Immersive analytics for oncology patient cohorts

This thesis proposes a novel interactive immersive analytics tool and methods to interrogate the cancer patient cohort in an immersive virtual environment, namely Virtual Reality to Observe Oncology data Models (VROOM). The overall objective is to develop an immersive analytics platform, which includes a data analytics pipeline from raw gene expression data to immersive visualisation on virtual and augmented reality platforms utilising a game engine. Unity3D has been used to implement the visualisation. Work in this thesis could provide oncologists and clinicians with an interactive visualisation and visual analytics platform that helps them to drive their analysis in treatment efficacy and achieve the goal of evidence-based personalised medicine. The thesis integrates the latest discovery and development in cancer patients’ prognoses, immersive technologies, machine learning, decision support system and interactive visualisation to form an immersive analytics platform of complex genomic data. For this thesis, the experimental paradigm that will be followed is in understanding transcriptomics in cancer samples. This thesis specifically investigates gene expression data to determine the biological similarity revealed by the patient's tumour samples' transcriptomic profiles revealing the active genes in different patients. In summary, the thesis contributes to i) a novel immersive analytics platform for patient cohort data interrogation in similarity space where the similarity space is based on the patient's biological and genomic similarity; ii) an effective immersive environment optimisation design based on the usability study of exocentric and egocentric visualisation, audio and sound design optimisation; iii) an integration of trusted and familiar 2D biomedical visual analytics methods into the immersive environment; iv) novel use of the game theory as the decision-making system engine to help the analytics process, and application of the optimal transport theory in missing data imputation to ensure the preservation of data distribution; and v) case studies to showcase the real-world application of the visualisation and its effectiveness

Review of innovative immersive technologies for healthcare applications

Immersive technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), can connect people using enhanced data visualizations to better involve stakeholders as integral members of the process. Immersive technologies have started to change the research on multidimensional genomic data analysis for disease diagnostics and treatments. Immersive technologies are highlighted in some research for health and clinical needs, especially for precision medicine innovation. The use of immersive technology for genomic data analysis has recently received attention from the research community. Genomic data analytics research seeks to integrate immersive technologies to build more natural human-computer interactions that allow better perception engagements. Immersive technologies, especially VR, help humans perceive the digital world as real and give learning output with lower performance errors and higher accuracy. However, there are limited reviews about immersive technologies used in healthcare and genomic data analysis with specific digital health applications. This paper contributes a comprehensive review of using immersive technologies for digital health applications, including patient-centric applications, medical domain education, and data analysis, especially genomic data visual analytics. We highlight the evolution of a visual analysis using VR as a case study for how immersive technologies step, can by step, move into the genomic data analysis domain. The discussion and conclusion summarize the current immersive technology applications’ usability, innovation, and future work in the healthcare domain, and digital health data visual analytics

A Game-Theoretical Approach to Clinical Decision Making with Immersive Visualisation

Cancer is a disease characterised by changes in combinations of genes within affected tumour cells. The deep understanding of genetic activity afforded to cancer specialists through complex genomics data analytics has advanced the clinical management of cancer by using deep machine learning algorithms and visualisation. However, most of the existing works do not integrate intelligent decision-making aids that can guide users in the analysis and exploration processes. This paper contributes a novel strategy that applies game theory within a VR-enabled immersive visualisation system designed as the decision support engine to mimic real-world interactions between stakeholders within complex relationships, in this case cancer clinicians. Our focus is to apply game theory to assist doctors in the decision-making process regarding the treatment options for rare-cancer patients. Nash Equilibrium and Social Optimality strategy profiles were used to facilitate complex analysis within the visualisation by inspecting which combination of genes and dimensionality reduction methods yields the best survival rate and by investigating the treatment protocol to form new hypotheses. Using a case simulation, we demonstrate the effectiveness of game theory in guiding the analyst with a patient cohort data interrogation system as compared to an analyst without a decision support system. Particularly, the strategy profile (t-SNE method and DNMT3B_ZBTB46_LAPTM4B gene) gains the highest payoff for the two doctors

Understanding cancer patient cohorts in virtual reality environment for better clinical decisions: a usability study

