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Assessment of tumor heterogeneity by CT texture analysis: Can the largest cross-sectional area be used as an alternative to whole tumor analysis?
European Journal of Radiology 82 (2013) 342–348
Contents lists available at SciVerse ScienceDirect
European Journal of Radiology journal homepage: www.elsevier.com/locate/ejrad
Assessment of tumor heterogeneity by CT texture analysis: Can the largest cross-sectional area be used as an alternative to whole tumor analysis? Francesca Ng a , Robert Kozarski b , Balaji Ganeshan c , Vicky Goh d,∗ a
Paul Strickland Scanner Centre, Mount Vernon Hospital, Northwood, Middlesex, UK CliCr, University of Hertfordshire, Hatfield, Hertfordshire, UK c Clinical Imaging Sciences, University of Sussex, Falmer, Sussex, UK d Cancer Imaging, Division of Imaging Sciences and Biomedical Engineering, Kings College London, King’s Health Partners, London, UK b
a r t i c l e
i n f o
Article history: Received 17 August 2012 Accepted 4 October 2012 Keywords: Computed tomography Texture analysis Colon Rectum Tumor
a b s t r a c t Objective: To determine if there is a difference between contrast enhanced CT texture features from the largest cross-sectional area versus the whole tumor, and its effect on clinical outcome prediction. Methods: Entropy (E) and uniformity (U) were derived for different filter values (1.0–2.5: fine to coarse textures) for the largest primary tumor cross-sectional area and the whole tumor of the staging contrast enhanced CT in 55 patients with primary colorectal cancer. Parameters were compared using nonparametric Wilcoxon test. Kaplan–Meier analysis was performed to determine the relationship between CT texture and 5-year overall survival. Results: E was higher and U lower for the whole tumor indicating greater heterogeneity at all filter levels (1.0–2.5): median (range) for E and U for whole tumor versus largest cross-sectional area of 7.89 (7.43–8.31) versus 7.62 (6.94–8.08) and 0.005 (0.004–0.01) versus 0.006 (0.005–0.01) for filter 1.0; 7.88 (7.22–8.48) versus 7.54 (6.86–8.1) and 0.005 (0.003–0.01) versus 0.007 (0.004–0.01) for filter 1.5; 7.88 (7.17–8.54) versus 7.48 (5.84–8.25) and 0.005 (0.003–0.01) versus 0.007 (0.004–0.02) for filter 2.0; and 7.83 (7.03–8.57) versus 7.42 (5.19–8.26) and 0.005 (0.003–0.01) versus 0.006 (0.004–0.03) for filter 2.5 respectively (p ≤ 0.001). Kaplan–Meier analysis demonstrated better separation of E and U for whole tumor analysis for 5-year overall survival. Conclusion: Whole tumor analysis appears more representative of tumor heterogeneity. © 2012 Published by Elsevier Ireland Ltd.
1. Introduction Intratumoral heterogeneity is a recognized feature of malignancy reflecting areas of high cell density, necrosis, haemorrhage and myxoid change [1]. Tumor heterogeneity is an important prognostic factor, as high intra-tumoral heterogeneity may be associated with higher tumor grades [2]. Thus, there is a need for tools which can assess this, and that may improve risk stratification of patients for treatment at staging. Various image processing methods may extract spatial information from medical images that may not be appreciated by the naked eye. Computed tomography (CT) texture analysis is one approach of quantifying spatial heterogeneity, extracting data of the pixel spatial intensity variations across a
∗ Corresponding author at: Imaging 2, Level 1, Lambeth Wing, St Thomas’ Hospital, Lambeth Palace Road, London, SE1 7EH, UK. Tel.: +44 207 1885538; fax: +44 207 188 5454. E-mail addresses: kher shing [email protected] (F. Ng), [email protected] (R. Kozarski), [email protected] (B. Ganeshan), [email protected] (V. Goh). 0720-048X/$ – see front matter © 2012 Published by Elsevier Ireland Ltd. http://dx.doi.org/10.1016/j.ejrad.2012.10.023
tissue of interest. Using different filters CT texture features that may be extracted including entropy (E) and uniformity (U): Entropy is a measure of texture irregularity, while uniformity is a measure of the gray level distribution within the tumor. Thus a heterogenous lesion will display high entropy and low uniformity. Previous studies in various cancer have shown that higher entropy and lower uniformity may predict for poorer survival [3–8]. These studies have been performed at a single axial level, typically the largest cross section of the lesion rather than the whole tumor. How representative this is of the whole tumor is unknown. Instinctively there is likely to be a discrepancy between these parameter values and those obtained from the whole tumor. Intuitively whole tumor analysis should provide a more comprehensive evaluation of underlying spatial heterogeneity. In colorectal cancer we have previously shown that whole tumor CT texture may reflect 5-year overall survival [9]. The aim of this substudy was to determine if there is a difference between contrast enhanced CT texture features from the largest cross-sectional area versus the whole tumor in primary colorectal cancer, and prediction of outcome.
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of texture. This produces a series of derived images displaying features at different spatial scales from fine to coarse texture within a region of interest (ROI) drawn around the colorectal tumor (Fig. 2). The scale was selected by tuning the filter parameter between 1.0 and 2.5; where 1.0 indicates fine texture (features of 4 pixels in width), 1.5 and 2.0 indicate medium textures (features of 6 and 10 pixels in width respectively) and 2.5 indicates coarse texture (features of 12 pixels in width). For each patient, a ROI was delineated around the largest cross-sectional area of the tumor outline and further refined by the exclusion of areas of air with a thresholding procedure that removed from analysis any pixels with attenuation values below -50 HU. The ROI was then propagated automatically to include the entire tumor volume. Where necessary the ROI was adjusted further manually at each individual level. Mean ROI size was 62,497 pixels (range, 6734–546,533 pixels). Heterogeneity within the ROI was quantified by calculating entropy (irregularity, E) and uniformity (distribution of gray level, U), with and without image filtration, for the largest cross-sectional area of the tumor and whole tumor volume respectively. Higher entropy and lower uniformity represent increased heterogeneity. 2.4. Follow up Fig. 1. Flowchart of processes detailing patient selection and inclusion into the study for further analysis.
2. Materials and methods
Patients were followed up as per usual clinical practice. If patients remained alive they were censored at 5 years post imaging. Date of death was recorded for deceased patients allowing overall survival at 5 years to be assessed.
