| Title: |
Personalized federated learning for predicting disability progression in multiple sclerosis using real-world routine clinical data |
| Authors: |
PIRMANI, Ashkan; DE BROUWER, Edward; Arany, Adam; Oldenhof, Martijn; Passemiers, Antoine; FAES, Axel; Kalincik, Tomas; Ozakbas, Serkan; Gouider, Riadh; Willekens, Barbara; Horakova, Dana; Havrdova, Eva Kubala; Patti, Francesco; Prat, Alexandre; Lugaresi, Alessandra; Tomassini, Valentina; Grammond, Pierre; Cartechini, Elisabetta; Roos, Izanne; Boz, Cavit; Alroughani, Raed; Amato, Maria Pia; Buzzard, Katherine; Lechner-Scott, Jeannette; Guimaraes, Joana; Solaro, Claudio; Gerlach, Oliver; Soysal, Aysun; Kuhle, Jens; Sanchez-Menoyo, Jose Luis; Spitaleri, Daniele; Csepany, Tunde; VAN WIJMEERSCH, Bart; Ampapa, Radek; Prevost, Julie; Khoury, Samia J.; Van Pesch, Vincent; John, Nevin; Maimone, Davide; Weinstock-Guttman, Bianca; Laureys, Guy; Mccombe, Pamela; Blanco, Yolanda; Altintas , Ayse; Al-Asmi, Abdullah; Garber, Justin; van der Walt, Anneke; Butzkueven, Helmut; de Gans, Koen; Rozsa, Csilla; Taylor, Bruce; Al-Harbi, Talal; Sas, Attila; Rajda, Cecilia; Gray, Orla; Decoo, Danny; Carroll, William M.; Kermode, Allan G.; Fabis-Pedrini, Marzena; Mason, Deborah; Perez-Sempere, Angel; Simu, Mihaela; Shuey, Neil; Singhal, Bhim; Cauchi, Marija; Hardy, Todd A.; Ramanathan, Sudarshini; Lalive, Patrice; Sirbu, Carmen-Adella; Hughes, Stella; Castillo Trivino, Tamara; PEETERS, Liesbet; Moreau, Yves |
| Contributors: |
Taylor, Bruce/0000-0003-2807-0070; PIRMANI, Ashkan; DE BROUWER, Edward; Arany, Adam; Oldenhof, Martijn; Passemiers, Antoine; FAES, Axel; Kalincik, Tomas; Ozakbas, Serkan; Gouider, Riadh; Willekens, Barbara; Horakova, Dana; Havrdova, Eva Kubala; Patti, Francesco; Prat, Alexandre; Lugaresi, Alessandra; Tomassini, Valentina; Grammond, Pierre; Cartechini, Elisabetta; Roos, Izanne; Boz, Cavit; Alroughani, Raed; Amato, Maria Pia; Buzzard, Katherine; Lechner-Scott, Jeannette; Guimaraes, Joana; Solaro, Claudio; Gerlach, Oliver; Soysal, Aysun; Kuhle, Jens; Sanchez-Menoyo, Jose Luis; Spitaleri, Daniele; Csepany, Tunde; VAN WIJMEERSCH, Bart; Ampapa, Radek; Prevost, Julie; Khoury, Samia J.; Van Pesch, Vincent; John, Nevin; Maimone, Davide; Weinstock-Guttman, Bianca; Laureys, Guy; Mccombe, Pamela; Blanco, Yolanda; Altintas , Ayse; Al-Asmi, Abdullah; Garber, Justin; van der Walt, Anneke; Butzkueven, Helmut; de Gans, Koen; Rozsa, Csilla; Taylor, Bruce; Al-Harbi, Talal; Sas, Attila; Rajda, Cecilia; Gray, Orla; Decoo, Danny; Carroll, William M.; Kermode, Allan G.; Fabis-Pedrini, Marzena; Mason, Deborah; Perez-Sempere, Angel; Simu, Mihaela; Shuey, Neil; Singhal, Bhim; Cauchi, Marija; Hardy, Todd A.; Ramanathan, Sudarshini; Lalive, Patrice; Sirbu, Carmen-Adella; Hughes, Stella; Castillo Trivino, Tamara; PEETERS, Liesbet; Moreau, Yves |
| Publisher Information: |
NATURE PORTFOLIO |
| Publication Year: |
2025 |
| Collection: |
Document Server@UHasselt (Universiteit Hasselt) |
| Description: |
Early prediction of disability progression in multiple sclerosis (MS) remains challenging despite its critical importance for therapeutic decision-making. We present the first systematic evaluation of personalized federated learning (PFL) for 2-year MS disability progression prediction, leveraging multi-center real-world data from over 26,000 patients. While conventional federated learning (FL) enables privacy-aware collaborative modeling, it remains vulnerable to institutional data heterogeneity. PFL overcomes this challenge by adapting shared models to local data distributions without compromising privacy. We evaluated two personalization strategies: a novel AdaptiveDualBranchNet architecture with selective parameter sharing, and personalized fine-tuning of global models, benchmarked against centralized and client-specific approaches. Baseline FL underperformed relative to personalized methods, whereas personalization significantly improved performance, with personalized FedProx and FedAVG achieving ROC-AUC scores of 0.8398 +/- 0.0019 and 0.8384 +/- 0.0014, respectively. These findings establish personalization as critical for scalable, privacy-aware clinical prediction models and highlight its potential to inform earlier intervention strategies in MS and beyond. ; Acknowledgments A.Pirmani., Y.M. and M.O. are funded by (1) VLAIO PM: Augmenting Therapeutic Effectiveness through Novel Analytics (HBC.2019.2528); Research Council KU Leuven: Symbiosis 4 (C14/22/125), Symbiosis3 (C14/ 18/092; CELSA—Active Learning (CELSA/21/019). Y.M., M.O., and A.Pirmani. are affiliated to Leuven.AI and received funding from the Flemish Government (AI Research Program, FWO SBO (S003422N), and ELIXIR Belgium (I002819N)). E.D.B. and A.Passiemers. was funded by a FWO-SB grant. R.G. received honoraria as consultant on scientific advisory boards or as speaker from Biogen, Celgene-BMS, Janssen, Merck, Novartis, Roche, Sanofi-Genzyme, Sandoz, Teva and Viatris. B.W. received consultancy and advisory board fees from Roche, Sanofi-Genzyme, ... |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| ISSN: |
40707601 |
| Relation: |
npj digital medicine, 8 (1) (Art N° 478); https://hdl.handle.net/1942/46540; 001536298500003 |
| DOI: |
10.1038/s41746-025-01788-8 |
| Availability: |
https://hdl.handle.net/1942/46540; https://doi.org/10.1038/s41746-025-01788-8 |
| Rights: |
The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/bync-nd/4.0/. ; info:eu-repo/semantics/openAccess |
| Accession Number: |
edsbas.B3B8343A |
| Database: |
BASE |