Gray-level Nonuniformity Research Articles

Prognostic Value of Functional Parameters of 18F-FDG-PET Images in Patients with Primary Renal/Adrenal Lymphoma.

Objectives The aim of this study is to explore the textural features that may identify the morphological changes in the lymphoma region and predict the prognosis of patients with primary renal lymphoma (PRL) and primary adrenal lymphoma (PAL). Methods This retrospective study comprised nineteen non-Hodgkin's lymphoma (NHL) patients undergoing 18F-FDG-PET/CT at West China Hospital from December 2013 to May 2017. 18F-FDG-PET images were reviewed independently by two board certificated radiologists of nuclear medicine, and the texture features were extracted from LifeX packages. The prognostic value of PET FDG-uptake parameters, patients' baseline characteristics, and textural parameters were analyzed using Kaplan–Meier analysis. Cox regression analysis was used to identify the independent prognostic factors among the imaging and clinical features. Results The overall survival of included patients was 18.84 ± 13.40 (mean ± SD) months. Univariate Cox analyses found that the tumor stage, GLCM (gray-level co-occurrence matrix) entropy, GLZLM_GLNU (gray-level nonuniformity), and GLZLM_ZLNU (zone length nonuniformity), values were significant predictors for OS. Among them, GLRLM_RLNU ≥216.6 demonstrated association with worse OS at multivariate analysis (HR 9.016, 95% CI 1.041–78.112, p=0.046). Conclusions The texture analysis of 18F-FDG-PET images could potentially serve as a noninvasive strategy to predict the overall survival of patients with PRL and PAL.

Radiomic Features of the Nigrosome-1 Region of the Substantia Nigra: Using Quantitative Susceptibility Mapping to Assist the Diagnosis of Idiopathic Parkinson's Disease.

Introduction: The loss of nigrosome-1, which is also referred to as the swallow tail sign (STS) in T2*-weighted iron-sensitive magnetic resonance imaging (MRI), has recently emerged as a new biomarker for idiopathic Parkinson's disease (IPD). However, consistent recognition of the STS is difficult due to individual variations and different imaging parameters. Radiomics might have the potential to overcome these shortcomings. Therefore, we chose to explore whether radiomic features of nigrosome-1 of substantia nigra (SN) based on quantitative susceptibility mapping (QSM) could help to differentiate IPD patients from healthy controls (HCs).Methods: Three-dimensional multi-echo gradient-recalled echo images (0.86 × 0.86 × 1.00 mm3) were obtained at 3.0-T MRI for QSM in 87 IPD patients and 77 HCs. Regions of interest (ROIs) of the SN below the red nucleus were manually drawn on both sides, and subsequently, volumes of interest (VOIs) were segmented (these ROIs included four 1-mm slices). Then, 105 radiomic features (including 18 first-order features, 13 shape features, and 74 texture features) of bilateral VOIs in the two groups were extracted. Forty features were selected according to the ensemble feature selection method, which combined analysis of variance, random forest, and recursive feature elimination. The selected features were further utilized to distinguish IPD patients from HC using the SVM classifier with 10 rounds of 3-fold cross-validation. Finally, the representative features were analyzed using an unpaired t-test with Bonferroni correction and correlated with the UPDRS-III scores.Results: The classification results from SVM were as follows: area under curve (AUC): 0.96 ± 0.02; accuracy: 0.88 ± 0.03; sensitivity: 0.89 ± 0.06; and specificity: 0.87 ± 0.07. Five representative features were selected to show their detailed difference between IPD patients and HCs: 10th percentile and median in IPD patients were higher than those in HCs (all p < 0.00125), while Gray Level Run Length Matrix (GLRLM)-Long Run Low Gray Level Emphasis, Gray Level Size Zone Matrix (GLSZM)–Gray Level Non-Uniformity, and volume (all p < 0.00125) in IPD patients were lower than those in HCs. The 10th percentile was positively correlated with UPDRS-III score (r = 0.35, p = 0.001).Conclusion: Radiomic features of the nigrosome-1 region of SN based on QSM could be useful in the diagnosis of IPD and could serve as a surrogate marker for the STS.

