Quantitative Texture Research Articles

Kinect-driven Patient-specific Head, Skull, and Muscle Network Modelling for Facial Palsy Patients

Background and ObjectiveFacial palsy negatively affects both professional and personal life qualities of involved patients. Classical facial rehabilitation strategies can recover facial mimics into their normal and symmetrical movements and appearances. However, there is a lack of objective, quantitative, and in-vivo facial texture and muscle activation bio-feedbacks for personalizing rehabilitation programs and diagnosing recovering progresses. Consequently, this study proposed a novel patient-specific modelling method for generating a full patient specific head model from a visual sensor and then computing the facial texture and muscle activation in real-time for further clinical decision making. MethodsThe modeling workflow includes (1) Kinect-to-head, (2) head-to-skull, and (3) muscle network definition & generation processes. In the Kinect-to-head process, subject-specific data acquired from a new user in neutral mimic were used for generating his/her geometrical head model with facial texture. In particular, a template head model was deformed to optimally fit with high-definition facial points acquired by the Kinect sensor. Moreover, the facial texture was also merged from his/her facial images in left, right, and center points of view. In the head-to-skull process, a generic skull model was deformed so that its shape was statistically fitted with his/her geometrical head model. In the muscle network definition & generation process, a muscle network was defined from the head and skull models for computing muscle strains during facial movements. Muscle insertion points and muscle attachment points were defined as vertex positions on the head model and the skull model respectively based on the standard facial anatomy. Three healthy subjects and two facial palsy patients were selected for validating the proposed method. In neutral positions, magnetic resonance imaging (MRI)-based head and skull models were compared with Kinect-based head and skull models. In mimic positions, infrared depth-based head models in smiling and [u]-pronouncing mimics were compared with appropriate animated Kinect-driven head models. The Hausdorff distance metric was used for these comparisons. Moreover, computed muscle lengths and strains in the tested facial mimics were validated with reported values in literature. ResultsWith the current hardware configuration, the patient-specific head model with skull and muscle network could be fast generated within 17.16±0.37s and animated in real-time with the framerate of 40 fps. In neutral positions, the best mean error was 1.91 mm for the head models and 3.21 mm for the skull models. On facial regions, the best mean errors were 1.53 mm and 2.82 mm for head and skull models respectively. On muscle insertion/attachment point regions, the best mean errors were 1.09 mm and 2.16 mm for head and skull models respectively. In mimic positions, these errors were 2.02 mm in smiling mimics and 2.00 mm in [u]-pronouncing mimics for the head models on facial regions. All above error values were computed on a one-time validation procedure. Facial muscles exhibited muscle shortening and muscle elongating for smiling and pronunciation of sound [u] respectively. Extracted muscle features (i.e. muscle length and strain) are in agreement with experimental and literature data. ConclusionsThis study proposed a novel modeling method for fast generating and animating patient-specific biomechanical head model with facial texture and muscle activation bio-feedbacks. The Kinect-driven muscle strains could be applied for further real-time muscle-oriented facial paralysis grading and other facial analysis applications.

A Paradigm Shift in Nuclear Chromatin Interpretation: From Qualitative Intuitive Recognition to Quantitative Texture Analysis of Breast Cancer Cell Nuclei.

Assessing the pattern of nuclear chromatin is essential for pathological investigations. However, the interpretation of nuclear pattern is subjective. In this study, we performed the texture analysis of nuclear chromatin in breast cancer samples to determine the nuclear pleomorphism score thereof. We used three different algorithms for extracting high‐level texture features: the gray‐level co‐occurrence matrix (GLCM), gray‐level run length matrix (GLRLM), and gray‐level size zone matrix (GLSZM). Using these algorithms, 12 GLCM, 11 GLRLM, and 16 GLSZM features were extracted from three scores of breast carcinoma (Scores 1–3). Classification accuracy was assessed using the support vector machine (SVM) and k‐nearest neighbor (KNN) classification models. Three features of GLCM, 11 of GLRLM, and 12 of GLSZM were consistent across the three nuclear pleomorphism scores of breast cancer. Comparing Scores 1 and 3, the GLSZM feature large zone high gray‐level emphasis showed the largest difference among breast cancer nuclear scores among all features of the three algorithms. The SVM and KNN classifiers showed favorable results for all three algorithms. A multiclass classification was performed to compare and distinguish between the scores of breast cancer. Texture features of nuclear chromatin can provide useful information for nuclear scoring. However, further validation of the correlations of histopathologic features, and standardization of the texture analysis process, are required to achieve better classification results. © 2021 The Authors. Cytometry Part A published by Wiley Periodicals LLC on behalf of International Society for Advancement of Cytometry.