2.1. Patients 3. Statistical analysis This retrospective analysis was performed on archival data acquired during a prospective research study conducted over the period from October 2001 to September 2005. Institutional ethical approval and patient informed consent was obtained for this research study. Sixty-two consecutive patients [30 male, mean age of 69.1 years (range 34.9–83.6); 32 female, mean age 66.8 years (range 28.1–84.7)] with primary colorectal cancer underwent contrast enhanced portal venous CT (thorax, abdomen, pelvis) as part of their standard imaging staging. The study group consisted 55 of these 62 patients for which their staging contrast enhanced CT was retrievable from the archive and for whom follow up data was available (Fig. 1). 2.2. Computed tomography All patients underwent contrast enhanced CT of the chest, abdomen and pelvis. This was performed on a 4-MDCT (GE Lightspeed Plus, GE Healthcare, Amersham, UK) following 100 mL of 340 mg/mL iodinated contrast (Niopam 340; Bracco, Milan, Italy) at 5 mL/s injection rate using a pump injector (Percupump Touchscreen; EZ-EM, Westbury, NY, USA). Image acquisition commenced 75 s after intravenous contrast injection using the following parameters: 120 kV, 280 mAs, 0.6 s rotation time, 5 mm slice collimation; FOV 300, matrix 512. The staging CT was reported as per usual institutional clinical practice at the time of acquisition. 2.3. Texture analysis The archival contrast enhanced CT studies were loaded onto a workstation for further textural analysis using TexRAD (University of Sussex, Falmer, Sussex). This was performed by a single observer (*) with 4 years’ experience of abdominal CT, and blinded to clinical information. The texture analysis technique used for this study was as previously described [10] (Appendix). This technique comprised of an initial image filtration step to selectively extract features of different sizes and intensity variation, followed by quantification
Median (range) for each texture features was derived. Texture features were compared between the largest tumor cross-sectional area and the whole tumor using non-parametric Wilcoxon test. A p-value of <0.05 was considered significant. Receiver operating curve (ROC) and Kaplan–Meier analysis was performed to determine the relationship, if any, between CT texture features of the tumor and 5-year overall survival. The optimum point on the ROC curve for each texture parameter at different filter values (fine, medium, coarse) was considered the threshold for predicting overall survival. 4. Results 4.1. Patients There were 8 Stage I, 19 Stage II, 17 Stage III and 11 Stage IV cancers. Of these, 26 were in the rectum, 12 in the sigmoid colon, 2 in the descending colon, 1 in the transverse colon, 3 in the ascending colon and 11 in the caecum. Twenty-six of the 55 patients died within 45 months of their initial CT examination: 17 were found to have distant metastases (9 at staging; 8 subsequent to treatment) and 1 had a synchronous tumor. The median (range) for E and U without filtration and for filter scale values of 1.0 (fine), 1.5, 2.0 (medium) and 2.5 (coarse) texture respectively are summarized in Table 1. E was higher and U lower for both the unfiltered (p = < 0.001), and filtered images (fine (p = < 0.001), medium (p = < 0.001) and coarse textures (p = < 0.001) for the whole tumor volume compared to the largest cross-sectional area, indicating greater heterogeneity values for the whole tumor. E values ranged from 3%, 4–6% and 7% higher for fine, medium and coarse features respectively, U values from 13–15%, 20–27% and 32% lower for fine, medium and coarse features respectively. The ROC and Kaplan Meier analysis at different scale values are summarized in Table 2. Kaplan Meier curves were significantly
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Fig. 2. (A) Contrast enhanced CT of a proximal ascending colon tumor and corresponding images displaying fine (b), medium (c) and coarse (d) textures obtained by using filter values of 1.0 (width, 4 pixels), 1.5 (width, 6 pixels) and 2.5 (width, 12 pixels). (B) Volume-rendered left sagittal view of (A) conventional contrast-enhanced CT whole tumor and corresponding (B) fine, (C) medium and (D) coarse texture in a colorectal cancer patient.
different for entropy and uniformity for fine texture (filter 1.0) of whole tumor volume but not for the largest cross-sectional area of tumor (Figs. 3 and 4). 5. Discussion In our study we have shown that whole tumor analysis may provide a more representative evaluation of tumor heterogeneity. Parameter values were significantly higher for entropy, a measure of texture irregularity for both unfiltered and filtered images. Conversely uniformity, a measure of gray level distribution was significantly lower. To date many published studies of CT texture analysis have been performed for the largest cross-sectional tumor area (Table 3). These studies have shown promising results in terms of biological correlates [5,14], prognostic [3,7,8] and predictive [4,6,15] potential. For example, in a study of 17 patients with non small cell lung cancer (NSCLC), unenhanced CT texture analysis coarse texture uniformity correlated negatively with tumor SUV and may reflect metabolic change associated with the presence of necrosis,
haemorrhage and myxoid change [5]. Several studies assessing the prognostic potential of texture analysis showed that high tumor heterogeneity is associated with poorer outcome. In a study involving 54 patients with NSCLC undergoing staging PET–CT, patients with heterogenous tumors at coarse texture uniformity < 0.624 did not survive more than 2.5 years [8]. A study of 21 patients with primary oesophageal cancer undergoing staging PET-CT demonstrated that patients with heterogenous tumors with coarse texture uniformity of ≤0.8477 had poorer survival and coarse texture uniformity was an independent predictor of survival. This study also showed that higher stage tumors exhibited significantly greater heterogeneity at medium but not fine textures [7]. Skogen et al. found that high grade gliomas demonstrated increased heterogeneity with a significant difference between high and low grade tumors observed at coarse texture entropy > 5.2 (with a sensitivity of 76% and specificity of 82%) and uniformity ≤ 0.025 (with a sensitivity of 64% and specificity of 91%) [6]. Goh et al. found that at coarse texture scale, percentage change in tumor uniformity in patients with metastatic renal cell cancer following 2 cycles of tyrosine kinase inhibitors is a significant independent predictor of time
