Investigación

Current International Research Projects

SYNTHIA Synthetic Data Generation Framework For Integrated Validation Of Use Cases And AI Healthcare Applications

Call: HORIZON-JU-IHI-2023-05-04
Investigador principal: Guillermo Sanz
Role: Coordinator
Year: 2024

SYNTHIA is an ambitious collaboration between public and private institutions to facilitate the responsible use of Synthetic Data (SD) in healthcare applications. The project will improve the methodological and technical aspects of SD Generation (SDG) by developing new techniques and advancing established ones for different data modalities, including genomics and imaging, to improve the generation of realistic multimodal and longitudinal data. This project will provide the research community with approaches for transparent benchmarking of alternative SDG methods for specific applications, identify and establish evaluation metrics and methodologies, and contribute to the standardisation of an evaluation assessment framework for SD. Robust evidence of SD applicability in a set of use cases across a broad spectrum of medical conditions will be crucial to demonstrate the potential of SD to accelerate data-driven solutions of equivalent quality to those derived from real patient data. Furthermore, legal and regulatory implications of SD use will be analysed with the aim of delivering an assurance framework to guide secure SD utilization in healthcare. These significant breakthroughs will be implemented through the open SYNTHIA federated platform, facilitating responsible SD use by the health research community. The platform will facilitate users´ long-term access to extensively validated, reusable synthetic datasets, as well as to SDG workflows and SD assessment frameworks. The federated infrastructure will rely on extended open-source frameworks for interoperability with other data-sharing infrastructures in the context of the European Health Data Space. A multidisciplinary collaboration of SDG developers, FAIR data experts, clinical researchers, developers of therapies and data-based tools, legal experts, socio-economic analysts, regulatory, policy advocacy, and communication experts will provide a 360º vision on how to advance healthcare applications through SD use.

EUCAIM European Federation for Cancer Images

Call: DIGITAL-2022-CLOUD-AI-02
Investigador principal: Luis Martí-Bonmatí
Role: Coordinator
Year: 2023

EUropean Federation for CAncer IMages (EUCAIM) joins 79 partners to deploy a pan-European digital federated infrastructure of FAIR cancer-related de-identified images from Real-World. The infrastructure is designed to preserve the data sovereignty of providers, and provide a platform, including an Atlas of Cancer Images, for the development and benchmarking of AI tools towards Precision Medicine. EUCAIM will address the fragmentation of existing cancer image repositories by building on repositories of the AI4HI initiative, European Research Infrastructures and national/regional repositories and include clinical images, pathology, molecular and laboratory data. EUCAIM targets clinicians, researchers, and innovators, providing the means to finally build up validated clinical decision-making systems supporting diagnosis, treatment, and predictive medicine to benefit citizens. EUCAIM will define the legal grounds for the operation on a pan-European scale, adapting to the particularities of different countries on managing clinical data. EUCAIM will implement a federation of providers compliant with this legal ground, defining common data models, ontologies, quality standards, FAIR principles and de-identification procedures. EUCAIM will provide a comprehensive dashboard for data discovery, federated search, metadata harvesting, annotation and distributed processing, including federated and privacy-preserving learning. EUCAIM will build a central hub hosting the Atlas of Cancer Images, to enable development of trustworthy AI tools. EUCAIM will support new providers in building the federation and monitoring the distributed infrastructure. EUCAIM will align with the European Health Data Spaces initiative toward a sustainable flagship repository of high-quality data and tools. EUCAIM brings together clinical data providers, researchers, Research Infrastructures, and industry with mature solutions addressing the challenges of implementing such a cancer imaging infrastructure.

RadioVal International Clinical Validation of Radiomics Artificial Intelligence for Breast Cancer Treatment Planning

Call: HORIZON-HLTH-2021-DISEASE-04-04
Investigador principal: Luis Martí-Bonmatí
Role: Participant
Year: 2022