Systematic analysis of bias and variability of texture measurements in computed tomography

Texture is a key radiomics measurement for quantification of disease and disease progression. The sensitivity of the measurements to image acquisition, however, is uncertain. We assessed bias and variability of computed tomography (CT) texture feature measurements across many clinical image acquisition settings and reconstruction algorithms. Diverse, anatomically informed textures (texture A, B, and C) were simulated across 1188 clinically relevant CT imaging conditions representing four in-plane pixel sizes (0.4, 0.5, 0.7, and 0.9mm), three slice thicknesses (0.625, 1.25, and 2.5mm), three dose levels ( 1.90, 3.75, and 7.50 mGy), and 33 reconstruction kernels. Imaging conditions corresponded to noise and resolution properties representative of five commercial scanners (GE LightSpeed VCT, GE Discovery 750 HD, GE Revolution, Siemens Definition Flash, and Siemens Force) in filtered backprojection and iterative reconstruction. About 21 texture features were calculated and compared between the ground-truth phantom (i.e., preimaging) and its corresponding images. Each feature was measured with four unique volumes of interest (VOIs) sizes (244, 579, 1000, and . To characterize the bias, the percentage relative difference [PRD(%)] in each feature was calculated between the imaged scenario and the ground truth for all VOI sizes. Feature variability was assessed in terms of (1) indicating the variability between the ground truth and simulated image scenario based on the PRD(%), (2) indicating the simulation-based variability, and (3) indicating the natural variability present in the ground-truth phantom. The PRD ranged widely from to 1220%, with an underlying variability ( ) of up to 241%. Features such as gray-level nonuniformity, texture entropy, sum average, and homogeneity exhibited low susceptibility to reconstruction kernel effects ( ) with relatively small ( ) across imaging conditions. The dynamic range of results indicates that image acquisition and reconstruction conditions of in-plane pixel sizes, slice thicknesses, dose levels, and reconstruction kernels can lead to significant bias and variability in feature measurements.

Software comparison to analyze bone radiomics from high resolution CBCT scans of mandibular condyles.

Radiomics refers to the extraction and analysis of advanced quantitative imaging from medical images to diagnose and/or predict diseases. In the dentistry field, the bone data from mandibular condyles could be computationally analyzed using the voxel information provided by high-resolution CBCT scans to increase the diagnostic power of temporomandibular joint (TMJ) conditions. However, such quantitative information demands innovative computational software, algorithm implementation, and validation. Our study's aim was to compare a newly developed BoneTexture application to two-consolidated software with previous applications in the medical field, Ibex and BoneJ, to extract bone morphometric and textural features from mandibular condyles. We used an imaging database of HR-CBCT TMJs scans with an isotropic voxel size of 0.08 mm3 . A single group with 66 distinct mandibular condyles composed the final sample. We calculated 18 variables for bone textural features and 5 for bone morphometric measurements using the Ibex, BoneJ and BoneTexture applications. Spearman correlation and Bland-Altman plot analyses were done to compare the agreement among software. The results showed a high Spearman correlation among the software applications ( r = 0.7-1), with statistical significance for all variables, except Grey Level Non-Uniformity and Short Run Emphasis. The Bland-Altman vertical axis showed, in general, good agreement between the software applications and the horizontal axis showed a narrow average distribution for Correlation, Long Run Emphasis and Long Run High Grey Level Emphasis. Our data showed consistency among the three applications to analyze bone radiomics in high-resolution CBCT. Further studies are necessary to evaluate the applicability of those variables as new bone imaging biomarkers to diagnose bone diseases affecting TMJs.

Texture Feature Extraction for Batik Images Using GLCM and GLRLM with Neural Network Classification

