Adapting federated radiomics models for radiation pneumonitis prediction in patients receiving thoracic radiotherapy with immunotherapy

Background and purpose: Radiation pneumonitis (RP) is one of the major dose-limiting toxicities of thoracic radiotherapy. Although multiple studies have attempted to predict RP, robust multicenter model development is often hindered by privacy regulations and data-transfer constraints, and many existing models are primarily derived from radiotherapy-alone populations, limiting applicability to contemporary regimens that incorporate immunotherapy. Therefore, this study aimed to develop an RP prediction model within a federated learning framework, incorporating sequential transfer learning strategies to enable separate risk assessment for radiotherapy patients with and without immunotherapy. Methods: Multicenter cohorts of lung cancer patients treated with definitive thoracic radiotherapy with or without immunotherapy were retrospectively collected and stratified by immunotherapy exposure. Radiomics features were extracted from whole-lung regions on pretreatment planning CT scans to construct RP prediction models. A federated learning framework was first applied to non-immunotherapy patients to learn common features of radiation pneumonitis without sharing raw data. The pretrained federated model was then sequentially transferred to immunotherapy treatment cohorts, with targeted fine-tuning to adapt to treatment specific RP patterns. Model performance was evaluated through internal validation and independent external validation, with SHAP analysis exploring feature importance differences across treatment settings. Results: A total of 610 patients were included from five multicenter cohorts. Using patients without immunotherapy for model development, the federated baseline model showed stable discrimination in external validation across non-immunotherapy cohorts (AUC = 0.77). When this baseline model was directly applied to the immunotherapy cohort without adaptation, performance dropped markedly (AUC = 0.43). After fine-tuning on immunotherapy data, the immunotherapy-adapted model achieved improved performance within the immunotherapy cohort (AUC = 0.76) and remained robust in an independent external immunotherapy validation cohort (AUC = 0.75). Feature attribution analysis showed a shift in model coefficients between immunotherapy-treated and non-immunotherapy patients. Conclusion: A federated modeling framework with treatment adaptation improves RP risk prediction across heterogeneous treatment settings under multicenter data constraints, particularly in immunotherapy-treated patients.