Texture analysis in non-contrast enhanced CT: Impact of malignancy on texture in apparently disease-free areas of the liver

Ganeshan, Balaji; Miles, Kenneth A.; Young, Rupert; Chatwin, Chris

doi:10.1016/j.ejrad.2007.12.005

Cited by 124 publications

(84 citation statements)

References 33 publications

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“…CTTA technique employed in this study uses a filtration-histogram technique where initial filtration step using Laplacian of Gaussian (LoG) spatial band-pass (non-orthogonal Wavelet) filter to selectively extract and enhance features of different sizes and intensity variation [19,30]. This produces a series of derived images that show features ranging from fine (spatial scaling factor, SSF 2 mm which corresponds to object size of 2 mm in radius), medium (SSF 3,4,5 mm which corresponds to object size of 3,4,5 mm in radius) to coarse (SSF 6, which corresponds to object size of approximately 6 mm in radius) texture maps ( Figure 1) [36,37]. This is followed by quantification using histogram-based statistical parameters (first, second and higher order) which includes mean gray level intensity (Mean), standard deviation (SD, dispersion from the mean), entropy (irregularity), mean of the positive pixels (MPP), skewness (asymmetry), and kurtosis (peakedness or sharpness) at each SSF value.…”

Section: Ct Texture Analysis (Ctta)mentioning

confidence: 99%

Texture analysis of the liver at MDCT for assessing hepatic fibrosis

et al. 2017

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Abstract:Purpose: To evaluate CT texture analysis (CTTA) for staging of hepatic fibrosis (stages F0-F4)Methods: Quantitative texture analysis (QTA) of the liver was performed on abdominal MDCT scans using commercially-available software (TexRAD), which uses a filtration histogram statistic-based technique. Single-slice ROI measurements of the total liver, Couinaud segments IV-VIII, and segments I-III were obtained. CTTA parameters were correlated against fibrosis stage (F0-F4), with biopsy performed within one year for all cases with intermediate fibrosis (F1-F3). Results:The study cohort consisted of 289 adults (158M/131W; mean age, 51 yrs), including healthy controls (F0, n=77), and patients with increasing stages of fibrosis (F1, n=42; F2 n=37; F3 n=53; F4 n=80). Mean of the pixel histogram increased with fibrosis stage, demonstrating an ROC AUC of 0.78 at medium filtration for F0 vs F1-4, with sensitivity and specificity of 74% and 74% at cut-off 0.18. For significant fibrosis (≥F2), mean showed AUCs ranging from 0.71-0.73 across medium and coarse filtered textures with sensitivity and specificity of 71% and 68% at cutoff of 0.3, with similar performance also observed for advanced fibrosis (≥F3). Entropy showed a similar trend. Conversely, kurtosis and skewness decreased with increasing fibrosis, particularly in cirrhotic patients. For cirrhosis (≥F4), kurtosis and skewness showed AUCs of 0.86 and 0.87 respectively at coarse filtered scale, with skewness showing a sensitivity and specificity of 84% and 75% at cutoff of 1.3. Conclusion:CTTA may be helpful in detecting the presence of hepatic fibrosis and discriminating between stages of fibrosis, particularly at advanced levels.

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Section: Ct Texture Analysis (Ctta)mentioning

confidence: 99%

Texture analysis of the liver at MDCT for assessing hepatic fibrosis

et al. 2017

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“…The authors performed texture analysis on CT images using in-house software, which has now became commercially available by the name of TexRAD (Tex-RAD Ltd, Cambridge, UK), and a number of studies using this software have been published. [32][33][34][35][36][37][38][39] The group suggested the use of five LoG filters ranging from fine to coarse textures (filter widths: fine 5 4 image pixels and coarse 5 12 image pixels) and quantifying tumour heterogeneity by measuring the mean, SD, skewness, mean value of positive pixels (MPP), uniformity and entropy from the CT slice with the largest cross section of the tumour. The group reported uniformity, which is inversely proportional to entropy, to show the most significant results in predicting patient survival.…”

