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.

The comparative response of three emerging dosimetry systems for clinical brachytherapy dose distribution measurement has been investigated. Ge-doped optical fibers have excellent spatial resolution for single-direction measurement but are currently too large for complex dose distribution assessment. The use of PRESAGE(®) with optical-CT readout gave promising results in the measurement of true 3D dose distributions but further development work is required to reduce noise and improve dynamic range for brachytherapy dose distribution measurements. EBT3 Gafchromic film with multichannel analysis demonstrated accurate and reproducible measurement of dose distributions in HDR brachytherapy. Calibrated dose measurements were possible with agreement within 1.5% of TPS dose calculations. The suitability of EBT3 as a dosimeter for 2D quality control or commissioning work has been demonstrated.

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.

This paper studies the sensitivity of a range of image texture parameters used in radiomics to: i) the number of intensity levels, ii) the method of quantisation to select the intensity levels and iii) the use of an intensity threshold. 43 commonly used texture features were studied for the gross target volume outlined on the CT component of PET/CT scans of 50 patients with non-small cell lung carcinoma (NSCLC). All cases were quantised for all values between 4 and 128 intensity levels using four commonly used quantisation methods. All results were analysed with and without a threshold range of-200 HU to 300 HU. Cases were ranked for each texture feature and for all quantisation methods with the Spearman's rank correlation coefficient determined to evaluate stability. Results showed large fluctuations in ranking, particularly for low numbers of levels, differences between quantisation methods and with the use of a threshold, with values Spearman's Rank Correlation for many parameters below 0.2. Our results demonstrated the sensitivity of radiomics features to the parameters used during analysis and highlight the risk of low reproducibility comparing studies with slightly different parameters. In terms of the lung cancer CT datasets, this study supports the use of 128 intensity levels, the same uniform quantiser applied to all scans and thresholding of the data. It also supports several of the features recommended in the literature for such studies such as skewness and kurtosis. A recommended framework is presented for curation of the data analysis process to ensure stability of results.

Several studies have recently reported on the value of CT texture analysis in predicting survival, although the topic remains controversial, with further validation needed in order to consolidate the evidence base. The aim of this study was to investigate the effect of varying the input parameters in the Kaplan-Meier analysis, to determine whether the resulting P-value can be considered to be a robust indicator of the parameter's prognostic potential. A retrospective analysis of the CT-based normalised entropy of 51 patients with lung cancer was performed and overall survival data for these patients were collected. A normalised entropy cut-off was chosen to split the patient cohort into two groups and log-rank testing was performed to assess the survival difference of the two groups. This was repeated for varying normalised entropy cut-offs and varying follow-up periods. Our findings were also compared with previously published results to assess robustness of this parameter in a multi-centre patient cohort. The P-value was found to be highly sensitive to the choice of cut-off value, with small changes in cut-off producing substantial changes in P. The P-value was also sensitive to follow-up period, with particularly noisy results at short follow-up periods. Using matched conditions to previously published results, a P-value of 0.162 was obtained. Survival analysis results can be highly sensitive to the choice in texture cut-off value in dichotomising patients, which should be taken into account when performing such studies to avoid reporting false positive results. Short follow-up periods also produce unstable results and should therefore be avoided to ensure the results produced are reproducible. Previously published findings that indicated the prognostic value of normalised entropy were not replicated here, but further studies with larger patient numbers would be required to determine the cause of the different outcomes.