Batik has a vast variety of motifs and colors. Aside from its popularity as being part of Indonesian culture, it has become the source of Indonesia’s income. Batik was more promising in the past years for the business opportunities. Batik has economic and high export value as the commodity. Batik has become the main part of national culture; however there is a lack of understanding for many people, as they are still unaware about batik motifs and patterns. Therefore, it is needed for building a model to identify batik motifs. This study aims to combine the features of texture and the feature of shapes’ ornament in batik to classify images using artificial neural networks. The value of texture features of images in batik is extracted using gray level co-occurrence matrices (GLCM) which include Contrast, Correlation, Homogeneity and Energy. And include the Gray level Run length matrices (GLRLM) which includes Gray Level Non-Uniformity (GLN), Long Run Emphasis (LRE), Short Run Emphasis (SRE), Run Percentage (RP). At this phase of the training and testing, we compare the value of a classification accuracy of neural networks in each class in batik with their texture features, and the combination of GLCM and GLRLM. From the three features used in the classification of batik image with artificial neural networks it includes Probabilistic Neural network, it was obtained that GLCM feature has the lowest accuracy rate of 78% and the combination of GLCM and GLRLM features produces a greater value of accuracy by 84%. The results obtained in this study indicate that there is an increase in accuracy of batik image classification using the probabilistic neural network with the combination of GLCM and GLRLM features in batik image.

Mandibular bone effects of botulinum toxin injections in masticatory muscles in adult.

Botulinum toxin (BTX) is injected into masticatory muscles to treat various conditions. Animal studies have demonstrated bone loss at the condylar and alveolar regions of the mandible after BTX injection into masticatory muscles. The aim of the present study was to investigate mandibular bone changes in patients who received BTX injections in masticatory muscles. Twelve adult patients who received BTX injections into masticatory muscles were included in this study. Cone beam computed tomography (CBCT) was performed before and 12 months after the injection. The condylar and alveolar regions of the mandible were analyzed by using texture analysis of the CBCT images with the run length method. Condylar cortical thickness was measured, and 3-dimensional analysis of the mandible was also performed. Six patients who did not receive BTX injections were used as controls. A run length parameter (gray level nonuniformity) was found to be increased in condylar and alveolar bones. A significant cortical thinning was found at the anterior portion of the right condyle. Three-dimensional analysis showed significant changes in the condylar bone and at the digastric fossa. No changes in mandibular angles were found. This study identified mandibular bone changes in adult patients who received BTX injection into masticatory muscles.

GLRLM features in CT texture analysis are prognostic factors: Evaluation of pancreatic cancer heterogeneity, retrospective cohort study

Introduction: Pancreatic cancer is an lethal disease. Tumor heterogeneity is an important indicator of the progression of chemoresistance. Although texture analyses can allow quantification of tumor heterogeneity, only a few studies reported the use of texture analysis in cases of pancreatic cancer. Therefore, we analyzed tumor heterogeneity in preoperative computed tomography (CT) scans by performing texture analysis using the gray-level run-length matrix (GLRLM). Method: We analyzed 116 consecutive patients who underwent surgical resection during 2001-2017 and had preoperative CT data available for analysis. An ROI was drawn on slices with a visible tumor and normal pancreas on the arterial phase CT scans and the correlation of pathological characteristics and GLRLM features was analyzed. We then performed Kaplan—Meier survival curve analysis among pancreatic cancer patients. Result: The gray-level non-uniformity (GLN) values in GLRLM features for tumors were higher than those for normal pancreas. High GLN values represent a non-uniform texture, i.e., heterogeneity. Recurrence-free survival was shorter in the group with high GLN135 values (p = 0.025). Conclusion: Tumor heterogeneity is a significant prognostic factor. Our analyses of the correlation between pathological outcomes and GLRLM features in pancreatic cancer patients showed that GLN values were a powerful indicator of prognosis.

Cardiac MRI Texture Analysis of T1 and T2 Maps in Patients with Infarctlike Acute Myocarditis.

