1. CHAIMELEON Project: Creation of a Pan-European Repository of Health Imaging Data for the Development of AI-Powered Cancer Management Tools
- Author
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Luis Martí Bonmatí, Ana Miguel, Amelia Suárez, Mario Aznar, Jean Paul Beregi, Laure Fournier, Emanuele Neri, Andrea Laghi, Manuela França, Francesco Sardanelli, Tobias Penzkofer, Phillipe Lambin, Ignacio Blanquer, Marion I. Menzel, Karine Seymour, Sergio Figueiras, Katharina Krischak, Ricard Martínez, Yisroel Mirsky, Guang Yang, Ángel Alberich-Bayarri, British Heart Foundation, Commission of the European Communities, European Research Council Horizon 2020, Innovative Medicines Initiative, Boehringer Ingelheim Ltd, Medical Research Council (MRC), Precision Medicine, and RS: GROW - R3 - Innovative Cancer Diagnostics & Therapy
- Subjects
Imatges tridimensionals en medicina ,Cancer Research ,INFORMATION ,ARTIFICIAL-INTELLIGENCE ,Intel·ligència artificial ,Artificial intelligence (AI) ,radiology ,ddc ,cancer imaging ,cancer management ,ComputingMethodologies_PATTERNRECOGNITION ,Oncology ,image harmonization ,artificial intelligence-AI, cancer imaging, cancer management, image harmonization, quantitative imaging biomarkers, radiology ,CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL ,artificial intelligence-AI ,quantitative imaging biomarkers ,1112 Oncology and Carcinogenesis ,Càncer - Abstract
[EN] 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. Subsequently, AI developers will enable a multimodal analytical data engine facilitating the interpretation, extraction and exploitation of the information stored at the repository. The development and implementation of AI-powered pipelines will enable advancement towards automating data deidentification, curation, annotation, integrity securing and image harmonization. By the end of the project, the usability and performance of the repository as a tool fostering AI experimentation will be technically validated, including a validation subphase by world-class European AI developers, participating in Open Challenges to the AI Community. Upon successful validation of the repository, a set of selected AI tools will undergo early in-silico validation in observational clinical studies coordinated by leading experts in the partner hospitals. Tool performance will be assessed, including external independent validation on hallmark clinical decisions in response to some of the currently most important clinical end points in cancer. The project brings together a consortium of 18 European partners including hospitals, universities, R & D centers and private research companies, constituting an ecosystem of infrastructures, biobanks, AI/in-silico experimentation and cloud computing technologies in oncology., CHAIMELEON has been funded by as a Horizon 2020 project (RIA, topic DT-TDS-05-2020-AI for Health Imaging; call SC1-FA-DTS-2019-1, under Grant Agreement No. 952172)
- Published
- 2022