Health data space nodes for privacy-preserving linkage of medical data to support collaborative secondary analyses.

Introduction: The potential for secondary use of health data to improve healthcare is currently not fully exploited. Health data is largely kept in isolated data silos and key infrastructure to aggregate these silos into standardized bodies of knowledge is underdeveloped. We describe the development, implementation, and evaluation of a federated infrastructure to facilitate versatile secondary use of health data based on Health Data Space nodes. Materials and methods: Our proposed nodes are self-contained units that digest data through an extract-transform-load framework that pseudonymizes and links data with privacy-preserving record linkage and harmonizes into a common data model (OMOP CDM). To support collaborative analyses a multi-level feature store is also implemented. A feasibility experiment was conducted to test the infrastructures potential for machine learning operations and deployment of other apps (e.g., visualization). Nodes can be operated in a network at different levels of sharing according to the level of trust within the network. Results: In a proof-of-concept study, a privacy-preserving registry for heart failure patients has been implemented as a real-world showcase for Health Data Space nodes at the highest trust level, linking multiple data sources including (a) electronical medical records from hospitals, (b) patient data from a telemonitoring system, and (c) data from Austria's national register of deaths. The registry is deployed at the tirol kliniken, a hospital carrier in the Austrian state of Tyrol, and currently includes 5,004 patients, with over 2.9 million measurements, over 574,000 observations, more than 63,000 clinical free text notes, and in total over 5.2 million data points. Data curation and harmonization processes are executed semi-automatically at each individual node according to data sharing policies to ensure data sovereignty, scalability, and privacy. As a feasibility test, a natural language processing model for classification of clinical notes was deployed and tested. Discussion: The presented Health Data Space node infrastructure has proven to be practicable in a real-world implementation in a live and productive registry for heart failure. The present work was inspired by the European Health Data Space initiative and its spirit to interconnect health data silos for versatile secondary use of health data.

Wearable activity data can predict functional recovery after musculoskeletal injury: Feasibility of a machine learning approach.

Delayed functional recovery after injury is associated with significant personal and socioeconomic burden. Identification of patients at risk for a prolonged recovery after a musculoskeletal injury is thus of high relevance. The aim of the current study was to show the feasibility of using a machine learning assisted model to predict functional recovery based on the pre- and immediate post injury patient activity as measured with wearable systems in trauma patients. Patients with a pre-existing wearable (smartphone and/or body-worn sensor), data availability of at least 7 days prior to their injury, and any musculoskeletal injury of the upper or lower extremity were included in this study. Patient age, sex, injured extremity, time off work and step count as activity data were recorded continuously both pre- and post-injury. Descriptive statistics were performed and a logistic regression machine learning model was used to predict the patient's functional recovery status after 6 weeks based on their pre- and post-injury activity characteristics. Overall 38 patients (7 upper extremity, 24 lower extremity, 5 pelvis, 2 combined) were included in this proof-of-concept study. The average follow-up with available wearable data was 85.4 days. Based on the activity data, a predictive model was constructed to determine the likelihood of having a recovery of at least 50 % of the pre-injury activity state by post injury week 6. Based on the individual activity by week 3 a predictive accuracy of over 80 % was achieved on an independent test set (F1=0,82; AUC=0,86; ACC=8,83). The employed model is feasible to assess the principal risk for a slower recovery based on readily available personal wearable activity data. The model has the potential to identify patients requiring additional aftercare attention early during the treatment course, thus optimizing return to the pre-injury status through focused interventions. Additional patient data is needed to adapt the model to more specifically focus on different fracture entities and patient groups.

Recent Advances in Pathology: the 2023 Annual Review Issue of The Journal of Pathology.

