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Published on: June 7, 2015
Center-specific Federated Learning for Radiation Pneumonitis: A Cross-Center Adaptive Alternating Framework
Meng Yan1, Zhixiang Wang2, Liyu Ning3
1Department of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Zhejiang Key Laboratory of Particle Radiotherapy Equipment, Hangzhou, China; Department of Radiation Oncology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, China; Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands.
Purpose:
Accurate prediction of symptomatic radiation pneumonitis (RP) is critical for radiation therapy, however, the generalization of deep learning models is hindered by restricted access to multicenter data. Although federated learning (FL) bypasses data sharing restrictions, standard FL algorithms underperform on highly heterogeneous clinical data across institutions. Therefore, this study aims to evaluate the clinical feasibility of a center-specific FL approach.
Methods And Materials:
We evaluated the Federated Cross-Center Adaptive Alternating Model (FCAAM), a tailored framework designed to decouple globally transferable representations from the center-specific adaptations. The framework uses a dynamic weighting mechanism to handle data heterogeneity and uses differential privacy for enhanced security. The proposed FCAAM was evaluated for the prediction of RP using planning computed tomography and dose images on a diverse cohort of 1238 patients from 4 data sets representing real-world temporal and spatial shifts. Its performance was compared with single-center model, centralized model, and standard federated average model.
Results:
FCAAM demonstrated improved cross-center performance and consistent robustness compared to baseline. It achieved a stable area under the curve across all 4 data set test sets (0.71-0.77), outperforming the single-center models (all area under the curves < 0.70) and federated averaging. FCAAM's performance was comparable to the centralized model and showed a relative improvement in sensitivity to small-sized data sets. Interpretability analysis confirmed that FCAAM learned clinically relevant features, and a web platform demonstrated the practical feasibility of applying FCAAM for multicenter collaboration.
Conclusions:
FCAAM provides a privacy-preserving, robust and interpretable solution for multicenter RP prediction. This center-specific strategy shows potential to enhance clinical decision-making and reduce cross-center performance gaps, supporting safer personalized radiation therapy.
