Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?

TLDR

Tumor spatial heterogeneity is a key prognostic factor that can be quantified by image texture analysis, revealing variations not visible to the naked eye. This review evaluates texture analysis methods across CT, MRI, and PET, aiming to determine their potential as a valuable clinical tool for oncologic imaging. Early evidence shows texture analysis can enhance diagnosis, staging, and therapy response assessment, but technical challenges limit its widespread adoption. Key teaching points: tumor heterogeneity is prognostic, texture analysis quantifies it, various statistical, model, and transform methods exist, and it may improve diagnosis, staging, and therapy response assessment.

Abstract

Tumor spatial heterogeneity is an important prognostic factor, which may be reflected in medical imagesImage texture analysis is an approach of quantifying heterogeneity that may not be appreciated by the naked eye. Different methods can be applied including statistical-, model-, and transform-based methods.Early evidence suggests that texture analysis has the potential to augment diagnosis and characterization as well as improve tumor staging and therapy response assessment in oncological practice.This review provides an overview of the application of texture analysis with different imaging modalities, CT, MRI, and PET, to date and describes the technical challenges that have limited its widespread clinical implementation so far. With further efforts to refine its application, image texture analysis has the potential to develop into a valuable clinical tool for oncologic imaging. TEACHING POINTS : • Tumor spatial heterogeneity is an important prognostic factor. • Image texture analysis is an approach of quantifying heterogeneity. • Different methods can be applied, including statistical-, model-, and transform-based methods. • Texture analysis could improve the diagnosis, tumor staging, and therapy response assessment.