The 2023 Annual Review Issue of The Journal of Pathology, Recent Advances in Pathology, contains 12 invited reviews on topics of current interest in pathology. This year, our subjects include immuno-oncology and computational pathology approaches for diagnostic and research applications in human disease. Reviews on the tissue microenvironment include the effects of apoptotic cell-derived exosomes, how understanding the tumour microenvironment predicts prognosis, and the growing appreciation of the diverse functions of fibroblast subtypes in health and disease. We also include up-to-date reviews of modern aspects of the molecular basis of malignancies, and our final review covers new knowledge of vascular and lymphatic regeneration in cardiac disease. All of the reviews contained in this issue are written by expert groups of authors selected to discuss the recent progress in their particular fields and all articles are freely available online (https://pathsocjournals.onlinelibrary.wiley.com/journal/10969896). © 2023 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Application of digital pathology-based advanced analytics of tumour microenvironment organisation to predict prognosis and therapeutic response.

In recent years, the application of advanced analytics, especially artificial intelligence (AI), to digital H&E images, and other histological image types, has begun to radically change how histological images are used in the clinic. Alongside the recognition that the tumour microenvironment (TME) has a profound impact on tumour phenotype, the technical development of highly multiplexed immunofluorescence platforms has enhanced the biological complexity that can be captured in the TME with high precision. AI has an increasingly powerful role in the recognition and quantitation of image features and the association of such features with clinically important outcomes, as occurs in distinct stages in conventional machine learning. Deep-learning algorithms are able to elucidate TME patterns inherent in the input data with minimum levels of human intelligence and, hence, have the potential to achieve clinically relevant predictions and discovery of important TME features. Furthermore, the diverse repertoire of deep-learning algorithms able to interrogate TME patterns extends beyond convolutional neural networks to include attention-based models, graph neural networks, and multimodal models. To date, AI models have largely been evaluated retrospectively, outside the well-established rigour of prospective clinical trials, in part because traditional clinical trial methodology may not always be suitable for the assessment of AI technology. However, to enable digital pathology-based advanced analytics to meaningfully impact clinical care, specific measures of 'added benefit' to the current standard of care and validation in a prospective setting are important. This will need to be accompanied by adequate measures of explainability and interpretability. Despite such challenges, the combination of expanding datasets, increased computational power, and the possibility of integration of pre-clinical experimental insights into model development means there is exciting potential for the future progress of these AI applications. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

The Relative Age Effect in Ice Hockey: Analysis of Its Presence, Its Fading and of a Reversal Effect among Junior and Professional Leagues.

This study analyzes the relative age effect (RAE) among the world's best junior hockey leagues and in the NHL. Despite the prevalence of RAE in ice hockey, past research suggests its fading-reversal over time, which may occur at later stages of athletic development. The hypothesis of the RAE reversal was tested with two sources of raw data files from the 2021-2022 season: 15 of the best international junior and minor professional leagues (N = 7 399) and the NHL (N = 812). Birth quartile distributions were analyzed to verify the prevalence of RAE and quantile regression was used to test the reversal of RAE hypotheses. Advanced hockey metrics were aggregated from multiple data sources and used to compare early born with late born players using birth quartiles. Prevalence of the RAE was verified with crosstabs analyses and quantile regression was used to test the reversal effect. Results indicated that the RAE still prevailed in ice hockey, with higher magnitude in Canadian leagues. Regression analyses showed that late-born junior and minor pro players, despite getting less exposure in terms of games played, attained levels of offensive production similar to those of early born players. Late-born players able to emerge in the NHL performed similarly and sometimes displayed better performance (in some markers). Results suggest that stakeholders should find ways to pay special attention to late born players in talent identification processes and offer them opportunities to develop at the highest levels.

Decentralized clinical trials and rare diseases: a Drug Information Association Innovative Design Scientific Working Group (DIA-IDSWG) perspective.

BACKGROUND: Traditional clinical trials require tests and procedures that are administered in centralized clinical research sites, which are beyond the standard of care that patients receive for their rare and chronic diseases. The limited number of rare disease patients scattered around the world makes it particularly challenging to recruit participants and conduct these traditional clinical trials. MAIN BODY: Participating in clinical research can be burdensome, especially for children, the elderly, physically and cognitively impaired individuals who require transportation and caregiver assistance, or patients who live in remote locations or cannot afford transportation. In recent years, there is an increasing need to consider Decentralized Clinical Trials (DCT) as a participant-centric approach that uses new technologies and innovative procedures for interaction with participants in the comfort of their home. CONCLUSION: This paper discusses the planning and conduct of DCTs, which can increase the quality of trials with a specific focus on rare diseases.

