CT Texture Analysis Research Articles

PD4-08 PRE-TREATMENT CT TEXTURAL ANALYSIS OF LARGE PRIMARY RENAL CELL CARCINOMAS; TUMOR HETEROGENEITY CORRELATES WITH HISTOLOGY AND CLINICAL OUTCOMES

You have accessJournal of UrologyImaging/Radiology: Uroradiology I1 Apr 2015PD4-08 PRE-TREATMENT CT TEXTURAL ANALYSIS OF LARGE PRIMARY RENAL CELL CARCINOMAS; TUMOR HETEROGENEITY CORRELATES WITH HISTOLOGY AND CLINICAL OUTCOMES E. Jason Abel, Meghan Lubner, Alejandro Munoz Del Rio, Nicholas Stabo, and Perry Pickhardt E. Jason AbelE. Jason Abel More articles by this author , Meghan LubnerMeghan Lubner More articles by this author , Alejandro Munoz Del RioAlejandro Munoz Del Rio More articles by this author , Nicholas StaboNicholas Stabo More articles by this author , and Perry PickhardtPerry Pickhardt More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2015.02.320AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES For patients with large RCC tumors, few methods are available to obtain prognostic information prior to surgery. Texture analysis evaluates the distribution and relationship of pixel or voxel-gray levels in the image. The objective of this study was to determine if CT texture features of newly-diagnosed primary renal cell carcinomas (RCC) correlate with pathologic features and oncologic outcomes. METHODS Images and clinical information for consecutive patients with large (>7 cm) RCC tumors treated surgically at our institution. Images were then sent to a texture analysis program (TexRAD Ltd, Somerset, UK). The software output includes a variety of histogram characteristics including mean pixel attenuation, standard deviation of the pixel histogram (sd), entropy, mean of the positive pixels (mpp), skewness (asymmetry) of the pixel histogram, and kurtosis (peakness) of pixel histogram and percent positive (pp) pixels at each spatial scaling factor. All patients were treated with nephrectomy and texture values were correlated with histologic subtype (clear cell, papillary, chromophobe, sarcomatoid features), Fuhrman grade, pathologic stage, and clinical outcomes. RESULTS Texture analysis was performed on pre-surgical CT scans for 157 patients with a mean tumor diameter of 9.9 cm. Mean patient age was 65.2 years and 62.9% of patients were male. Entropy on portal venous phase was positively associated with clear cell histology at coarse filter values (ssf 6, OR 134, 16–1110 p<0.001) and negatively associated with the papillary and chromophobe subtypes (papillary ssf 6, OR 0.016, 0.002-0.132, p<0.001). Mpp and sd were also significantly correlated with histologic cell type in a similar fashion. Entropy, sd, and mpp were correlated with Fuhrman grade, most prominently in the fine-medium texture filters (p-values <0.05). There was significant association of non-contrast texture features including sd, mpp and entropy with time to recurrence and death from disease at coarse filters (eg, entropy ssf 6 HR 3.5 1.5-7.8 p=0.002). CONCLUSIONS CT texture features (entropy, mpp and sd) correlate with tumor histology, Fuhrman grade, and outcome measures. © 2015 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 193Issue 4SApril 2015Page: e89 Advertisement Copyright & Permissions© 2015 by American Urological Association Education and Research, Inc.MetricsAuthor Information E. Jason Abel More articles by this author Meghan Lubner More articles by this author Alejandro Munoz Del Rio More articles by this author Nicholas Stabo More articles by this author Perry Pickhardt More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

Preliminary data using computed tomography texture analysis for the classification of hypervascular liver lesions: generation of a predictive model on the basis of quantitative spatial frequency measurements--a work in progress.

