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Millions of diagnostic imaging studies are being performed each year in the EU, but only 100,000 of these are being used for research purposes. A new European project powered by artificial intelligence (AI) plans to make this data available to clinicians and researchers to advance cancer care, Luis Martí Bonmatí, Professor of Radiology at La Fe University and Polytechnic Hospital Valencia, Spain told delegates at ECR 2023.

Luis Martí Bonmatí, Professor of Radiology at La Fe University and Polytechnic Hospital Valencia

With nearly four billion diagnostic imaging studies performed each year worldwide (1), the amount of data that can be mined to improve outcomes in patients with cancer is dazzling. But to drill into this goldmine, systems must be made able to integrate and share the data. And these two steps have proved quite challenging so far, Martí Bonmatí explained in a dedicated session. “We need to integrate existing repositories and projects that have generated large databases and databanks,” he told the audience. “A major issue is how to create data sharing agreements in clinical environments to incite hospitals to share their data. They’re still reluctant with GDPR and the potential risk of patient re-identification.”

The degree of digital maturity varies from one hospital to another. But generally, it is very low. “We tend to believe that hospitals and clinical infrastructures are more technologically mature than they really are,” he said. ‘The data there is often unstructured, scattered and complicated to link. That causes many problems.”

Even when the data from radiology, pathological anatomy, radiotherapy and patient outcome has been put together, and a repository with 5,000 cases has been created, that data is often forgotten as soon as the project is finished.

A European solution to tackle data privacy

To reuse existing data and encourage researchers to produce new information, the EUCAIM consortium and the European Commission have recently launched the European Cancer Imaging Initiative (EUCAIM), an infrastructure deployment project that feeds on federated learning, an AI technique that tackles all issues related to data privacy and security.

The European Cancer Imaging Initiative (EUCAIM) is an infrastructure deployment project that feeds on federated learning to tackle all issues related to data privacy and security.

“Federated learning enables multiple actors to build a common and robust machine learning model without sharing data, allowing to address data privacy, security and access rights,” said Martí Bonmatí, who serves as EUCAIM scientific director. “We will address the fragmentation of existing cancer image repositories and establish a distributed Atlas of Cancer Imaging, with over 60 million anonymized cancer image data from over 100,000 patients.”

The data will be accessible to clinicians, researchers and innovators across the EU for the development and benchmarking of trustworthy AI tools. The project builds upon the results of the “AI for Health Imaging” (AI4HI) Network, which consists of five large EU-funded projects on big data and AI in cancer imaging – Chaimeleon, EuCanImage, ProCancer-I, Incisive and Primage.

EUCAIM brings together 76 partners from 14 EU member states, covering competences in cancer imaging and care; big data in medical imaging; FAIR data management; ethical and legal aspects of medical data; development and deployment of research infrastructures; AI and machine learning; and dissemination, communication and stakeholder outreach in biomedical imaging. “The idea is to bring the AI solutions to the data warehouses of the hospitals, so that they compute there and build the model,” he said.

EUCAIM is coordinated by the European Institute for Biomedical Imaging Research (EIBIR) in Vienna and it is the cornerstone of the European Commission’s European Cancer Imaging Initiative, a flagship of Europe’s Beating Cancer Plan. It is a first step towards putting some order into the hospitals, Martí Bonmatí believes. “Hospitals in Europe have to consolidate a data warehouse that allows them to use the data they generate every day to optimize hospital management and to encourage investment in data,” he said.

The development of AI is a consequence, he concluded. “You need to have your hospital well organized to know how to use the data. Having an organized warehouse is the basis.” eu/en/policies/cancer-imaging


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