance: Public health services, including cancer screening tests, have been affected by the onset of the COVID-19 epidemic. Objective: To investigate the pandemic's association with cancer screening worldwide. Data Sources: In this systematic review and meta-analysis, databases such as PubMed, ProQuest, and Scopus were searched comprehensively for articles published between January 1, 2020, and December 12, 2021. Study Selection: Observational studies and articles that reported data from cancer registries that compared the number of screening tests performed before and during the pandemic for breast, cervical, and colorectal cancer were included. Data Extraction and Synthesis: Two pairs of independent reviewers extracted data from the selected studies. The weighted average of the percentage variation was calculated between the 2 periods to assess the change in the number of cancer screening tests performed during the pandemic. Stratified analysis was performed by geographic area, period, and type of setting. The systematic review and meta-analysis was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. Main Outcomes and Measures: The main outcome was the weighted average percentage variation in the number of screening tests performed between January and October 2020 compared with the previous period. Results: The review comprised 39 publications. There was an overall decrease of -46.7% (95% CI, -55.5% to -37.8%) for breast cancer screening, -44.9% (95% CI, -53.8% to -36.1%) for colorectal cancer screening, and -51.8% (95% CI, -64.7% to -38.9%) for cervical cancer screening during the pandemic. For all 3 cancers, a U-shaped temporal trend was identified; for colorectal cancer, a significant decrease was still apparent after May 2020 (in June to October, the decrease was -23.4% [95% CI, -44.4% to -2.4%]). Differences by geographic area and screening setting were also identified. Conclusions and Relevance: A summary estimate of the downscaling of cancer screening tests since the onset of the COVID-19 pandemic is provided in this systematic review and meta-analysis. This could be associated with an increase in the number of avoidable cancer deaths. Effective interventions are required to restore the capacity of screening services to the prepandemic level.

Effect of cancer on outcome of COVID-19 patients: a systematic review and meta-analysis of studies of unvaccinated patients.

Background: Since the beginning of the SARS-CoV-2 pandemic, cancer patients affected by COVID-19 have been reported to experience poor prognosis; however, a detailed quantification of the effect of cancer on outcome of unvaccinated COVID-19 patients has not been performed. Methods: To carry out a systematic review of the studies comparing the outcome of unvaccinated COVID-19 patients with and without cancer, a search string was devised which was used to identify relevant publications in PubMed up to December 31, 2020. We selected three outcomes: mortality, access to ICU, and COVID-19 severity or hospitalization. We considered results for all cancers combined as well as for specific cancers. We conducted random-effects meta-analyses of the results, overall and after stratification by region. We also performed sensitivity analyses according to quality score and assessed publication bias. Results: For all cancer combined, the pooled odds ratio (OR) for mortality was 2.32 (95% confidence interval [CI] 1.82-2.94, I2 for heterogeneity 90.1%, 24 studies), that for ICU admission was 2.39 (95% CI 1.90-3.02, I2 0.0%, 5 studies), that for disease severity or hospitalization was 2.08 (95% CI 1.60-2.72, I2 92.1%, 15 studies). The pooled mortality OR for hematologic neoplasms was 2.14 (95% CI 1.87-2.44, I2 20.8%, 8 studies). Data were insufficient to perform a meta-analysis for other cancers. In the mortality meta-analysis for all cancers, the pooled OR was higher for studies conducted in Asia than studies conducted in Europe or North America. There was no evidence of publication bias. Conclusions: Our meta-analysis indicates a twofold increased risk of adverse outcomes (mortality, ICU admission, and severity of COVID-19) in unvaccinated COVID-19 patients with cancer compared to COVID-19 patients without cancer. These results should be compared with studies conducted in vaccinated patients; nonetheless, they argue for special effort to prevent SARS-CoV-2 infection in patients with cancer. Funding: No external funding was obtained.

Effectiveness Error: Measuring and Improving RadViz Visual Effectiveness.

RadViz contributes to multidimensional analysis by using 2D points for encoding data elements and interpreting them along the original data dimensions. For these characteristics it is used in different application domains, like clustering, anomaly detection, and software visualization. However, it is likely that using the dimension arrangement that comes with the data will produce a plot that leads users to make inaccurate conclusions about points values and data distribution. This article attacks this problem without altering the original RadViz design: It defines, for both a single point and a set of points, the metric of effectiveness error, and uses it to define the objective function of a dimension arrangement strategy, arguing that minimizing it increases the overall RadViz visual quality. This article investigated the intuition that reducing the effectiveness error is beneficial for other well-known RadViz problems, like points clumping toward the center, many-to-one plotting of non-proportional points, and cluster separation. It presents an algorithm that reduces to zero the effectiveness error for a single point and a heuristic that approximates the dimension arrangement minimizing the effectiveness error for an arbitrary set of points. A set of experiments based on 21 real datasets has been performed, with the goals of analyzing the advantages of reducing the effectiveness error, comparing the proposed dimension arrangement strategy with other related proposals, and investigating the heuristic accuracy. The Effectiveness Error metric, the algorithm, and the heuristic presented in this article have been made available in a d3.js plugin at https://aware-diag-sapienza.github.io/d3-radviz.

Molecular networks in Network Medicine: Development and applications.

Network Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein-protein interaction networks, correlation-based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in identifying key genes within genetic association regions, and limited applications to human diseases. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Translational, Genomic, and Systems Medicine > Translational Medicine Analytical and Computational Methods > Analytical Methods Analytical and Computational Methods > Computational Methods.

Vulnus: Visual Vulnerability Analysis for Network Security.

Vulnerabilities represent one of the main weaknesses of IT systems and the availability of consolidated official data, like CVE (Common Vulnerabilities and Exposures), allows for using them to compute the paths an attacker is likely to follow. However, even if patches are available, business constraints or lack of resources create obstacles to their straightforward application. As a consequence, the security manager of a network needs to deal with a large number of vulnerabilities, making decisions on how to cope with them. This paper presents VULNUS (VULNerabilities visUal aSsessment), a visual analytics solution for dynamically inspecting the vulnerabilities spread on networks, allowing for a quick understanding of the network status and visually classifying nodes according to their vulnerabilities. Moreover, VULNUS computes the approximated optimal sequence of patches able to eliminate all the attack paths and allows for exploring sub-optimal patching strategies, simulating the effect of removing one or more vulnerabilities. VULNUS has been evaluated by domain experts using a lab-test experiment, investigating the effectiveness and efficiency of the proposed solution.

An Enhanced Visualization Process Model for Incremental Visualization.

With today's technical possibilities, a stable visualization scenario can no longer be assumed as a matter of course, as underlying data and targeted display setup are much more in flux than in traditional scenarios. Incremental visualization approaches are a means to address this challenge, as they permit the user to interact with, steer, and change the visualization at intermediate time points and not just after it has been completed. In this paper, we put forward a model for incremental visualizations that is based on the established Data State Reference Model, but extends it in ways to also represent partitioned data and visualization operators to facilitate intermediate visualization updates. In combination, partitioned data and operators can be used independently and in combination to strike tailored compromises between output quality, shown data quantity, and responsiveness-i.e., frame rates. We showcase the new expressive power of this model by discussing the opportunities and challenges of incremental visualization in general and its usage in a real world scenario in particular.