Computed tomography texture analysis (CTTA) is a method of quantifying lesion heterogeneity based on distribution of pixel intensities within a region of interest. This study investigates the ability of CTTA to distinguish different hypervascular liver lesions and compares CTTA parameters by creating a proof-of-concept model to distinguish between different lesions. Following institutional review board approval, CTTA software (TexRAD Ltd) was used to retrospectively analyze 17 cases of focal nodular hyperplasia, 19 hepatic adenomas, 25 hepatocellular carcinomas, and 19 cases of normal liver parenchyma using arterial phase scans. Two radiologists read the same image series used by the CTTA software and reported their best guess diagnosis. Computed tomography texture analysis parameters were computed from regions of interest using spatial band-pass filters to quantify heterogeneity. Random-forest method was used to construct a predictive model from these parameters, and a separate regression model was created using a subset of parameters. The random-forest model successfully distinguished the 3 lesion types and normal liver with predicted classification performance accuracy for 91.2% for adenoma, 94.4% for focal nodular hyperplasia, and 98.6% for hepatocellular carcinoma. This error prediction was generated using a subset of data points not used in generation of the model, but not on discrete prospective cases. In contrast, the 2 human readers using the same image series data analyzed by the CTTA software had lower accuracies, of 72.2% and 65.6%, respectively. The explicit regression model with a subset of image parameters had intermediate overall accuracy of 84.9%. Computed tomography texture analysis may prove valuable in lesion characterization. Differentiation between common hypervascular lesion types could be aided by the judicious incorporation of texture parameters into clinical analysis.

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

Liver metastases limit survival in colorectal cancer. Earlier detection of (occult) metastatic disease may benefit treatment and survival. The objective of this article is to evaluate the potential of whole-liver CT texture analysis of apparently disease-free liver parenchyma for discriminating between colorectal cancer (CRC) patients with and without hepatic metastases. The primary staging CT examinations of 29 CRC patients were retrospectively analysed. Patients were divided into three groups: patients without liver metastases (n = 15), with synchronous liver metastases (n = 10) and metachronous liver metastases within 18 months following primary staging (n = 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 = fine, 1.5 = medium, 2.5 = coarse). Mean E1.5 and E2.5 for the whole liver in patients with synchronous metastases were significantly higher compared with the non-metastatic patients (p = 0.02 and p = 0.01). Mean U1.5 and U2.5 were significantly lower in the synchronous metastases group compared with the non-metastatic group (p = 0.04 and p = 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 E1.5, E2.5 and decreased U1.5, U2.5 values. Areas under the ROC curve for the diagnosis of synchronous metastatic disease based on the texture parameters E1.5,2.5 and U1.5,2.5 ranged between 0.73 and 0.78. 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.

CT texture analysis of pulmonary lesions in patients suspected for lung cancer

Methods Using texture analysis, we analysed 104 lesions in 104 patients suspected for lung cancer with a positive CT correlate. The analysis was performed using TexRAD (developed by TexRAD Ltd. UK). Histology was our reference standard. Malignancy was present in 92 lesions. CTTA comprised a filtration-histogram technique where filtration extracted and enhanced features of different sizes (fine, medium, coarse – scales) followed by histogram analysis using mean (M), entropy (E), uniformity (U), total number of voxels and kurtosis (K) within the entire volume of the suspected lesion. The operator performing the CTTA was blinded to the histological results.

CT Texture Analysis of Renal Masses: Pilot Study Using Random Forest Classification for Prediction of Pathology

Computed tomography texture analysis (CTTA) allows quantification of heterogeneity within a region of interest. This study investigates the possibility of distinguishing between several common renal masses using CTTA-derived parameters by developing and validating a predictive model. CTTA software was used to analyze 20 clear cell renal cell carcinomas (RCCs), 20 papillary RCCs, 20 oncocytomas, and 20 renal cysts. Regions of interest were drawn around each mass on multiple slices in the arterial, venous, and delayed phases on renal mass protocol CT scans. Unfiltered images and spatial band-pass filtered images were analyzed to quantify heterogeneity. Random forest method was used to construct a predictive model to classify lesions using quantitative parameters. The model was externally validated on a separate set of 19 unknown cases. The random forest model correctly categorized oncocytomas in 89% of cases (sensitivity=89%, specificity=99%), clear cell RCCs in 91% of cases (sensitivity=91%, specificity=97%), cysts in 100% of cases (sensitivity=100%, specificity=100%), and papillary RCCs in 100% of cases (sensitivity=100%, specificity=98%). CTTA, in conjunction with random forest modeling, demonstrates promise as a tool to characterize lesions. Various renal masses were accurately classified using quantitative information derived from routine scans.

Quantitative CT texture and shape analysis: can it differentiate benign and malignant mediastinal lymph nodes in patients with primary lung cancer?

