Literature DB >> 28987701

Comparison of perioperative automated versus manual two-dimensional tumor analysis in glioblastoma patients.

Frauke Kellner-Weldon1, Christoph Stippich2, Roland Wiest3, Vera Lehmann3, Raphael Meier4, Jürgen Beck5, Philippe Schucht5, Andreas Raabe5, Mauricio Reyes4, Andrea Bink2.   

Abstract

OBJECTIVES: Current recommendations for the measurement of tumor size in glioblastoma continue to employ manually measured 2D product diameters of enhancing tumor. To overcome the rater dependent variability, this study aimed to evaluate the potential of automated 2D tumor analysis (ATA) compared to highly experienced rater teams in the workup of pre- and postoperative image interpretation in a routine clinical setting.
MATERIALS AND METHODS: From 92 patients with newly diagnosed GB and performed surgery, manual rating of the sum product diameter (SPD) of enhancing tumor on magnetic resonance imaging (MRI) contrast enhanced T1w was compared to automated machine learning-based tumor analysis using FLAIR, T1w, T2w and contrast enhanced T1w.
RESULTS: Preoperative correlation of SPD between two rater teams (1 and 2) was r=0.921 (p<0.0001). Difference among the rater teams and ATA (p=0.567) was not statistically significant. Correlation between team 1 vs. automated tumor analysis and team 2 vs. automated tumor analysis was r=0.922 and r=0.897, respectively (p<0.0001 for both). For postoperative evaluation interrater agreement between team 1 and 2 was moderate (Kappa 0.53). Manual consensus classified 46 patients as completely resected enhancing tumor. Automated tumor analysis agreed in 13/46 (28%) due to overestimation caused by hemorrhage and choroid plexus enhancement.
CONCLUSIONS: Automated 2D measurements can be promisingly translated into clinical trials in the preoperative evaluation. Immediate postoperative SPD evaluation for extent of resection is mainly influenced by postoperative blood depositions and poses challenges for human raters and ATA alike.
Copyright © 2017 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Computer assisted reading; MRI; automated data analysis; glioblastoma; machine learning

Mesh:

Year:  2017        PMID: 28987701     DOI: 10.1016/j.ejrad.2017.07.028

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  4 in total

1.  Deep learning for automatic brain tumour segmentation on MRI: evaluation of recommended reporting criteria via a reproduction and replication study.

Authors:  Emilia Gryska; Isabella Björkman-Burtscher; Asgeir Store Jakola; Tora Dunås; Justin Schneiderman; Rolf A Heckemann
Journal:  BMJ Open       Date:  2022-07-18       Impact factor: 3.006

2.  Structured Reporting in Neuroradiology: Intracranial Tumors.

Authors:  Andrea Bink; Jan Benner; Julia Reinhardt; Anthony De Vere-Tyndall; Bram Stieltjes; Nicolin Hainc; Christoph Stippich
Journal:  Front Neurol       Date:  2018-02-06       Impact factor: 4.003

Review 3.  Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review.

Authors:  Emilia Gryska; Justin Schneiderman; Isabella Björkman-Burtscher; Rolf A Heckemann
Journal:  BMJ Open       Date:  2021-01-29       Impact factor: 2.692

4.  Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence.

Authors:  Peter M Maloca; Philipp L Müller; Aaron Y Lee; Adnan Tufail; Konstantinos Balaskas; Stephanie Niklaus; Pascal Kaiser; Susanne Suter; Javier Zarranz-Ventura; Catherine Egan; Hendrik P N Scholl; Tobias K Schnitzer; Thomas Singer; Pascal W Hasler; Nora Denk
Journal:  Commun Biol       Date:  2021-02-05
  4 in total

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