Analysis of heterogeneity in T2-weighted MR images can differentiate pseudoprogression from progression in glioblastoma
May
Analysis of heterogeneity in T2-weighted MR images can differentiate pseudoprogression from progression in glioblastoma
Thomas C. Booth 1 2
Timothy J. Larkin 1 2
Yinyin Yuan 2
Mikko I. Kettunen 1 2
Sarah N. Dawson 0 2
Daniel Scoffings 2
Holly C. Canuto 1 2
Sarah L. Vowler 2
Heide Kirschenlohr 1 2
Michael P. Hobson 2
Florian Markowetz 2
Sarah Jefferies 2
Kevin M. Brindle 1 2
0 Cambridge Clinical Trials Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom, 4 Department of Radiology, Addenbrooke's Hospital , Cambridge , United Kingdom , 5 Battock Centre for Experimental Astrophysics, Cavendish Laboratory, University of Cambridge, Cambridge, United Kingdom, 6 Department of Oncology, Addenbrooke's Hospital , Cambridge , United Kingdom
1 Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom, 2 Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre , Cambridge , United Kingdom
2 Editor: Jonathan H Sherman, George Washington University , UNITED STATES
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Data Availability Statement: Data are available
from Addenbrooke's Hospital (after Ethics
Committee approval) for researchers who meet the
criteria for access to confidential data. Queries may
be sent to .
The UK National Research Ethics Service did not
provide permission to make the minimal data set
publicly available. Thomas C Booth or Kevin M
Brindle may be contacted for readers to request the
data; there is confirmation that data will be
available upon request to all interested researchers
provided the UK National Research Ethics Service
Purpose
To develop an image analysis technique that distinguishes pseudoprogression from true
progression by analyzing tumour heterogeneity in T2-weighted images using topological
descriptors of image heterogeneity called Minkowski functionals (MFs).
Methods
Results
Using a retrospective patient cohort (n = 50), and blinded to treatment response outcome,
unsupervised feature estimation was performed to investigate MFs for the presence of
outliers, potential confounders, and sensitivity to treatment response. The progression and
pseudoprogression groups were then unblinded and supervised feature selection was
performed using MFs, size and signal intensity features. A support vector machine model was
obtained and evaluated using a prospective test cohort.
The model gave a classification accuracy, using a combination of MFs and size features, of
more than 85% in both retrospective and prospective datasets. A different feature selection
method (Random Forest) and classifier (Lasso) gave the same results. Although not
apparent to the reporting radiologist, the T2-weighted hyperintensity phenotype of those patients
with progression was heterogeneous, large and frond-like when compared to those with
pseudoprogression.
Conclusion
Analysis of heterogeneity, in T2-weighted MR images, which are acquired routinely in
the clinic, has the potential to detect an earlier treatment response allowing an early
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Funding: Funded by Medical Research Council/
Royal College of Radiologists (UK) Clinical
Research Fellowship (G1000265); Cancer
Research UK Clinical Research Fellowship;
Addenbrookes Charitable Trust Award to TCB.
Cancer Research UK Programme grant (C197/
A3514) to KMB. www.mrc.ac.uk; www.
cancerresearchuk.org/; http://www.
act4addenbrookes.org.uk. The funders had no role
in study design, data collection and analysis,
decision to publish, or preparation of the
manuscript.
change in treatment strategy. Prospective validation of this technique in larger datasets is
required.
Introduction
The commonest primary malignant brain tumour, glioblastoma, is a devastating disease with a
progression free-survival of 15% at 1 year.[
1
] Maximal debulking surgery and radiotherapy,
with concomitant and adjuvant temozolomide, is the standard of care[
2
] but is associated with
pseudoprogression. This describes false-positive progressive disease within 6 months of
chemoradiotherapy, typically determined by changes in contrast enhancement on T1-weighted
MR images, representing non-specific blood-brain barrier disruption.[
3
] Pseudoprogression
confounds response assessment and may affect clinical management. An imaging technique
that reliably differentiates responders from non-responders would allow an early change in
treatment strategy with prompt termination of ineffective treatment and the option of
implementing novel therapies.[
4
] To achieve this, we describe a method that is simple to implement,
requires little computational effort, is intuitive to interpret[
5
] and only requires T2-weighted
images that are acquired routinely during patient follow-up and which more accurately detect
glioblastoma infiltration than contrast-enhanced T1-weighted images.[6;7] This is because
glioblastoma cell infiltration, which can cause hyperintensity in T2-weighted images, does
not (...truncated)