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Glioblastoma Surgery Imaging-Reporting and Data System: Validation and Performance of the Automated Segmentation Task

Abstract

For patients with presumed glioblastoma, essential tumor characteristics are determined from preoperative MR images to optimize the treatment strategy. This procedure is time-consuming and subjective, if performed by crude eyeballing or manually. The standardized GSI-RADS aims to provide neurosurgeons with automatic tumor segmentations to extract tumor features rapidly and objectively. In this study, we improved automatic tumor segmentation and compared the agreement with manual raters, describe the technical details of the different components of GSI-RADS, and determined their speed. Two recent neural network architectures were considered for the segmentation task: nnU-Net and AGU-Net. Two preprocessing schemes were introduced to investigate the tradeoff between performance and processing speed. A summarized description of the tumor feature extraction and standardized reporting process is included. The trained architectures for automatic segmentation and the code for computing the standardized report are distributed as open-source and as open-access software. Validation studies were performed on a dataset of 1594 gadolinium-enhanced T1-weighted MRI volumes from 13 hospitals and 293 T1-weighted MRI volumes from the BraTS challenge. The glioblastoma tumor core segmentation reached a Dice score slightly below 90%, a patientwise F1-score close to 99%, and a 95th percentile Hausdorff distance slightly below 4.0 mm on average with either architecture and the heavy preprocessing scheme. A patient MRI volume can be segmented in less than one minute, and a standardized report can be generated in up to five minutes. The proposed GSI-RADS software showed robust performance on a large collection of MRI volumes from various hospitals and generated results within a reasonable runtime.

Category

Academic article

Language

English

Author(s)

  • David Bouget
  • Roelant Eijgelaar
  • André Pedersen
  • Ivar Kommers
  • Hilko Ardon
  • Frederik Barkhof
  • Lorenzo Bello
  • Mitchel S. Berger
  • Marco Conti Nibali
  • Julia Furtner
  • Even Hovig Fyllingen
  • Shawn Hervey-Jumper
  • Albert J. S. Idema
  • Barbara Kiesel
  • Alfred Kloet
  • Emmanuel Mandonnet
  • Domenique M. J. Müller
  • Pierre Robe
  • Marco Rossi
  • Lisa Millgård Sagberg
  • Tommaso Sciortino
  • Wimar A. van den Brink
  • Michiel Wagemakers
  • Georg Widhalm
  • Marnix G. Witte
  • Aeilko H. Zwinderman
  • Ingerid Reinertsen
  • Philip C De Witt Hamer
  • Ole Solheim

Affiliation

  • SINTEF Digital / Health Research
  • Vrije Universiteit Amsterdam Medical Center
  • Netherlands
  • University College London
  • University of Milan
  • University of California, San Francisco
  • Medical University of Vienna
  • Norwegian University of Science and Technology
  • St. Olavs Hospital, Trondheim University Hospital
  • Haaglanden Medical Center
  • France
  • University Medical Center Utrecht
  • University Medical Center Groningen
  • Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital

Date

17.09.2021

Year

2021

Published in

Cancers

ISSN

2072-6694

Publisher

MDPI

Volume

13

Issue

18

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