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Table 1 Median (range) for entropy and uniformity without filtration and for absolute filter scale values depicting fine (1.0), medium (1.5, 2.0) and coarse (2.5) textures respectively. Texture features were compared between that of the largest cross-sectional area of tumor and whole tumor volume using Wilcoxon text; significance at 5%. Texture parameter
No filtration Entropy Uniformity Filter value = 1.0 (fine) Entropy Uniformity Filter value = 1.5 (medium) Entropy Uniformity Filter value = 2.0 (medium) Entropy Uniformity Filter value = 2.5 (coarse) Entropy Uniformity
Median (range) texture values
p-Value (Wilcoxon test)
Largest cross-sectional area
Whole tumor volume
6.81 (6.32–7.25) 0.01 (0.008–0.02)
7.07 (6.59–7.55) 0.009 (0.006–0.01)
<0.001
7.62 (6.94–8.08) 0.006 (0.005–0.01)
7.89 (7.43–8.31) 0.005 (0.004–0.007)
<0.001
7.54 (6.86–8.1) 0.007 (0.005–0.01)
7.88 (7.22–8.48) 0.005 (0.003–0.008)
<0.001
7.48 (5.84–8.25) 0.007 (0.004–0.02)
7.88 (7.17–8.54) 0.005 (0.003–0.009)
<0.001
7.42 (5.19–8.26) 0.006 (0.004–0.003)
7.83 (7.03–8.57) 0.005 (0.003–0.01)
<0.001
to progression, and a better predictor of response than the current response assessment methods (RECIST and modified CHOI) [15]. Nevertheless the use of the largest cross-sectional area rather than the whole tumor to extract texture features is a limitation given the overall aim to quantify heterogeneity. Previous pathological study of 1075 patients with advanced colorectal cancer who had no distant metastasis at time of surgery has shown that the extent of the poorly differentiated component in colorectal cancer—defined as a region where a tumor has no glandular formation—positively correlates with T-stage and N-stage grading of the tumor. The 5-year survival rate was significantly different (p < 0.0001) for grades I (99.3%), II (86%) and III (65.5%)
<0.001
<0.001
<0.001
<0.001
<0.001
tumors with tumor grade being the highest in mucinous carcinoma [16]. Other pathological studies in rectal cancer have shown that increased tumor microvessel density and de-differentiation of tumor cells at the advancing edge of the tumor correlate with increasing depth of tumor invasion and had poorer survival [17,18]. In the past the use of the largest cross-sectional area has been a pragmatic one, given the time consuming nature of whole tumor analysis. However, with development of commercial platforms facilitating whole tumor analysis with automated processing, e.g. segmentation, it is important to highlight that whole tumor analysis may improve clinical evaluation. In our study we found that separation of the K–M curve for 5 year
Table 2 Summary of ROC and Kaplan–Meier (KM) analysis for mean gray-level intensity, entropy and uniformity at different scale values for the largest cross-sectional area of tumor and whole tumor volume. Texture parameter
Largest cross section
Whole tumor volume
ROC threshold
p-Value (KM)
ROC threshold
No filtration Entropy Uniformity
≤6.72 ≤0.01
0.10 0.32
≤7.06 >0.01
0.73 0.49
Filter value = 1.0 (fine) Entropy Uniformity
≤7.90 >0.01
0.09 0.47
≤7.89 >0.01
0.002 0.02
Filter value = 1.5 (medium) Entropy Uniformity
≤7.5 >0.01
0.29 0.18
≤7.92 >0.01
0.01 0.19
Filter value = 2.0 (medium) Entropy Uniformity
≤7.49 >0.01
0.43 0.73
≤7.98 >0.01
0.049 0.37
Filter value = 2.5 (coarse) Entropy Uniformity
≤7.49 ≤0.01
0.45 0.75
≤7.80 >0.01
0.14 0.15
p-Value (KM)
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F. Ng et al. / European Journal of Radiology 82 (2013) 342–348
Fig. 3. Kaplan–Meier curves at filter value 1.0 for entropy showing a significant difference in survival in whole tumor volume analysis (a), and no significant difference in survival using largest cross sectional area of tumor (b).
Fig. 4. Kaplan–Meier curves at filter value 1.0 for uniformity showing a significant difference in survival in whole tumor volume analysis (a), and no significant difference in survival using largest cross sectional area of tumor (b).
Table 3 Table showing published studies of CT texture analysis that have been performed for the largest cross-sectional tumor area. Tumor type
Analysis
Feature
Author
Year
NSCLC NSCLC Hepatic tumors Lymph node texture in patients with rectal cancer Liver texture in patients with colorectal cancer but no known metastases Liver texture in patients with colorectal cancer Liver texture in patients with colorectal cancer Liver texture in patients with colorectal cancer Oesophageal cancer Glioma Metastatic renal cell cancer
Largest cross-sectional area Largest cross-sectional area Largest cross-sectional area Largest cross-sectional area Largest cross-sectional area
E, U, MGI U Autocovariance function Fractal dimension, MGI E, U, MGI
Ganeshan et al. [5] Ganeshan et al. [8] Huang et al. [11] Cui et al. [12] Ganeshan et al. [3]
2010 2012 2006 2011 2007
Largest cross-sectional area Largest cross-sectional area Largest cross-sectional area Largest cross-sectional area Largest cross-sectional area Largest cross sectional area
U, MGI E, U, MGI U, MGI E, U E, U, MGI, TP, PPP, S, K, SD E, U
Miles et al. [4] Ganeshan et al. [13] Ganeshan et al. [14] Ganeshan et al. [7] Skogen et al. [6] Goh et al. [15]
2009 2011 2007 2012 2011 2011
Abbreviations. MGI, mean gray level intensity; E, entropy; U, uniformity; TP, total pixels; PPP, proportion of positive pixels; S, skewness of pixel distribution; SD, standard deviation of pixels distribution; K, kurtosis of pixel distribution; NSCLC, non small cell lung cancer.
