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Category

Life Sciences
Articles

Written by

Sibylle Pichot

Head of Communication — Resilience Care

Summary

Title

A Multicenter Real-World Database to Advance Clinical Research

March 16, 2026

3 minutes

The national uro-oncology cohort aims to establish a French reference for multicenter real-world data on urological cancers. Led by Prof. Stéphane Oudard, the ARTIC association, and Resilience Care, this large-scale project combines clinical excellence, technological innovation, and collaborative research to advance uro-oncology and accelerate medical breakthroughs.

A National Reference Tool to Support Innovation

Initiated by Prof. Stéphane Oudard (HEGP, APHP) and the non profit association ARTIC, this project aims to build a multicenter real-world database dedicated to urological cancers (prostate, bladder, kidney, etc.). Supported by Resilience Care, the program aims to recruit and monitor up to 5,000 patients annually across more than 30 French centers (university hospitals, cancer centers, public/private centers).

The main objective: map care practices nationwide and generate high-quality, structured, longitudinal data for academic and industrial research. The program will also identify prognostic and predictive factors (clinical, biological, genetic) to optimize treatments personalization, evaluate the health-economic impact of therapeutic and organizational innovations, and foster translational projects (biobanks, multi-omics analyses, AI, etc.).

The project builds on existing databases that will be expanded to cover a broader data scope and geographic network, ensuring national representation of practices, increasing statistical power, and enabling targeted studies by pathology, subtype, and treatment line.

The goal is to create a national reference tool that supports innovation in uro-oncology and accelerates knowledge transfer into clinical practice.

An Innovative Approach to Data Collection and Use

The initiative relies on four key pillars:

  • Clinical, biological, and Patient Reported Outcomes (PRO) data that enable detailed analyses by pathology, subtype, or treatment line. Collected data will cover demographics, genetics, histology, successive treatments, biological monitoring, progression events, and PRO (quality of life, symptomatic toxicities).
  • Natural Language Processing (NLP) to automatically extract and structure clinical data from Electronic Health Records. This technology reduces time-consuming manual data entry and eases the administrative burden on healthcare professionals.[1]
  • Scientific, technological, and regulatory excellence from the project leaders. ARTIC has 25 years of experience in clinical research and national database management. Resilience Care operates the third Health Data Warehouse authorized by CNIL to be operated by a private company in France, ensuring GDPR-compliant data use.
  • Collaborative governance that brings together a multidisciplinary Scientific Committee, composed of key opinion leaders in urology, oncology, radiotherapy, anatomic pathology, genomics, QoL, biostatistics, ethics & methodology—and a strategic Steering Committee. Together, they ensure scientific relevance and operational success.

Structured Collaborative Research in Uro-Oncology

Working with expert networks and leading uro-oncology centers in France, this project offers a secure, scalable, and interoperable framework for leveraging real-world data and accelerating medical innovations.

For academic centers, it provides easy access to a high-quality, comprehensive, structured, and current national database, along with opportunities to co-develop high-impact studies, publications, and multi-center research.

For industrial partners, the program enables secure access to robust real-world data (RWD) for evaluating treatments and devices, as well as conducting observational and health-economic studies within a controlled regulatory framework.

The launch of this national cohort represents a major opportunity to structure and advance collaborative, innovative research in uro-oncology, ultimately improving and personalizing patient care.

Source

[1] C. Vinot, C. Ferté, T. Gaboriaud, A. Minvielle-Sebastia, A. Dubois, R. Schwob, S. Mallah, M. Pajiep, J.M. Alliot, A. Ferreira, E. Pons-Tostivint, J. Mazieres, A. Yazigi. Automated extraction of temporalized tumor evolution from oncology EMRs using natural language processing. ESMO Real World Data and Digital Oncology, Volume 11, 2026, 100660, ISSN 2949-8201, https://doi.org/10.1016/j.esmorw.2025.100660.

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