Estimating Prevalence and Characteristics of Statin Intolerance among High and Very High Cardiovascular Risk Patients in Germany (2017 to 2020).

Statin intolerance (SI) (partial and absolute) could lead to suboptimal lipid management. The lack of a widely accepted definition of SI results into poor understanding of patient profiles and characteristics. This study aims to estimate SI and better understand patient characteristics, as reflected in clinical practice in Germany using supervised machine learning (ML) techniques. This retrospective cohort study utilized patient records from an outpatient setting in Germany in the IQVIA™ Disease Analyzer. Patients with a high cardiovascular risk, atherosclerotic cardiovascular disease, or hypercholesterolemia, and those on lipid-lowering therapies between 2017 and 2020 were included, and categorized as having "absolute" or "partial" SI. ML techniques were applied to calibrate prevalence estimates, derived from different rules and levels of confidence (high and low). The study included 292,603 patients, 6.4% and 2.8% had with high confidence absolute and partial SI, respectively. After deploying ML, SI prevalence increased approximately by 27% and 57% (p < 0.00001) in absolute and partial SI, respectively, eliciting a maximum estimate of 12.5% SI with high confidence. The use of advanced analytics to provide a complementary perspective to current prevalence estimates may inform the identification, optimal treatment, and pragmatic, patient-centered management of SI in Germany.

Rapid Quantitation of Four Nitrosamine Impurities in Angiotensin Receptor Blocker Drug Substances.

Starting in July 2018, the FDA alerted patients and health care professionals to the recall of ARBs such as valsartan by several pharmaceutical companies because of their potential contamination with carcinogenic nitrosamine impurities, including: (1) N-nitrosodimethylamine (NDMA), (2) N-nitrosodiethylamine (NDEA), (3) N-nitrosoethylisopropylamine (NEIPA), (4) N-nitrosodiisopropylamine (NDIPA), (5) N-nitrosodibutylamine (NDBA) and (6) N-nitroso-N-methyl-4-aminobutyric acid (NMBA). The FDA initiated a laboratory investigation to develop analytical procedures to test multiple lots of marketed ARB drugs to determine the possible presence of carcinogenic impurities and, if present, quantitate the levels of these impurities. Here the FDA laboratory developed and validated an automated micro-solid phase extraction MS/MS method, where all the analytes are not separated prior to elution to the MS, to simultaneously quantify NEIPA, NDIPA, NDBA and NMBA in ARB drug substances with an instrument sample analysis time of 12 seconds. The method was validated according to the ICH Q2(R1) guideline, and was determined to be specific, accurate, precise and linear over the corresponding nitrosamine analytical ranges. The method has been successfully implemented to quantitate the four nitrosamine impurities in 129 generic losartan, valsartan, olmesartan, irbesartan and telmisartan drug substance samples from 32 lots; and 32 losartan and valsartan drug product samples from 6 lots.

Analytical and toxicological aspects of nanomaterials in different product groups: Challenges and opportunities.

The widespread integration of engineered nanomaterials into consumer and industrial products creates new challenges and requires innovative approaches in terms of design, testing, reliability, and safety of nanotechnology. The aim of this review article is to give an overview of different product groups in which nanomaterials are present and outline their safety aspects for consumers. Here, release of nanomaterials and related analytical challenges and solutions as well as toxicological considerations, such as dose-metrics, are discussed. Additionally, the utilization of engineered nanomaterials as pharmaceuticals or nutraceuticals to deliver and release cargo molecules is covered. Furthermore, critical pathways for human exposure to nanomaterials, namely inhalation and ingestion, are discussed in the context of risk assessment. Analysis of NMs in food, innovative medicine or food contact materials is discussed. Specific focus is on the presence and release of nanomaterials, including whether nanomaterials can migrate from polymer nanocomposites used in food contact materials. With regard to the toxicology and toxicokinetics of nanomaterials, aspects of dose metrics of inhalation toxicity as well as ingestion toxicology and comparison between in vitro and in vivo conclusions are considered. The definition of dose descriptors to be applied in toxicological testing is emphasized. In relation to potential exposure from different products, opportunities arising from the use of advanced analytical techniques in more unique scenarios such as release of nanomaterials from medical devices such as orthopedic implants are addressed. Alongside higher product performance and complexity, further challenges regarding material characterization and safety, as well as acceptance by the general public are expected.

