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.