Analysis of heterogeneity in T-2-weighted MR images can differentiate pseudoprogression from progression in glioblastoma

作者：Booth Thomas C; Larkin Timothy J; Yuan Yinyin; Kettunen Mikko I; Dawson Sarah N; Scoffings Daniel; Canuto Holly C; Vowler Sarah L; Kirschenlohr Heide; Hobson Michael P; Markowetz Florian; Jefferies Sarah; Brindle Kevin M^*

来源：PLos One, 2017, 12(5): e0176528.

DOI：10.1371/journal.pone.0176528

Purpose
To develop an image analysis technique that distinguishes pseudoprogression from true progression by analyzing tumour heterogeneity in T-2-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 T-2-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 T-2-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.

• 出版日期2017-5-17