To assess the accuracy of CT texture and shape analysis in the differentiation of benign and malignant mediastinal nodes in lung cancer. Forty-three patients with biopsy-proven primary lung malignancy with pathological mediastinal nodal staging and unenhanced CT of the thorax were studied retrospectively. Grey-level co-occurrence and run-length matrix textural features, as well as morphological features, were extracted from 72 nodes. Differences between benign and malignant features were assessed using Mann-Whitney U tests. Receiver operating characteristic (ROC) curves for each were constructed and the area under the curve (AUC) calculated with histopathology diagnosis as outcome. Combinations of features were also entered as predictors in logistic regression models and optimal threshold criteria were used to estimate sensitivity and specificity. Using optimum-threshold criteria, the combined textural and shape features identified malignant mediastinal nodes with 81% sensitivity and 80% specificity (AUC = 0.87, P < 0.0001). Using this combination, 84% malignant and 71% benign nodes were correctly classified. Quantitative CT texture and shape analysis has the potential to accurately differentiate malignant and benign mediastinal nodes in lung cancer. • Mediastinal nodal staging is crucial in the management of lung cancer • Mediastinal nodal metastasis affects prognosis and suitability for surgical treatment • Computed tomography (CT) is limited for mediastinal nodal staging • Texture analysis measures tissue heterogeneity not perceptible to human vision • CT texture analysis may accurately differentiate malignant and benign mediastinal nodes.

CT textural analysis of abdominal aortic aneurysms as a biomarker for aneurysm growth

BackgroundSize of abdominal aortic aneurysm (AAA) does not account entirely for rate of expansion or risk of rupture. More accurate methods of risk stratification are needed. Textural analysis of aneurysm CT data (CTTA) provides one approach. CTTA measures heterogeneity within standard CT images. Greater heterogeneity in tumours corresponds with adverse pathological features such as hypoxia and neovascularisation, and predicts reduced survival. In this study, we prospectively tested CTTA in AAA, assessing its association with annual expansion and need for intervention. In addition, we assessed the correlation between CTTA and 18F-fluorodeoxyglucose (FDG) uptake on PET, a marker of AAA inflammation. MethodsPatients with small and medium AAA underwent contrast CT and FDG PET with usual surveillance. CTTA (TexRAD software) was used on CT images from the sac and shoulder of each aneurysm. Fine, medium, and coarse CT textures were quantified with histogram-based parameters (standard deviation [SD], kurtosis, and entropy). Non-parametric statistical testing was used to assess significant correlations. Findings63 patients with a mean AAA diameter of 4·3 cm (SD 0·7) were included. Median annual expansion was 0·1 cm (IQR 0–0·3), with 13 patients requiring surgery across a median follow-up of 2·3 years (1·9–3·3). Shoulder CTTA kurtosis for medium texture was positively correlated with annual AAA growth (r=0·338 p=0·027). Shoulder CTTA entropy was significantly higher in patients who went on to require surgery (unfiltered images p=0·035), suggesting more heterogeneity in these patients. There were significant correlations between CTTA parameters and PET standardised uptake value in both the sac and shoulder—positive correlations with SD (sac, unfiltered r=0·313, p=0·012; shoulder, fine textures r=0·270, p=0·033), and inverse correlations with kurtosis (sac, unfiltered r=−0·330, p=0·008; sac, fine textures r=−3·03, p=0·017; shoulder, fine textures r=−0·247, p=0·051). InterpretationOur findings suggest that CTTA analysis can provide useful prognostic information about AAA. CT heterogeneity might be correlated with corresponding AAA FDG PET signal. CTTA parameters can be derived from standard CT images, potentially providing a straightforward and cost-effective means of risk stratification. FundingNational Institute for Health Research, Higher Education Funding Council for England.

High Order Texture-Based Analysis in Biomedical Images

There are several different types of medical imaging modalities, among others magnetic resonance imaging (MRI), positron emission tomography (PET), ultrasound, computed tomography (CT) or two-dimensional electrophoresis images (2D-electrophoresis). The number of images is increasing rapidly and the development of automatic image processing systems is necessary in order to aid in diagnostic decisions and therapy assessments. One of the most important features in an image is texture, thus it is one of the central concepts in computer vision and should always be taken into account as an innate property. There are various methods of extracting textural features from images; this work considers statistical methods for texture analysis. Those methods analyze the spatial distribution of gray values by computing local features. Depending on the number of pixels, statistical methods can be classified into first- (one pixel), second- (two pixels) and high-order (three or more pixels). Second- and high-order statistics estimate properties of two or more pixel values occurring at specific locations relative to each other. This paper is focused on the high-order statistics texture analysis of CT, MRI, PET and 2D-electrophoresis images. Keywords: Texture analysis, biomedical images, feature selection, Kernel-Based techniques, Higher-order statistics, feature extraction, biomedical imaging, computer vision.

