A Machine Learning Model to Harmonize Volumetric Brain MRI Data for Quantitative Neuroradiologic Assessment of Alzheimer Disease.

Journal: Radiology. Artificial intelligence
Published Date: Jan 1, 2025

Abstract

Purpose To extend a previously developed machine learning algorithm for harmonizing brain volumetric data of individuals undergoing neuroradiologic assessment of Alzheimer disease not encountered during model training. Materials and Methods Neuroharmony is a recently developed method that uses image quality metrics as predictors to remove scanner-related effects in brain-volumetric data using random forest regression. To account for the interactions between Alzheimer disease pathology and image quality metrics during harmonization, the authors developed a multiclass extension of Neuroharmony for individuals with and without cognitive impairment. Cross-validation experiments were performed to benchmark performance against other available strategies using data from 20 864 participants with and without cognitive impairment, spanning 11 prospective and retrospective cohorts and 43 scanners. Evaluation metrics assessed the ability to remove scanner-related variations in brain volumes (marker concordance between scanner pairs) while retaining the ability to delineate different diagnostic groups (preserving disease-related signal). Results For each strategy, marker concordances between scanners were significantly better ( < .001) compared with preharmonized data. The proposed multiclass model achieved significantly higher concordance (mean, 0.75 ± 0.09 [SD]) than the Neuroharmony model trained on individuals without cognitive impairment (mean, 0.70 ± 0.11) and preserved disease-related signal (∆AUC [area under the receiver operating characteristic curve] = -0.006 ± 0.027) better than the Neuroharmony model trained on individuals with and without cognitive impairment that did not use the proposed extension (∆AUC = -0.091 ± 0.036). The marker concordance was better in scanners seen during training (concordance > 0.97) than unseen (concordance < 0.79), independent of cognitive status. Conclusion In a large-scale multicenter dataset, the proposed multiclass Neuroharmony model outperformed other available strategies for harmonizing brain volumetric data from unseen scanners in a clinical setting. Image Postprocessing, MR Imaging, Dementia, Random Forest Published under a CC BY 4.0 license See also commentary by Haller in this issue.

Authors

  • Damiano Archetti
    From the Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Via Pilastroni 4, Brescia 25125, Italy (D.A., A.R.); Alzheimer Centre Amsterdam, Neurology, Vrije Universiteit, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands (V.V., W.M.v.d.F., B.M.T.); Amsterdam Neuroscience, Neurodegeneration, Amsterdam, the Netherlands (V.V., W.M.v.d.F., B.M.T.); Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary (B.W., T.A., Z.V.); Biomatics and Applied Artificial Intelligence Institute, John von Neumann Faculty of Informatics, Óbuda University, Budapest, Hungary (B.W.); The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, Australia (P.B.); School of Psychology, University of Surrey, Guildford, United Kingdom (T.A.); Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France (S.D.); Department of Epidemiology and Data Science, Vrije Universiteit, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands (W.M.v.d.F.); Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands (F.B.); Queen Square Institute of Neurology, University College London, United Kingdom (F.B.); and UCL Hawkes Institute, Department of Medical Physics and Biomedical Engineering and Department of Computer Science, University College London, London, United Kingdom (F.B., D.C.A., A.A., N.P.O.).
  • Vikram Venkatraghavan
    From the Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Via Pilastroni 4, Brescia 25125, Italy (D.A., A.R.); Alzheimer Centre Amsterdam, Neurology, Vrije Universiteit, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands (V.V., W.M.v.d.F., B.M.T.); Amsterdam Neuroscience, Neurodegeneration, Amsterdam, the Netherlands (V.V., W.M.v.d.F., B.M.T.); Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary (B.W., T.A., Z.V.); Biomatics and Applied Artificial Intelligence Institute, John von Neumann Faculty of Informatics, Óbuda University, Budapest, Hungary (B.W.); The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, Australia (P.B.); School of Psychology, University of Surrey, Guildford, United Kingdom (T.A.); Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France (S.D.); Department of Epidemiology and Data Science, Vrije Universiteit, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands (W.M.v.d.F.); Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands (F.B.); Queen Square Institute of Neurology, University College London, United Kingdom (F.B.); and UCL Hawkes Institute, Department of Medical Physics and Biomedical Engineering and Department of Computer Science, University College London, London, United Kingdom (F.B., D.C.A., A.A., N.P.O.).
  • Béla Weiss
    Brain Imaging Centre, Research Centre for Natural Sciences, Budapest 1117, Hungary. Electronic address: weiss.bela@ttk.hu.
  • Pierrick Bourgeat
    CSIRO Health and Biosecurity, The Australian e-Health & Research Centre, Herston, QLD, Australia. Electronic address: pierrick.bourgeat@csiro.au.
  • Tibor Auer
    School of Psychology, University of Surrey, United Kingdom.
  • Zoltán Vidnyánszky
    Brain Imaging Centre, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok körútja 2., 1117 Budapest, Hungary.
  • Stanley Durrleman
    Inria, ARAMIS Project-team, F-75013, Paris, France; Institut du Cerveau et de la Moelle épinière, F-75013, Paris, France; Inserm, U1127, F-75013, Paris, France; CNRS, UMR 7225, F-75013, Paris, France; Sorbonne Université, F-75013, Paris, France.
  • Wiesje M van der Flier
    Afdeling Neurologie, Alzheimercentrum Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, locatie VUmc.
  • Frederik Barkhof
    MS Center Amsterdam, Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, the Netherlands.
  • Daniel C Alexander
    Centre for Medical Image Computing and Dept of Computer Science, University College London, Gower Street, London WC1E 6BT, UK.
  • Andre Altmann
    Centre for Medical Image Computing, University College London, London, UK.
  • Alberto Redolfi
    Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
  • Betty M Tijms
    From the Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Via Pilastroni 4, Brescia 25125, Italy (D.A., A.R.); Alzheimer Centre Amsterdam, Neurology, Vrije Universiteit, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands (V.V., W.M.v.d.F., B.M.T.); Amsterdam Neuroscience, Neurodegeneration, Amsterdam, the Netherlands (V.V., W.M.v.d.F., B.M.T.); Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary (B.W., T.A., Z.V.); Biomatics and Applied Artificial Intelligence Institute, John von Neumann Faculty of Informatics, Óbuda University, Budapest, Hungary (B.W.); The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, Australia (P.B.); School of Psychology, University of Surrey, Guildford, United Kingdom (T.A.); Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France (S.D.); Department of Epidemiology and Data Science, Vrije Universiteit, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands (W.M.v.d.F.); Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands (F.B.); Queen Square Institute of Neurology, University College London, United Kingdom (F.B.); and UCL Hawkes Institute, Department of Medical Physics and Biomedical Engineering and Department of Computer Science, University College London, London, United Kingdom (F.B., D.C.A., A.A., N.P.O.).
  • Neil P Oxtoby
    UCL Centre for Medical Image Computing and Department of Computer Science, University College London, Gower Street, London, UK.

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