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Fig. A1. The 2-D forms of the LoG filter in the spatial and frequency domain at filter value of 2.0.
overall survival was better for whole tumor than largest crosssectional area analysis particularly at filter levels of 1.0 reflecting fine texture. This suggests that in some tumors the largest crosssectional area values may not be representative of the tumor as a whole. Nevertheless there are still some challenges to clinical application. These results are from a retrospective analysis on a relatively small number of patients. Reproducibility and generalisability of whole tumor analysis has yet to be established. However results are promising for clinical practice. In conclusion, assessment of spatial heterogeneity by texture analysis may be best reflected by whole tumor analysis. Acknowledgements The authors thank the University of Sussex for software provision. The corresponding author acknowledges the financial support from the Department of Health via the National Institute of Health Research (NIHR) Biomedical Research Centre award to Guys’ and St Thomas’ NHS Foundation Trust, in partnership with King’s College London and King’s College Hospital NHS Foundation Trust; and Comprehensive Cancer Imaging Centre, funded by the Cancer Research UK and Engineering and Physical Sciences Research Council in association with the Medical Research Council and Department of Health (England)
Appendix A. Appendix Textural analysis consists of two main stages: (a) image filtration and (b) quantification of texture. For image filtration, a Laplacian of Gaussian (LoG) band-pass filter is chosen, and modulated to highlight different spatial scales between fine texture (filter value = 1.0) and coarse texture (filter value = 2.5). This scale can be considered as the width at which structures in the image will be highlighted and enhanced, while structures less than this width will become blurred (Fig. A1). The two-dimensional (2D) Gaussian distribution G is given by: G(x, y) = e−((x
2 +y2 )/2 2 )
(E1)
where x, y are the spatial coordinates of the image matrix and is the standard deviation of the Gaussian distribution. The 2D Gaussian distribution effectively blurs the image, wiping out all structures at scales much smaller than the sigma of the Gaussian. This distribution has the desirable characteristics of being smooth and localized in both the spatial and frequency domains, and is therefore less likely to introduce any changes to the original image. Thus, the Gaussian distribution corresponding to a particular value allows highlighting of only texture features of a particular scale in contrast agent-enhanced CT images. We have used this filtration technique to filter out textural features of varying scale; fine scale enhances parenchyma, while medium-to-coarse scale enhances blood vessels.
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Table A1 Filter values and the corresponding widths of the filter in pixels. Filter value
Filter width (pixels)
1 (fine) 1.5 (medium) 2 (medium) 2.5 (coarse)
4 6 10 12
by calculating entropy (irregularity, e) and uniformity (distribution of gray level, u) using the equations below: k
e=−
u= (2 )
The reason for using the Laplacian is that it is the lowestorder orientation-independent (isotropic) differential operator and inherently has less computational burden and can be used to detect intensity changes in an image that correspond to the zero crossings of the filter. Following filtration, any pixels with negative values were assigned a value of zero. 2 G is the Laplacian of Gaussian filter, a circularly symmetric, Mexican-hat-shaped filter (see Fig. A1 for spatial and frequency domain representations of the filter) whose distribution in the 2D spatial domain is given by: −1 ∇ G(x, y) = 4 2
x 2 + y2 1− 2 2
e−((x
[p(l)]log2 [p(l)]
(E3)
l−1
2 +y2 )/2 2 )
k
[p(l)]
2
(E4)
l=1
where l is the pixel value (between l = 1 to k in the ROI, and p(l) is the probability of the occurrence of that pixel value. Higher entropy and lower uniformity represent increased heterogeneity. Entropy and uniformity were recorded for absolute scale values for each patient’s tumor on the baseline CT and CT following 2 cycles of treatment. The percentage change in image entropy and uniformity was also recorded. References
(E2)
From the mathematical expression of this circularly symmetric filter at different values, the number of pixels representing the width between the diametrically opposite zero-crossing points in this filter can be calculated (Fig. A1). The width of the filter at different values is obtained by evaluating the Laplacian of the Gaussian spatial distribution along the x and y directions. The lower the value, the smaller is the width of the filter in the spatial domain and the larger the pass-band region of the filter in the frequency domain, highlighting fine details or features in the filtered image in the spatial domain. Similarly in the spatial domain, a higher value allows coarse features to be highlighted in the filtered image. Filtration can be done in the spatial or frequency domain. In the spatial domain, the filter mask is convolved with the image, which involves intensive computation. It is more efficient to use the filter in the frequency domain, as convolution of the filter mask and the image in the spatial domain is equivalent to multiplication of the Fourier transforms of the filter mask and image in the frequency domain. The inverse Fourier transform of the filtered spectrum gives the resultant filtered image in the spatial domain. Also, the accuracy of this filtering operation is improved when used in the frequency domain, as the quantization errors arising from the convolution of the filter, especially for small values in the spatial domain, would distort the image. In our study, the filter was applied in the frequency domain. Quantification of texture Regions of interest (ROIs) were delineated around the tumor outline. The ROI was further refined by the exclusion of areas of air with a thresholding procedure that removed from analysis any pixels with attenuation values below −50 HU. The ROIs for texture analysis were stored as a binary mask and assigned to the corresponding to the individual patient metastasis. This ensured that the same patient-specific ROI was used for the texture analyses of different values. The resulting images underwent 2D band-pass filtering with the Laplacian of Gaussian filter using each of the following filter values: 1.0, 1.5, 2.0, and 2.5 (Table A1). Heterogeneity within this ROI was quantified with and without image filtration
[1] McArdle CS, Hole D, Hansell D, et al. Prospective study of colorectal cancer in the west of Scotland: 10-year follow-up. British Journal of Surgery 1990;77:280–2. [2] Eccles SA, Welch DR. Metastasis: recent discoveries and novel treatment strategies. Lancet 2007;369:1742–57. [3] Ganeshan B, Miles KA, Young RC, et al. Hepatic enhancement in colorectal cancer: texture analysis correlates with hepatic haemodynamics and patient survival. Academic Radiology 2007;14:1520–30. [4] Miles KA, Ganeshan B, Griffiths MR, et al. Colorectal cancer: texture analysis of portal phase hepatic CT images as a potential marker of survival. Radiology 2009;250:444–52. [5] Ganeshan B, Abaleke S, Young R, et al. Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage. Cancer Imaging 2010;10:137–43. [6] Skogen K, Ganeshan B, Good T, et al. Imaging heterogeneity in gliomas using texture analysis. Cancer Imaging 2011;11:S113. Spec No. A. [7] Ganeshan B, Skogen K, Pressney I, et al. Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage and survival. Clinical Radiology 2012;67:157–64. [8] Ganeshan B, Panayiotou E, Burnand K, et al. Tumour heterogeneity in non-small cell lung cancer assessed by CT texture analysis: a potential marker of survival. European Radiology 2012;22:796–802. [9] Ng F, Ganeshan B, Kozarski R, Miles KA, Goh V., Assessment of Primary Colorectal Cancer Heterogeneity by Using Whole-Tumor Texture Analysis: Contrast-enhanced CT Texture as a Biomarker of 5-year Survival. Radiology. 2012. [November 14 Epub ahead of print]. [10] Ganeshan B, Miles KA, Young RC, et al. In search of biologic correlates for liver texture on portal phase CT. Academic Radiology 2007;14:1058–68. [11] Huang YL, Chen JH, Shen WC. Diagnosis of hepatic tumors with texture analysis in nonenhanced computed tomography images. Academic Radiology 2006;13:713–20. [12] Cui C, Cai H, Liu L, et al. Quantitative analysis and prediction of regional lymph node status in rectal cancer based on computed tomography imaging. European Radiology 2011;21:2318–25. [13] Ganeshan B, Burnand K, Young R, et al. Dynamic contrast-enhanced texture analysis of the liver: initial assessment in colorectal cancer. Investigative Radiology 2011;46:160–8. [14] Ganeshan B, Miles KA, Young RC, et al. In search of biological correlates for liver texture on portal-phase CT. Academic Radiology 2007;14:1058–68. [15] Goh V, Ganeshan B, Nathan P, et al. Assessment of response to tyrosine kinase inhibitors (TKIs) in metastatic renal cell cancer: CT texture as a predictive biomarker. Radiology 2011;261:165–71. [16] Ueno H, Mochizuki H, Hashiguchi Y, et al. Histological grading of colorectal cancer: a simple and objective method. Annals of Surgery 2008;247: 811–8. [17] Rasheed S, Harris AL, Tekkis PP, et al. Assessment of microvessel density and carbonic anhydrase-9 (CA-9) expression in rectal cancer. Pathology, Research and Practice 2009;205:1–9. [18] Ueno H, Price AB, Wilkinson KH, et al. A new prognostic staging system for rectal cancer. Annals of Surgery 2004;240:832–9.