How can Big Data Analytics Support People-Centred and Integrated Health Services: A Scoping Review.

Introduction: Health systems in high-income countries face a variety of challenges calling for a systemic approach to improve quality and efficiency. Putting people in the centre is the main idea of the WHO model of people-centred and integrated health services. Integrating health services is fuelled by an integration of health data with great potentials for decision support based on big data analytics. The research question of this paper is "How can big data analytics support people-centred and integrated health services?" Methods: A scoping review following the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses - Scoping Review (PRISMA-ScR) statement was conducted to gather information on how big data analytics can support people-centred and integrated health services. The results were summarized in a role model of a people-centred and integrated health services platform illustrating which data sources might be integrated and which types of analytics might be applied to support the strategies of the people-centred and integrated health services framework to become more integrated across the continuum of care. Additional rapid literature reviews were conducted to generate frequency distributions of the most often used data types and analytical methods in the medical literature. Finally, the main challenges connected with big data analytics were worked out based on a content analysis of the results from the scoping literature review. Results: Based on the results from the rapid literature reviews the most often used data sources for big data analytics (BDA) in healthcare were biomarkers (39.3%) and medical images (30.9%). The most often used analytical models were support vector machines (27.3%) and neural networks (20.4%). The people-centred and integrated health services framework defines different strategic interventions for health services to become more integrated. To support all aspects of these interventions a comparably integrated platform of health-related data would be needed, so that a role model labelled as people-centred health platform was developed. Based on integrated data the results of the scoping review (n = 72) indicate, that big data analytics could for example support the strategic intervention of tailoring personalized health plans (43.1%), e.g. by predicting individual risk factors for different therapy options. Also BDA might enhance clinical decision support tools (31.9%), e.g. by calculating risk factors for disease uptake or progression. BDA might also assist in designing population-based services (26.4% by clustering comparable individuals in manageable risk groups e.g. mentored by specifically trained, non-medical professionals. The main challenges of big data analytics in healthcare were categorized in regulatory, (information-) technological, methodological, and cultural issues, whereas methodological challenges were mentioned most often (55.0%), followed by regulatory challenges (43.7%). Discussion: The BDA applications presented in this literature review are based on findings which have already been published. For some important components of the framework on people-centred care like enhancing the role of community care or establishing intersectoral partnerships between health and social care institutions only few examples of enabling big data analytical tools were found in the literature. Quite the opposite does this mean that these strategies have less potential value, but rather that the source systems in these fields need to be further developed to be suitable for big data analytics. Conclusions: Big data analytics can support people-centred and integrated health services e.g. by patient similarity stratifications or predictions of individual risk factors. But BDA fails to unfold its full potential until data source systems are still disconnected and actions towards a comprehensive and people-centred health-related data platform are politically insufficiently incentivized. This work highlighted the potential of big data analysis in the context of the model of people-centred and integrated health services, whereby the role model of the person-centered health platform can be used as a blueprint to support strategies to improve person-centered health care. Likely because health data is extremely sensitive and complex, there are only few practical examples of platforms to some extent already capable of merging and processing people-centred big data, but the integration of health data can be expected to further proceed so that analytical opportunities might also become reality in the near future.

Staff scheduling for residential care under pandemic conditions: The case of COVID-19.

The COVID-19 pandemic severely impacted residential care delivery all around the world. This study investigates the current scheduling methods in residential care facilities in order to enhance them for pandemic conditions. We first define the basic problem that addresses decisions associated with the assignment and scheduling of staff members, who perform a set of tasks required by residents during a planning horizon. This problem includes the minimization of costs associated with the salary of part-time staff members, total overtime, and violations of service time windows. Subsequently, we adapt the basic problem to pandemic conditions by considering the impacts of communal spaces (e.g., shared rooms) and a cohorting policy (classification of residents based on their risk of infection) on the spread of infectious diseases. We introduce a new objective function that minimizes the number of distinct staff members serving each room of residents. Likewise, we propose a new objective function for the cohorting policy that aims to minimize the number of distinct cohorts served by each staff member. A new constraint is incorporated that forces staff members to serve only one cohort within a shift. We present a population-based heuristic algorithm to solve this problem. Through a comparison with two benchmark solution approaches (a mathematical programme and a non-dominated archiving ant colony optimization algorithm), the superiority of the heuristic algorithm is shown regarding solution quality and CPU time. Finally, we conduct numerical analyses to present managerial implications.