Differentiating Hepatic Epithelioid Angiomyolipoma From Hepatocellular Carcinoma and Focal Nodular Hyperplasia via Radiomics Models.

Background: We conduct a study in developing and validating two radiomics-based models to preoperatively distinguish hepatic epithelioid angiomyolipoma (HEAML) from hepatic carcinoma (HCC) as well as focal nodular hyperplasia (FNH).Methods: Totally, preoperative contrast-enhanced computed tomography (CT) data of 170 patients and preoperative contrast-enhanced magnetic resonance imaging (MRI) data of 137 patients were enrolled in this study. Quantitative texture features and wavelet features were extracted from the regions of interest (ROIs) of each patient imaging data. Then two radiomics signatures were constructed based on CT and MRI radiomics features, respectively, using the random forest (RF) algorithm. By integrating radiomics signatures with clinical characteristics, two radiomics-based fusion models were established through multivariate linear regression and 10-fold cross-validation. Finally, two diagnostic nomograms were built to facilitate the clinical application of the fusion models.Results: The radiomics signatures based on the RF algorithm achieved the optimal predictive performance in both CT and MRI data. The area under the receiver operating characteristic curves (AUCs) reached 0.996, 0.879, 0.999, and 0.925 for the training as well as test cohort from CT and MRI data, respectively. Then, two fusion models simultaneously integrated clinical characteristics achieved average AUCs of 0.966 (CT data) and 0.971 (MRI data) with 10-fold cross-validation. Through decision curve analysis, the fusion models were proved to be excellent models to distinguish HEAML from HCC and FNH in comparison between the clinical models and radiomics signatures.Conclusions: Two radiomics-based models derived from CT and MRI images, respectively, performed well in distinguishing HEAML from HCC and FNH and might be potential diagnostic tools to formulate individualized treatment strategies.

Crystal Growth and Crystallization Time Scales of the Panzhihua Layered Intrusion: Constraint from Crystal Size Distribution

AbstractThe Panzhihua layered intrusions is generated closely related to the Emeishan LIPs. This paper analyzes the spatial distribution of plagioclase and pyroxene. The quantitative texture analysis of 2209 plagioclase shows that the characteristic length of plagioclase is 0.54 to 0.96 mm, the intercept variation range is large, from –0.67 to 0.96, and the slope is –1.85 to –1.04, the Aspect Ratio shows from 1.84 to 2.59 and fractal dimension D is 1.908–1.933. The quantitative texture analysis of 2342 pyroxene shows that the characteristic length of pyroxene is 0.38–0.64 mm, the intercept shows from 0.46 to 2.26, The slope ranges from –2.6 to –1.47, the Aspect Ratio value varies from 1.53 to 1.71, the fractal dimension D is 0.93 to 1.13. All the CSDs results of the Panzhihua intrusions indicate that plagioclase and pyroxene form in an open magma system and undergo four replenishment of magma injection. The plagioclase crystals do not grow as the lathlike shape, and the fractal growth leads to complex crystal surface. The plagioclase undergoes deformation compaction during the crystal process, and then is oriented. The pyroxene crystals grow along an approximately triaxial ratio and undergo texture adjustment and small crystal dissolution reabsorption. When all crystals in magma system grows up to 2 mm, the pyroxene undergoes cumulation in the Panzhihua layered intrusions. The plagioclase crystallization time scale is 171.23–304.41 years, representing that the crystallization is the more uniform in central part of the melt. The nucleation density continuously increases during the crystallization process of the magma system. The time scale to reach the final maximum crystal nucleation density is 15.28–58.98 years.

Diagnosis of Diabetic Retinopathy Utilizing Computer-Aided Diagnosis System

Purpose: Diabetes is considered one of most diseases spread among people; blindness is considered the most resulted effect. Diabetes can damage the retinal blood vessels and cause severe problems to the eyes, which may end with sight loss. Such medical condition is known as "diabetic retinopathy" (DR). In such a diagnosis, the retinal microvascular go through several stages of change threat. In the early stages of the DR, detecting the formation that happened to the retinal blood vessels helps prevent the disease's dangerous effects. Therefore, producing a method to diagnose the disease in the early stages is helpful. So, this work aimed to develop a system of detecting and classifying the retina formation, trying to avoid relevant effects.Methodology: The current method depends on vascular edges map extracted from images of retina captured by a fundus camera. Such a map been utilized to extract quantitative texture features. The system tested two independent groups of the region of interest in normal and abnormal images. The two sets were extracted from ground truth images of the 89 fundus images. Fundus images were annotated images from the Standard Diabetic Retinopathy Database (DIARETDB1).Findings: The system provided an accuracy of 71% with a sensitivity of 75%.Recommendation: The current work may open an opportunity for improvement for future work. Other methods may be reached to raise the accuracy and the sensitivity of the system. Besides, the current system may be tested on a larger sample size to study such effects. Finally, the ease of the current method makes it faster in adoption in the appropriate diagnosis especially in the early stages.

