Role Of Texture Analysis Research Articles

Texture Analysis of Adenomatous and Metastatic Adrenal Lesions on Native and Contrast-enhanced Computed Tomography

Differentiating lipid-poor adrenal adenomas from adrenal metastases is not possible without performing a dedicated contrast-enhanced CT (CECT) protocol. Our purpose is to evaluate the possible role of texture analysis in classifying adrenal lesions.
 This is a retrospective study. We evaluated 33 patients with 47 metastases and 43 patients with 47 adenomas. Seven of the adenomas were lipid-poor, and the rest – lipid-rich adenomas. We used commercially available software for lesion segmentation and texture analysis on native and arterial phase CT images with a slice thickness of 2 mm. The segmentation was semi-automated, and features were computed on the resulting regions of interest (ROI) for each lesion on both native and arterial phases. Two conventional (HU standard deviation, Histogram Entropy) and six second-order texture features (GLCM – Homogeneity, Energy, Entropy log 10, Contrast, Dissimilarity, NGDLM–Busyness) were calculated. For statistical analysis the IBM SPSS 19 package was used to compare the following groups in both phases: 1. Adenomas--Metastasis; 2. Lipid-rich adenomas (LRA)--metastases; 3. Lipid-rich--lipid-poor adenomas (LPA); 4. Lipid-poor adenomas--metastases.
 There was a statistically significant difference in the distribution of some conventional (Histogram Entropy) and second-order features (GLCM – Homogeneity, Energy, Entropy log 10, Dissimilarity) in the first group on the native as well as on the enhanced images. The results were similar when comparing lipid-rich adenomas to metastases. Only native phase derived features were discriminative between lipid-rich and lipid-poor adenomas, with no difference between parameters on CECT. No difference was found between any of the texture features in lipid-poor adenomas and metastases for both phases.
 First- and second-order texture features were graded based on their potential for serving as classifier tools. Unenhanced features ranked higher. Further research and validation are needed to discover the most robust set of features for differentiating between lipid-poor adenomas and metastases.

Magnetic resonance imaging in naso-oropharyngeal carcinoma: role of texture analysis in the assessment of response to radiochemotherapy, a preliminary study

ObjectiveIdentifying MRI texture parameters able to distinguish inflammation, fibrosis, and residual cancer in patients with naso-oropharynx carcinoma after radiochemotherapy (RT-CHT).Material and methodsIn this single-centre, observational, retrospective study, texture analysis was performed on ADC maps and post-gadolinium T1 images of patients with histological diagnosis of naso-oropharyngeal carcinoma treated with RT-CHT. An initial cohort of 99 patients was selected; 57 of them were later excluded. The final cohort of 42 patients was divided into 3 groups (inflammation, fibrosis, and residual cancer) according to MRI, 18F-FDG-PET/CT performed 3–4 months after RT-CHT, and biopsy. Pre-RT-CHT lesions and the corresponding anatomic area post-RT-CHT were segmented with 3D slicer software from which 107 textural features were derived. T-Student and Wilcoxon signed-rank tests were performed, and features with p-value < 0.01 were considered statistically significant. Cut-off values—obtained by ROC curves—to discriminate post-RT-CHT non-tumoural changes from residual cancer were calculated for the parameters statistically associated to the diseased status at follow-up.ResultsTwo features—Energy and Grey Level Non-Uniformity—were statistically significant on T1 images in the comparison between ‘positive’ (residual cancer) and ‘negative’ patients (inflammation and fibrosis). Energy was also found to be statistically significant in both patients with fibrosis and residual cancer. Grey Level Non-Uniformity was significant in the differentiation between residual cancer and inflammation. Five features were statistically significant on ADC maps in the differentiation between ‘positive’ and ‘negative’ patients. The reduction in values of such features between pre- and post-RT-CHT was correlated with a good response to therapy.ConclusionsTexture analysis on post-gadolinium T1 images and ADC maps can differentiate residual cancer from fibrosis and inflammation in early follow-up of naso-oropharyngeal carcinoma treated with RT-CHT.

Computed Tomography for the Diagnosis of Intraperitoneal Infected Fluid Collections after Surgery for Gastric Cancer. Role of Texture Analysis.

