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.

Radiomics Features of Multiparametric MRI as Novel Prognostic Factors in Advanced Nasopharyngeal Carcinoma

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T2w‐MRI signal normalization affects radiomics features reproducibility

“…For this reason, radiomic features that present high classification/prediction power should also present high reliability, since both these properties are necessary to build a reliable radiomic signature. 6,7 In the previous years, several works have dealt with the assessment of radiomic features reliability, both considering the evaluation of repeatability, i.e features that remain the same when calculated multiple times in the same subject with the same conditions, and reproducibility, i.e. features that remain the same when calculated using different acquisition or processing conditions.…”

Texture analysis of T1‐w and T2‐w MR images allows a quantitative evaluation of radiation‐induced changes of internal obturator muscles after radiotherapy for prostate cancer

“…Due to its novel and recent development, there are no standard procedures yet for image processing and features choice and computation. For this reason, the choices made about ROI identification and features calculation have a great impact on the final results . In the present work, particular attention was paid to the image registration and contour propagation processes, in order to correctly and accurately identify the ROIs volumes in which texture analysis was applied, without introducing image interpolation that can alter the gray‐level information.…”

“…[12][13][14] Mp-MRI could have a compelling role in this context, due to its potentiality to fully characterize the soft tissues from different points of view, such as their anatomical, structural, and functional properties described by the information carried out by different acquisition techniques. Moreover, the combination of mp-MRI and radiomic techniques 15,16 allows the quantification of the tissue's spatial organization by the use of texture analysis. This can improve the knowledge of the muscle response to radiation, by an assessment of the structural changes, which may be dependent on the received dose.…”