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

PLOS ONE, Dec 2019

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.

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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 - 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 give permission. Interested researchers may contact this agency at . 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)


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Thomas C. Booth, Timothy J. Larkin, Yinyin Yuan, Mikko I. Kettunen, Sarah N. Dawson, Daniel Scoffings, Holly C. Canuto, Sarah L. Vowler, Heide Kirschenlohr, Michael P. Hobson, Florian Markowetz, Sarah Jefferies, Kevin M. Brindle. Analysis of heterogeneity in T2-weighted MR images can differentiate pseudoprogression from progression in glioblastoma, PLOS ONE, 2017, Volume 12, Issue 5, DOI: 10.1371/journal.pone.0176528