Abstract Background Visualising patient genomic data in a cohort with embedding data analytics models can provide relevant and sensible patient comparisons to assist a clinician with treatment decisions. As immersive technology is actively used around the medical world, there is a rising demand for an efficient environment that can effectively display genomic data visualisations on immersive devices such as a Virtual Reality (VR) environment. The VR technology will allow clinicians, biologists, and computer scientists to explore a cohort of individual patients within the 3D environment. However, demonstrating the feasibility of the VR prototype needs domain users’ feedback for future user-centred design and a better cognitive model of human–computer interactions. There is limited research work for collecting and integrating domain knowledge into the prototype design. Objective A usability study for the VR prototype–-Virtual Reality to Observe Oncology data Models (VROOM) was implemented. VROOM was designed based on a preliminary study among medical users. The goals of this usability study included establishing a baseline of user experience, validating user performance measures, and identifying potential design improvements that are to be addressed to improve efficiency, functionality, and end-user satisfaction. Methods The study was conducted with a group of domain users (10 males, 10 females) with portable VR devices and camera equipment. These domain users included medical users such as clinicians and genetic scientists and computing domain users such as bioinformatics and data analysts. Users were asked to complete routine tasks based on a clinical scenario. Sessions were recorded and analysed to identify potential areas for improvement to the data visual analytics projects in the VR environment. The one-hour usability study included learning VR interaction gestures, running visual analytics tool, and collecting before and after feedback. The feedback was analysed with different methods to measure effectiveness. The statistical method Mann–Whitney U test was used to analyse various task performances among the different participant groups, and multiple data visualisations were created to find insights from questionnaire answers. Results The usability study investigated the feasibility of using VR for genomic data analysis in domain users’ daily work. From the feedback, 65% of the participants, especially clinicians (75% of them), indicated that the VR prototype is potentially helpful for domain users’ daily work but needed more flexibility, such as allowing them to define their features for machine learning part, adding new patient data, and importing their datasets in a better way. We calculated the engaged time for each task and compared them among different user groups. Computing domain users spent 50% more time exploring the algorithms and datasets than medical domain users. Additionally, the medical domain users engaged in the data visual analytics parts (approximately 20%) longer than the computing domain users

Visual analytics of genomic and cancer data : a systematic review

Visual analytics and visualisation can leverage the human perceptual system to interpret and uncover hidden patterns in big data. The advent of next-generation sequencing technologies has allowed the rapid production of massive amounts of genomic data and created a corresponding need for new tools and methods for visualising and interpreting these data. Visualising genomic data requires not only simply plotting of data but should also offer a decision or a choice about what the message should be conveyed in the particular plot; which methodologies should be used to represent the results must provide an easy, clear, and accurate way to the clinicians, experts, or researchers to interact with the data. Genomic data visual analytics is rapidly evolving in parallel with advances in high-throughput technologies such as artificial intelligence (AI) and virtual reality (VR). Personalised medicine requires new genomic visualisation tools, which can efficiently extract knowledge from the genomic data and speed up expert decisions about the best treatment of individual patient’s needs. However, meaningful visual analytics of such large genomic data remains a serious challenge. This article provides a comprehensive systematic review and discussion on the tools, methods, and trends for visual analytics of cancer-related genomic data. We reviewed methods for genomic data visualisation including traditional approaches such as scatter plots, heatmaps, coordinates, and networks, as well as emerging technologies using AI and VR. We also demonstrate the development of genomic data visualisation tools over time and analyse the evolution of visualising genomic data

Cancer from the Inside Out: Using Virtual Reality to Create Certainty in Clinical Decisions using Complex Genomics

Background: The human genome is complex. It is within this complex data that information will be found that identify patients as individuals, the very basis of ‘precision medicine’. Functionally, this complex genome underpins the biological mechanisms of an individual patient's cancer. Aim: To build a framework for personalised treatment of disease, where the complexity of the genome is negotiated in meaningful and actionable ways. Methods: Machine learning analysis of complex genomic data embed the high dimensional features into low dimensional (3D) space to visualise the interrelationships between patients (eg PCA, tSNE, UMAP). Here we present VROOM (Virtual Reality to Observe Oncology Models), a novel VR prototype that allows the complete immersion of the user within the data 3D model for a cohort of patients which allows for comparative data analysis, visualisation and clinical interpretation of individual patients.Results: VROOM is built on strong design principles and practical needs that allow analysts to move into the 3D environment to explore within the cohort for individual patients of interest. Tested on models involving gene expression data derived from 400 acute myeloid leukaemia patients, users can select individuals to compare within the 3D virtual genomic world. The system allows for the extraction of patient specific gene expression values which are then transferred to a virtual 2D working board that displays patient-specific information as well as different graphical representations, teasing out all user-defined data comparisons for the individuals in question. Stakeholder usability studies highlight ease of use and the unique merits of the application

