ArticleScientific reports2025
A generalized heterogeneous federated model for identifying patients with postoperative progression of early-stage non-small cell lung cancer.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Artificial intelligence in thoracic surgery: a narrative review of clinical advances and applications in 2025.Journal of thoracic disease · 2026Review
- Explainable Split-Learning-Based Framework for Accurate Pulmonary Nodule Classification.Bioengineering (Basel, Switzerland) · 2026Article
- Application of deep learning in prognostic prediction of non-small cell lung cancer.Frontiers in cell and developmental biology · 2026Review
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Authors and funding
13 authors.
Funding
Abstract
In the postoperative management of early-stage non-small cell lung cancer (NSCLC), accurate identification of patients at high risk of progression is essential for developing personalized follow-up schedules and adjuvant treatment strategies. However, in multi-center settings, model and data heterogeneity limit the flexibility and generalizability of traditional federated learning. To address this, we propose a heterogeneous federated learning model (HFLM) that enables centers to adopt different model architectures while using a robust feature transfer strategy to alleviate the impact of heterogeneous data. Using CT images from 892 early-stage NSCLC patients across four medical institutions, HFLM achieved AUCs of 0.863 (95% CI, 0.8072–0.9192), 0.837 (95% CI, 0.7204–0.9530), 0.846 (95% CI, 0.7349–0.9564), and 0.847 (95% CI, 0.6971–0.9963). Cross-validation and stratified analyses further confirm its strong generalization and stability across centers.
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Registered trials
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