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Triple-negative breast cancer (TNBC) is an aggressive subtype with poor prognosis and limited treatments, for which accurate pre-operative prediction is essential for guiding therapy. While multiparametric MRI is highly sensitive, its use in multi-center AI workflows is hampered by inter-scanner variability. This study explores Federated Learning with radiomic features from DCE-MRI, and assesses the role of image standardization in improving TNBC classification performance. Data were split across 5 virtual clients to simulate hospitals, each training locally within a federated MLP framework. Results show that image standardization markedly improves TNBC classification, highlighting the role of preprocessing in federated AI pipelines.

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Federated Learning for Pre-operative Detection of Triple-Negative Breast Cancer from Multiparametric MRI: Preliminary Results

  • 会议论文

  • 发表日期：

    2026年

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  • Giorgio De Nunzio,
  • Luana Conte,
  • Vincenzo Taormina,
  • Alessandro Crisci,
  • Giovanni Vincenzo Donatiello,
  • Rocco Rizzo,
  • Donato Cascio

摘要

Triple-negative breast cancer (TNBC) is an aggressive subtype with poor prognosis and limited treatments, for which accurate pre-operative prediction is essential for guiding therapy. While multiparametric MRI is highly sensitive, its use in multi-center AI workflows is hampered by inter-scanner variability. This study explores Federated Learning with radiomic features from DCE-MRI, and assesses the role of image standardization in improving TNBC classification performance. Data were split across 5 virtual clients to simulate hospitals, each training locally within a federated MLP framework. Results show that image standardization markedly improves TNBC classification, highlighting the role of preprocessing in federated AI pipelines.