Purpose To assess the diagnostic potential of texture analysis applied to T1 and T2 maps obtained with cardiac MRI for the diagnosis of acute infarctlike myocarditis. Materials and Methods This prospective study from August 2012 to May 2015 included 39 participants (overall mean age ± standard deviation, 34.7 years ± 12.2 [range, 18-63 years]; mean age of women, 46.1 years ± 10.8 [range, 24-63 years]; mean age of men, 29.8 years ± 9.2 [range, 18-56 years]) from the Magnetic Resonance Imaging in Myocarditis (MyoRacer) trial with clinical suspicion of acute myocarditis and infarctlike presentation. Participants underwent biventricular endomyocardial biopsy, cardiac catheterization, and cardiac MRI at 1.5 T, in which native T1 and T2 mapping as well as Lake Louise criteria (LLC) were assessed. Texture analysis was applied on T1 and T2 maps by using a freely available software package. Stepwise dimension reduction and texture feature selection was performed for selecting features enabling the diagnosis of myocarditis by using endomyocardial biopsy as the reference standard. Results Endomyocardial biopsy confirmed the diagnosis of acute myocarditis in 26 patients, whereas 13 participants had no signs of acute inflammation. Mean T1 and T2 values and LLC showed a low diagnostic performance, with area under the curve in receiver operating curve analyses as follows: 0.65 (95% confidence interval [CI]: 0.45, 0.85) for T1, 0.67 (95% CI: 0.49, 0.85) for T2, and 0.62 (95% CI: 0.42, 0.79) for LLC. Combining the texture features T2 run-length nonuniformity and gray-level nonuniformity resulted in higher diagnostic performance with an area under the curve of 0.88 (95% CI: 0.73, 1.00) (P < .001) and a sensitivity and specificity of 89% [95% CI: 81%, 93%] and 92% [95% CI: 77%, 93%], respectively. Conclusion Texture analysis of T2 maps shows high sensitivity and specificity for the diagnosis of acute infarctlike myocarditis. © RSNA, 2018 Online supplemental material is available for this article.

Radiomics of liver MRI predict metastases in mice

BackgroundThe purpose of this study was to investigate whether any texture features show a correlation with intrahepatic tumor growth before the metastasis is visible to the human eye.MethodsEight male C57BL6 mice (age 8–10 weeks) were injected intraportally with syngeneic MC-38 colon cancer cells and two mice were injected with phosphate-buffered saline (sham controls). Small animal magnetic resonance imaging (MRI) at 4.7 T was performed at baseline and days 4, 8, 12, 16, and 20 after injection applying a T2-weighted spin-echo sequence. Texture analysis was performed on the images yielding 32 texture features derived from histogram, gray-level co-occurrence matrix, gray-level run-length matrix, and gray-level size-zone matrix. The features were examined with a linear regression model/Pearson correlation test and hierarchical cluster analysis. From each cluster, the feature with the lowest variance was selected.ResultsTumors were visible on MRI after 20 days. Eighteen features from histogram and the gray-level-matrices exhibited statistically significant correlations before day 20 in the experiment group, but not in the control animals. Cluster analysis revealed three distinct clusters of independent features. The features with the lowest variance were Energy, Short Run Emphasis, and Gray Level Non-Uniformity.ConclusionsTexture features may quantitatively detect liver metastases before they become visually detectable by the radiologist.

Texture analysis and machine learning of non-contrast T1-weighted MR images in patients with hypertrophic cardiomyopathy—Preliminary results

PurposeTo test in a first proof-of-concept study whether texture analysis (TA) allows for the detection of myocardial tissue alterations in hypertrophic cardiomyopathy (HCM) on non-contrast T1-weighted cardiac magnetic resonance (CMR) images using machine learning based approaches. MethodsThis retrospective, IRB-approved study included 32 patients with known HCM. Thirty patients with normal CMR served as controls. Regions-of-interest for TA encompassing the left ventricle were drawn on short-axis non-contrast T1-weighted images using a freely available software package. Step-wise dimension reduction and texture feature selection was performed for selecting features enabling the detection of myocardial tissue alterations in HCM patients on non-contrast T1-weighted CMR images. ResultsComparing HCM patients and controls, four texture features were identified showing significant differences between groups (Grey-level Non-uniformity [GLevNonU]: 74 ± 17 vs. 38 ± 9, p < .001; Energy of wavelet coefficients in low-frequency sub-bands [WavEnLL]: 58 ± 5 vs. 48 ± 10, p < .001; Fraction: 0.70 ± 0.07 vs. 0.78 ± 0.05, p < .001; Sum Average: 16.6 ± 0.4 vs. 17.0 ± 0.5, p = .007). A model containing the single parameter GLevNonU proved to be the best for differentiating between HCM patients and controls with a sensitivity/specificity of 91%/93%. A cut-off of GLevNonU ≥46 allowed for distinguishing HCM patients from controls with a sensitivity/specificity of 94%/90%. Even in patients without late gadolinium enhancement (LGE), the defined cut-off led to a differentiation of LGE- patients from healthy controls with 100% sensitivity and 90% specificity. ConclusionsTA on non-contrast T1-weighted images allows for the detection of myocardial tissue alterations in the setting of HCM with excellent accuracy, delivering potential novel parameters for a non-contrast assessment of myocardial texture alterations.

