A generalized heterogeneous federated model for identifying patients with postoperative progression of early-stage non-small cell lung cancer

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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Publication Details

Journal
Scientific Reports
Published
2025-12-01
DOI
https://doi.org/10.1038/s41598-025-30565-6
Primary Topic
Lung Cancer Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00
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article

A generalized heterogeneous federated model for identifying patients with postoperative progression of early-stage non-small cell lung cancer

Jun Xu, Yehang Chen, Qinggeng Jin, Fei Wu et al.
Scientific Reports
Lung Cancer Diagnosis and Treatment
article

A generalized heterogeneous federated model for identifying patients with postoperative progression of early-stage non-small cell lung cancer

Jun Xu, Yehang Chen, Qinggeng Jin, Fei Wu, Qiong Li, Wansheng Long, Xiangmeng Chen, Xiaojuan Chen, Zhaole Yu, Huan Lin, Bao Feng, Kunwei Li, Senliang Lu
article en

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.

Scientific ReportsVol. 16(1)
Guangxi University (CN), Sun Yat-sen University (CN), WuXi AppTec (China) (CN), Guangdong General Hospital (CN), Guilin University of Aerospace Technology (CN), Guilin University of Technology (CN), Fifth Affiliated Hospital of Sun Yat-sen University (CN), Sun Yat-sen University Cancer Center (CN), Jiangmen Central Hospital (CN), Guilin University of Electronic Technology (CN)
Openalex Percentile: Top 44%
Lung Cancer Diagnosis and Treatment
DOIOpen Access PDFOpenAlex
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