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

M.F.J.A. van der Lubbe*, A. Vaidyanathan, M. de Wit, E.L. van den Burg, A.A. Postma, T.D. Bruintjes, M.A.L. Bilderbeek-Beckers, P.F.M. Dammeijer, S. Vanden Bossche, V. Van Rompaey, P. Lambin, M. van Hoof, R. van de Berg

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Purpose This study investigated the feasibility of a new image analysis technique (radiomics) on conventional MRI for the computer-aided diagnosis of Meniere's disease. Materials and methods A retrospective, multicentric diagnostic case-control study was performed. This study included 120 patients with unilateral or bilateral Meniere's disease and 140 controls from four centers in the Netherlands and Belgium. Multiple radiomic features were extracted from conventional MRI scans and used to train a machine learning-based, multi-layer perceptron classification model to distinguish patients with Meniere's disease from controls. The primary outcomes were accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the classification model. Results The classification accuracy of the machine learning model on the test set was 82%, with a sensitivity of 83%, and a specificity of 82%. The positive and negative predictive values were 71%, and 90%, respectively. Conclusion The multi-layer perceptron classification model yielded a precise, high-diagnostic performance in identifying patients with Meniere's disease based on radiomic features extracted from conventional T2-weighted MRI scans. In the future, radiomics might serve as a fast and noninvasive decision support system, next to clinical evaluation in the diagnosis of Meniere's disease.
Original languageEnglish
Pages (from-to)72-82
Number of pages11
JournalRadiologia medica
Volume127
Issue number1
Early online date25 Nov 2021
DOIs
Publication statusPublished - Jan 2022

Keywords

  • Meniere's disease
  • Magnetic resonance imaging
  • Radiomics
  • Machine learning
  • ENDOLYMPHATIC HYDROPS
  • IMAGES

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