Analysis of the Data Consistency of Medical Imaging Information Systems: An Exploratory Study

In the context of medical imaging, a considerable amount of data is created on daily basis, which are produced and managed by multiple medical imaging and information systems. The professional and department performance, as well as the respective characterization, are supported by these data, which makes their quality a critical aspect to take into account. The work developed had the objective of analysing the existence of nonconformities related to the consistency of the Radiology Information Systems (RIS) and Picture and Communication System (PACS) databases, as well as their precision, accuracy, integrity and associated risks for the healthcare practice. Data belonging to 1068 computed radiography studies were analysed and ten nonconformities were identified, related to the patient name, patient age, patient date of birth, patient gender, study date, study time, institution identification, radiologist identification, number of radiographic projections performed, and billing code. The work carried out allowed identifying some lack of data quality, sometimes evidenced in the absence of consistency, precision, accuracy and integrity.publishe

Quantitative imaging in radiation oncology

Artificially intelligent eyes, built on machine and deep learning technologies, can empower our capability of analysing patients’ images. By revealing information invisible at our eyes, we can build decision aids that help our clinicians to provide more effective treatment, while reducing side effects. The power of these decision aids is to be based on patient tumour biologically unique properties, referred to as biomarkers. To fully translate this technology into the clinic we need to overcome barriers related to the reliability of image-derived biomarkers, trustiness in AI algorithms and privacy-related issues that hamper the validation of the biomarkers. This thesis developed methodologies to solve the presented issues, defining a road map for the responsible usage of quantitative imaging into the clinic as decision support system for better patient care

From multisource data to clinical decision aids in radiation oncology:The need for a clinical data science community

Big data are no longer an obstacle; now, by using artificial intelligence (AI), previously undiscovered knowledge can be found in massive data collections. The radiation oncology clinic daily produces a large amount of multisource data and metadata during its routine clinical and research activities. These data involve multiple stakeholders and users. Because of a lack of interoperability, most of these data remain unused, and powerful insights that could improve patient care are lost. Changing the paradigm by introducing powerful AI analytics and a common vision for empowering big data in radiation oncology is imperative. However, this can only be achieved by creating a clinical data science community in radiation oncology. In this work, we present why such a community is needed to translate multisource data into clinical decision aids

Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer

Quantitative extraction of high-dimensional mineable data from medical images is a process known as radiomics. Radiomics is foreseen as an essential prognostic tool for cancer risk assessment and the quantification of intratumoural heterogeneity. In this work, 1615 radiomic features (quantifying tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET and CT images of 300 patients from four different cohorts were analyzed for the risk assessment of locoregional recurrences (LR) and distant metastases (DM) in head-and-neck cancer. Prediction models combining radiomic and clinical variables were constructed via random forests and imbalance-adjustment strategies using two of the four cohorts. Independent validation of the prediction and prognostic performance of the models was carried out on the other two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88). Furthermore, the results obtained via Kaplan-Meier analysis demonstrated the potential of radiomics for assessing the risk of specific tumour outcomes using multiple stratification groups. This could have important clinical impact, notably by allowing for a better personalization of chemo-radiation treatments for head-and-neck cancer patients from different risk groups.Comment: (1) Paper: 33 pages, 4 figures, 1 table; (2) SUPP info: 41 pages, 7 figures, 8 table