CT signal heterogeneity of abdominal aortic aneurysm as a possible predictive biomarker for expansion

ObjectiveThere is a need for prognostic biomarkers for risk assessment of small abdominal aortic aneurysm (AAA). Since CT textural analysis of tissue is a recognized feature of adverse biology and patient outcome in other diseases, we investigated it as a possible biomarker in small AAA. MethodsFifty consecutive patients (46-men, 4-woman, median-age 75y, range 56–85) with small AAA (3–5.5 cm) under surveillance undergoing serial ultrasound were prospectively recruited and assessed at baseline with CT texture analysis (CTTA) and 18F-Fluorodeoxyglucose positron emission tomography (18F-FDG-PET). We followed forty patients (36-men, 4-woman, median-age = 74 y, range 60–85, participation rate = 80% for 1 year. For each axial image, CTTA using the filtration-histogram technique was carried out using a software algorithm that selectively extracts texture features of different coarseness (fine, medium and coarse) and intensity variation. Standard-deviation (SD) and kurtosis (K) at each feature-scale were measured. The maximum standardized uptake value (SUVmax) of 18F-FDG in each axial image of the AAA was also measured with corrections for blood pool 18F-FDG activity to assess AAA metabolic activity. Specificity, sensitivity, and c-statistics were calculated with 95% confidence intervals for prediction of significant AAA expansion (≥2 mm) by CTTA measures before and after adjusting for clinical variables. ResultsThe median aneurysm expansion at 12 months was 2.0 mm, (IQR 0.0–4.0). Coarse texture SD correlated inversely with AAA SUVmax (rs = −0.456, P = 0.003). Medium coarse texture K correlated significantly with future AAA expansion adjusted for baseline size (rs = 0.343, P = 0.030). AAA SUVmax correlated inversely with AAA expansion corrected for baseline size (rs = −0.383, P = 0.015). Medium texture K was a strong predictor of significant AAA expansion (area under the Receiver-operating-characteristic (ROC) curve was 0.813) after adjusting for clinical variables. ConclusionWe have shown evidence that CT signal heterogeneity measurements in small aortic aneurysm may be considered as a risk stratification tool in future prospective studies to identify aneurysms at risk of significant expansion. CT textural data appears to reflect AAA metabolism measured by PET.

Computed tomography texture analysis capabilities in diagnosing a chronic obstructive pulmonary disease

The possibility of application of different textural features for the lung disease automatic diagnosis on the basis of the 2D digital computed tomography (CT) images was studied. Histogram features, covariance features, Haralick’s features and run length features were used. A procedure based on the discriminant analysis criterion was used for the selection of the best features group. We experimentally showed that the approach offered is convenient to use for solving the problem of automatic diagnosis on a 160-image set received during examination of patients with a chronic obstructive pulmonary disease. The resulting group of effective features includes two Haralick’s features and three run length features, providing the error rate of 0.11, which is better than similar results obtained without a feature selection procedure.

Textural Features on Computed Tomography Scans Predict Overall Survival in Patients with Esophageal Cancer

Abstract Purpose: To predict overall survival (OS) in non-metastatic esophageal cancer using texture analysis of pre-therapy computed tomography (CT) images. Materials and Methods: Records from 762 non-metastatic esophageal cancer patients with non-contrast CT scans (obtained from 1998-2011) before receiving chemoradiation were retrospectively reviewed. 328 quantitative image features were extracted from the esophageal gross tumor volume (GTV). A random survival forest model compared how well five of these features (entropy, histogram 10th percentile, volume, volume-to-area ratio, fraction GTV pruned after thresholding) predicted OS versus all 328 features. Cox proportional hazards modeling was used to derive scores, based on these five features, which could stratify patients by survival in a training set consisting of 50% of the 762 cases, chosen randomly from the data. This model was then tested in a validation set (remaining 50% of cases). Multivariate analysis was done with the image-derived score and other prognostic variables. Results: CT texture analysis based on the five image-derived features yielded a similar concordance rate for predicting OS (56%) as did all 328 features (56%), and in fact showed higher concordance for predicting OS than disease stage alone (44%). This image-derived score was also able to significantly stratify OS (P<0.05) in both the training and validation set, as well as independently predict OS in multivariate analysis (HR 1.61, 95% CI 1.13-2.29, P=0.009), along with stage, treatment with surgery, tumor grade, and radiation modality. Conclusions: Texture features from pretreatment CT images can independently predict OS in patients with non-metastatic esophageal carcinoma.

