Analysis of heterogeneity in T2-weighted MR images can differentiate pseudoprogression from progression in glioblastoma

PloS One
Thomas C BoothKevin M Brindle

Abstract

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). 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. Analysis of heterogeneity, in T2-weighte...Continue Reading

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Methods Mentioned

BETA
imaging technique
surgical resection
biopsy
feature extraction
imaging techniques

Software Mentioned

SIMCA
XLSTAT
R
Graphpad Prism
LIBSVM
P +
nlme
MATLAB
Random Forest
Stata

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