Traditional Machine Learning Methods versus Deep Learning for Meningioma Classification, Grading, Outcome Prediction, and Segmentation: A Systematic Review and Meta-Analysis.

Journal: World neurosurgery
Published Date: Aug 12, 2023

Abstract

BACKGROUND: Meningiomas are common intracranial tumors. Machine learning (ML) algorithms are emerging to improve accuracy in 4 primary domains: classification, grading, outcome prediction, and segmentation. Such algorithms include both traditional approaches that rely on hand-crafted features and deep learning (DL) techniques that utilize automatic feature extraction. The aim of this study was to evaluate the performance of published traditional ML versus DL algorithms in classification, grading, outcome prediction, and segmentation of meningiomas.

Authors

  • Krish M Maniar
    Department of Neurosurgery, Computational Neurosciences Outcomes Center (CNOC), Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, United States.
  • Philipp LassarĂ©n
    Department of Neurosurgery, Computational Neurosciences Outcomes Center (CNOC), Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, United States; Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
  • Aakanksha Rana
    From the Computational Neuroscience Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 60 Fenwood Rd, Boston, MA 02115 (C.M.W.G., A.M.L., A.H.D., A.W.S., O.A., M.W.G., T.R.S., H.A.Z., A.R., A.B.); and Spine Research Department, Department of Neurosurgery, Leiden University Medical Center, Leiden, the Netherlands (C.M.W.G., C.L.A.V.L.).
  • Yuxin Yao
    Department of Computer Science, University College London, London, United Kingdom.
  • Ishaan A Tewarie
    Department of Neurosurgery, Computational Neurosciences Outcomes Center (CNOC), Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, United States; Department of Neurosurgery, Haaglanden Medical Center, The Hague, The Netherlands; Faculty of Medicine, Erasmus University Rotterdam/Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands.
  • Jakob V E Gerstl
    Department of Neurosurgery, Computational Neurosciences Outcomes Center (CNOC), Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, United States.
  • Camila M Recio Blanco
    Department of Neurosurgery, Computational Neurosciences Outcomes Center (CNOC), Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, United States; Northeast National University, Corrientes, Argentina; Prisma Salud, Puerto San Julian, Santa Cruz, Argentina.
  • Liam H Power
    Department of Neurosurgery, Computational Neurosciences Outcomes Center (CNOC), Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, United States; School of Medicine, Tufts University, Boston, Massachusetts, United States.
  • Marco Mammi
    Computational Neuroscience Outcomes Center, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
  • Heather Mattie
    Harvard T H Chan School of Public Health, Harvard University, Cambridge, MA, USA.
  • Timothy R Smith
    Computational Neurosciences Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Rania A Mekary
    Department of Neurosurgery, Computational Neurosciences Outcomes Center (CNOC), Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, United States; Department of Pharmaceutical Business and Administrative Sciences, School of Pharmacy, Massachusetts College of Pharmacy and Health Sciences University, Boston, Massachusetts, United States. Electronic address: rania.mekary@channing.harvard.edu.

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