The prognostic value of CT radiomic features for patients with pulmonary adenocarcinoma treated with EGFR tyrosine kinase inhibitors.

PurposeTo determine if the radiomic features on CT can predict progression-free survival (PFS) in epidermal growth factor receptor (EGFR) mutant adenocarcinoma patients treated with first-line EGFR tyrosine kinase inhibitors (TKIs) and to identify the incremental value of radiomic features over conventional clinical factors in PFS prediction.MethodsIn this institutional review board–approved retrospective study, pretreatment contrast-enhanced CT and first follow-up CT after initiation of TKIs were analyzed in 48 patients (M:F = 23:25; median age: 61 years). Radiomic features at baseline, at 1st first follow-up, and the percentage change between the two were determined. A Cox regression model was used to predict PFS with nonredundant radiomic features and clinical factors, respectively. The incremental value of radiomic features over the clinical factors in PFS prediction was also assessed by way of a concordance index.ResultsRoundness (HR: 3.91; 95% CI: 1.72, 8.90; P = 0.001) and grey-level nonuniformity (HR: 3.60; 95% CI: 1.80, 7.18; P<0.001) were independent predictors of PFS. For clinical factors, patient age (HR: 2.11; 95% CI: 1.01, 4.39; P = 0.046), baseline tumor diameter (HR: 1.03; 95% CI: 1.01, 1.05; P = 0.002), and treatment response (HR: 0.46; 95% CI: 0.24, 0.87; P = 0.017) were independent predictors. The addition of radiomic features to clinical factors significantly improved predictive performance (concordance index; combined model = 0.77, clinical-only model = 0.69, P<0.001).ConclusionsRadiomic features enable PFS estimation in EGFR mutant adenocarcinoma patients treated with first-line EGFR TKIs. Radiomic features combined with clinical factors provide significant improvement in prognostic performance compared with using only clinical factors.

Validation of Voxel-Size Normalization of CT Radiomic Features in Lung Cancer Patients

Radiomic features are potential candidates for therapy response assessment in radiation oncology. However, the robustness of these features with imaging parameters is not well established. For example, some radiomic features related to intensity, GLRLM and NGTDM were suggested as potential biomarkers in recent studies. However, all these features were found to be intrinsically dependent on voxel size as indicated by a recent texture phantom study. Normalization by voxel size is a potential method to remove these voxel-size dependencies. The purpose of this study was to validate the voxel size normalization in a patient cohort. Eight patients with non-small lung cancer (NSCLC) of varying tumor volumes were analyzed. The segmented volume of interest (VOI) along with original CT scan of each patient was down and up-sampled to various voxel sizes using linear interpolation. For each patient, VOI was resampled to 5 different pixel sizes from 0.4 to 1.0 mm and 5 different slice thicknesses from 1 to 4 mm. There was a total of 88 CT data sets used in this study. Ten features including entropy, energy, TGV, contrast from intensity histogram; mean and inverse variance from GLCM; gray level non-uniformity (GLNU) and run length non-uniformity (RLNU) from GLRLM; and coarseness and texture strength from NGTDM were extracted from 3D VOIs using an in-house program. To investigate the usefulness of voxel-size normalization, each feature was modified to include voxel size or number of voxels in its definition. The absolute value of the spearman rank correlation coefficient (rs) was calculated for each feature to determine which features were correlated with voxel size or number of voxels in VOI before and after normalization. The features having values rs > 0.9, 0.5 < rs < 0.9 and rs< 0.5 were respectively categorized as high, moderate and no correlations with voxel size. Without voxel size normalization, eight out of 10 features: Intensity-energy, Intensity-entropy, Intensity-TGV, GLCM-mean, GLCM-inverse variance, GLRLM-GLNU, GLRLM-RLNU and NGTDM-coarseness were highly correlated with voxel size (rs > 0.9). Intensity based contrast and NGTDM based texture strength were moderately correlated with voxel size (0.5 < rs < 0.9) before normalization. After voxel size normalization, all ten features showed low values of spearman rank correlation coefficient (rs< 0.5), indicating intrinsic dependencies of these features on voxel size were reduced or eliminated. We conclude that voxel size normalization results from a texture phantom study also apply to lung patients cohort. This study highlights the importance of investigating the robustness of radiomic features with CT imaging parameters and exploring methods to fix this variability before using them for prognostic and predictive studies.

