Unipi Team Leader: Prof. Emanuele Neri, Dipartimento di Ricerca Traslazionale e delle Nuove Tecnologie in Medicina e Chirurgia
The goal of the EU-funded EuCanImage project is to build a secure, large-scale European cancer imaging platform with capabilities that will advance the application of artificial intelligence (AI) in oncology. The platform will be populated with new data from 25 000 subjects, enabling the investigation of unmet clinical needs, such as the detection of small liver lesions and metastases of colorectal cancer or the evaluation of the molecular subtypes of breast tumours. The platform will be cross-linked to biological and health repositories through the European Genome–phenome Archive, allowing the development of multiscale AI solutions that integrate organ-level, molecular and clinical predictors into novel patient-specific cancer fingerprints. The consortium will build upon several European initiatives in data sharing for personalised medicine research.
Objective
The goal of EuCanImage is to build a highly secure, federated and large-scale European cancer imaging platform, with capabilities that will greatly enhance the potential of artificial intelligence (AI) in oncology. Firstly, the EuCanImage platform will be populated with a completely new data resource totaling over 25,000 single subjects, which will allow to investigate unmet clinical needs like never before, such as for the detection of small liver lesions and metastases of colorectal cancer, or for estimating molecular subtypes of breast tumours and pathological complete response. Secondly, the cancer imaging platform, built by leveraging the well-established Euro-Bioimaging infrastructure, will be cross-linked to biological and health repositories through the European Genome-phenome Archive, allowing to develop multi-scale AI solutions that integrate organ-level, molecular and other clinical predictors into dense patient-specific cancer fingerprints.
To deliver this platform, the consortium will build upon several key European initiatives in data sharing for personalised medicine research, including EUCANCAn (cancer genomics and health data sharing), euCanSHare (cardiac imaging and omics data sharing) and EUCAN-Connect (federated data analytics). Furthermore, to foster international cooperation and leverage existing success stories, the consortium comprises the coordinators of The Cancer Imaging Archive (TCIA), the US cancer imaging repository funded by the National Cancer Institute. This will allow EuCanImage to leverage a unique 10-year long experience in cancer imaging storage, anonymisation, curation and management. Finally, a close collaboration between world renown clinical, radiomics, AI and legal experts within the consortium and beyond will establish well-needed guidelines for AI development and validation named FUTURE, for delivering Fair, Universal, Traceable, Usable, Robust and Explainable decision support systems for future cancer care.
Coordinator
UNIVERSITAT DE BARCELONA, Spain
Participants
- UNIVERSITEIT MAASTRICHT, Netherlands
- ERASMUS UNIVERSITAIR MEDISCH CENTRUM ROTTERDAM, Netherlands
- BARCELONA SUPERCOMPUTING CENTER - CENTRO NACIONAL DE SUPERCOMPUTACION, Spain
- FUNDACIO CENTRE DE REGULACIO GENOMICA, Spain
- University of Arkansas, United States
- BIOBANKS AND BIOMOLECULAR RESOURCES RESEARCH INFRASTRUCTURE CONSORTIUM (BBMRI-ERIC), Austria
- UNIVERSIDAD DEL PAIS VASCO/ EUSKAL HERRIKO UNIBERTSITATEA, Spain
- LYNKEUS, Italy
- COLLECTIVE MINDS RADIOLOGY AB, Sweden
- ONCORADIOMICS, Belgium
- SIEMENS HEALTHCARE GMBH, Germany
- EIBIR GEMEINNUTZIGE GMBH ZUR FORDERUNG DER ERFORSCHUNG DER BIOMEDIZINISCHEN BILDGEBUNG, Austria
- EUROPEAN SOCIETY OF ONCOLOGIC IMAGING - ESOI EUROPAISCHE GESELLSCHAFTFUR ONKOLOGISCHE BILDEBUNG, Austria
- EUROPEAN ASSOCIATION FOR CANCER RESSEARCH, United Kingdom
- UNIVERSITA DI PISA, Italy
- FUNDACIO CLINIC PER A LA RECERCA BIOMEDICA, Spain
- UMEA UNIVERSITET, Sweden
- GDANSKI UNIWERSYTET MEDYCZNY, Poland
- LIETUVOS SVEIKATOS MOKSLU UNIVERSITETO LIGONINE KAUNO KLINIKOS, Lithuania
Start date 1 October 2020
End date 30 September 2024
Project cost € 9 994 358,50
Project funding € 9 994 358,50
Unipi quota € 481 250
Call title H2020-SC1-FA-DTS-2019-1
Funding Scheme RIA - Research and Innovation action
Unipi role Partner