Contents lists available at SciVerse ScienceDirect
European Journal of Radiology journal homepage: www.elsevier.com/locate/ejrad
Assessment of tumor heterogeneity by CT texture analysis: Can the largest cross-sectional area be used as an alternative to whole tumor analysis? Francesca Ng a , Robert Kozarski b , Balaji Ganeshan c , Vicky Goh d,∗ a
Paul Strickland Scanner Centre, Mount Vernon Hospital, Northwood, Middlesex, UK CliCr, University of Hertfordshire, Hatfield, Hertfordshire, UK c Clinical Imaging Sciences, University of Sussex, Falmer, Sussex, UK d Cancer Imaging, Division of Imaging Sciences and Biomedical Engineering, Kings College London, King’s Health Partners, London, UK b
a r t i c l e
i n f o
Article history: Received 17 August 2012 Accepted 4 October 2012 Keywords: Computed tomography Texture analysis Colon Rectum Tumor
a b s t r a c t Objective: To determine if there is a difference between contrast enhanced CT texture features from the largest cross-sectional area versus the whole tumor, and its effect on clinical outcome prediction. Methods: Entropy (E) and uniformity (U) were derived for different filter values (1.0–2.5: fine to coarse textures) for the largest primary tumor cross-sectional area and the whole tumor of the staging contrast enhanced CT in 55 patients with primary colorectal cancer. Parameters were compared using nonparametric Wilcoxon test. Kaplan–Meier analysis was performed to determine the relationship between CT texture and 5-year overall survival. Results: E was higher and U lower for the whole tumor indicating greater heterogeneity at all filter levels (1.0–2.5): median (range) for E and U for whole tumor versus largest cross-sectional area of 7.89 (7.43–8.31) versus 7.62 (6.94–8.08) and 0.005 (0.004–0.01) versus 0.006 (0.005–0.01) for filter 1.0; 7.88 (7.22–8.48) versus 7.54 (6.86–8.1) and 0.005 (0.003–0.01) versus 0.007 (0.004–0.01) for filter 1.5; 7.88 (7.17–8.54) versus 7.48 (5.84–8.25) and 0.005 (0.003–0.01) versus 0.007 (0.004–0.02) for filter 2.0; and 7.83 (7.03–8.57) versus 7.42 (5.19–8.26) and 0.005 (0.003–0.01) versus 0.006 (0.004–0.03) for filter 2.5 respectively (p ≤ 0.001). Kaplan–Meier analysis demonstrated better separation of E and U for whole tumor analysis for 5-year overall survival. Conclusion: Whole tumor analysis appears more representative of tumor heterogeneity. © 2012 Published by Elsevier Ireland Ltd.
1. Introduction Intratumoral heterogeneity is a recognized feature of malignancy reflecting areas of high cell density, necrosis, haemorrhage and myxoid change [1]. Tumor heterogeneity is an important prognostic factor, as high intra-tumoral heterogeneity may be associated with higher tumor grades [2]. Thus, there is a need for tools which can assess this, and that may improve risk stratification of patients for treatment at staging. Various image processing methods may extract spatial information from medical images that may not be appreciated by the naked eye. Computed tomography (CT) texture analysis is one approach of quantifying spatial heterogeneity, extracting data of the pixel spatial intensity variations across a
∗ Corresponding author at: Imaging 2, Level 1, Lambeth Wing, St Thomas’ Hospital, Lambeth Palace Road, London, SE1 7EH, UK. Tel.: +44 207 1885538; fax: +44 207 188 5454. E-mail addresses: kher shing [email protected] (F. Ng), [email protected] (R. Kozarski), [email protected] (B. Ganeshan), [email protected] (V. Goh). 0720-048X/$ – see front matter © 2012 Published by Elsevier Ireland Ltd. http://dx.doi.org/10.1016/j.ejrad.2012.10.023
tissue of interest. Using different filters CT texture features that may be extracted including entropy (E) and uniformity (U): Entropy is a measure of texture irregularity, while uniformity is a measure of the gray level distribution within the tumor. Thus a heterogenous lesion will display high entropy and low uniformity. Previous studies in various cancer have shown that higher entropy and lower uniformity may predict for poorer survival [3–8]. These studies have been performed at a single axial level, typically the largest cross section of the lesion rather than the whole tumor. How representative this is of the whole tumor is unknown. Instinctively there is likely to be a discrepancy between these parameter values and those obtained from the whole tumor. Intuitively whole tumor analysis should provide a more comprehensive evaluation of underlying spatial heterogeneity. In colorectal cancer we have previously shown that whole tumor CT texture may reflect 5-year overall survival [9]. The aim of this substudy was to determine if there is a difference between contrast enhanced CT texture features from the largest cross-sectional area versus the whole tumor in primary colorectal cancer, and prediction of outcome.
