The role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning

Abstract

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.

Texture analysis of medical images for radiotherapy applications

“…This was highlighted in some recent reviews, 5,7,74,75 and some of the latest results in the field are schematically summarized in Table 3 and discussed in the following sections.…”

“…A more detailed description about their computation can be found in previous reviews. 3,5,24 Textural features can be estimated by calculating a single value for the whole ROI or by building a texture map. In the first case, the ROI-based approach provides a unique index for the description of the texture in the whole structure.…”

“…9 Even if texture analysis was carried out in the most rigorous way and if significant models that could predict tissue response to radiation or provide a characterization of tumour heterogeneity were found, the biological interpretation of these parameters remains one of the major questions about texture analysis. In fact, up to now, the biological origin of the features is still not completely understood, as reported in the review of Alobaidli et al 5 In this context, some studies try to assess possible correlations between texture analysis and genetic or proteomic data 4,108,109 or between texture and histological image analysis. 30 In particular, it was found that pathophysiological properties determined by genomic analysis were significantly related to texture analysis on CT, 4 PET 109 and DCE-MRI images, 108 suggesting that heterogeneity features were strongly correlated with cell cycling pathways.…”

“…3,[5][6][7][8][9] Despite the fact that it seems a promising technique, it has not been completely exploited, since some possible limitations, especially regarding factors that can reduce its repeatability and robustness, need to be explored in more detail.…”

Computerized PET/CT image analysis in the evaluation of tumour response to therapy

“…49 Adding structural features (the presence or absence of marked central necrosis) to morphology, attenuation and size features in CECT was found to be more accurate than response assessment by RECIST in renal cell carcinoma. 50 CT texture features characterizing the spatial variations of tissue density were shown to be prognostic factors in NSCLC 51,52 and oesophageal cancer. 53 Positron emission tomography features In recent years, 18 F-FDG-PET imaging, which measures functional (metabolic activity) changes, has shown advantages over anatomic imaging as a response evaluation tool in many malignancies.…”