Non-invasive assessment of glioma microstructure using VERDICT MRI: correlation with histology

Research output: Contribution to journalArticle

Authors

  • Fulvio Zaccagna
  • Frank Riemer
  • Andrew N Priest
  • Mary A McLean
  • Kieren Allinson
  • James T Grist
  • Carmen Dragos
  • Tomasz Matys
  • Jonathan H Gillard
  • Stephen J Price
  • Martin J Graves
  • Ferdia A Gallagher

Colleges, School and Institutes

External organisations

  • Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK. fz247@cam.ac.uk.
  • Department of Radiology, School of Clinical Medicine, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
  • Department of Radiology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom.
  • Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge Department of Oncology, University of Cambridge, Addenbrooke's Hospital, Cambridge Cambridge Experimental Cancer Medicine Centre and NIHR Cambridge Biomedical Research Centre, Cambridge, UK.
  • Department of Pathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
  • Department of Neurosurgery, Birmingham Brain Cancer Program, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, B15 2TT, UK.
  • University of Cambridge

Abstract

PURPOSE: This prospective study evaluated the use of vascular, extracellular and restricted diffusion for cytometry in tumours (VERDICT) MRI to investigate the tissue microstructure in glioma. VERDICT-derived parameters were correlated with both histological features and tumour subtype and were also used to explore the peritumoural region.

METHODS: Fourteen consecutive treatment-naïve patients (43.5 years ± 15.1 years, six males, eight females) with suspected glioma underwent diffusion-weighted imaging including VERDICT modelling. Tumour cell radius and intracellular and combined extracellular/vascular volumes were estimated using a framework based on linearisation and convex optimisation. An experienced neuroradiologist outlined the peritumoural oedema, enhancing tumour and necrosis on T2-weighted imaging and contrast-enhanced T1-weighted imaging. The same regions of interest were applied to the co-registered VERDICT maps to calculate the microstructure parameters. Pathology sections were analysed with semi-automated software to measure cellularity and cell size.

RESULTS: VERDICT parameters were successfully calculated in all patients. The imaging-derived results showed a larger intracellular volume fraction in high-grade glioma compared to low-grade glioma (0.13 ± 0.07 vs. 0.08 ± 0.02, respectively; p = 0.05) and a trend towards a smaller extracellular/vascular volume fraction (0.88 ± 0.07 vs. 0.92 ± 0.04, respectively; p = 0.10). The conventional apparent diffusion coefficient was higher in low-grade gliomas compared to high-grade gliomas, but this difference was not statistically significant (1.22 ± 0.13 × 10-3 mm2/s vs. 0.98 ± 0.38 × 10-3 mm2/s, respectively; p = 0.18).

CONCLUSION: This feasibility study demonstrated that VERDICT MRI can be used to explore the tissue microstructure of glioma using an abbreviated protocol. The VERDICT parameters of tissue structure correlated with those derived on histology. The method shows promise as a potential test for diagnostic stratification and treatment response monitoring in the future.

KEY POINTS:
• VERDICT MRI is an advanced diffusion technique which has been correlated with histopathological findings obtained at surgery from patients with glioma in this study.
• The intracellular volume fraction measured with VERDICT was larger in high-grade tumours compared to that in low-grade tumours.
• The results were complementary to measurements from conventional diffusion-weighted imaging, and the technique could be performed in a clinically feasible timescale.

Details

Original languageEnglish
Pages (from-to)5559-5566
Number of pages8
JournalEuropean Radiology
Volume29
Issue number10
Early online date19 Mar 2019
Publication statusPublished - 1 Oct 2019

Keywords

  • Brain neoplasms, Cancer, Diagnostic imaging, Diffusion magnetic resonance imaging, Glioma