Progressive disease in glioblastoma: Benefits and limitations of semi-automated volumetry

PLoS One. 2017 Feb 28;12(2):e0173112. doi: 10.1371/journal.pone.0173112. eCollection 2017.

Abstract

Purpose: Unambiguous evaluation of glioblastoma (GB) progression is crucial, both for clinical trials as well as day by day routine management of GB patients. 3D-volumetry in the follow-up of GB provides quantitative data on tumor extent and growth, and therefore has the potential to facilitate objective disease assessment. The present study investigated the utility of absolute changes in volume (delta) or regional, segmentation-based subtractions for detecting disease progression in longitudinal MRI follow-ups.

Methods: 165 high resolution 3-Tesla MRIs of 30 GB patients (23m, mean age 60.2y) were retrospectively included in this single center study. Contrast enhancement (CV) and tumor-related signal alterations in FLAIR images (FV) were semi-automatically segmented. Delta volume (dCV, dFV) and regional subtractions (sCV, sFV) were calculated. Disease progression was classified for every follow-up according to histopathologic results, decisions of the local multidisciplinary CNS tumor board and a consensus rating of the neuro-radiologic report.

Results: A generalized logistic mixed model for disease progression (yes / no) with dCV, dFV, sCV and sFV as input variables revealed that only dCV was significantly associated with prediction of disease progression (P = .005). Delta volume had a better accuracy than regional, segmentation-based subtractions (79% versus 72%) and a higher area under the curve by trend in ROC curves (.83 versus .75).

Conclusion: Absolute volume changes of the contrast enhancing tumor part were the most accurate volumetric determinant to detect progressive disease in assessment of GB and outweighed FLAIR changes as well as regional, segmentation-based image subtractions. This parameter might be useful in upcoming objective response criteria for glioblastoma.

MeSH terms

  • Aged
  • Brain Neoplasms / pathology*
  • Contrast Media
  • Female
  • Glioblastoma / pathology*
  • Humans
  • Magnetic Resonance Imaging
  • Male
  • Middle Aged
  • Models, Theoretical
  • Retrospective Studies

Substances

  • Contrast Media

Grants and funding

This work was supported by the German Research Foundation (DFG) and the Technical University of Munich (TUM) in the framework of the Open Access Publishing Program. BW is supported through the KKF scholarship of the TUM. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.