Lessons Learned From European Health Data Projects With Cancer Use Cases: Implementation of Health Standards and Internet of Things Semantic Interoperability.

The adoption of the European Health Data Space (EHDS) regulation has made integrating health data critical for both primary and secondary applications. Primary use cases include patient diagnosis, prognosis, and treatment, while secondary applications support research, innovation, and regulatory decision-making. Additionally, leveraging large datasets improves training quality for artificial intelligence (AI) models, particularly in cancer prevention, prediction, and treatment personalization.

Empowering cancer research in Europe: the EUCAIM cancer imaging infrastructure.

Artificial intelligence (AI) is a powerful technology with the potential to disrupt cancer detection, diagnosis and treatment. However, the development of new AI algorithms requires access to large and complex real-world datasets. Although such datasets are constantly being generated, access to them is limited by data fragmentation across numerous repositories and sites, heterogeneity, lack of annotations, and potential privacy issues.

Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects.

Artificial intelligence (AI) is transforming the field of medical imaging and has the potential to bring medicine from the era of 'sick-care' to the era of healthcare and prevention. The development of AI requires access to large, complete, and harmonized real-world datasets, representative of the population, and disease diversity. However, to date, efforts are fragmented, based on single-institution, size-limited, and annotation-limited datasets.

CHAIMELEON Project: Creation of a Pan-European Repository of Health Imaging Data for the Development of AI-Powered Cancer Management Tools.

The CHAIMELEON project aims to set up a pan-European repository of health imaging data, tools and methodologies, with the ambition to set a standard and provide resources for future AI experimentation for cancer management. The project is a 4 year long, EU-funded project tackling some of the most ambitious research in the fields of biomedical imaging, artificial intelligence and cancer treatment, addressing the four types of cancer that currently have the highest prevalence worldwide: lung, breast, prostate and colorectal. To allow this, clinical partners and external collaborators will populate the repository with multimodality (MR, CT, PET/CT) imaging and related clinical data.

Serverless Workflows for Containerised Applications in the Cloud Continuum.

This paper introduces an open-source platform to support serverless computing for scientific data-processing workflow-based applications across the Cloud continuum (i.e. simultaneously involving both on-premises and public Cloud platforms to process data captured at the edge).

ReMindCare App for Early Psychosis: Pragmatic Real World Intervention and Usability Study.

Background: eHealth interventions are widely used in clinical trials and increasingly in care settings as well; however, their efficacy in real-world contexts remains unknown. ReMindCare is a smartphone app that has been systematically implemented in a first episode of psychosis program (FEPP) for patients with early psychosis since 2018.

Objective: The objective of this study was to assess the efficacy of ReMindCare after 19 months of use in the clinic and varying use by individual patients.

ReMindCare, an app for daily clinical practice in patients with first episode psychosis: A pragmatic real-world study protocol.

Aim: Despite the potential benefits of e-health interventions for patients with psychosis, the integration of these applications into the clinical workflow and analysis of their long-term effects still face significant challenges. To address these issues, we developed the ReMindCare app. This app aims to improve the treatment quality for patients with psychosis.

PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers.

PRIMAGE is one of the largest and more ambitious research projects dealing with medical imaging, artificial intelligence and cancer treatment in children. It is a 4-year European Commission-financed project that has 16 European partners in the consortium, including the European Society for Paediatric Oncology, two imaging biobanks, and three prominent European paediatric oncology units. The project is constructed as an observational in silico study involving high-quality anonymised datasets (imaging, clinical, molecular, and genetics) for the training and validation of machine learning and multiscale algorithms.

Differences in the Use and Opinions About New eHealth Technologies Among Patients With Psychosis: Structured Questionnaire.

Background: Despite a growing interest in the use of technology in order to support the treatment of psychotic disorders, limited knowledge exists about the viability and acceptability of these eHealth interventions in relation to the clinical characteristics of patients.

Objective: The objective of this study was to assess the access and use of, as well as experiences and interest in, new technologies using a survey of patients diagnosed with early psychosis compared with a survey of patients diagnosed with chronic psychotic disorders.

Methods: We designed a structured questionnaire.

Use of mobile technologies in patients with psychosis: A systematic review.

Unlabelled: There is a growing interest in mobile Health interventions (m-Health) in patients with psychosis. The aim of this study is to conduct a systematic review in order to analysethe current state of research in this area. The search of articles was carried out following the PRISMA criteria, focusing on those studies that used mobile technologies in patients with psychosis during the period from 1990 to 2016.

Increasing the Efficiency on Producing Radiology Reports for Breast Cancer Diagnosis by Means of Structured Reports. A Comparative Study.

Background: Radiology reports are commonly written on free-text using voice recognition devices. Structured reports (SR) have a high potential but they are usually considered more difficult to fill-in so their adoption in clinical practice leads to a lower efficiency. However, some studies have demonstrated that in some cases, producing SRs may require shorter time than plain-text ones.