Statistical Modeling for Quality Risk Assessment of Clinical Trials: Follow-Up at the Era of Remote Auditing.

BACKGROUND: As investigator site audits have largely been conducted remotely during the COVID-19 pandemic, remote quality monitoring has gained some momentum. To further facilitate the conduct of remote quality assurance (QA) activities for clinical trials, we developed new quality indicators, building on a previously published statistical modeling methodology. METHODS: We modeled the risk of having an audit or inspection finding using historical audits and inspections data from 2011 to 2019. We used logistic regression to model finding risk for 4 clinical impact factor (CIF) categories: Safety Reporting, Data Integrity, Consent and Protecting Endpoints. RESULTS: We could identify 15 interpretable factors influencing audit finding risk of 4 out of 5 CIF categories. They can be used to realistically predict differences in risk between 25 and 43% for different sites which suffice to rank sites by audit and inspection finding risk. CONCLUSION: Continuous surveillance of the identified risk factors and resulting risk estimates could be used to complement remote QA strategies for clinical trials and help to manage audit targets and audit focus also in post-pandemic times.

AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems.

Artificial intelligence (AI) is a leading technology of the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR), with the capability of incorporating human behavior and intelligence into machines or systems. Thus, AI-based modeling is the key to build automated, intelligent, and smart systems according to today's needs. To solve real-world issues, various types of AI such as analytical, functional, interactive, textual, and visual AI can be applied to enhance the intelligence and capabilities of an application. However, developing an effective AI model is a challenging task due to the dynamic nature and variation in real-world problems and data. In this paper, we present a comprehensive view on "AI-based Modeling" with the principles and capabilities of potential AI techniques that can play an important role in developing intelligent and smart systems in various real-world application areas including business, finance, healthcare, agriculture, smart cities, cybersecurity and many more. We also emphasize and highlight the research issues within the scope of our study. Overall, the goal of this paper is to provide a broad overview of AI-based modeling that can be used as a reference guide by academics and industry people as well as decision-makers in various real-world scenarios and application domains.

Pandemic Analytics: How Countries are Leveraging Big Data Analytics and Artificial Intelligence to Fight COVID-19?

Emergence of coronavirus in December 2019 and its spread across the world in the following months has made it a global health concern. The uncertainty about its evolution, transmission and effect of SARS-CoV-2, has left the countries and their governments in a worrisome state. Ambiguity about the strategies that would work towards mitigating the impact of virus has prompted them to use data-driven methods. Several countries started applying big data and advanced analytics technology for management of the crisis. This study aims to understand how different nations have employed analytics to deal with COVID-19. This paper reviews various strategies employed by different governments and organizations across nations that use advanced analytics to tackle pandemic. In the current emergency of corona virus, there have been several measures that organizations have taken to mitigate its impact, thanks to the evolution of computing technology. Big data and analytical tools provide various solutions like detection of existing COVID-19 cases, prediction of future outbreak, anticipation of potential preventive and therapeutic agents, and assistance in informed decision-making. This review discusses the big data analytics and artificial intelligence approaches that policy makers, researchers, epidemiologists and private organizations have adopted. By examining the different ways and areas where data analytics has been utilized, this study provides the other nations with the progressive scheme to address the pandemic.

COVID-19 Intelligence-Driven Operational Response Platform: Experience of a Large Tertiary Multihospital System in the Middle East.