Breast cancer is now the most common cancer worldwide, surpassing lung cancer in 2020 for the first time. It is responsible for almost 30% of all cancers in women and current trends show its increasing incidence. Neoadjuvant chemotherapy (NAC) has shown promise in reducing mortality for advanced cases, but the therapy is associated with a high rate of over-treatment, as well as with significant side effects for the patients. For predicting NAC respondents and improving patient selection, artificial intelligence (AI) approaches based on radiomics have shown promising preclinical evidence, but existing studies have mostly focused on evaluating model accuracy, all-too-often in homogeneous populations. RadioVal is the first multi-centre, multi-continental and multi-faceted clinical validation of radiomics-driven estimation of NAC response in breast cancer. The project builds on the repositories, tools and results of five EU-funded projects from the AI for Health Imaging (AI4HI) Network, including a large multi-centre cancer imaging dataset on NAC treatment in breast cancer. To test applicability as well as transferability, the validation with take place in eight clinical centres from three high-income EU countries (Sweden, Austria, Spain), two emerging EU countries (Poland, Croatia), and three countries from South America (Argentina), North Africa (Egypt) and Eurasia (Turkey). RadioVal will develop a comprehensive and standardised methodological framework for multi-faceted radiomics evaluation based on the FUTURE-AI Guidelines, to assess Fairness, Universality, Traceability, Usability, Robustness and Explainability. Furthermore, the project will introduce new tools to enable transparent and continuous evaluation and monitoring of the radiomics tools over time. The RadioVal study will be implemented through a multi-stakeholder approach, taking into account clinical and healthcare needs, as well as socio-ethical and regulatory requirements from day one.

NextMRI Truly portable MRI for extremity and brain imaging anywhere & everywhere

Call: HORIZON-EIC-2023-TRANSITIONOPEN-01
Investigador principal: Luis Martí-Bonmatí
Role: Participant
Year: 2023

Mobile medical imaging devices are invaluable for clinical diagnostic purposes both inside and outside healthcare institutions. Unfortunately, Magnetic Resonance Imaging (MRI), the gold standard for numerous neurological and musculoskeletal conditions, is not readily portable. Recently, low-field MRI companies have demonstrated the first decisive steps towards portability within medical facilities and vehicles, but the scanners' weight and dimensions are still incompatible with more demanding use cases such as in remote and developing regions, sports facilities and events, medical and military camps, or home healthcare. Within the Histo-MRI and PR Scanner projects, we have recently demonstrated in vivo images taken with a light, small footprint, low-field extremity MRI scanner outside the controlled settings provided by medical facilities, including the first-ever MRI images obtained at a patient's home, and even in open air connected to a gasoline generator. This has opened a path towards highly accessible MRI under circumstances previously unrealistic. In this project we will take the necessary technical, industrial and entrepreneurial steps to tackle the massive deployment of low-field portable systems for point-of-care and bedside imaging in clinics large and small, home and hospice care, rural areas, sports facilities and events, field hospitals, or NGO and military camps, making MRI available to a large fraction of the world population with no or insufficient access. To advance towards this highly ambitious goal, the objectives of the NextMRI project include: i) expanding the current technology to both extremity and brain imaging; ii) improving the diagnostic capabilities with machine learning; iii) improving the portability and usability to meet end-user needs; iv) optimizing production costs; v) gathering medical evidence of the technology performance with clinical trials; and vi) developing a sustainable business case and model towards commer

MATTO-GBM (FCAECC) Multimodality Artificial intelligence open-source Tools for Radiation Treatment Optimization in patients with Glioblastoma

Call: Joint Transnational Call for Proposals 2022 (JTC 2022)
Investigador principal: Luis Martí-Bonmatí
Role: Participant
Year: 2023

High-grade gliomas (HGG) are very aggressive brain tumors with poor overall survival rates of 16 months on average and 70-80% local recurrence (LR). Treatment concepts have remained almost unchanged for decades. The correlation of tumor imaging hallmarks with treatment response could allow personalized adapted therapies to prevent disease progression. However, the best imaging modality for HGG remains an unanswered question. HGG diagnosis and therapy has been commonly based on magnetic resonance (MR). Positron emission tomography (PET) has been proposed to overcome the MR limitations (50% specificity), when differentiating LR from radiogenic alterations. In this context, the use of hybrid PET/MR systems for simultaneously imaging HGG permits a more accurate comparison of the information provided by both modalities. Our main motivation is the identification of biologically active tumor tissue associated with LR in HGG, in order to replace the homogeneous dose distribution conventionally delivered in radiotherapy treatment, by a dose distribution scaled based on the patient´s specific risk profile of LR. With this purpose, our project aims to identify the best modality for HGG segmentation and for LR prediction. As result of the project, an open-source software is expected, which combines PETand MR- based HGG segmentation and LR prediction to support clinical decisions regarding personalized treatment options. In our proposal, two patient cohorts are involved: 232 patients of the existing prospective GLIAA trial (Freiburg) and small (25) exploratory study part of the current project (Málaga and Valencia). Artificial intelligence (AI) is applied to develop PET- (Freiburg) and MR- (Valencia) based tumor segmentation and radiomics models for LR prediction. Integrated PET/MR models are also evaluated. The expertise of the academia partner (Vienna) focuses on the improvement of the robustness of AI-algorithms and the development of an open-source visualization tool.