Several computed tomographic (CT) imaging features have been proposed to describe the infection of postoperative abdominal fluid collections; however, these features are vague, and there is a significant overlap between infected and non-infected collections. We assessed the role of textural parameters as additional diagnostic tools for distinguishing between infected and non-infected peritoneal collections in patients operated for gastric cancer. From 527 patients operated for gastric cancer, we retrospectively selected 82 cases with intraperitoneal collections who underwent CT exams. The fluid component was analyzed through a novel method (texture analysis); different patterns of pixel intensity and distribution were extracted and processed through a dedicated software (MaZda). A univariate analysis comparing the parameters of texture analysis between the two groups was performed. Afterwards, a multivariate analysis was performed for the univariate statistically significant parameters. The study included 82 patients with bacteriologically verified infected (n=40) and noninfected (n=42) intraperitoneal effusions. The univariate analysis evidenced statistically significant differences between all the parameters involved. The multivariate analysis highlighted 10 parameters as being statistically significant, adjusted to Bonferroni correction. Our evidence supports the fact that textural analysis can be used as a complementary diagnostic tool for the detection of infected fluid collections after gastric cancer surgery. Further studies are required to validate the accuracy of this method.

Ultrasonography in the Differentiation of Endometriomas from Hemorrhagic Ovarian Cysts: The Role of Texture Analysis.

The ultrasonographic (US) features of endometriomas and hemorrhagic ovarian cysts (HOCs) are often overlapping. With the emergence of new computer-aided diagnosis techniques, this is the first study to investigate whether texture analysis (TA) could improve the discrimination between the two lesions in comparison with classic US evaluation. Fifty-six ovarian cysts (endometriomas, 30; HOCs, 26) were retrospectively included. Four classic US features of endometriomas (low-level internal echoes, perceptible walls, no solid components, and less than five locules) and 275 texture parameters were assessed for every lesion, and the ability to identify endometriomas was evaluated through univariate, multivariate, and receiver operating characteristics analyses. The sensitivity (Se) and specificity (Sp) were calculated with 95% confidence intervals (CIs). The texture model, consisting of seven independent predictors (five variations of difference of variance, image contrast, and the 10th percentile; 100% Se and 100% Sp), was able to outperform the ultrasound model composed of three independent features (low-level internal echoes, perceptible walls, and less than five locules; 74.19% Se and 84.62% Sp) in the diagnosis of endometriomas. The TA showed statistically significant differences between the groups and high diagnostic value, but it remains unclear if the textures reflect the intrinsic histological characteristics of the two lesions.

Ultrasonography in the Diagnosis of Adnexal Lesions: The Role of Texture Analysis.

The classic ultrasonographic differentiation between benign and malignant adnexal masses encounters several limitations. Ultrasonography-based texture analysis (USTA) offers a new perspective, but its role has been incompletely evaluated. This study aimed to further investigate USTA’s capacity in differentiating benign from malignant adnexal tumors, as well as comparing the workflow and the results with previously-published research. A total of 123 adnexal lesions (benign, 88; malignant, 35) were retrospectively included. The USTA was performed on dedicated software. By applying three reduction techniques, 23 features with the highest discriminatory potential were selected. The features’ ability to identify ovarian malignancies was evaluated through univariate, multivariate, and receiver operating characteristics analyses, and also by the use of the k-nearest neighbor (KNN) classifier. Three parameters were independent predictors for ovarian neoplasms (sum variance, and two variations of the sum of squares). Benign and malignant lesions were differentiated with 90.48% sensitivity and 93.1% specificity by the prediction model (which included the three independent predictors), and with 71.43–80% sensitivity and 87.5–89.77% specificity by the KNN classifier. The USTA shows statistically significant differences between the textures of the two groups, but it is unclear whether the parameters can reflect the true histopathological characteristics of adnexal lesions.

Computed tomography in the diagnosis of intraperitoneal effusions: The role of texture analysis.

