Texture analysis of medical images for radiotherapy applications

Abstract

The high-throughput extraction of quantitative information from medical images, known as radiomics, has grown in interest due to the current necessity to quantitatively characterize tumour heterogeneity. In this context, texture analysis, consisting of a variety of mathematical techniques that can describe the grey-level patterns of an image, plays an important role in assessing the spatial organization of different tissues and organs. For these reasons, the potentiality of texture analysis in the context of radiotherapy has been widely investigated in several studies, especially for the prediction of the treatment response of tumour and normal tissues. Nonetheless, many different factors can affect the robustness, reproducibility and reliability of textural features, thus limiting the impact of this technique. In this review, an overview of the most recent works that have applied texture analysis in the context of radiotherapy is presented, with particular focus on the assessment of tumour and tissue response to radiations. Preliminary, the main factors that have an influence on features estimation are discussed, highlighting the need of more standardized image acquisition and reconstruction protocols and more accurate methods for region of interest identification. Despite all these limitations, texture analysis is increasingly demonstrating its ability to improve the characterization of intratumour heterogeneity and the prediction of clinical outcome, although prospective studies and clinical trials are required to draw a more complete picture of the full potential of this technique.

Figure 1.

Texture analysis procedure from tissue identification on medical images to features extraction (here in terms of first-, second- and higher-order statistical features, fractal dimension and wavelet filters) and analysis (such as cross-correlations matrix, receiver operating characteristic curves).

Figure 2.

Estimation of entropy from grey-level co-occurrence matrix in a cervical lymph node (in red) using a region of interest-based approach (left) and a pixel-wise approach (right). Colours appear only in the online version.

Figure 3.

Effect of image registration and contour propagation on region of interest identification. Pelvic T₂ weighted (T2w)-MR images acquired before and after radiotherapy were co-registered and bladder contour was automatically propagated using four different algorithms, resulting in four different bladder contours, represented by different colours (on the right). Colours appear only in the online version.

Figure 4.

Perfusion maps derived from intravoxel incoherent motion images. From left to right: apparent diffusion coefficient (ADC), true diffusion coefficient (Dt), pseudodiffusion coefficient (Dp) and perfusion fraction (Pf). The ADC and Dt maps are more robust and less noisy than Dp and Pf maps.