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

TY - JOUR

T1 - Analysis of heterogeneity in T2-weighted MR images can differentiate pseudoprogression from progression in glioblastoma

AU - Booth, Thomas C.

AU - Larkin, Timothy J.

AU - Yuan, Yinyin

AU - Kettunen, Mikko I.

AU - Dawson, Sarah N.

AU - Scoffings, Daniel

AU - Canuto, Holly C.

AU - Vowler, Sarah L.

AU - Kirschenlohr, Heide

AU - Hobson, Michael P.

AU - Markowetz, Florian

AU - Jefferies, Sarah

AU - Brindle, Kevin M.

PY - 2017/5/17

Y1 - 2017/5/17

N2 - 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 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. Results 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 change in treatment strategy. Prospective validation of this technique in larger datasets is required.

AB - 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 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. Results 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 change in treatment strategy. Prospective validation of this technique in larger datasets is required.

U2 - 10.1371/journal.pone.0176528

DO - 10.1371/journal.pone.0176528

M3 - Article

SN - 1932-6203

VL - 12

SP - e0176528

JO - PloS one

JF - PloS one

IS - 5

ER -