Discrete spherical harmonic functions for texture representation and analysis

A basis of discrete harmonic functions for efficient representation and analysis of crystallographic texture is presented. Discrete harmonics are a numerical representation of the harmonics on the sphere. A finite element formulation is utilized to calculate these orthonormal basis functions, which provides several advantageous features for quantitative texture analysis. These include high-precision numerical integration, a simple implementation of the non-negativity constraint and computational efficiency. Simple examples of pole figure and texture interpolation and of Fourier filtering using these basis sets are presented.

Anomalous texture development induced by grain yielding anisotropy in Ni and Ni-Mo alloys

During the annealing of Ni and Ni-Mo films, a {110} texture, usually considered to be both of high surface energy and high strain energy, was found to develop. Quantitative thermodynamic model calculations showed that the anomalous formation of this {110} texture originates fundamentally from the grain yielding anisotropy of Ni. Based on extensive grain yielding anisotropy model calculations, a “texture map” based on {111}, {100}, and {110} textures was constructed to predict the texture transition temperatures for different film thicknesses. A kinetic analysis of the texture evolution in the films is further presented, revealing that the texture evolution is controlled by the self-diffusion of atoms at grain boundaries. These findings pave the way for the achievement of unusual surface orientations through the quantitative texture design of thin films.

Effect of phase of enhancement on texture analysis in renal masses evaluated with non-contrast-enhanced, corticomedullary, and nephrographic phase-enhanced CT images.

To compare texture analysis (TA) features of solid renal masses on renal protocol (non-contrast enhanced [NECT], corticomedullary [CM], nephrographic [NG]) CT. A total of 177 consecutive solid renal masses (116 renal cell carcinoma [RCC]; 51 clear cell [cc], 40 papillary, 25 chromophobe, and 61 benign masses; 49 oncocytomas, 12 fat-poor angiomyolipomas) with three-phase CT between 2012 and 2017 were studied. Two blinded radiologists independently assessed tumor heterogeneity (5-point Likert scale) and segmented tumors. TA features (N = 25) were compared between groups and between phases. Accuracy (area under the curve [AUC]) for RCC versus benign and cc-RCC versus other masses was compared. Subjectively, tumor heterogeneity differed between phases (p < 0.01) and between tumors within the same phase (p = 0.03 [NECT] and p < 0.01 [CM, NG]). Inter-observer agreement was moderate to substantial (intraclass correlation coefficient = 0.55-0.73). TA differed in 92.0% (23/25) features between phases (p < 0.05) except for GLNU and f6. More TA features differed significantly on CM (80.0% [20/25]) compared with NG (40.0% [10/25]) and NECT (16.0% [4/25]) (p < 0.01). For RCC versus benign, AUCs of texture features did not differ comparing CM and NG (p > 0.05), but were higher for 20% (5/25) and 28% (7/25) of features comparing CM and NG with NECT (p < 0.05). For cc-RCC versus other, 36% (9/25) and 40% (10/25) features on CM had higher AUCs compared with NECT and NG images (p < 0.05). Texture analysis of renal masses differs, when evaluated subjectively and quantitatively, by phase of CT enhancement. The corticomedullary phase had the highest discriminatory value when comparing masses and for differentiating cc-RCC from other masses. • Subjectively evaluated renal tumor heterogeneity on CT differs by phase of enhancement. • Quantitative CT texture analysis features in renal tumors differ by phases of enhancement with the corticomedullary phase showing the highest number and most significant differences compared with non-contrast-enhanced and nephrographic phase images. • For diagnosis of clear cell RCC, corticomedullary phase texture analysis features had improved accuracy of classification in approximately 40% of features studied compared with non-contrast-enhanced and nephrographic phase images.