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of texture. This produces a series of derived images displaying features at different spatial scales from fine to coarse texture within a region of interest (ROI) drawn around the colorectal tumor (Fig. 2). The scale was selected by tuning the filter parameter between 1.0 and 2.5; where 1.0 indicates fine texture (features of 4 pixels in width), 1.5 and 2.0 indicate medium textures (features of 6 and 10 pixels in width respectively) and 2.5 indicates coarse texture (features of 12 pixels in width). For each patient, a ROI was delineated around the largest cross-sectional area of the tumor outline and further refined by the exclusion of areas of air with a thresholding procedure that removed from analysis any pixels with attenuation values below -50 HU. The ROI was then propagated automatically to include the entire tumor volume. Where necessary the ROI was adjusted further manually at each individual level. Mean ROI size was 62,497 pixels (range, 6734–546,533 pixels). Heterogeneity within the ROI was quantified by calculating entropy (irregularity, E) and uniformity (distribution of gray level, U), with and without image filtration, for the largest cross-sectional area of the tumor and whole tumor volume respectively. Higher entropy and lower uniformity represent increased heterogeneity. 2.4. Follow up Fig. 1. Flowchart of processes detailing patient selection and inclusion into the study for further analysis.
2. Materials and methods
Patients were followed up as per usual clinical practice. If patients remained alive they were censored at 5 years post imaging. Date of death was recorded for deceased patients allowing overall survival at 5 years to be assessed.
2.1. Patients 3. Statistical analysis This retrospective analysis was performed on archival data acquired during a prospective research study conducted over the period from October 2001 to September 2005. Institutional ethical approval and patient informed consent was obtained for this research study. Sixty-two consecutive patients [30 male, mean age of 69.1 years (range 34.9–83.6); 32 female, mean age 66.8 years (range 28.1–84.7)] with primary colorectal cancer underwent contrast enhanced portal venous CT (thorax, abdomen, pelvis) as part of their standard imaging staging. The study group consisted 55 of these 62 patients for which their staging contrast enhanced CT was retrievable from the archive and for whom follow up data was available (Fig. 1). 2.2. Computed tomography All patients underwent contrast enhanced CT of the chest, abdomen and pelvis. This was performed on a 4-MDCT (GE Lightspeed Plus, GE Healthcare, Amersham, UK) following 100 mL of 340 mg/mL iodinated contrast (Niopam 340; Bracco, Milan, Italy) at 5 mL/s injection rate using a pump injector (Percupump Touchscreen; EZ-EM, Westbury, NY, USA). Image acquisition commenced 75 s after intravenous contrast injection using the following parameters: 120 kV, 280 mAs, 0.6 s rotation time, 5 mm slice collimation; FOV 300, matrix 512. The staging CT was reported as per usual institutional clinical practice at the time of acquisition. 2.3. Texture analysis The archival contrast enhanced CT studies were loaded onto a workstation for further textural analysis using TexRAD (University of Sussex, Falmer, Sussex). This was performed by a single observer (*) with 4 years’ experience of abdominal CT, and blinded to clinical information. The texture analysis technique used for this study was as previously described [10] (Appendix). This technique comprised of an initial image filtration step to selectively extract features of different sizes and intensity variation, followed by quantification
Median (range) for each texture features was derived. Texture features were compared between the largest tumor cross-sectional area and the whole tumor using non-parametric Wilcoxon test. A p-value of <0.05 was considered significant. Receiver operating curve (ROC) and Kaplan–Meier analysis was performed to determine the relationship, if any, between CT texture features of the tumor and 5-year overall survival. The optimum point on the ROC curve for each texture parameter at different filter values (fine, medium, coarse) was considered the threshold for predicting overall survival. 4. Results 4.1. Patients There were 8 Stage I, 19 Stage II, 17 Stage III and 11 Stage IV cancers. Of these, 26 were in the rectum, 12 in the sigmoid colon, 2 in the descending colon, 1 in the transverse colon, 3 in the ascending colon and 11 in the caecum. Twenty-six of the 55 patients died within 45 months of their initial CT examination: 17 were found to have distant metastases (9 at staging; 8 subsequent to treatment) and 1 had a synchronous tumor. The median (range) for E and U without filtration and for filter scale values of 1.0 (fine), 1.5, 2.0 (medium) and 2.5 (coarse) texture respectively are summarized in Table 1. E was higher and U lower for both the unfiltered (p = < 0.001), and filtered images (fine (p = < 0.001), medium (p = < 0.001) and coarse textures (p = < 0.001) for the whole tumor volume compared to the largest cross-sectional area, indicating greater heterogeneity values for the whole tumor. E values ranged from 3%, 4–6% and 7% higher for fine, medium and coarse features respectively, U values from 13–15%, 20–27% and 32% lower for fine, medium and coarse features respectively. The ROC and Kaplan Meier analysis at different scale values are summarized in Table 2. Kaplan Meier curves were significantly
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Fig. 2. (A) Contrast enhanced CT of a proximal ascending colon tumor and corresponding images displaying fine (b), medium (c) and coarse (d) textures obtained by using filter values of 1.0 (width, 4 pixels), 1.5 (width, 6 pixels) and 2.5 (width, 12 pixels). (B) Volume-rendered left sagittal view of (A) conventional contrast-enhanced CT whole tumor and corresponding (B) fine, (C) medium and (D) coarse texture in a colorectal cancer patient.
