Cite this article as: Alobaidli S, McQuaid S, South C, Prakash V, Evans P, Nisbet A. The role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning. Br J Radiol 2014;87:20140369. REVIEW ARTICLEThe role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning ABSTRACTPredicting a tumour's response to radiotherapy prior to the start of treatment could enhance clinical care management by enabling the personalization of treatment plans based on predicted outcome. In recent years, there has been accumulating evidence relating tumour texture to patient survival and response to treatment. Tumour texture could be measured from medical images that provide a non-invasive method of capturing intratumoural heterogeneity and hence could potentially enable a prior assessment of a patient's predicted response to treatment. In this article, work presented in the literature regarding texture analysis in radiotherapy in relation to survival and outcome is discussed. Challenges facing integrating texture analysis in radiotherapy planning are highlighted and recommendations for future directions in research are suggested.

Attenuation-correction artefacts were seen in PET and SPECT images, with PET being more severely affected. The most severe artefacts were produced from mismatches in cardiac and liver position, whereas lung mismatches were less critical. Both cardiac and liver positions must, therefore, be correctly matched during attenuation correction.

We have developed a new method of compensating for effects of partial volume and spillover in dual-modality imaging. The approach requires segmentation of just a few tissue types within a small VOI surrounding a lesion; the algorithm estimates simultaneously, from projection data, the activity concentration within each segmented tissue inside the VOI. Measured emission projections were fitted to the sum of resolution-blurred projections of each such tissue, scaled by its unknown activity concentration, plus a global background contribution obtained by reprojection through the reconstructed image volume outside the VOI. The method was evaluated using multiple-pinhole μSPECT data simulated for the MOBY mouse phantom containing two spherical lung tumors and one liver tumor, as well as using multiple-bead phantom data acquired on μSPECT and μCT scanners. Each VOI in the simulation study was 4.8 mm (12 voxels) cubed and, depending on location, contained up to four tissues (tumor, liver, heart, lung) with different values of relative 99mTc concentration. All tumor activity estimates achieved <3% bias after ~15 OSEM iterations (× 10 subsets), with better than 8% precision (≤25% greater than the Cramer-Rao lower bound). The projection-based fitting approach also outperformed three SUV-like metrics, one of which was corrected for count spillover. In the bead phantom experiment, the mean ± standard deviation of the bias of VOI estimates of bead concentration were 0.9 ± 9.5%, comparable to those of a perturbation geometric transfer matrix (pGTM) approach (-5.4 ± 8.6%); however, VOI estimates were more stable with increasing iteration number than pGTM estimates, even in the presence of substantial axial misalignment between μCT and μSPECT image volumes.

Lung cancer is the leading cause of cancer mortality worldwide. Treatment pathways include regular cross-sectional imaging, generating large data sets which present intriguing possibilities for exploitation beyond standard visual interpretation. This additional data mining has been termed "radiomics" and includes semantic and agnostic approaches. Textural analysis (TA) is an example of the latter, and uses a range of mathematically derived features to describe an image or region of an image. Often TA is used to describe a suspected or known tumour. TA is an attractive tool as large existing image sets can be submitted to diverse techniques for data processing, presentation, interpretation and hypothesis testing with annotated clinical outcomes. There is a growing anthology of published data using different TA techniques to differentiate between benign and malignant lung nodules, differentiate tissue subtypes of lung cancer, prognosticate and predict outcome and treatment response, as well as predict treatment side effects and potentially aid radiotherapy planning. The aim of this systematic review is to summarize the current published data and understand the potential future role of TA in managing lung cancer.

Mismatches between PET and CT datasets due to respiratory effects can lead to artefactual perfusion defects. To overcome this, we have proposed a method of aligning a single CT with each frame of a gated PET study in a semi-automatic manner, incorporating a statistical shape model of the diaphragm and a rigid registration of the heart. This ensures that the structures that could influence the appearance of the reconstructed cardiac activity are correctly matched between emission and transmission datasets. When tested on two patient studies, it was found in both cases that attenuation correction using the proposed technique resulted in PET images that were closer to the gold standard of attenuation correction with a gated CT, compared with scenarios where only heart matching was considered (and not the diaphragm) or where no transformation was performed (i.e. where a single CT frame was used to attenuation-correct all PET frames). These preliminary results suggest that diaphragm matching between PET and CT improves the quantitative accuracy of reconstructed PET images and that the proposed method of using a statistical shape model to describe the diaphragm shape and motion, in combination with a rigid registration to determine respiratory-induced heart motion, is a feasible method of achieving this.

Obtaining the best possible task performance using reconstructed SPECT images requires optimization of both the collimator and reconstruction parameters. The goal of this study is to determine how to perform this optimization, namely whether the collimator parameters can be optimized solely from projection data, or whether reconstruction parameters should also be considered. In order to answer this question, and to determine the optimal collimation, a digital phantom representing a human torso with 16-mm-diameter hot lesions (activity ratio 8:1) was generated and used to simulate clinical SPECT studies with parallel-hole collimation. Two approaches to optimizing the SPECT system were then compared in a lesion quantification task: sequential-optimization, where collimation was optimized on projection data using the Cramer-Rao bound, and joint-optimization, which simultaneously optimized collimator and reconstruction parameters. For every condition, quantification performance in reconstructed images was evaluated using the root-mean-squared-error of 400 estimates of lesion activity. Compared to the joint-optimization approach, the sequential-optimization approach favoured a poorer resolution collimator, which, under some conditions, resulted in sub-optimal estimation performance. This implies that inclusion of the reconstruction parameters in the optimization procedure is important in obtaining the best possible task performance; in this study, this was achieved with a collimator resolution similar to that of a general-purpose (LEGP) collimator. This collimator was found to outperform the more commonly used high-resolution (LEHR) collimator, in agreement with other task-based studies, using both quantification and detection tasks.

This implies that PD can be detected earlier with the dedicated system than with the conventional system; therefore, earlier identification of PD progression should be possible with the high-sensitivity dedicated SPECT camera.

A new method of compensating for tissue-fraction and count-spillover effects, which requires tissue segmentation only within a small volume surrounding the primary lesion of interest, was evaluated for SPECT imaging. Tissue-activity concentration estimates are obtained by fitting the measured projection data to a statistical model of the segmented tissue projections. Multiple realizations of two simulated human-torso phantoms, each containing 20 spherical “tumours”, 1.6 cm in diameter, with tumour-to-background ratios of 8:1 and 4:1, were simulated. Estimates of tumour- and background-activity concentration values for homogeneous as well as inhomogeneous tissue activities were compared to standard SUV metrics on the basis of accuracy and precision. For perfectly registered, high-contrast, superficial lesions in a homogeneous background without scatter, the method yielded accurate (<0.4% bias) and precise (<6.1%) recovery of the simulated activity values, significantly outperforming the SUV metrics. Tissue inhomogeneities, greater tumour depth and lower contrast ratios degraded precision (up to 11.7%), but the estimates remained almost unbiased. The method was comparable in accuracy but more precise than a well-established matrix inversion approach, even when errors in tumor size and position were introduced to simulate moderate inaccuracies in segmentation and image registration. Photon scatter in the object did not significantly affect the accuracy or precision of the estimates.