A non-invasive, automated diagnosis of Menière's disease using radiomics and machine learning on conventional magnetic resonance imaging: A multicentric, case-controlled feasibility study.

Abstract

Authors

• Marly F J A van der Lubbe
  Department of Otolaryngology and Head and Neck Surgery, Maastricht University Medical Center, Maastricht, The Netherlands.
• Akshayaa Vaidyanathan
  The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, The Netherlands. akshayaa.vaidyanathan@oncoradiomics.com.
• Marjolein de Wit
  Department of Otolaryngology and Head and Neck Surgery, Maastricht University Medical Center +, Maastricht, The Netherlands.
• Elske L van den Burg
  Department of Otolaryngology and Head and Neck Surgery, Maastricht University Medical Center +, Maastricht, The Netherlands.
• Alida A Postma
  Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands.
• Tjasse D Bruintjes
  Department of Otorhinolaryngology, Gelre Hospital, Apeldoorn, The Netherlands.
• Monique A L Bilderbeek-Beckers
  Department of Radiology, Viecuri Medical Center, Venlo, The Netherlands.
• Patrick F M Dammeijer
  Department of Otorhinolaryngology, Viecuri Medical Center, Venlo, The Netherlands.
• Stephanie Vanden Bossche
  Department of Radiology, Antwerp University Hospital, Antwerp, Belgium.
• Vincent van Rompaey
  Department of Otorhinolaryngology and Head & Neck Surgery, Antwerp University Hospital, Antwerp, Belgium.
• Philippe Lambin
  Department of Radiation Oncology (MAASTRO Clinic), Dr. Tanslaan 12, Maastricht, The Netherlands.
• Marc van Hoof
  Department of Otolaryngology and Head and Neck Surgery, Maastricht University Medical Center, Maastricht, The Netherlands.
• Raymond van de Berg
  Department of Otolaryngology and Head and Neck Surgery, Maastricht University Medical Center, Maastricht, The Netherlands.

Keywords

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