The COVID-19 pandemic has resulted in global disruptions within healthcare systems, leading to quick dynamic fluctuations in hospital operations and supply chain management. During the early months of the pandemic, tertiary multihospital systems were highly viewed as the go-to hospitals for handling these rapid healthcare challenges caused by the rapidly increasing number of COVID-19 cases. Yet, this pandemic has created an urgent need for coordinated mechanisms to alleviate increasing pressures on these large multihospital systems and ensure services remain high-quality, accessible, and sustainable. Digital health solutions have been identified as promising approaches to address these challenges. This case report describes results for developing multidisciplinary visualizations to support digital health operations in one of the largest tertiary multihospital systems in the Middle East. The report concludes with some lessons and insights learned from the rapid development and delivery of this user-centric COVID-19 multihospital operations intelligent platform.

Using Methods From Computational Decision-making to Predict Nonadherence to Fitness Goals: Protocol for an Observational Study.

BACKGROUND: Can methods from computational models of decision-making be used to build a predictive model to identify individuals most likely to be nonadherent to personal fitness goals? Such a model may have significant value in the global battle against obesity. Despite growing awareness of the impact of physical inactivity on human health, sedentary behavior is increasingly linked to premature death in the developed world. The annual impact of sedentary behavior is significant, causing an estimated 2 million deaths. From a global perspective, sedentary behavior is one of the 10 leading causes of mortality and morbidity. Annually, considerable funding and countless public health initiatives are applied to promote physical fitness, with little impact on sustained behavioral change. Predictive models developed from multimodal methodologies combining data from decision-making tasks with contextual insights and objective physical activity data could be used to identify those most likely to abandon their fitness goals. This has the potential to enable development of more targeted support to ensure that those who embark on fitness programs are successful. OBJECTIVE: The aim of this study is to determine whether it is possible to use decision-making tasks such as the Iowa Gambling Task to help determine those most likely to abandon their fitness goals. Predictive models built using methods from computational models of decision-making, combining objective data from a fitness tracker with personality traits and modeling from decision-making games delivered via a mobile app, will be used to ascertain whether a predictive algorithm can identify digital personae most likely to be nonadherent to self-determined exercise goals. If it is possible to phenotype these individuals, it may be possible to tailor initiatives to support these individuals to continue exercising. METHODS: This is a siteless study design based on a bring your own device model. A total of 200 healthy adults who are novice exercisers and own a Fitbit (Fitbit Inc) physical activity tracker will be recruited via social media for this study. Participants will provide consent via the study app, which they will download from the Google Play store (Alphabet Inc) or Apple App Store (Apple Inc). They will also provide consent to share their Fitbit data. Necessary demographic information concerning age and sex will be collected as part of the recruitment process. Over 12 months, the scheduled study assessments will be pushed to the subjects to complete. The Iowa Gambling Task will be administered via a web app shared via a URL. RESULTS: Ethics approval was received from Dublin City University in December 2020. At manuscript submission, study recruitment was pending. The expected results will be published in 2022. CONCLUSIONS: It is hoped that the study results will support the development of a predictive model and the study design will inform future research approaches. TRIAL REGISTRATION: ClinicalTrials.gov NCT04783298; https://clinicaltrials.gov/ct2/show/NCT04783298.

Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective.

The digital world has a wealth of data, such as internet of things (IoT) data, business data, health data, mobile data, urban data, security data, and many more, in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting knowledge or useful insights from these data can be used for smart decision-making in various applications domains. In the area of data science, advanced analytics methods including machine learning modeling can provide actionable insights or deeper knowledge about data, which makes the computing process automatic and smart. In this paper, we present a comprehensive view on "Data Science" including various types of advanced analytics methods that can be applied to enhance the intelligence and capabilities of an application through smart decision-making in different scenarios. We also discuss and summarize ten potential real-world application domains including business, healthcare, cybersecurity, urban and rural data science, and so on by taking into account data-driven smart computing and decision making. Based on this, we finally highlight the challenges and potential research directions within the scope of our study. Overall, this paper aims to serve as a reference point on data science and advanced analytics to the researchers and decision-makers as well as application developers, particularly from the data-driven solution point of view for real-world problems.

Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment.