LEOPARD Liver Electronic Offering Platform with Artificial intelligence-based Devices

Call: HORIZON-HLTH-2022-TOOL-12-two-stage
Investigador principal: Luis Martí-Bonmatí
Role: Participant
Year: 2023

Liver transplantation (LT) is a life-saving procedure for decompensated cirrhosis (DC) and hepato-cellular carcinoma (HCC). Its efficacy is hampered by the risk of death/drop-out on the Wait List (WL). This risk is driven by organ shortage and is mitigated by organ offering schemes. According to a sickest first policy, offering schemes prioritize LT candidates with the highest risk of dying, as assessed by predictive models. To drive allocation, Organ Sharing Organizations (OSOs) use a 20-year-old model, the MELD, predicting mortality in DC but not in HCC. Because of a dramatic increase in % of HCC candidates (40% against 10% in early 20ties), MELD schemes are increasingly inaccurate, with persisting 15 to 30% mortality in countries with low/medium donation rate. This scenario, together with advances in prognosis in DC and HCC candidates and statistics, prompts LT community to look for up-dated algorithms to refine offering schemes. To address this issue, key European LT stakeholders including OSOs, experts in LT, Statisticians, Research Labs and SME joined LEOPARD. Building on an innovative, harmonized OSOs pre-LT dataset and advances in modeling, LEOPARD propose to design and validate 1) an AI-based LEOPARD predictive algorithm outperforming current allocation models by better stratifying patients on the risk of mortality, to be proposed OSOs to drive allocation; 2) DC & HCC LEOPARD calculators available for professional for assistance in complex decision-making processes; 3) OMICs/radiomics predictive signatures integrated in a prototype 3rd-generation exploratory model. We expect to generate computational tools improving candidates' outcomes, with more patients transplanted on time. Adoption of these tools should result in harmonization of European heterogeneous prioritization schemes, and in a signification reduction in disparities of access to LT, a major objective pointed out by EC. LEOPARD should place Europe in leading position for organ offering schemes.

ARTEMIS AcceleRating the Translation of virtual twins towards a pErsonalised Management of fatty lIver patients

Call: HORIZON-HLTH-2023-TOOL-05
Investigador principal: Luis Martí-Bonmatí
Role: Participant
Year: 2024

The ARTEMIs project aims to consolidate existing computational mechanistic and machine-learning models at different scales to deliver 'virtual twins' embedded in a clinical decision support system (CDSS). The CDSS will provide clinically meaningful information to clinicians, for a more personalised management of the whole spectrum of Metabolic Associated Fatty Liver Disease (MAFLD). MAFLD, with an estimated prevalence of about 25%, goes from an undetected sleeping disease to inflammation (hepatitis), to fibrosis development (cirrhosis) and/or hepatocellular carcinoma (HCC), decompensated cirrhosis and HCC being the final stages of the disease. However, many MAFLD patients do not die from the liver disease itself, but from cardiovascular comorbidities or complications.

The ARTEMIs will contribute to the earlier management of MAFLD patients, by prognosing the development of more advanced forms of the disease and cardiovascular comorbidities, promoting active surveillance of patients at risk. The system will predict the impact of novel drug treatments or procedures, or simply better life habits. The system will therefore not only serve as a clinical decision aid tool, but also as an educational tool for patients, to promote better nutritional and lifestyle behaviors. In more advanced forms of the disease, therapeutic interventions include TIPPS to manage portal hypertension, partial hepatectomy, partial or complete liver transplant. ARTEMIs will contribute to predict per- or post-intervention heart failure, building on existing microcirculation hemodynamics models. The model developers will benefit from a large distributed patient cohort and data exploration environment to identify patterns in data, draw new theories on the liver-heart metabolic axis and validate the performance of their models. The project includes a proof-of-concept feasibility study assessing the utility of the integrated virtual twins and CDSS in the clinical context.

LaFe-CISA Oficina de Internacional del IISLAFE: Convirtiendo la Estrategia de Internacionalización en Acción.