SU-E-T-280: CT Texture Analysis of Non-Small Cell Lung Carcinoma Is Not Predictive of SNP Status

Purpose: Several single‐nucleotide polymorphisms (SNPs) have been shown to be associated with overall survival in patients with non‐small cell lung cancer. Also, CT texture analysis of the tumor has shown that tumor heterogeneity is associated with overall survival. Here we investigate whether there is any association between texture features and SNP status. Methods: 139 NSCLC patients were identified who had been treated for chemo‐radiation therapy, and for whom both SNP data and 4DCT simulation images with consistent imaging parameters were available. 14 geometric and texture features, selected from a previous reproducibility/redundancy study, were extracted from the physician‐drawn target on the exhale‐phase CT images. The data were randomly divided into a training set consisting of 2/3 of the patients (n=93), and a test set consisting of the remaining 50 cases. For each SNP and texture feature, the Kruskal‐Wallis test was used to investigate whether there were differences in the texture features between patients of different genotypes. To get an estimate of how many false positives would be expected per SNP, permutation tests were performed, where the SNP genotypes were randomly permuted among the patients and the Kruskal‐Wallis tests were repeated. This process was repeated 500 times. Results: 6 comparisons met the nominal P<0.05 in the training set, but none of these were confirmed in the test data, indicating that there are no association between texture features and SNPs. When the analysis was repeated with the entire dataset, 9 comparisons were significant at the P<0.05 level, but permutation analysis indicated that 8.1 comparisons would have P<0.05 by chance. Conclusion: There is no strong evidence for an association between any of the 12 SNPs and 14 texture features investigated here. Our next step is to investigate whether combining SNPs and CT textures has a better predictive capability than using SNPs only.

SU-E-CAMPUS-I-06: Imaging Biomarkers of CT Textural Analysis Correlate to Genomic Expression in Oral Cavity Squamous Cell Carcinoma

Purpose: To determine whether correlations exist between CT‐generated texture features and genetic biomarkers in oral cavity squamous cell carcinoma (OCSCC). Methods: We retrospectively analyzed 27 OCSCC patients with surplus frozen tissue and preoperative CT for genetic expression of vascular endothelial growth factor (VEGF) ligands A B, and C, VEGF receptors 1, 2, and 3, Cyclin D1 protein, and epidermal growth factor receptor (EGFR). Each tumor was segmented, blinded to the results of the gene expression analysis, on their preoperative CT and was modified into a 6‐bit grayscale image prior to texture feature extraction. Seven calculated texture features were computed based on intensity histograms (IHIST) and co‐occurrence matrices (COM). Pearson correlation coefficients (PC) were calculated between texture features and genetic expression. A sub‐group analysis (n = 17) was performed excluding patients that had a CT slice thickness greater than 1mm. Results: Correlations were found between IHIST‐kurtosis and VEGF‐receptor1 (PC = 0.42, p = 0.03) as well as between IHIST‐mean and COM‐correlation with VEGF‐receptor 1 (PC = 0.40, p = 0.04 and ‐ 0.39, p = 0.04, respectively). In the subset analysis with more controlled preoperative CT scans, the correlation between IHIST‐kurtosis and VEGF‐receptor1 remained (PC = 0.54, p = 0.03) while the correlations with VEGF‐receptor 1 did not. Additionally, IHIST‐entropy was found to correlate with VEGF ligand A (PC = −0.51, p = 0.04). This correlation is arguably the most useful as an established chemotherapy agent exists for VEGFA (bevacizumab). However, no correlations survived a Bonferroni correction for multiple hypothesis testing in the initial or subset analyses. Conclusion: This study provides some preliminary data indicating that CT‐based texture features may be correlated with genetic expression in OCSCC. In future studies, a larger sample size and more focused hypothesis of texture correlations with specific genetic targets should be implemented.

