Privacy-Preserving Linkage of Distributed Biological, Clinical, and Imaging Data Supporting Artificial Intelligence in Pediatric Oncology.

Autores de IIS La Fe

• Blanca Martínez De Las Heras

  Autor

• Adela Cañete Nieto

  Autor

• Ana María Jimenez Pastor

  Autor

• 

  Luis Martí Bonmatí

  Autor

Participantes ajenos a IIS La Fe

• Hayn D
• Baumgartner M
• Kreiner K
• Sandner E
• Jammerbund B
• Schalling M
• Poetschger U
• Duester V
• Ladenstein R
• Schreier G

Grupos

• Investigacion Biomédica en Imagen (GIBI 2-30)

• Investigación Clínica y Traslacional en Cáncer

Abstract

BACKGROUND: Cancer remains the leading cause of disease-related mortality in children over the age of one in Europe, with over 35,000 new pediatric cases and more than 6,000 deaths annually. Due to the rarity of pediatric cancers, clinical trial protocols often substitute for formal treatment guidelines, resulting in many children being enrolled in multiple trials, with biological samples and genomic data stored in various biobanks. Data collection in pediatric oncology is challenging, with sparse data acquired over extended periods, underscoring the need for optimal utilization of all available information through linked, privacy-preserving datasets. METHODS: Here, we report the development of a distributed, privacy-preserving data infrastructure for the PRIMAGE project, a European initiative aimed at supporting artificial intelligence (AI)-driven image analysis for pediatric cancer prognostics. The infrastructure leverages the European Patient Identity (EUPID) Services for Privacy-Preserving Record Linkage, enabling pseudonymized data integration across clinical, biological, and imaging sources. The system incorporates EUPID's hashing and phonetic matching protocols to pseudonymize patient identifiers and link distributed datasets, facilitating secondary data use in compliance with the General Data Protection Regulation. RESULTS: Data from over 700 neuroblastoma patients from European trials and hospitals were linked and uploaded to the PRIMAGE platform, where AI models predict clinical outcomes. CONCLUSION: This infrastructure successfully facilitated AI model development, advancing pediatric oncology research, and offering a scalable framework for future European health data initiatives, such as the European Health Data Space.

The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).