Musib Siddique

TL;DR: This review focuses on the literature describing the emerging methods of texture analysis in 18FDG PET/CT, as well as other imaging modalities, and how the measurement of spatial variation of voxel grey-scale intensity within an image may provide additional predictive and prognostic information, and postulate the underlying biological mechanisms.

TL;DR: Experimental results provide initial evidence that convolutional neural networks have the potential to extract PET imaging representations that are highly predictive of response to therapy, and compare the performance of two competing radiomics strategies.

TL;DR: This review describes the following imaging techniques that may be used to assess body composition: dual-energy X-ray absorptiometry (DXA), computed tomography (CT) and magnetic resonance imaging (MRI).

TL;DR: Some early data suggest that extraction of additional quantitative data from PET images may offer further predictive and prognostic information in individual patients, but a number of potential limitations need to be recognised and overcome.

TL;DR: Smoothing and segmentation have only a small effect on the precision of heterogeneity measurements in 18F-FDG PET data, however, quantisation often has larger effects, highlighting a need for further evaluation and standardisation of parameters for multicentre studies.