A lung graph model for the radiological assessment of chronic thromboembolic pulmonary hypertension in CT

Chronic thromboembolic pulmonary hypertension (CTEPH) is a possible complication of pulmonary embolism (PE), with poor prognosis if left untreated. Surgical curative treatment is available, particularly in the early stages of the disease. However, most cases are not diagnosed until specific symptoms become evident. A small number of computed tomography (CT) findings, such as a widened pulmonary artery and mosaicism in the lung parenchyma, have been correlated with pulmonary hypertension (PH). Quantitative texture analysis in the CT scans of these patients could provide complementary sub-visual information of the vascular changes taking place in the lungs. For this task, a lung graph model was developed with texture descriptors from 37 CT scans with confirmed CTEPH diagnosis and 48 CT scans from PE patients who did not develop PH. The probability of presenting CTEPH, computed with the graph model, outperformed a convolutional neural network approach using 10 different train/test splits of the data set. An accuracy of 0.76 was obtained with the proposed texture analysis, and was then compared to the visual assessment of CT findings, manually identified by a team of three expert radiologists, commonly associated with pulmonary hypertension. This graph-based score combined with the information attained from the radiological findings resulted in a Cohen’s kappa coefficient of 0.47 when differentiating patients with confirmed CTEPH from those with PE who did not develop the disease. The proposed texture quantification could be an objective measurement, complementary to the current analysis of radiologists for the early detection of CTEPH and thus improve patient outcome.

On the correlation between second order texture features and human observer detection performance in digital images

Image texture, the relative spatial arrangement of intensity values in an image, encodes valuable information about the scene. As it stands, much of this potential information remains untapped. Understanding how to decipher textural details would afford another method of extracting knowledge of the physical world from images. In this work, we attempt to bridge the gap in research between quantitative texture analysis and the visual perception of textures. The impact of changes in image texture on human observer’s ability to perform signal detection and localization tasks in complex digital images is not understood. We examine this critical question by studying task-based human observer performance in detecting and localizing signals in tomographic breast images. We have also investigated how these changes impact the formation of second-order image texture. We used digital breast tomosynthesis (DBT) an FDA approved tomographic X-ray breast imaging method as the modality of choice to show our preliminary results. Our human observer studies involve localization ROC (LROC) studies for low contrast mass detection in DBT. Simulated images are used as they offer the benefit of known ground truth. Our results prove that changes in system geometry or processing leads to changes in image texture magnitudes. We show that the variations in several well-known texture features estimated in digital images correlate with human observer detection–localization performance for signals embedded in them. This insight can allow efficient and practical techniques to identify the best imaging system design and algorithms or filtering tools by examining the changes in these texture features. This concept linking texture feature estimates and task based image quality assessment can be extended to several other imaging modalities and applications as well. It can also offer feedback in system and algorithm designs with a goal to improve perceptual benefits. Broader impact can be in wide array of areas including imaging system design, image processing, data science, machine learning, computer vision, perceptual and vision science. Our results also point to the caution that must be exercised in using these texture features as image-based radiomic features or as predictive markers for risk assessment as they are sensitive to system or image processing changes.

Physiological changes in newborn skin after natural oil massage in rural Nepal

# Background Natural oils are applied daily to newborn infant skin during massage in certain cultures. In hospitalized premature infants, topical oils reduced infection and improved skin condition. A community study among neonates in rural Nepal compared sunflower and mustard oils for effects on skin integrity. Particularly notable was the rapid increase in skin rash that peaked at day 14 and lessened by day 28 for both oils, a finding not previously reported. # Methods To investigate the rash physiology, high-resolution digital images were collected for a subset of 216 premature and full-term infants. Image features were characterized for type and severity, quantified for color and surface texture and compared to measures of skin barrier integrity. # Results Skin features were observed in 403 of 811 images, including localized erythema, discrete papules and an occasional pustule. Miliaria crystallina was noted in 32.2%. A second texture, with raised, “pebble” features, characterized 26%. Of 216 subjects, 61% had “pebble” on at least one visit. Miliaria severity increased and day 14 was higher than 1 and 7 (P<0.05). Pebble texture severity increased. Day 28 was higher than 1, 3 and 7 (P<0.05). Skin pH was lower and erythema, rash and massage numbers were higher for miliaria than without it (P<0.05). Subjects with and without pebble texture did not differ for skin integrity measures. # Conclusions An understanding of the maturational changes in qualitative and quantitative skin color and texture with routine neonatal oil massage provides the basis for leveraging this commonly practiced ritual to optimize skin care.