different for entropy and uniformity for fine texture (filter 1.0) of whole tumor volume but not for the largest cross-sectional area of tumor (Figs. 3 and 4). 5. Discussion In our study we have shown that whole tumor analysis may provide a more representative evaluation of tumor heterogeneity. Parameter values were significantly higher for entropy, a measure of texture irregularity for both unfiltered and filtered images. Conversely uniformity, a measure of gray level distribution was significantly lower. To date many published studies of CT texture analysis have been performed for the largest cross-sectional tumor area (Table 3). These studies have shown promising results in terms of biological correlates [5,14], prognostic [3,7,8] and predictive [4,6,15] potential. For example, in a study of 17 patients with non small cell lung cancer (NSCLC), unenhanced CT texture analysis coarse texture uniformity correlated negatively with tumor SUV and may reflect metabolic change associated with the presence of necrosis,
haemorrhage and myxoid change [5]. Several studies assessing the prognostic potential of texture analysis showed that high tumor heterogeneity is associated with poorer outcome. In a study involving 54 patients with NSCLC undergoing staging PET–CT, patients with heterogenous tumors at coarse texture uniformity < 0.624 did not survive more than 2.5 years [8]. A study of 21 patients with primary oesophageal cancer undergoing staging PET-CT demonstrated that patients with heterogenous tumors with coarse texture uniformity of ≤0.8477 had poorer survival and coarse texture uniformity was an independent predictor of survival. This study also showed that higher stage tumors exhibited significantly greater heterogeneity at medium but not fine textures [7]. Skogen et al. found that high grade gliomas demonstrated increased heterogeneity with a significant difference between high and low grade tumors observed at coarse texture entropy > 5.2 (with a sensitivity of 76% and specificity of 82%) and uniformity ≤ 0.025 (with a sensitivity of 64% and specificity of 91%) [6]. Goh et al. found that at coarse texture scale, percentage change in tumor uniformity in patients with metastatic renal cell cancer following 2 cycles of tyrosine kinase inhibitors is a significant independent predictor of time
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Table 1 Median (range) for entropy and uniformity without filtration and for absolute filter scale values depicting fine (1.0), medium (1.5, 2.0) and coarse (2.5) textures respectively. Texture features were compared between that of the largest cross-sectional area of tumor and whole tumor volume using Wilcoxon text; significance at 5%. Texture parameter
No filtration Entropy Uniformity Filter value = 1.0 (fine) Entropy Uniformity Filter value = 1.5 (medium) Entropy Uniformity Filter value = 2.0 (medium) Entropy Uniformity Filter value = 2.5 (coarse) Entropy Uniformity
Median (range) texture values
p-Value (Wilcoxon test)
Largest cross-sectional area
Whole tumor volume
6.81 (6.32–7.25) 0.01 (0.008–0.02)
7.07 (6.59–7.55) 0.009 (0.006–0.01)
<0.001
7.62 (6.94–8.08) 0.006 (0.005–0.01)
7.89 (7.43–8.31) 0.005 (0.004–0.007)
<0.001
7.54 (6.86–8.1) 0.007 (0.005–0.01)
7.88 (7.22–8.48) 0.005 (0.003–0.008)
<0.001
7.48 (5.84–8.25) 0.007 (0.004–0.02)
7.88 (7.17–8.54) 0.005 (0.003–0.009)
<0.001
7.42 (5.19–8.26) 0.006 (0.004–0.003)
7.83 (7.03–8.57) 0.005 (0.003–0.01)
<0.001
to progression, and a better predictor of response than the current response assessment methods (RECIST and modified CHOI) [15]. Nevertheless the use of the largest cross-sectional area rather than the whole tumor to extract texture features is a limitation given the overall aim to quantify heterogeneity. Previous pathological study of 1075 patients with advanced colorectal cancer who had no distant metastasis at time of surgery has shown that the extent of the poorly differentiated component in colorectal cancer—defined as a region where a tumor has no glandular formation—positively correlates with T-stage and N-stage grading of the tumor. The 5-year survival rate was significantly different (p < 0.0001) for grades I (99.3%), II (86%) and III (65.5%)
<0.001
<0.001
<0.001
<0.001
<0.001
tumors with tumor grade being the highest in mucinous carcinoma [16]. Other pathological studies in rectal cancer have shown that increased tumor microvessel density and de-differentiation of tumor cells at the advancing edge of the tumor correlate with increasing depth of tumor invasion and had poorer survival [17,18]. In the past the use of the largest cross-sectional area has been a pragmatic one, given the time consuming nature of whole tumor analysis. However, with development of commercial platforms facilitating whole tumor analysis with automated processing, e.g. segmentation, it is important to highlight that whole tumor analysis may improve clinical evaluation. In our study we found that separation of the K–M curve for 5 year
Table 2 Summary of ROC and Kaplan–Meier (KM) analysis for mean gray-level intensity, entropy and uniformity at different scale values for the largest cross-sectional area of tumor and whole tumor volume. Texture parameter
Largest cross section
Whole tumor volume
ROC threshold
p-Value (KM)
ROC threshold
No filtration Entropy Uniformity
≤6.72 ≤0.01
0.10 0.32
≤7.06 >0.01
0.73 0.49
Filter value = 1.0 (fine) Entropy Uniformity
≤7.90 >0.01
0.09 0.47
≤7.89 >0.01
0.002 0.02
Filter value = 1.5 (medium) Entropy Uniformity
≤7.5 >0.01
0.29 0.18
≤7.92 >0.01
0.01 0.19
Filter value = 2.0 (medium) Entropy Uniformity
≤7.49 >0.01
0.43 0.73
≤7.98 >0.01
0.049 0.37
Filter value = 2.5 (coarse) Entropy Uniformity
≤7.49 ≤0.01
0.45 0.75
≤7.80 >0.01
0.14 0.15
p-Value (KM)
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Fig. 3. Kaplan–Meier curves at filter value 1.0 for entropy showing a significant difference in survival in whole tumor volume analysis (a), and no significant difference in survival using largest cross sectional area of tumor (b).
Fig. 4. Kaplan–Meier curves at filter value 1.0 for uniformity showing a significant difference in survival in whole tumor volume analysis (a), and no significant difference in survival using largest cross sectional area of tumor (b).