Can Laws Be a Potential PET Image Texture Analysis Approach for Evaluation of Tumor Heterogeneity and Histopathological Characteristics in NSCLC?

We investigated the association between the textural features obtained from 18F-FDG images, metabolic parameters (SUVmax, SUVmean, MTV, TLG), and tumor histopathological characteristics (stage and Ki-67 proliferation index) in non-small cell lung cancer (NSCLC). The FDG-PET images of 67 patients with NSCLC were evaluated. MATLAB technical computing language was employed in the extraction of 137 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), and Laws' texture filters. Textural features and metabolic parameters were statistically analyzed in terms of good discrimination power between tumor stages, and selected features/parameters were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). We showed that one textural feature (gray-level nonuniformity, GLN) obtained using GLRLM approach and nine textural features using Laws' approach were successful in discriminating all tumor stages, unlike metabolic parameters. There were significant correlations between Ki-67 index and some of the textural features computed using Laws' method (r=0.6, p=0.013). In terms of automatic classification of tumor stage, the accuracy was approximately 84% with k-NN classifier (k=3) and SVM, using selected five features. Texture analysis of FDG-PET images has a potential to be an objective tool to assess tumor histopathological characteristics. The textural features obtained using Laws' approach could be useful in the discrimination of tumor stage.

SEM-image textural features and drug release behavior of Eudragit-based matrix pellets

Matrix pellets of acetaminophen were prepared by extrusion-spheronization using a high proportion of several varieties of Eudragit; namely, 40% RSPO or RLPO, and 17% RS30D or RL30D. The pellets were characterized using textural analysis of gray-level scanning electron microscopy (SEM) images and through the cumulative pore volume distribution obtained by mercury intrusion porosimetry. Surface roughness and distribution of pores at pellet surface were characterized in terms of gray-level non uniformity (GLN) and fractal dimension of pellet surface. Both parameters were strongly dependent on the procedure followed to incorporate the polymer (in powder form or as liquid dispersion) and, to a lesser extent, on the polymer variety (RL or RS). The curing of pellets at 105 °C for 30 min led to the smoothing out and to a decrease in the pore size at the surface, which notably slowed down acetaminophen release rate. Strong correlations between the drug release rate and the surface roughness of the pellets were found.

Predictive Value of Standardized Intratumoral Metabolic Heterogeneity in Locally Advanced Cervical Cancer Treated With Chemoradiation.

The aim of this study was to propose and evaluate a novel image metric for quantifying spatial heterogeneity of tumor F-fluorodeoxyglucose (FDG) uptake within the context of predicting response to chemoradiation in locally advanced cervical cancer. Ninety patients with locally advanced cervical cancer treated with concomitant chemoradiation were included in this study. Each patient underwent two whole-body F-FDG positron emission tomography/computed tomography scans with one before the initiation of treatment for staging and the other at 12 weeks after treatment completion for response assessment. Patients were categorized in terms of response to chemoradiation into two major groups: complete metabolic responders and noncomplete metabolic responders. The capacity of the proposed intratumoral heterogeneity metric to differentiate patients with respect to response to therapy was evaluated and compared with the use of standardized uptake value indices and various texture parameters that had been previously introduced for predicting tumor response to chemoradiation. At baseline, the proposed intratumoral heterogeneity metric along with four texture features, including entropy and energy derived from gray-level co-occurrence matrices and gray-level nonuniformity and zone size nonuniformity from gray-level zone size matrices, was capable of differentiating responders' groups with P values of 0.0026, 0.0252, 0.0240, 0.0234, and 0.0188, respectively. Furthermore, when compared with the texture features exhibiting significant difference between the responders' groups, the proposed metric demonstrated larger area under receiver operating characteristic curve. The proposed metric with quantifying spatial heterogeneity of intratumoral FDG accumulation in a normalized manner may be associated with predictive value of poor response to concurrent chemoradiation in locally advanced cervical cancer.

