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.
Highlights
The use of Artificial Intelligence (AI) on health data is generating promising tools to assist clinicians in cancer management, as an increasing number of health imaging-based AI approaches are proving to have vast potential to become useful clinical tools in different areas of application [1]
An advisory board consisting of a recognized group of experts in the fields of oncology and AI applied to cancer management has been designated to give general advice and guidance to the consortium
Open Challenges will be organized promoting other world-class developers to use CHAIMELEON resources to train their own models. This project will follow a methodological approach of continuous learning, allowing a smooth update of the models including new data annotations and training to progressively improve performance over time
Summary
The use of Artificial Intelligence (AI) on health data is generating promising tools to assist clinicians in cancer management, as an increasing number of health imaging-based AI approaches are proving to have vast potential to become useful clinical tools in different areas of application [1] These include recurrence and survival prediction using multidimensional heterogeneous data [2] prediction of tumor molecular features and association with tumor spread [3, 4], stratification of patients based on risk [5], and prediction of treatment response [6] among many others. Despite these major advancements, the development of imaging-based AI tools relies on the availability of large, qualitycontrolled datasets [7], which currently still remains a major challenge. The need for the creation of a fully FAIR (Findable, Accessible, Interoperable, Reusable), GDPR compliant, European imaging repository still stands [18]
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