Call: Ayudas para la preparación y gestión de proyectos europeos y facilitar la atracción de talento internacional en las instituciones de I+D 2023
Investigador principal: Rita Diranzo
Role: Individual
Year: 2024

The general objective of CISA-LaFe is to increase the participation of the IIS La Fe researchers in Horizon Europe Program projects, as well as in other international programs, increasing their quality and success rate in the international research environment. This is intended to be achieved by promoting international cooperation, supporting the search for international partners and consortia, as well as promoting emerging research groups at IIS La Fe through scientific and technical advice that allows them to improve their potential and opportunities, relying on qualified management structures and international promotion of R&D&I projects. In addition, internationalization also encompasses technological lines/products/services developed by IIS La Fe researchers and the international valorization of their innovation together with the OTCof IIS La Fe.

METEV Circulating Extracellular vesicles (cEVs): a novel nutrient delivery mechanism and therapeutic target in metabolic pathologies (METEV)

Call: HORIZON-MSCA-2023-PF-01-01
Investigador principal: Pilar Sepúlveda
Role: Individual
Year: 2024

The sensing of blood nutrient availability, storage, and delivery requires robust inter-tissue crosstalk mechanisms which, for the last 70 years, we have still trying to understand in health and disease. When these mechanisms fail, metabolic pathologies such as Metabolic Syndrome (MetSy) ensue. MetSy is a complex entity that has emerged as a worldwide epidemic. The lack of knowledge of its nature, the absence of any efficient treatment, and the increasing prevalence imply a major public health concern. MetSy is defined as abdominal obesity, insulin resistance, misbalance in lipid profile, and high cardiovascular and diabetes type 2 risks. These factors, dysregulated simultaneously, suggest an underlying mechanism linking all these metabolic impairments. In 2019, I published an article proposing a novel nutrient delivery mechanism from blood to tissues based on extracellular vesicles (EVs) biology. Since then, other groups have corroborated our findings, supporting the idea that, as a complementation of the classical nutrient delivery mechanisms, EVs in circulation (cEVs) are able to deliver metabolic fuels from blood to tissues. While this new cEVs-based mechanism is occurring in healthy subjects, we don´t know if it is falling in MetSy patients, or even if it is relevant enough to alter human physiopathology. METEV aims to study these last two concepts, to address a better understanding of metabolic pathologies, in particular, the role of cEVs in the development of MetSy. My main personal objective is to learn (training) and take advantage of cutting-edge technologies to improve the knowledge of metabolic pathologies with potential relevant implications in multiple fields of medicine. This will allow forging future interdisciplinary collaborations with professionals from different areas. The present MSCA enables the development of my own collaborative network, as I will work at the Hospital La Fe (Spain), with a secondment at Aalborg University Hospital (Denmark).

IMPACT-AML Research and Innovation actions supporting the implementation of the Mission on Cancer

Call: HORIZON-MISS-2022-CANCER-01
Investigador principal: Pau Montesinos
Role: Participant
Year: 2023

In IMPACT-AML, a multidisciplinar R/R AML represents a model of high-impact disease, in which no standard of care exists, and where we have an urgent need for new evidence on possible therapies; AML offers the setting in which methodological innovation will combine powerful instruments of clinical trials with personalized medicine through academic efforts. Hereby, we propose to create an inclusive master framework for relapsed or refractory acute myeloid leukemia (STREAM) to include patients with R/R AML across Europe proficiently acquire an unselected population for clinical trials and monitor outcomes including neglected cohorts. Thereafter we will conduct a prospective randomized pragmatic clinical trial (RPCT) that will compare the classical "high intensity" rescue chemotherapy with biology-driven, "low intensity" rescue to obtain "real world" data on the benefit of one of the two different strategies in term of survival also considering patients and caregivers preferences, patient-reported outcomes (PRO), accessibility, affordability, and social cost. RPCT will aim to evaluate the effectiveness of real-world clinical alternatives in routine care. In addition to retaining the high internal validity of traditional randomized trials, it will maximize external validity, i. e. generalizability of results to many settings. In this context, the inclusion of an RPCT in the master framework will allow a dynamic inclusion and the collection of the excluded population as an instrument to predict the real-life applicability of clinical trial results. We will offer Europe results from an ambitious project, that will go beyond the state of the art in R/R AML demonstrating the superiority of a strategy in a first-of-his-kind clinical trial, providing strong data that will be delivered to national health care providers, policymakers, and health authorities data on optimized and affordable treatment for R/R AML and promoting the implementation of the selected better option.