Manufacturing High Entropy Alloys: Pathway to Industrial Competitiveness

High entropy alloys (HEAs) provide a transformative opportunity to design materials that are custom tailored to the distinct needs of a given application, thereby shifting the paradigm from “apply the material you have” to “engineer the material you need.” HEAs will enable high-performance manufactured goods that are competitive in the international marketplace through extraordinary material properties and unique property combinations. HEAs deliver new choices to manufacturers to create alternatives to materials that are rare, hazardous, expensive, or subject to international restrictions or conflict. The potential benefits of HEAs span diverse fields and applications, and show promise to not only accelerate economic growth and domestic competitive advantage, but also address pressing societal challenges. These include solid state cooling, liquefied natural gas handling, nuclear degradation- resistant materials, corrosion-resistant heat exchangers, and efficiency gains from high temperature performance that advance national energy goals; high-performance aerospace materials and ultra- hardness ballistics that support national security; and strong, corrosion-resistant medical devices and advances in magnetic resonance imaging that are essential to national health priorities. Research advances are setting the stage to realize each of these vital areas. However, research advances made to-date to produce lab-scale prototypes do not lend themselves to manufacturing at scale. For Americans to fully benefit from HEAs, the emerging technologies must be translated into products manufactured at scale in the United States. However, manufacturers and HEA experts who are working to bridge this gap are encountering cross-cutting barriers in manufacturing processes, testing, data, and access to the necessary resources. Through strategic public- and private- sector research and investment, these barriers can be overcome. The United States has invested in both HEA research and advanced materials resources, such as material sample creation at the Ames Laboratory Materials Preparation Center, material characterization at Oak Ridge National Laboratory’s Neutron User Facilities, and modeling and analysis through the National Institute of Standards and Technology’s Material Genome Initiative. A vast array of research and expertise has been fostered at federal laboratories and universities, yielding promising alloys, manufacturing processes, and analysis methods.National Science Foundation, Grant No. 1552534https://deepblue.lib.umich.edu/bitstream/2027.42/146747/1/Manufacturing-HEAs.pdf-1Description of Manufacturing-HEAs.pdf : Main articl

The holistic perspective of the INCISIVE Project: artificial intelligence in screening mammography

Finding new ways to cost-effectively facilitate population screening and improve cancer diagnoses at an early stage supported by data-driven AI models provides unprecedented opportunities to reduce cancer related mortality. This work presents the INCISIVE project initiative towards enhancing AI solutions for health imaging by unifying, harmonizing, and securely sharing scattered cancer-related data to ensure large datasets which are critically needed to develop and evaluate trustworthy AI models. The adopted solutions of the INCISIVE project have been outlined in terms of data collection, harmonization, data sharing, and federated data storage in compliance with legal, ethical, and FAIR principles. Experiences and examples feature breast cancer data integration and mammography collection, indicating the current progress, challenges, and future directions.This research received funding mainly from the European Union’s Horizon 2020 research and innovation program under grant agreement no 952179. It was also partially funded by the Ministry of Economy, Industry, and Competitiveness of Spain under contracts PID2019-107255GB and 2017-SGR-1414.Peer ReviewedArticle signat per 30 autors/es: Ivan Lazic (1), Ferran Agullo (2), Susanna Ausso (3), Bruno Alves (4), Caroline Barelle (4), Josep Ll. Berral (2), Paschalis Bizopoulos (5), Oana Bunduc (6), Ioanna Chouvarda (7), Didier Dominguez (3), Dimitrios Filos (7), Alberto Gutierrez-Torre (2), Iman Hesso (8), Nikša Jakovljević (1), Reem Kayyali (8), Magdalena Kogut-Czarkowska (9), Alexandra Kosvyra (7), Antonios Lalas (5) , Maria Lavdaniti (10,11), Tatjana Loncar-Turukalo (1),Sara Martinez-Alabart (3), Nassos Michas (4,12), Shereen Nabhani-Gebara (8), Andreas Raptopoulos (6), Yiannis Roussakis (13), Evangelia Stalika (7,11), Chrysostomos Symvoulidis (6,14), Olga Tsave (7), Konstantinos Votis (5) Andreas Charalambous (15) / (1) Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia; (2) Barcelona Supercomputing Center, 08034 Barcelona, Spain; (3) Fundació TIC Salut Social, Ministry of Health of Catalonia, 08005 Barcelona, Spain; (4) European Dynamics, 1466 Luxembourg, Luxembourg; (5) Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece; (6) Telesto IoT Solutions, London N7 7PX, UK: (7) School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (8) Department of Pharmacy, Kingston University London, London KT1 2EE, UK; (9) Timelex BV/SRL, 1000 Brussels, Belgium; (10) Nursing Department, International Hellenic University, 57400 Thessaloniki, Greece; (11) Hellenic Cancer Society, 11521 Athens, Greece; (12) European Dynamics, 15124 Athens, Greece; (13) German Oncology Center, Department of Medical Physics, Limassol 4108, Cyprus; (14) Department of Digital Systems, University of Piraeus, 18534 Piraeus, Greece; (15) Department of Nursing, Cyprus University of Technology, Limassol 3036, CyprusPostprint (published version