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. 2012 Dec;3(6):573-89.
doi: 10.1007/s13244-012-0196-6. Epub 2012 Oct 24.

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

Fergus Davnall  1 , Connie S P Yip, Gunnar Ljungqvist, Mariyah Selmi, Francesca Ng, Bal Sanghera, Balaji Ganeshan, Kenneth A Miles, Gary J Cook, Vicky Goh
Affiliations

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

Fergus Davnall et al. Insights Imaging. 2012 Dec.

Abstract

Background: Tumor spatial heterogeneity is an important prognostic factor, which may be reflected in medical images

Methods: Image 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.

Results: 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.

Conclusion: 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.

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Figures

Fig. 1
Fig. 1
Non-small-cell lung cancer showing spatial variation in staining for angiogenesis (CD34), pimonidazole (hypoxia), and glucose transporter protein expression (Glut-1)
Fig. 2
Fig. 2
Texture analysis of contrast-enhanced CT images of a colon cancer with the application of different filters highlighting fine, medium, and coarse textures
Fig. 3
Fig. 3
Texture analysis of a T2-weighted MRI image of rectal cancer
Fig. 4
Fig. 4
Dynamic contrast-enhanced CT (perfusion CT) blood flow parametric map (a); 2D image (b); segmented and thresholded image (c) for fractal analysis
Fig. 5
Fig. 5
Changes in texture features of esophageal cancer following neoadjuvant chemotherapy: baseline (a) and following chemotherapy (b). An increase in homogeneity is noted with treatment
Fig. 6
Fig. 6
Changes in texture features of metastatic renal cancer following two cycles of a tyrosine kinase inhibitor: baseline (left) and following therapy (right). An increase in homogeneity is noted with treatment
Fig. 7
Fig. 7
Changes in breast tumor texture following neoadjuvant chemotherapy on T2W-weighted MRI. Tumor shrinkage and an increase in homogeneity are noted following completion of chemotherapy (right)
See this image and copyright information in PMC

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