gms | German Medical Science

72. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie (DGNC)
Joint Meeting mit der Polnischen Gesellschaft für Neurochirurgie

Deutsche Gesellschaft für Neurochirurgie (DGNC) e. V.

06.06. - 09.06.2021

Artificial intelligence for the differentiation between multiple sclerosis and glioma II° or III° using 18F-FET-PET imaging

Differenzierung von Multipler Sklerose und Gliomen WHO II°/III° anhand von AI-basierten 18F-FET-PET Untersuchungen

Meeting Abstract

  • presenting/speaker Laurèl Rauschenbach - Universitätsmedizin Essen, Neurochirurgie und Wirbelsäulenchirurgie Essen, Essen, Deutschland
  • Sied Kebir - Universitätsmedizin Essen, Neurologie, Essen, Deutschland
  • Manuel Weber - Universitätsmedizin Essen, Nuklearmedizin, Essen, Deutschland
  • Lazaros Lazaridis - Universitätsmedizin Essen, Neurologie, Essen, Deutschland
  • Teresa Schmidt - Universitätsmedizin Essen, Neurologie, Essen, Deutschland
  • Kathy Keyvani - Universitätsmedizin Essen, Neuropathologie, Essen, Deutschland
  • Niklas Schäfer - Universitätsklinikum Bonn, Klinik für Neurologie, Bonn, Deutschland
  • Lale Umutlu - Universitätsmedizin Essen, Diagnostische und interventionelle Radiologie, Essen, Deutschland
  • Daniela Pierscianek - Universitätsmedizin Essen, Neurochirurgie und Wirbelsäulenchirurgie Essen, Essen, Deutschland
  • Martin Stuschke - Universitätsmedizin Essen, Strahlentherapie, Essen, Deutschland
  • Michael Forsting - Universitätsmedizin Essen, Diagnostische und interventionelle Radiologie, Essen, Deutschland
  • Ulrich Sure - Universitätsmedizin Essen, Neurochirurgie und Wirbelsäulenchirurgie Essen, Essen, Deutschland
  • Christoph Kleinschnitz - Universitätsmedizin Essen, Neurologie, Essen, Deutschland
  • Gerald Antoch - Universitätsklinikum Düsseldorf, Diagnostische und interventionelle Radiologie, Düsseldorf, Deutschland
  • Patrick Colletti - University of Southern California, Radiology, Los Angeles, CA, Vereinigte Staaten
  • Domenico Rubello - S. Maria della Misericordia Hospital, Nuclear Medicine, Radiology, Neuroradiology, Clinical Pathology, Rovigo, Italien
  • Ken Herrmann - Universitätsmedizin Essen, Nuklearmedizin, Essen, Deutschland
  • Ulrich Herrlinger - Universitätsklinikum Bonn, Klinik für Neurologie, Bonn, Deutschland
  • Björn Scheffler - Universitätsmedizin Essen, DKTK Translationale Neuroonkologie, Essen, Deutschland
  • Ralph Bundschuh - Universitätsklinikum Bonn, Nuklearmedizin, Bonn, Deutschland
  • Martin Glas - Universitätsmedizin Essen, Neurologie, Essen, Deutschland

Deutsche Gesellschaft für Neurochirurgie. 72. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie (DGNC), Joint Meeting mit der Polnischen Gesellschaft für Neurochirurgie. sine loco [digital], 06.-09.06.2021. Düsseldorf: German Medical Science GMS Publishing House; 2021. DocV099

doi: 10.3205/21dgnc096, urn:nbn:de:0183-21dgnc0967

Published: June 4, 2021

© 2021 Rauschenbach et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. See license information at http://creativecommons.org/licenses/by/4.0/.


Outline

Text

Objective: To evaluate the diagnostic significance of 18F-FET-PET imaging for the differentiation between multiple sclerosis (MS) and World Health Organization (WHO) grade 2/3 glioma (glioma II°/III°).

Methods: We retrospectively screened for patients in whom 18F-FET-PET imaging was performed for the diagnostic workup of newly-diagnosed lesions evident on magnetic resonance imaging (MRI) and suspicious for glioma. Among those, we identified patients with histologically confirmed glioma II°/III° and those who later turned out to have multiple sclerosis pursuant to the revised McDonald criteria from 2017. For each group, the mean and maximum tumor-to-brain ratio (TBR) of 18F-FET were determined. Moreover, we used a support-vector machine (SVM) based machine learning algorithm trained on a development and evaluated on a validation subgroup. Receiver operating characteristic (ROC) analysis with area under the curve (AUC) metric was used to assess model performance.

Results: A total of n=33 patients met inclusion criteria. Subsequent diagnostics confirmed MS in n=7 and glioma II°/III° in n=26 patients. TBRmean and TBRmax were significantly higher in the glioma group (TBRmean glioma group: 2.16±0.93, MS group: 1.33±0.14, p = 0.03; TBRmax glioma group: 2.01±0.79, MS group: 1.23±0.19, p = 0.02). In a subgroup analysis, TBRmean and TBRmax significantly differentiated between MS and oligodendroglioma (OG) II°/III° (TBRmean OG group: 2.75±0.91, MS group: 1.33±0.14, p = 0.002; TBRmax OG group: 2.47±0.71, TBRmean MS group: 1.23±0.19, p = 0.003). As shown on ROC analysis, the ability to differentiate between MS and glioma increased from 78.6% using standard TBR analysis to 93.0% using a SVM based machine learning algorithm.

Conclusion: 18F-FET-PET imaging using an SVM based machine learning algorithm enhanced classification performance for the differentiation of MS from glioma. Future studies with larger sample sizes are needed to confirm this observation.