Analysis of heterogeneity in T2_2-weighted MR images can differentiate pseudoprogression from progression in glioblastoma

$\textbf{Purpose}$ 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). $\textbf{Methods}$ 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. $\textbf{Results}$ 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. $\textbf{Conclusion}$ 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.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

Metabolomic analysis of human disease and its application to the eye

Metabolomics, the analysis of the metabolite profile in body fluids or tissues, is being applied to the analysis of a number of different diseases as well as being used in following responses to therapy. While genomics involves the study of gene expression and proteomics the expression of proteins, metabolomics investigates the consequences of the activity of these genes and proteins. There is good reason to think that metabolomics will find particular utility in the investigation of inflammation, given the multi-layered responses to infection and damage that are seen. This may be particularly relevant to eye disease, which may have tissue specific and systemic components. Metabolomic analysis can inform us about ocular or other body fluids and can therefore provide new information on pathways and processes involved in these responses. In this review, we explore the metabolic consequences of disease, in particular ocular conditions, and why the data may be usefully and uniquely assessed using the multiplexed analysis inherent in the metabolomic approach