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Center-specific Federated Learning for Radiation Pneumonitis:A Cross-Center Adaptive Alternating Framework
Meng Yan1, Zhixiang Wang2, Liyu Ning3
1Department of Radiation Oncology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China; Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands.
A new federated learning approach (FCAAM) improves radiation pneumonitis (RP) prediction across institutions. This privacy-preserving method enhances clinical decision-making for safer radiotherapy by overcoming data heterogeneity challenges.
Area of Science:
- Medical Physics
- Artificial Intelligence in Medicine
- Radiotherapy Oncology
Background:
- Accurate prediction of symptomatic radiation pneumonitis (RP) is crucial for radiotherapy planning.
- Deep learning model generalization is limited by restricted multi-center data access.
- Standard federated learning (FL) algorithms struggle with heterogeneous clinical data.
Purpose of the Study:
- To evaluate the clinical feasibility of a center-specific federated learning approach for RP prediction.
- To assess the performance of the Federated Cross-Center Adaptive Alternating Model (FCAAM) in a multi-center setting.
- To address the limitations of standard FL in handling data heterogeneity and privacy concerns.
Main Methods:
- Evaluation of FCAAM, a framework decoupling global representations from center-specific adaptations.
- Utilized dynamic weighting for data heterogeneity and differential privacy for security.
- Tested FCAAM on 1,238 patients across four diverse datasets for RP prediction using CT and dose images.
Main Results:
- FCAAM demonstrated superior cross-center performance and robustness compared to single-center and FedAvg models.
- Achieved a stable AUC of 0.71-0.77 across test sets, outperforming baseline models.
- FCAAM's performance was comparable to centralized models and showed improved sensitivity on smaller datasets.
Conclusions:
- FCAAM offers a privacy-preserving, robust, and interpretable solution for multi-center RP prediction.
- This center-specific strategy can enhance clinical decision-making and reduce performance disparities.
- The approach supports safer, personalized radiotherapy through improved multi-center collaboration.
