Federated foundation models for multi-center CT staging of pancreatic cancer

Abstract

Collaborators: University Medical Center Göttingen, Charité – Universitätsmedizin Berlin, Peter MacCallum Cancer Centre

This project addresses preoperative staging of pancreatic ductal adenocarcinoma (PDAC), where lymph node metastasis is a major prognostic factor but CT-based assessment suffers from low sensitivity and high inter-observer variability. Together with partners across three German centers, we established a retrospective multi-center cohort of 546 patients to enable evaluation under real-world heterogeneity while preserving data privacy. Methodologically, we combined CT foundation model transfer learning (pre-training on 148,000 volumetric CT scans) with heterogeneity-aware federated optimization. The resulting system improved federated performance by +12.6% balanced accuracy over FedAvg and recovered most of the centralized reference performance (centralized BA 0.601; DOR 3.45).

Perspective

The project establishes a sustainable multi-center collaboration for privacy-preserving oncologic imaging in Germany. Ongoing work extends the framework toward multimodal integration and more robust harmonization strategies, and a joint DFG proposal with the clinical partners has been submitted to further advance federated foundation models for PDAC staging and translation.