Intelligent and immersive visual analytics of health data

Massive amounts of health data have been created together with the advent of computer technologies and next generation sequencing technologies. Analytical techniques can significantly aid in the processing, integration and interpretation of the complex data. Visual analytics field has been rapidly evolving together with the advancement in automated analysis methods such as data mining, machine learning and statistics, visualization, and immersive technologies. Although automated analysis processes greatly support the decision making, conservative domains such as medicine, banking, and insurance need trusts on machine learning models. Explainable artificial intelligence could open the black boxes of the machine learning models to improve the trusts for decision makers. Immersive technologies allow the users to engage naturally with the blended reality in where they can look the information in different angles in addition to traditional screens. This chapter reviews and discusses the intelligent visualization, artificial intelligence and immersive technologies in health domain. We also illustrate the ideas with various case studies in genomic data visual analytics

Safety, adherence and efficacy of PCSK9 inhibitors: a retrospective real-world study

Introduction PCSK9 inhibitors demonstrated their effectiveness in reducing low-density lipoprotein cholesterol (LDL-C) and cardiovascular events in landmark trials. It remains unclear whether the results can be translated to Asian populations. This study was designed to assess the real-world safety, adherence and efficacy of PCSK9 inhibitors. Methods A retrospective review for patients newly initiated on PCSK9 inhibitors between 1st June 2017 and 31st July 2021 was conducted in a tertiary cardiology centre. Patients aged ≥ 21 years with a minimum one-month follow-up were included. Adverse drug reactions (ADRs), drug discontinuation, adherence patterns and efficacy between evolocumab and alirocumab groups were compared. Multivariable and propensity score adjusted Cox regression analyses were applied to analyse the outcomes. Results Of 87 patients screened, 80 (51 evolocumab; 29 alirocumab) were included. There were no significant differences between evolocumab and alirocumab groups in ADRs (11.8% vs 3.4%, adjusted HR, 2.97; 95% CI, 0.34 – 25.89 in multivariable analysis; adjusted HR, 3.24; 95% CI, 0.38–27.69 after propensity score adjustment) and discontinuation rates (27.5% vs 34.5%, adjusted HR, 0.89; 95% CI, 0.40–2.02 in multivariable analysis; adjusted HR, 0.88; 95% CI, 0.39–1.99 after propensity score adjustment). High medication cost was the main reason for discontinuation. One-third of patients had inadequate adherence to PCSK9 inhibitors. Both groups showed significant reductions of LDL-C compared to baseline. Conclusions PCSK9 inhibitors are efficacious, safe and well tolerated. Further studies are warranted to examine the cost-effectiveness of PCSK9 inhibitors to rationalise their sustainable use for cardiovascular prevention

Virtual reality for the observation of oncology models (VROOM) : immersive analytics for oncology patient cohorts

The significant advancement of inexpensive and portable virtual reality (VR) and augmented reality devices has re-energised the research in the immersive analytics field. The immersive environment is different from a traditional 2D display used to analyse 3D data as it provides a unified environment that supports immersion in a 3D scene, gestural interaction, haptic feedback and spatial audio. Genomic data analysis has been used in oncology to understand better the relationship between genetic profile, cancer type, and treatment option. This paper proposes a novel immersive analytics tool for cancer patient cohorts in a virtual reality environment, virtual reality to observe oncology data models. We utilise immersive technologies to analyse the gene expression and clinical data of a cohort of cancer patients. Various machine learning algorithms and visualisation methods have also been deployed in VR to enhance the data interrogation process. This is supported with established 2D visual analytics and graphical methods in bioinformatics, such as scatter plots, descriptive statistical information, linear regression, box plot and heatmap into our visualisation. Our approach allows the clinician to interrogate the information that is familiar and meaningful to them while providing them immersive analytics capabilities to make new discoveries toward personalised medicine