Table 3 Table showing published studies of CT texture analysis that have been performed for the largest cross-sectional tumor area. Tumor type
Analysis
Feature
Author
Year
NSCLC NSCLC Hepatic tumors Lymph node texture in patients with rectal cancer Liver texture in patients with colorectal cancer but no known metastases Liver texture in patients with colorectal cancer Liver texture in patients with colorectal cancer Liver texture in patients with colorectal cancer Oesophageal cancer Glioma Metastatic renal cell cancer
Largest cross-sectional area Largest cross-sectional area Largest cross-sectional area Largest cross-sectional area Largest cross-sectional area
E, U, MGI U Autocovariance function Fractal dimension, MGI E, U, MGI
Ganeshan et al. [5] Ganeshan et al. [8] Huang et al. [11] Cui et al. [12] Ganeshan et al. [3]
2010 2012 2006 2011 2007
Largest cross-sectional area Largest cross-sectional area Largest cross-sectional area Largest cross-sectional area Largest cross-sectional area Largest cross sectional area
U, MGI E, U, MGI U, MGI E, U E, U, MGI, TP, PPP, S, K, SD E, U
Miles et al. [4] Ganeshan et al. [13] Ganeshan et al. [14] Ganeshan et al. [7] Skogen et al. [6] Goh et al. [15]
2009 2011 2007 2012 2011 2011
Abbreviations. MGI, mean gray level intensity; E, entropy; U, uniformity; TP, total pixels; PPP, proportion of positive pixels; S, skewness of pixel distribution; SD, standard deviation of pixels distribution; K, kurtosis of pixel distribution; NSCLC, non small cell lung cancer.
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Fig. A1. The 2-D forms of the LoG filter in the spatial and frequency domain at filter value of 2.0.
overall survival was better for whole tumor than largest crosssectional area analysis particularly at filter levels of 1.0 reflecting fine texture. This suggests that in some tumors the largest crosssectional area values may not be representative of the tumor as a whole. Nevertheless there are still some challenges to clinical application. These results are from a retrospective analysis on a relatively small number of patients. Reproducibility and generalisability of whole tumor analysis has yet to be established. However results are promising for clinical practice. In conclusion, assessment of spatial heterogeneity by texture analysis may be best reflected by whole tumor analysis. Acknowledgements The authors thank the University of Sussex for software provision. The corresponding author acknowledges the financial support from the Department of Health via the National Institute of Health Research (NIHR) Biomedical Research Centre award to Guys’ and St Thomas’ NHS Foundation Trust, in partnership with King’s College London and King’s College Hospital NHS Foundation Trust; and Comprehensive Cancer Imaging Centre, funded by the Cancer Research UK and Engineering and Physical Sciences Research Council in association with the Medical Research Council and Department of Health (England)
Appendix A. Appendix Textural analysis consists of two main stages: (a) image filtration and (b) quantification of texture. For image filtration, a Laplacian of Gaussian (LoG) band-pass filter is chosen, and modulated to highlight different spatial scales between fine texture (filter value = 1.0) and coarse texture (filter value = 2.5). This scale can be considered as the width at which structures in the image will be highlighted and enhanced, while structures less than this width will become blurred (Fig. A1). The two-dimensional (2D) Gaussian distribution G is given by: G(x, y) = e−((x
2 +y2 )/2 2 )
(E1)
where x, y are the spatial coordinates of the image matrix and is the standard deviation of the Gaussian distribution. The 2D Gaussian distribution effectively blurs the image, wiping out all structures at scales much smaller than the sigma of the Gaussian. This distribution has the desirable characteristics of being smooth and localized in both the spatial and frequency domains, and is therefore less likely to introduce any changes to the original image. Thus, the Gaussian distribution corresponding to a particular value allows highlighting of only texture features of a particular scale in contrast agent-enhanced CT images. We have used this filtration technique to filter out textural features of varying scale; fine scale enhances parenchyma, while medium-to-coarse scale enhances blood vessels.
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Table A1 Filter values and the corresponding widths of the filter in pixels. Filter value
Filter width (pixels)
1 (fine) 1.5 (medium) 2 (medium) 2.5 (coarse)
4 6 10 12
by calculating entropy (irregularity, e) and uniformity (distribution of gray level, u) using the equations below: k
e=−
u= (2 )
The reason for using the Laplacian is that it is the lowestorder orientation-independent (isotropic) differential operator and inherently has less computational burden and can be used to detect intensity changes in an image that correspond to the zero crossings of the filter. Following filtration, any pixels with negative values were assigned a value of zero. 2 G is the Laplacian of Gaussian filter, a circularly symmetric, Mexican-hat-shaped filter (see Fig. A1 for spatial and frequency domain representations of the filter) whose distribution in the 2D spatial domain is given by: −1 ∇ G(x, y) = 4 2
x 2 + y2 1− 2 2
e−((x
[p(l)]log2 [p(l)]
(E3)
l−1
2 +y2 )/2 2 )
k
[p(l)]
2
(E4)
l=1
where l is the pixel value (between l = 1 to k in the ROI, and p(l) is the probability of the occurrence of that pixel value. Higher entropy and lower uniformity represent increased heterogeneity. Entropy and uniformity were recorded for absolute scale values for each patient’s tumor on the baseline CT and CT following 2 cycles of treatment. The percentage change in image entropy and uniformity was also recorded. References
(E2)
From the mathematical expression of this circularly symmetric filter at different values, the number of pixels representing the width between the diametrically opposite zero-crossing points in this filter can be calculated (Fig. A1). The width of the filter at different values is obtained by evaluating the Laplacian of the Gaussian spatial distribution along the x and y directions. The lower the value, the smaller is the width of the filter in the spatial domain and the larger the pass-band region of the filter in the frequency domain, highlighting fine details or features in the filtered image in the spatial domain. Similarly in the spatial domain, a higher value allows coarse features to be highlighted in the filtered image. Filtration can be done in the spatial or frequency domain. In the spatial domain, the filter mask is convolved with the image, which involves intensive computation. It is more efficient to use the filter in the frequency domain, as convolution of the filter mask and the image in the spatial domain is equivalent to multiplication of the Fourier transforms of the filter mask and image in the frequency domain. The inverse Fourier transform of the filtered spectrum gives the resultant filtered image in the spatial domain. Also, the accuracy of this filtering operation is improved when used in the frequency domain, as the quantization errors arising from the convolution of the filter, especially for small values in the spatial domain, would distort the image. In our study, the filter was applied in the frequency domain. Quantification of texture Regions of interest (ROIs) were delineated around the tumor outline. The ROI was further refined by the exclusion of areas of air with a thresholding procedure that removed from analysis any pixels with attenuation values below −50 HU. The ROIs for texture analysis were stored as a binary mask and assigned to the corresponding to the individual patient metastasis. This ensured that the same patient-specific ROI was used for the texture analyses of different values. The resulting images underwent 2D band-pass filtering with the Laplacian of Gaussian filter using each of the following filter values: 1.0, 1.5, 2.0, and 2.5 (Table A1). Heterogeneity within this ROI was quantified with and without image filtration
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