Enabling AI-Based Prediction of Neoadjuvant Treatment Response: A FAIR Multimodal Dataset Within EUCAIM.
Authors
González López M,Álvarez Pérez RM,Rey Garduño F,Jiménez-Hoyuela García JM,Castell Monsalve FJ,Parra Calderón CL
Affiliations (3)
Affiliations (3)
- Computational Health Informatics Group. IBiS/HUVR/CSIC/US.
- Molecular Imaging, Theragnosis and Precision Nuclear Medicine Group. IBiS/Department of Nuclear Medicine. HUVR/CSIC/US.
- Department of Radiology. HUVR.
Abstract
This work presents a FAIR-compliant, multimodal PET-CT and clinical dataset developed within the EUCAIM framework to support future AI-based prediction of response to NST in LABC patients. A real-world dataset was curated, harmonized, and integrated into the EUCAIM CDM within a federated infrastructure. While predictive modeling is ongoing, this study focuses on data preparation and infrastructure, key bottlenecks for AI development, demonstrating the feasibility of integrating local hospital data into a European federated ecosystem to enable future multicentric AI applications.
Topics
Neoadjuvant TherapyPositron Emission Tomography Computed TomographyBreast NeoplasmsArtificial IntelligenceJournal Article