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. 2013 Mar 26;13(1):140-9.
doi: 10.1102/1470-7330.2013.0015.

Quantifying tumour heterogeneity with CT

Balaji Ganeshan  1 , Kenneth A Miles
Affiliations

Quantifying tumour heterogeneity with CT

Balaji Ganeshan et al. Cancer Imaging. .

Abstract

Heterogeneity is a key feature of malignancy associated with adverse tumour biology. Quantifying heterogeneity could provide a useful non-invasive imaging biomarker. Heterogeneity on computed tomography (CT) can be quantified using texture analysis which extracts spatial information from CT images (unenhanced, contrast-enhanced and derived images such as CT perfusion) that may not be perceptible to the naked eye. The main components of texture analysis can be categorized into image transformation and quantification. Image transformation filters the conventional image into its basic components (spatial, frequency, etc.) to produce derived subimages. Texture quantification techniques include structural-, model- (fractal dimensions), statistical- and frequency-based methods. The underlying tumour biology that CT texture analysis may reflect includes (but is not limited to) tumour hypoxia and angiogenesis. Emerging studies show that CT texture analysis has the potential to be a useful adjunct in clinical oncologic imaging, providing important information about tumour characterization, prognosis and treatment prediction and response.

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Figures

Figure 1
Figure 1
Conventional CT image of a CRC lesion (A) and corresponding images selectively displaying fine (B), medium (C) and coarse (D) texture obtained by using LoG filter values of 1.0 (width, 4 pixels or 3.9 mm), 1.5 (width, 6 pixels or 5.9 mm) and 2.5 (width, 12 pixels or 11.8 mm), respectively (courtesy of Professor Ashley Groves, Institute of Nuclear Medicine, University College Hospital, London, UK).
Figure 2
Figure 2
The histogram displays the range and frequency of pixel intensity values within the medium filtered (filter value 1.5) image of a CRC lesion (courtesy of Professor Ashley Groves, Institute of Nuclear Medicine, University College Hospital, London, UK).
Figure 3
Figure 3
An illustration of positive and negative skewed histogram (courtesy of http://en.wikipedia.org/wiki/Skewness).
Figure 4
Figure 4
An illustration of positive and negative kurtosis (courtesy of http://en.wikipedia.org/wiki/Kurtosis).
See this image and copyright information in PMC

References

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