MuffinInfo: HTML5-Based Statistics Extractor from Next-Generation Sequencing Data.

Usually, the information known a priori about a newly sequenced organism is limited. Even resequencing the same organism can generate unpredictable output. We introduce MuffinInfo, a FastQ/Fasta/SAM information extractor implemented in HTML5 capable of offering insights into next-generation sequencing (NGS) data.

Fast inexact mapping using advanced tree exploration on backward search methods.

Background: Short sequence mapping methods for Next Generation Sequencing consist on a combination of seeding techniques followed by local alignment based on dynamic programming approaches. Most seeding algorithms are based on backward search alignment, using the Burrows Wheeler Transform, the Ferragina and Manzini Index or Suffix Arrays. All these backward search algorithms have excellent performance, but their computational cost highly increases when allowing errors.

NeuPAT: an intranet database supporting translational research in neuroblastic tumors.

Translational research in oncology is directed mainly towards establishing a better risk stratification and searching for appropriate therapeutic targets. This research generates a tremendous amount of complex clinical and biological data needing speedy and effective management. The authors describe the design, implementation and early experiences of a computer-aided system for the integration and management of data for neuroblastoma patients.

A service-based BLAST command tool supported by cloud infrastructures.

Notwithstanding the benefits of distributed-computing infrastructures for empowering bioinformatics analysis tools with the needed computing and storage capability, the actual use of these infrastructures is still low. Learning curves and deployment difficulties have reduced the impact on the wide research community. This article presents a porting strategy of BLAST based on a multiplatform client and a service that provides the same interface as sequential BLAST, thus reducing learning curve and with minimal impact on their integration on existing workflows.

Integrating TRENCADIS components in gLite to share DICOM medical images and structured reports.

The problem of sharing medical information among different centres has been tackled by many projects. Several of them target the specific problem of sharing DICOM images and structured reports (DICOM-SR), such as the TRENCADIS project. In this paper we propose sharing and organizing DICOM data and DICOM-SR metadata benefiting from the existent deployed Grid infrastructures compliant with gLite such as EGEE or the Spanish NGI.

Using grid-enabled distributed metadata database to index DICOM-SR.

Integrating medical data at inter-centre level implies many challenges that are being tackled from many disciplines and technologies. Medical informatics have applied an important effort on describing and standardizing Electronic Health Records, and specially standardisation has achieved an important extent on Medical Imaging. Grid technologies have been extensively used to deal with multi-domain authorisation issues and to provide single access points for accessing DICOM Medical Images, enabling the access and processing to large repositories of data.

SHARE road map for HealthGrids: methodology.

The SHARE(1) project (http://www.eu-share.org) was asked to identify the key developments needed to achieve wide adoption and deployment of HealthGrids throughout Europe.

Metagenomics reveals our incomplete knowledge of global diversity.

Javier.tamames@uv.es

A highly optimized grid deployment: the metagenomic analysis example.

Computational resources and computationally expensive processes are two topics that are not growing at the same ratio. The availability of large amounts of computing resources in Grid infrastructures does not mean that efficiency is not an important issue. It is necessary to analyze the whole process to improve partitioning and submission schemas, especially in the most critical experiments.

SHARE, from vision to road map: technical steps.

We present the 'HealthGrid' initiative and briefly review work carried out in various European healthgrid projects. We report on joint work with numerous European collaborators. Since the European Commission's Information Society Technologies programme funded the first gridbased health and medical projects, the HealthGrid movement has flourished in Europe.

SHARE roadmap 1: towards a debate.

We present the 'HealthGrid' initiative and review work carried out in various European projects. Since the European Commission's Information Society Technologies programme funded the first grid-based health and medical projects, the HealthGrid movement has flourished in Europe. Many projects have now been completed and 'Healthgrid' consulted a number of experts to compile and publish a 'White Paper' which establishes the foundations, potential scope and prospects of an approach to health informatics based on a grid infrastructure.

Roadmap for a European healthgrid.

This paper proposes a 10-year roadmap to achieve the goal to offer to healthcare professionals an environment created through the sharing of resources, in which heterogeneous and dispersed health data as well as applications can be accessed by all users as a tailored information providing system according to their authorisation and without loss of information. The paper identifies milestones and presents short term objectives on the road to this healthgrid.

Data integration in eHealth: a domain/disease specific roadmap.

The paper documents a series of data integration workshops held in 2006 at the UK National e-Science Centre, summarizing a range of the problem/solution scenarios in multi-site and multi-scale data integration with six HealthGrid projects using schizophrenia as a domain-specific test case. It outlines emerging strategies, recommendations and objectives for collaboration on shared ontology-building and harmonization of data for multi-site trials in this domain.