This partnership is a significant step forward in the fight against cancer. The project will provide a central hub and a federated infrastructure that will link EU-level and national initiatives, hospital networks, and research repositories with cancer images data to help improve early cancer detection and treatment options.
Quibim will adapt its platform to explore and visualize the imaging and clinical data included in the repository, analyze them, and extract predictive models of patient outcomes.
Clinicians, researchers and innovators will have cross-border access to an interoperable, privacy-preserving and secure infrastructure for federated, distributed analysis of cancer imaging data.
Valencia, January 24, 2023 – Quibim, a leading medical imaging technology partner, joins the European Commission’s European Federation for Cancer Images (EUCAIM) project directing major tasks focused on governance and implementation of the Central Hub and data federation and interoperability. This first-of-its-kind European ‘pan-cancer’ project aims to create an easy-access to cancer images for clinicians, researchers and innovators to streamline cancer diagnosis, treatment and care.
The infrastructure will be populated by observational studies from hospitals (21 clinical sites in 12 EU countries), include clinical images and link with pathology, molecular and laboratory data and will be expanded to at least 30 distributed data providers from 15 countries by the end of the 4-year project.
Quibim is strategically positioned in EUCAIM as the first company in funding resources, leveraging its experience implementing QP-Discovery®, their imaging and multi-omics platform with cutting-edge technology for transforming imaging data into actionable predictions.
The company will adapt its platform and provide the marketplace to meet the requirements related to the analysis, annotation and exploitation of data and third-party data transfer services through QP-Discovery®. Quibim will lead the EUCAIM Data Preprocessing tools and services contributing to the development of imaging quality control and curation.
In addition, Quibim will be in charge of the image and feature harmonization pipelines to guarantee the minimization of batch effects on the datasets used to train models in the EUCAIM infrastructure, providing tools for the harmonization of feature-based datasets.
This project aligns with the company’s primary goal: to turn imaging into a catalyzer of precision medicine. Quibim is a crucial ally in this initiative that aims to promote innovation and the deployment of digital technologies in the treatment and care of cancer to achieve more accurate and faster clinical decision-making, diagnosis, treatment and predictive medicine for cancer patients.
EUCAIM: Europe’s most prominent medical image biobank
The EUCAIM project is a pan-cancer initiative that analyzes common elements between different types of tumors to develop more effective cancer therapies. The project, which has 76 partners, aims to create a digital infrastructure for storing and analyzing cancer images without any identifying patient data. Furthermore, it’s the cornerstone of the European Commission initiated European Cancer Imaging Initiative, a flagship of the Europe’s Beating Cancer Plan (EBCP), which aims to foster innovation and deployment of digital technologies in cancer treatment and care, to achieve more precise and faster clinical decision-making, diagnostics, treatments and predictive medicine for cancer patients.
The project brings together clinical data providers, researchers, research infrastructures and industry partners with innovative solutions that address the challenges of implementing such a cancer imaging infrastructure. The infrastructure will feature complex and simple oncological anomalies, anonymize the collected images (with over 60 million anonymized cancer image data), and include data from more than 100,000 patients.
EUCAIM will provide access to health professionals, researchers, and innovators worldwide, while prioritizing data privacy and compliance with the General Data Protection Regulation (GDPR). European laws will govern the infrastructure and consider different countries’ unique data management regulations. Quibim, with its extensive experience in data de-identification, will also provide tools for image de-identification to guarantee compliance with GDPR and for image annotation by means of segmentation masks.
The project builds upon the results of the work of the “AI for Health Imaging” (AI4HI) Network which consists of 5 large EU-funded projects on big data and Artificial Intelligence in cancer imaging: Chaimeleon, EuCanImage, ProCancer-I, Incisive and Primage.
“As a company, we seek to leverage the power of medical imaging to improve diagnosis of cancer and prediction of patient outcomes. We offer solutions that analyze these images in depth, performing ‘virtual’ and non-invasive biopsies, extracting critical information to diagnose diseases,” points out Angel Alberich-Bayarri, CEO of Quibim. “By participating in the EUCAIM consortium, we seek to provide universal access to this information and make precision medicine available to different healthcare organizations” he concludes.
In addition, the project plans to implement at least 50 artificial intelligence (AI) algorithms, AI tools and clinical prediction models for researchers within the infrastructure by the end of the project. These AI tools can detect diseases before they become evident on radiological images, providing early detection and improving the chances of successful treatment. One example of these tools is Quibim’s QP Prostate® and QP Brain® solutions, which can detect prostate and brain diseases by comparing new images with similar images from a database of known diagnoses.
Quibim, through its imaging biomarker panels, can analyze the mechanisms of diseases such as cancer, improve drug programs and monitor treatment efficacy. Using a whole-body analysis approach for each patient, Quibim is driving tissue-agnostic precision medicine, regardless of the type of cancer or where in the body it originated.
This work was co-funded by the European Union under Grant Agreement number 1011100633. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.