The morphological changes suggesting peritoneal carcinomatosis are inconsistent and may be visible only at later stages of the disease. However, malignant ascites represents an early sign, and this fluid exhibits specific histological characteristics. This study aimed to quantify the fluid properties on computed tomography (CT) images of intraperitoneal effusions through texture analysis and evaluate its utility in differentiating benign from malignant collections. Fifty-two patients with histologically proven benign (n=29) and malignant (n=23) intraperitoneal effusions who underwent CT examinations were retrospectively included. Texture analysis of the fluid component was performed on the non-enhanced phase of each examination using dedicated software. Fisher and the probability of classification error and average correlation coefficients were used to select two sets of ten texture features, whose ability to distinguish between the two types of collections were tested using a k-nearest-neighbor classifier. Also, each of the selected feature’s diagnostic power was assessed using univariate and receiver operating characteristics analysis based on the calculation of the area under the curve. The k-nearest-neighbor classifier was able to distinguish between the two entities with 71.15% accuracy, 73.91% sensitivity, and 68.97% specificity. The highest-ranked texture parameter was Inverse Difference Moment (p=0.0023; area under the curve=0.748), based on which malignant collections could be diagnosed with 95.65% sensitivity and 44.83% specificity. Although successful, the texture assessment of benign and malignant collections is less effective in reflecting the cytological differences between the two groups.

The Role of Texture Analysis and Radiomics in Prediction of Bone Marrow Infiltration in Patients with Lymphoma

Purpose. To explore the ability of image-based parameters with texture parameters in the differentiation of bone marrow infiltration by positron emission tomography-computed tomography (PET/CT). Procedures. We retrospectively evaluated the baseline PET/CT scan of 44 patients with histologically proven lymphoma. Seventy-three of features were extracted using CGITA software and statistical analysis were carried out on SPSS program. Results. Spearman correlation analysis revealed a strong positive correlation between conventional PET metrics and texture features (19 significant association features) and inverse correlation was found (one parameter significant association feature). Area under the curve and p-value of receive operating characteristics showed that (HILRE (4-bin), HILZE (64-bin), LRE (64-bin), LZE (64-bin), max spectrum (8-bin), busyness (64-bin),code similarity (32-bin & 64-bin)) were significant discriminator of bone marrow infiltration among other features (AUC>0.682, p < 0.05). Univariate analyses of texture features showed that code similarity and LRE (both 64 bin) has significant prediction to BMI. Multivariate analyses revealed that LRE (64 bin) p= 0.031; odds ratio: 1.022; 95% CI, 1.002-1.043) were independent variables for bone marrow infiltration. Conclusions. Significant associations emerged between PET features and bone marrow infiltration in lymphoma. Texture analysis on PET/CT shows potential to differentiate between bone marrow infiltration in patients with Lymphoma.

Differentiation of Endometriomas from Ovarian Hemorrhagic Cysts at Magnetic Resonance: The Role of Texture Analysis.

Background and Objectives: To assess ovarian cysts with texture analysis (TA) in magnetic resonance (MRI) images for establishing a differentiation criterion for endometriomas and functional hemorrhagic cysts (HCs) that could potentially outperform their classic MRI diagnostic features. Materials and Methods: Forty-three patients with known ovarian cysts who underwent MRI were retrospectively included (endometriomas, n = 29; HCs, n = 14). TA was performed using dedicated software based on T2-weighted images, by incorporating the whole lesions in a three-dimensional region of interest. The most discriminative texture features were highlighted by three selection methods (Fisher, probability of classification error and average correlation coefficients, and mutual information). The absolute values of these parameters were compared through univariate, multivariate, and receiver operating characteristic analyses. The ability of the two classic diagnostic signs (“T2 shading” and “T2 dark spots”) to diagnose endometriomas was assessed by quantifying their sensitivity (Se) and specificity (Sp), following their conventional assessment on T1-and T2-weighted images by two radiologists. Results: The diagnostic power of the one texture parameter that was an independent predictor of endometriomas (entropy, 75% Se and 100% Sp) and of the predictive model composed of all parameters that showed statistically significant results at the univariate analysis (100% Se, 100% Sp) outperformed the ones shown by the classic MRI endometrioma features (“T2 shading”, 75.86% Se and 35.71% Sp; “T2 dark spots”, 55.17% Se and 64.29% Sp). Conclusion: Whole-lesion MRI TA has the potential to offer a superior discrimination criterion between endometriomas and HCs compared to the classic evaluation of the two lesions’ MRI signal behaviors.

