Optimizing Federated Learning Configurations for MRI Prostate Segmentation and Cancer Detection: A Simulation Study.

Journal: Radiology. Artificial intelligence
Published Date: Jul 30, 2025

Abstract

Purpose To develop and optimize a federated learning (FL) framework across multiple clients for biparametric MRI prostate segmentation and clinically significant prostate cancer (csPCa) detection. Materials and Methods A retrospective study was conducted using Flower FL to train a nnU-Net-based architecture for MRI prostate segmentation and csPCa detection, using data collected from January 2010 to August 2021. Model development included training and optimizing local epochs, federated rounds, and aggregation strategies for FL-based prostate segmentation on T2-weighted MRIs (four clients, 1294 patients) and csPCa detection using biparametric MRIs (three clients, 1440 patients). Performance was evaluated on independent test sets using the Dice score for segmentation and the Prostate Imaging: Cancer Artificial Intelligence (PI-CAI) score, defined as the average of the area under the receiver operating characteristic curve and average precision, for csPCa detection. values for performance differences were calculated using permutation testing. Results The FL configurations were independently optimized for both tasks, showing improved performance at 1 epoch 300 rounds using FedMedian for prostate segmentation and 5 epochs 200 rounds using FedAdagrad, for csPCa detection. Compared with the average performance of the clients, the optimized FL model significantly improved performance in prostate segmentation (Dice score increase from 0.73 ± 0.06 to 0.88 ± 0.03; ≤ .01) and csPCa detection (PI-CAI score increase from 0.63 ± 0.07 to 0.74 ± 0.06; ≤ .01) on the independent test set. The optimized FL model showed higher lesion detection performance compared with the FL-baseline model (PICAI score increase from 0.72 ± 0.06 to 0.74 ± 0.06; ≤ .01), but no evidence of a difference was observed for prostate segmentation (Dice scores, 0.87 ± 0.03 vs 0.88 ± 03; > .05). Conclusion FL enhanced the performance and generalizability of MRI prostate segmentation and csPCa detection compared with local models, and optimizing its configuration further improved lesion detection performance. ©RSNA, 2025.

Authors

  • Ashkan Moradi
    Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Olav Kyrres gate 9, 7030 Trondheim, Norway.
  • Fadila Zerka
    The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.
  • Joeran Sander Bosma
    Diagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands.
  • Mohammed R S Sunoqrot
    Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Olav Kyrres gate 9, 7030 Trondheim, Norway.
  • Bendik S Abrahamsen
    Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Olav Kyrres gate 9, 7030 Trondheim, Norway.
  • Derya Yakar
    Department of Radiology, Medical Imaging Center, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. Electronic address: d.yakar@umcg.nl.
  • Jeroen Geerdink
    Department of Health & Information Technology, Ziekenhuisgroep Twente, Almelo, The Netherlands.
  • Henkjan Huisman
    Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Tone Frost Bathen
    Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Olav Kyrres gate 9, 7030 Trondheim, Norway.
  • Mattijs Elschot
    Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Tronheim, Norway; Department of Radiology and Nuclear Medicine, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.

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