CHAIMELEON Project: Creation of a Pan-European Repository of Health Imaging Data for the Development of AI-Powered Cancer Management Tools.
CHAIMELEON Project: Creation of a Pan-European Repository of Health Imaging Data for the Development of AI-Powered Cancer Management Tools.
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DOI：
10.3389/fonc.2022.742701
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发表时间：
2022
影响因子：
4.7
通讯作者：
Alberich-Bayarri Á
中科院分区：
文献类型：
--
作者：
Bonmatí LM;Miguel A;Suárez A;Aznar M;Beregi JP;Fournier L;Neri E;Laghi A;França M;Sardanelli F;Penzkofer T;Lambin P;Blanquer I;Menzel MI;Seymour K;Figueiras S;Krischak K;Martínez R;Mirsky Y;Yang G;Alberich-Bayarri Á
chaimelon项目旨在建立一个泛欧健康成像数据、工具和方法储存库，旨在为未来的癌症管理人工智能实验制定标准并提供资源。该项目为期4年，由欧盟资助，致力于生物医学成像、人工智能和癌症治疗领域一些最雄心勃勃的研究，解决目前全球患病率最高的四种癌症：肺癌、乳腺癌、前列腺癌和结直肠癌。为了实现这一点，临床合作伙伴和外部合作者将用多模态（MR， CT, PET/CT）成像和相关临床数据填充存储库。随后，人工智能开发人员将启用多模态分析数据引擎，促进存储在存储库中的信息的解释、提取和利用。人工智能管道的开发和实施将推动数据去识别、管理、注释、完整性保护和图像协调的自动化。到项目结束时，存储库作为促进人工智能实验的工具的可用性和性能将在技术上得到验证，包括由世界级的欧洲人工智能开发人员参与人工智能社区开放挑战的验证子阶段。在成功验证存储库后，一组选定的人工智能工具将在由合作医院的主要专家协调的观察性临床研究中进行早期计算机验证。将对工具的性能进行评估，包括针对一些目前最重要的癌症临床终点的标志性临床决策的外部独立验证。该项目汇集了18个欧洲合作伙伴，包括医院、大学、研发中心和私营研究公司，构成了肿瘤领域基础设施、生物银行、人工智能/硅实验和云计算技术的生态系统。
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.
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