Predictive role of CT texture analysis in patients with metastatic urothelial cancer treated with PD-1/PD-L1 inhibitors.

424 Background: Reliable biomarkers to predict response of urothelial cancer to PD-1/PD-L1 inhibitors are still being investigated. Texture analysis represents underlying tumor heterogeneity and may serve as a predictor of response in urothelial cancer. The purpose of this study was to assess predictive ability of CT texture analysis for disease progression in patients with metastatic urothelial cancer treated with PD-1/PD-L1 inhibitor. Methods: In this IRB-approved HIPAA-compliant retrospective study, from total 93 consecutive patients with metastatic urothelial cancer treated with PD-1/PD-L1 inhibitors from 2013-2018, 43 patients with measurable disease per RECIST 1.1 criteria who had contrast-enhanced CT performed within three months after starting treatment were included. Progression-free survival was calculated based on serial follow-up CTs, and 11 patients without progression who did not reach 1 year follow-up were excluded. Texture features of measurable lesions on first follow-up CT were extracted (TexRAD Ltd, Feedback Plc, Cambridge, UK). Stepwise logistic regression analysis to identify patients who had progressive disease (PD) at 12 months was performed and performance assessed using receiver operator curves. Results: Of 32 included patients (24 men, 8 women; median age: 65 years) who had total 80 measurable lesions, 22 progressed by 12 months. On first follow-up CT, the entropy and mean of the lesions were higher (p = 0.04, p = 0.02) for patients with PD by 12 months. Calculated specificity and sensitivity of entropy (AUC = 0.79) were 90%, and 63%; of mean (AUC = 0.81) were 90%, and 50%. A predictive model including mean and entropy yielded 95% sensitivity, 80% specificity, 91% PPV, 89% NPV and 91% accuracy (AUC = 0.863) to identify patients with PD at 12 months. Conclusions: Texture analysis of CT performed within three months after starting PD-1/PD-L1 can help predict patients who progress by 12 months with high accuracy. Further studies investigating the correlation of texture analysis with survival endpoints may help validate the role of texture analysis as a biomarker to predict response to PD1/PD-L1 inhibitors.

Role of texture analysis in breast MRI as a cancer biomarker: A review.

Breast cancer is a known heterogeneous disease. Current clinically utilized histopathologic biomarkers may undersample tumor heterogeneity, resulting in higher rates of misdiagnosis for breast cancer. MRI can provide a whole-tumor sampling of disease burden and is widely utilized in clinical care. Texture analysis can provide a localized description of breast cancer, with particular emphasis on quantifying breast lesion heterogeneity. The object of this review is to provide an overview of texture analysis applications towards breast cancer diagnosis, prognosis, and treatment response evaluation and review the role of image-based texture features as noninvasive prognostic and predictive biomarkers. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:927-938.

P044] Imaging biomarker analysis of structural MR images for glioma grading

Purpose Preoperative differentiation between low and high grade gliomas is critical for adapting appropriate treatment strategies. However biopsy based diagnosis presents limitations in assessing tumor diffusive and solid growth patterns, respectively, due to the potential subsampling of tumor region as well as the associated patient risk. The purpose of this study is to investigate the value of imaging biomarker analysis in terms of a 3D texture analysis of T1 pre/post-contrast, T2, and DTI maps in the evaluation of tumor heterogeneity towards glioma grading. Methods In this study a semi-automated segmentation technique, based on T1 pre/post-contrast, T2, and DTI parametric maps, is used for the identification of the tumor core and peritumoral edema regions from 3 Tesla MR Data. 3D Texture analysis was performed on 10 low grade and 10 high grade glioma patients, considering several 3D Histogram, Gray Level Co-Occurrence Matrices (GLCM) and Gray Level Run Length Matrices (GLRLM) derived features. Results Initial evaluation of the proposed method is performed in terms of textural features statistical significant differences between the two tumor groups, with the prospect of implementing an advance classification scheme for identifying more complex correlations. The preliminary results suggest that 3D Texture Analysis findings might be utilized as a surrogate biomarker for the global non-invasive assessment of glioma grading. Conclusions Results demonstrate the potential role of Texture Analysis of 3D tumor imaging appearance in structural MRI, for pre-treatment gliomas grade differentiation.

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

Predicting 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.