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. 2022 Feb 24:12:742701.
doi: 10.3389/fonc.2022.742701. eCollection 2022.

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

Luis Martí Bonmatí  1 , Ana Miguel  1 , Amelia Suárez  2 , Mario Aznar  2 , Jean Paul Beregi  3 , Laure Fournier  3 , Emanuele Neri  4 , Andrea Laghi  5 , Manuela França  6 , Francesco Sardanelli  7 , Tobias Penzkofer  8 , Phillipe Lambin  9 , Ignacio Blanquer  10 , Marion I Menzel  11   12 , Karine Seymour  13 , Sergio Figueiras  14 , Katharina Krischak  15 , Ricard Martínez  16 , Yisroel Mirsky  17 , Guang Yang  18 , Ángel Alberich-Bayarri  19
Affiliations

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

Luis Martí Bonmatí et al. Front Oncol. .

Abstract

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.

Keywords: artificial intelligence-AI; cancer imaging; cancer management; image harmonization; quantitative imaging biomarkers; radiology.

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Conflict of interest statement

Authors AS, MA were employed by Matical Innovation SL. MM was employed by GE Healthcare. KS was employed by Medexprim. SF way employed by Bahia Software S.L.U. AA-B way employed by QUIBIM SL. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer KL declared a past co-authorship with several of the authors LM, PL to the handling editor.

Figures

Figure 1
Figure 1
Project overview.
Figure 2
Figure 2
Distributed architecture and IT infrastructure. (A) and (B) refer to two different types of hospitals based on their capacity to curate, complete, and anonymize data prior to their ingestion into the central repository. (A) Data processing is done on site. (B) Data processing is done via an intermediation platform.
Figure 3
Figure 3
High-level architecture of the central repository and technologies used.
See this image and copyright information in PMC

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