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

Autor: Booth TC; Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom.; Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, United Kingdom., Larkin TJ; Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom.; Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, United Kingdom., Yuan Y; Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, United Kingdom., Kettunen MI; Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom.; Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, United Kingdom., Dawson SN; Cambridge Clinical Trials Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom., Scoffings D; Department of Radiology, Addenbrooke's Hospital, Cambridge, United Kingdom., Canuto HC; Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom.; Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, United Kingdom., Vowler SL; Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, United Kingdom., Kirschenlohr H; Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom., Hobson MP; Battock Centre for Experimental Astrophysics, Cavendish Laboratory, University of Cambridge, Cambridge, United Kingdom., Markowetz F; Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, United Kingdom., Jefferies S; Department of Oncology, Addenbrooke's Hospital, Cambridge, United Kingdom., Brindle KM; Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom.; Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, United Kingdom.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2017 May 17; Vol. 12 (5), pp. e0176528. Date of Electronic Publication: 2017 May 17 (Print Publication: 2017).
DOI: 10.1371/journal.pone.0176528
Abstrakt: 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.
Databáze: MEDLINE
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