CT texture analysis using the filtration-histogram method: what do the measurements mean?

Analysis of texture within tumours on computed tomography (CT) is emerging as a potentially useful tool in assessing prognosis and treatment response for patients with cancer. This article illustrates the image and histological features that correlate with CT texture parameters obtained from tumours using the filtration-histogram approach, which comprises image filtration to highlight image features of a specified size followed by histogram analysis for quantification. Computer modelling can be used to generate texture parameters for a range of simple hypothetical images with specified image features. The model results are useful in explaining relationships between image features and texture parameters. The main image features that can be related to texture parameters are the number of objects highlighted by the filter, the brightness and/or contrast of highlighted objects relative to background attenuation, and the variability of brightness/contrast of highlighted objects. These relationships are also demonstrable by texture analysis of clinical CT images. The results of computer modelling may facilitate the interpretation of the reported associations between CT texture and histopathology in human tumours. The histogram parameters derived during the filtration-histogram method of CT texture analysis have specific relationships with a range of image features. Knowledge of these relationships can assist the understanding of results obtained from clinical CT texture analysis studies in oncology.

Quantifying tumour heterogeneity with CT

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.

Measurements of heterogeneity in gliomas on computed tomography relationship to tumour grade

To undertake a preliminary study that uses CT texture analysis (CTTA) to quantify heterogeneity in gliomas on contrast-enhanced CT and to assess the relationship between tumour heterogeneity and grade. Retrospective analysis of contrast enhanced CT images was performed in 44 patients with histologically proven cerebral glioma between 2007 and 2010. 11 tumours were low grade (Grade I = 3; Grade II, = 8) and 33 high grade (Grade III = 10, Grade IV = 23). CTTA assessment of tumour heterogeneity was performed using a proprietary software algorithm (TexRAD) that selectively filters and extracts textures at different anatomical scales between filter values 1.0 (fine detail) and 2.5 (coarse features). Heterogeneity was quantified as standard deviation (SD) with or without filtration. Tumour heterogeneity, size and attenuation were correlated with tumour grade. For each parameter, receiver operating characteristics characterised the diagnostic performance for discrimination of high grade from low grade glioma and of grade III tumours from grade IV. Further the CTTA was compared to the radiological diagnosis. Tumour heterogeneity correlated significantly with grade (SD without filtration rs = 0.664, p < 0.001, SD with coarse filtration (rs = 0.714, p < 0.001). Tumour size and attenuation showed only moderate correlations with tumour grade (rs = 0.426, p = 0.004 and rs = 0.447, p = 0.002 respectively). Coarse texture was the best discriminator between high and low grade tumours (AUC 0.832, p < 0.0001) and between grade III and grade IV gliomas (AUC = 0.878 p = 0.0001). Compared to the radiological diagnosis, CTTA further characterised the indetermined cases. By quantifying tumour heterogeneity, CTTA has the potential to provide a marker of tumour grade for patients with cerebral glioma. By differentiating between high and low grade tumours, CTTA could possibly assist clinical management.

Assessment of tumor heterogeneity by CT texture analysis: Can the largest cross-sectional area be used as an alternative to whole tumor analysis?

ObjectiveTo 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. MethodsEntropy (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 non-parametric Wilcoxon test. Kaplan–Meier analysis was performed to determine the relationship between CT texture and 5-year overall survival. ResultsE 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. ConclusionWhole tumor analysis appears more representative of tumor heterogeneity.

Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival

To establish the potential for tumour heterogeneity in non-small cell lung cancer (NSCLC) as assessed by CT texture analysis (CTTA) to provide an independent marker of survival for patients with NSCLC. Tumour heterogeneity was assessed by CTTA of unenhanced images of primary pulmonary lesions from 54 patients undergoing (18)F-fluorodeoxyglucose (FDG) PET-CT for staging of NSCLC. CTTA comprised image filtration to extract fine, medium and coarse features with quantification of the distribution of pixel values (uniformity) within the filtered images. Receiver operating characteristics identified thresholds for PET and CTTA parameters that were related to patient survival using Kaplan-Meier analysis. The median (range) survival was 29.5 (1-38) months. 24, 10, 14 and 6 patients had tumour stages I, II, III and IV respectively. PET stage and tumour heterogeneity assessed by CTTA were significant independent predictors of survival (PET stage: Odds ratio 3.85, 95% confidence limits 0.9-8.09, P = 0.002; CTTA: Odds ratio 56.4, 95% confidence limits 4.79-666, p = 0.001). SUV was not a significantly associated with survival. Assessment of tumour heterogeneity by CTTA of non-contrast enhanced images has the potential for to provide a novel, independent predictor of survival for patients with NSCLC. Computed tomography is a routine staging procedure in non-small cell lung cancer. CT texture analysis (CTTA) can quantify heterogeneity within these lung tumours. CTTA seems to offer a novel independent predictor of survival for NSCLC. CTTA could contribute to disease risk-stratification for patients with NSCLC.

Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: Preliminary evidence of an association with tumour metabolism, stage, and survival

To undertake a pilot study assessing whether tumour heterogeneity evaluated using computed tomography texture analysis (CTTA) has the potential to provide a marker of tumour aggression and prognosis in oesophageal cancer. In 21 patients, unenhanced CT images of the primary oesophageal lesion obtained using positron-emission tomography (PET)-CT examinations underwent CTTA. CTTA was carried out using a software algorithm that selectively filters and extracts textures at different anatomical scales between filter values 1.0 (fine detail) and 2.5 (coarse features) with quantification as entropy and uniformity (measures image heterogeneity). Texture parameters were correlated with average tumour 2-[(18)F]-fluoro-2-deoxy-d-glucose (FDG) uptake [standardized uptake values (SUV(mean) and SUV(max))] and clinical staging as determined by endoscopic ultrasound (nodal involvement) and PET-CT (distant metastases). The relationship between tumour stage, FDG uptake, and texture with survival was assessed using Kaplan-Meier analysis. Tumour heterogeneity correlated with SUV(max) and SUV(mean). The closest correlations were found for SUV(mean) measured as uniformity and entropy with coarse filtration (r=-0.754, p<0.0001; and r=0.748, p=0.0001 respectively). Heterogeneity was also significantly greater in patients with clinical stage III or IV for filter values between 1.0 and 2.0 (maximum difference at filter value 1.5: entropy: p=0.027; uniformity p=0.032). The median (range) survival was 21 (4-34) months. Tumour heterogeneity assessed by CTTA (coarse uniformity) was an independent predictor of survival [odds ratio (OR)=4.45 (95% CI: 1.08, 18.37); p=0.039]. CTTA assessment of tumour heterogeneity has the potential to identify oesophageal cancers with adverse biological features and provide a prognostic indicator of survival.

Imaging heterogeneity in gliomas using texture analysis

Aim: To preliminary assess whether computed tomography texture analysis (CTTA) has the potential to quantify tumour heterogeneity and thereby differentiate high-grade from low-grade gliomas. Methods: The study comprised 44 patients with brain tumours. Histology identified 33 as high-grade and 11 as low-grade gliomas. Contrast-enhanced CT images were acquired prior to brain biopsy. CTTA was carried out using a proprietary software algorithm (TexRAD) that selectively filters and extracts textures at different anatomical scales between filter values 1.0 (fine detail) and 2.5 (coarse features). Heterogeneity within the CT image (with or without filtration) was quantified using the following statistical parameters: entropy (indicates irregularity), uniformity (distribution of grey levels), total pixels (TP; indicates size), mean grey-level value and proportion of only those pixels with positive values (MPP and PPP), standard-deviation (SD), kurtosis (indicates peakedness) and skewness (indicates asymmetry). Texture parameters from high and low grade were compared with each other. Results: High grade demonstrated increased heterogeneity as measured from CTTA (entropy ↑, uniformity ↓, TP ↑, PPP ↓, SD ↑, skewness↑). Significant differences between tumour heterogeneity for high and low grade was observed for medium to coarse textures, with the highest being coarse textures; quantified as entropy (P = 0.0015), uniformity (P = 0.0026). Coarse texture values for entropy of >5.2(sensitivity 76%, specificity 82%), ≤0.025(sensitivity 64%, specificity 91%) for uniformity, was suggestive of high-grade glioma. TP (sensitivity 70%, specificity 73%) was higher in high grade than low grade, however less significant than texture. Conclusion: CTTA (e.g. TexRAD) has the potential to quantify tumour heterogeneity more so than size and thereby differentiate between high- and low-grade gliomas.