Respiration-Averaged CT for Attenuation Correction of PET Images - Impact on PET Texture Features in Non-Small Cell Lung Cancer Patients.

PurposeWe compared attenuation correction of PET images with helical CT (PET/HCT) and respiration-averaged CT (PET/ACT) in patients with non-small-cell lung cancer (NSCLC) with the goal of investigating the impact of respiration-averaged CT on 18F FDG PET texture parameters.Materials and MethodsA total of 56 patients were enrolled. Tumors were segmented on pretreatment PET images using the adaptive threshold. Twelve different texture parameters were computed: standard uptake value (SUV) entropy, uniformity, entropy, dissimilarity, homogeneity, coarseness, busyness, contrast, complexity, grey-level nonuniformity, zone-size nonuniformity, and high grey-level large zone emphasis. Comparisons of PET/HCT and PET/ACT were performed using Wilcoxon signed-rank tests, intraclass correlation coefficients, and Bland-Altman analysis. Receiver operating characteristic (ROC) curves as well as univariate and multivariate Cox regression analyses were used to identify the parameters significantly associated with disease-specific survival (DSS). A fixed threshold at 45% of the maximum SUV (T45) was used for validation.ResultsSUV maximum and total lesion glycolysis (TLG) were significantly higher in PET/ACT. However, texture parameters obtained with PET/ACT and PET/HCT showed a high degree of agreement. The lowest levels of variation between the two modalities were observed for SUV entropy (9.7%) and entropy (9.8%). SUV entropy, entropy, and coarseness from both PET/ACT and PET/HCT were significantly associated with DSS. Validation analyses using T45 confirmed the usefulness of SUV entropy and entropy in both PET/HCT and PET/ACT for the prediction of DSS, but only coarseness from PET/ACT achieved the statistical significance threshold.ConclusionsOur results indicate that 1) texture parameters from PET/ACT are clinically useful in the prediction of survival in NSCLC patients and 2) SUV entropy and entropy are robust to attenuation correction methods.

Diagnosis of Sarcomatoid Renal Cell Carcinoma With CT: Evaluation by Qualitative Imaging Features and Texture Analysis.

The objective of our study was to determine whether CT findings, including texture analysis, can differentiate sarcomatoid renal cell carcinoma (RCC) from clear cell RCC. A retrospective case-control study was performed of consecutive patients with a histologic diagnosis of sarcomatoid RCC (n = 20) and clear cell RCC (n = 25) who underwent preoperative CT over a 3-year period. The CT images were independently reviewed by two blinded abdominal radiologists; they evaluated the following: tumor heterogeneity, tumor margin, calcification, intratumoral neovascularity, peritumoral neovascularity, renal sinus invasion, renal vein invasion, and adjacent organ invasion. Interobserver agreement was assessed using the Cohen kappa coefficient, and results were compared between groups using an independent Student t test and the chi-square test with a Bonferroni correction. For texture analysis, gray-level co-occurrence and run-length matrix features were extracted and compared using Mann-Whitney U tests. ROC curves for each tumor were constructed, and AUCs were calculated. Overall, sarcomatoid RCCs were larger than clear cell RCCs, measuring 77 ± 27 mm (mean ± SD) compared with 50 ± 29 mm (p = 0.003), respectively; however, there was no difference in tumor size between the tumors that were compared using texture analysis or subjective analysis (p = 0.06 and 0.03, respectively). From the subjective analysis, only peritumoral neovascularity (readers 1 and 2: 70% and 70% sarcomatoid RCCs vs 0% and 41.6% clear cell RCCs, respectively; p = 0.001) and the size of the peritumoral vessels (p < 0.001) differed between sarcomatoid RCCs and clear cell RCCs, and interobserver agreement was fair (κ = 0.38). Other subjective imaging features did not differ between the tumors (p > 0.005). There was greater run-length nonuniformity and greater gray-level nonuniformity in sarcomatoid RCCs than in clear cell RCCs (p = 0.03 and p = 0.04, respectively). The combined textural features identified sarcomatoid RCC with an AUC of 0.81 ± 0.08 (standard error) (p < 0.0001). Large tumor size, the presence of peritumoral neovascularity, and larger peritumoral vessels are features that are more commonly associated with sarcomatoid RCCs than with clear cell RCCs. Sarcomatoid RCCs are also more heterogeneous by texture analysis than clear cell RCCs.

