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

Kellner-Weldon, Frauke; Stippich, Christoph; Wiest, Roland; Lehmann, Vera; Meier, Raphael; Beck, Jürgen; Schucht, Philippe; Raabe, Andreas; Reyes, Mauricio; Bink, Andrea (2017). Comparison of perioperative automated versus manual two-dimensional tumor analysis in glioblastoma patients. European journal of radiology, 95, pp. 75-81. Elsevier 10.1016/j.ejrad.2017.07.028

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute for Surgical Technology & Biomechanics ISTB
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology
04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Neurosurgery

UniBE Contributor:

Kellner-Weldon, Frauke; Stippich, Christoph; Wiest, Roland; Meier, Raphael; Beck, Jürgen; Schucht, Philippe; Raabe, Andreas and Reyes, Mauricio

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health

ISSN:

1872-7727

Publisher:

Elsevier

Language:

English

Submitter:

Martin Zbinden

Date Deposited:

14 Nov 2017 15:31

Last Modified:

14 Nov 2017 15:38

Publisher DOI:

10.1016/j.ejrad.2017.07.028

PubMed ID:

28987701

Uncontrolled Keywords:

Computer assisted reading MRI automated data analysis glioblastoma machine learning

BORIS DOI:

10.7892/boris.106178

URI:

https://boris.unibe.ch/id/eprint/106178

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