PURPOSE: Advanced medical image analytics is increasingly used to predict clinical outcome in patients diagnosed with gastrointestinal tumors. This review provides an overview on the value of radiomics in predicting response to treatment in patients with gastrointestinal tumors. METHODS: A systematic review was conducted, according to PRISMA guidelines. The protocol was prospectively registered (PROSPERO: CRD42019128408). PubMed, Embase, and Cochrane databases were searched. Original studies reporting on the value of radiomics in predicting response to treatment in patients with a gastrointestinal tumor were included. A narrative synthesis of results was conducted. Results were stratified by tumor type. Quality assessment of included studies was performed, according to the radiomics quality score. RESULTS: The comprehensive literature search identified 1360 unique studies, of which 60 articles were included for analysis. In 37 studies, radiomics models and individual radiomic features showed good predictive performance for response to treatment (area under the curve or accuracy > 0.75). Various strategies to construct predictive models were used. Internal validation of predictive models was often performed, while the majority of studies lacked external validation. None of the studies reported predictive models implemented in clinical practice. CONCLUSION: Radiomics is increasingly used to predict response to treatment in patients suffering from gastrointestinal cancer. This review demonstrates its great potential to help predict response to treatment and improve patient selection and early adjustment of treatment strategy in a non-invasive manner.

Harnessing the Power of Quality Assurance Data: Can We Use Statistical Modeling for Quality Risk Assessment of Clinical Trials?

BACKGROUND: The increasing number of clinical trials and their complexity make it challenging to detect and identify clinical quality issues timely. Despite extensive sponsor audit programs and monitoring activities, issues related to data integrity, safety, sponsor oversight and patient consent have recurring audit and inspection findings. Recent developments in data management and IT systems allow statistical modeling to provide insights to clinical Quality Assurance (QA) professionals to help mitigate some of the key clinical quality issues more holistically and efficiently. METHODS: We used findings from a curated data set from Roche/Genentech operational and quality assurance study data, covering a span of 8 years (2011-2018) and grouped them into 5 clinical impact factor categories, for which we modeled the risk with a logistic regression using hand crafted features. RESULTS: We were able to train 5 interpretable, cross-validated models with several distinguished risk factors, many of which confirmed field observations of our quality professionals. Our models were able to reliably predict a decrease in risk by 12-44%, with 2-8 coefficients each, despite a low signal-to-noise ratio in our data set. CONCLUSION: We proposed a modeling strategy that could provide insights to clinical QA professionals to help them mitigate key clinical quality issues (e.g., safety, consent, data integrity) in a more sustained data-driven way, thus turning the traditional reactive approach to a more proactive monitoring and alerting approach. Also, we are calling for cross-sponsors collaborations and data sharing to improve and further validate the use of statistical models in clinical QA.

In silico approaches for enhancing retrieval analysis as a source for discovery of implant reactivity-related mechanisms and biomarkers.

The ability to characterize implant debris in conjunction with corresponding immune and tissue-destructive responses renders retrieval analysis as an important tool for evaluating orthopedic devices. We applied advanced analytics and in silico approaches to illustrate the retrieval-based potential to elucidate host responses and enable discovery of corresponding biomarkers indicative of in vivo implant performance. Hip retrieval analysis was performed using variables based on immunostaining, polarized microscopy, and fretting-corrosion and oxidation analyses. Statistical analyses were performed in R. Hierarchical/k-means clustering and principal component analysis were used for data analysis and visualization. Correlation Engine (CE) and Ingenuity Pathway Analysis (IPA) were employed for in silico corroboration of putative biomarkers. Higher giant cell and histiocyte scores and positivity for CD68 and CD3 indicating infiltration with macrophages and T-cells, respectively, were detected mainly among older generation hips with higher ultra-high-molecular-weight-polyethylene loads. Our in silico analysis using pre-existing data on wear particle-induced loosening substantiated the role of CD68 in implant-induced innate responses and identified the CD68-related molecular signature that can be indicative of development of aseptic loosening and can be further corroborated for diagnostic/prognostic testing in clinical setting. Thus, this study confirmed the great potential of advanced analytics and in silico approaches for enhancing retrieval analysis applications to discovery of new biomarkers for optimizing implant-related preclinical testing and clinical management. © 2019 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater 108B:263-271, 2020.