Section: Texture Analysis In Ctmentioning

confidence: 99%

The role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning

Alobaidli

McQuaid²,

South³

et al. 2014

BJR

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Cite this article as: Alobaidli S, McQuaid S, South C, Prakash V, Evans P, Nisbet A. The role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning. Br J Radiol 2014;87:20140369. REVIEW ARTICLEThe role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning ABSTRACTPredicting a tumour's response to radiotherapy prior to the start of treatment could enhance clinical care management by enabling the personalization of treatment plans based on predicted outcome. In recent years, there has been accumulating evidence relating tumour texture to patient survival and response to treatment. Tumour texture could be measured from medical images that provide a non-invasive method of capturing intratumoural heterogeneity and hence could potentially enable a prior assessment of a patient's predicted response to treatment. In this article, work presented in the literature regarding texture analysis in radiotherapy in relation to survival and outcome is discussed. Challenges facing integrating texture analysis in radiotherapy planning are highlighted and recommendations for future directions in research are suggested.

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“…Apart from morphologically visible metastases, the presence of tumour may also affect the spatial heterogeneity of the remaining -apparently non-diseased -part of the liver. 21,22 Previous studies in CRC patients without metastases have demonstrated that texture parameters (entropy and uniformity) of the normal liver parenchyma were significantly correlated with the hepatic hemodynamic status and glucose metabolism 23,24 and may also be used as a predictor of survival. 25 Furthermore, results from a study by Ganeshan et al suggest that -in patients with known CRC liver metastases -texture parameters may be more sensitive to tumour-related changes in the apparently disease-free areas of the liver parenchyma than measurements of hepatic attenuation or perfusion.…”

Section: Introductionmentioning

confidence: 99%

Whole‐liver CT texture analysis in colorectal cancer: Does the presence of liver metastases affect the texture of the remaining liver?

et al. 2014

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Background: Liver metastases limit survival in colorectal cancer. Earlier detection of (occult) metastatic disease may benefit treatment and survival. Objective: The objective of this article is to evaluate the potential of whole-liver CT texture analysis of apparently diseasefree liver parenchyma for discriminating between colorectal cancer (CRC) patients with and without hepatic metastases. Methods: The primary staging CT examinations of 29 CRC patients were retrospectively analysed. Patients were divided into three groups: patients without liver metastases (n ¼ 15), with synchronous liver metastases (n ¼ 10) and metachronous liver metastases within 18 months following primary staging (n ¼ 4). Whole-liver texture analysis was performed by delineation of the apparently non-diseased liver parenchyma (excluding metastases or other focal liver lesions) on portal phase images. Mean grey-level intensity (M), entropy (E) and uniformity (U) were derived with no filtration and different filter widths (0.5 ¼ fine, 1.5 ¼ medium, 2.5 ¼ coarse). Results: Mean E 1.5 and E 2.5 for the whole liver in patients with synchronous metastases were significantly higher compared with the non-metastatic patients (p ¼ 0.02 and p ¼ 0.01). Mean U 1.5 and U 2.5 were significantly lower in the synchronous metastases group compared with the non-metastatic group (p ¼ 0.04 and p ¼ 0.02). Texture parameters for the metachronous metastases group were not significantly different from the non-metastatic group or synchronous metastases group (p > 0.05), although -similar to the synchronous metastases group -there was a subtle trend towards increased E 1.5 , E 2.5 and decreased U 1.5 , U 2.5 values. Areas under the ROC curve for the diagnosis of synchronous metastatic disease based on the texture parameters E 1.5,2.5 and U 1.5,2.5 ranged between 0.73 and 0.78. Conclusion: Texture analysis of the apparently non-diseased liver holds promise to differentiate between CRC patients with and without metastatic liver disease. Further research is required to determine whether these findings may be used to benefit the prediction of metachronous liver disease.

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Texture analysis in non-contrast enhanced CT: Impact of malignancy on texture in apparently disease-free areas of the liver

Cited by 124 publications

References 33 publications

Texture analysis of the liver at MDCT for assessing hepatic fibrosis

Texture analysis of the liver at MDCT for assessing hepatic fibrosis

The role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning

Whole‐liver CT texture analysis in colorectal cancer: Does the presence of liver metastases affect the texture of the remaining liver?

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