Validation of CBCT for the computation of textural biomarkers.

Osteoarthritis (OA) is associated with significant pain and 42.6% of patients with TMJ disorders present with evidence of TMJ OA. However, OA diagnosis and treatment remain controversial, since there are no clear symptoms of the disease. The subchondral bone in the TMJ is believed to play a major role in the progression of OA. We hypothesize that the textural imaging biomarkers computed in high resolution Conebeam CT (hr-CBCT) and μCT scans are comparable. The purpose of this study is to test the feasibility of computing textural imaging biomarkers in-vivo using hr-CBCT, compared to those computed in μCT scans as our Gold Standard. Specimens of condylar bones obtained from condylectomies were scanned using μCT and hr-CBCT. Nine different textural imaging biomarkers (four co-occurrence features and five run-length features) from each pair of μCT and hr-CBCT were computed and compared. Pearson correlation coefficients were computed to compare textural biomarkers values of μCT and hr-CBCT. Four of the nine computed textural biomarkers showed a strong positive correlation between biomarkers computed in μCT and hr-CBCT. Higher correlations in Energy and Contrast, and in GLN (grey-level non-uniformity) and RLN (run length non-uniformity) indicate quantitative texture features can be computed reliably in hr-CBCT, when compared with μCT. The textural imaging biomarkers computed in-vivo hr-CBCT have captured the structure, patterns, contrast between neighboring regions and uniformity of healthy and/or pathologic subchondral bone. The ability to quantify bone texture non-invasively now makes it possible to evaluate the progression of subchondral bone alterations, in TMJ OA.

3D Texture Analysis of Corpus Caliosum Based on MR Images Inpatients with Alzheimer’s Disease and Mild Cognitive Impairment

This study investigated three-dimensional (3D) texture as a possible diagnostic marker of Alzheimers disease (AD). Methods: T1-weighted MRI of 18 AD patients, 18 Mild Cognitive Impairment (MCI) patients and 18 normal controls (NC) were selected.3D Texture parameters of the corpus callosum,including contrast, inverse difference moment , entropy, short run emphasis, long run emphasis, grey level nonuniformity, run length nonuniformity and fraction were extracted from the gray level co-occurrence matrix and run length matrix. Finally statistic significance was tested among three groups, and the correlations between parameters and Mini-Mental State Examination (MMSE) scores were calculated. Results: The results showed that the 3D texture features had significant differences (p&lt;0.05) among three groups except grey level nonuniformity and run length nonuniformity that the difference was not significant (p&gt;0.05) between MCI and NC or AD and MCI , and they were correlated with MMSE scores.Conclusions: 3D texture analysis can reflect the pathological changes of corpus callosum in patients with AD and MCI, and it may be helpful to AD early diagnosis.

Reproducibility and Prognosis of Quantitative Features Extracted from CT Images

We study the reproducibility of quantitative imaging features that are used to describe tumor shape, size, texture from computed tomography (CT) scans of non-small cell lung cancer (NSCLC). CT images are dependent on various scanning factors. We focus on characterizing image features that are reproducible in the presence of variations due to patient factors and segmentation methods. Thirty-two NSCLC nonenhanced lung CT scans were obtained from the Reference Image Database to Evaluate Response data set. The tumors were segmented using both manual (radiologist expert) and ensemble (software-automated) methods. A set of features (219 threedimensional and 110 two-dimensional) was computed, quantitative image features were statistically filtered to identify a subset of reproducible and nonredundant features. The variability in the repeated experiment was measured by the test-retest concordance correlation coefficient (CCCTreT). The natural range in the features, normalized to variance, was measured by the dynamic range (DR). In this study, there were 29 features across segmentation methods found with CCCTreT and DR ≥ 0.9 and R2Bet ≥ 0.95. These reproducible features were tested for predicting radiologist prognostic score; some texture features (run-length and Laws kernels) had an area under the curve of 0.9. The representative features were tested for their prognostic capabilities using an independent NSCLC data set (59 lung adenocarcinomas), where one of the texture features, run-length gray-level nonuniformity, was statistically significant in separating the samples into survival groups (P ≤ .046).