Evolution of microstructure and texture of moderately warm-rolled pure tungsten during annealing at 1300 °C

The mechanical behavior, microstructure and texture evolution were investigated during isothermal annealing at 1300 °C of pure tungsten moderately warm-rolled to 67% thickness reduction. The degradation of the mechanical properties is characterized by hardness testing. The microstructure and texture evolution during heat treatment were characterized by Electron Backscatter Diffraction. During annealing of the moderately warm-rolled tungsten, recrystallization occurred first, quickly followed by relatively slow grain growth. The recrystallized volume fractions determined from hardness measurements and microstructural characterization were essentially the same. The evolution of the grain sizes during recrystallization was analyzed independently for deformed and recrystallized grains. Quantitative texture analysis showed that the overall texture strength is enhanced after recrystallization. As recrystallization strongly affects the mechanical properties of tungsten, such insights in the annealing behavior of warm-rolled tungsten plates are valuable for an understanding of their performance as potential plasma-facing materials in future fusion reactors.

Radiomics for the Diagnosis and Differentiation of Pancreatic Cystic Lesions.

Radiomics, also known as quantitative imaging or texture analysis, involves extracting a large number of features traditionally unmeasured in conventional radiological cross-sectional images and converting them into mathematical models. This review describes this approach and its use in the evaluation of pancreatic cystic lesions (PCLs). This discipline has the potential of more accurately assessing, classifying, risk stratifying, and guiding the management of PCLs. Existing studies have provided important insight into the role of radiomics in managing PCLs. Although these studies are limited by the use of retrospective design, single center data, and small sample sizes, radiomic features in combination with clinical data appear to be superior to the current standard of care in differentiating cyst type and in identifying mucinous PCLs with high-grade dysplasia. Combining radiomic features with other novel endoscopic diagnostics, including cyst fluid molecular analysis and confocal endomicroscopy, can potentially optimize the predictive accuracy of these models. There is a need for multicenter prospective studies to elucidate the role of radiomics in the management of PCLs.

Non-invasive evaluation for benign and malignant subcentimeter pulmonary ground-glass nodules (≤1 cm) based on CT texture analysis.

To investigate potential diagnostic model for predicting benign or malignant status of subcentimeter pulmonary ground-glass nodules (SPGGNs) (≤1 cm) based on CT texture analysis. A total of 89 SPGGNs from 89 patients were included; 51 patients were diagnosed with adenocarcinoma, and 38 were diagnosed with inflamed or infected benign SPGGNs. Analysis Kit software was used to manually delineate the volume of interest of lesions and extract a total of 396 quantitative texture parameters. The statistical analysis was performed using R software. The SPGGNs were randomly divided into a training set (n = 59) and a validation set (n = 30). All pre-normalized (Z-score) feature values were subjected to dimension reduction using the LASSO algorithm,and the most useful features in the training set were selected. The selected imaging features were then combined into a Rad-score, which was further assessed by ROC curve analysis in the training and validation sets. Four characteristic parameters (ClusterShade_AllDirection_offset4_SD, ShortRunEmphasis_angle45_offset1, Maximum3DDiameter, SurfaceVolumeRatio) were further selected by LASSO (p < 0.05). As a cluster of imaging biomarkers, the above four parameters were used to form the Rad-score. The AUC for differentiating between benign and malignant SPGGNs in the training set was 0.792 (95% CI: 0.671, 0.913), and the sensitivity and specificity were 86.10 and 65.20%, respectively. The AUC in the validation set was 72.9% (95% CI: 0.545, 0.913), and the sensitivity and specificity were 86.70 and 60%, respectively. The present diagnostic model based on the cluster of imaging biomarkers can preferably distinguish benign and malignant SPGGNs (≤1 cm). Texture analysis based on CT images provide a new and credible technique for accurate identification of subcentimeter pulmonary ground-glass nodules.

Breast lesion characterization using Quantitative Ultrasound (QUS) and derivative texture methods.

PurposeAccurate and timely diagnosis of breast cancer is extremely important because of its high incidence and high morbidity. Early diagnosis of breast cancer through screening can improve overall prognosis. Currently, biopsy remains as the gold standard for tumor pathological confirmation. Development of diagnostic imaging techniques for rapid and accurate characterization of breast lesions is required. We aim to evaluate the usefulness of texture-derivate features of QUS spectral parametric images for non-invasive characterization of breast lesions.MethodsQUS Spectroscopy was used to determine parametric images of mid-band fit (MBF), spectral slope (SS), spectral intercept (SI), average scatterer diameter (ASD), and average acoustic concentration (AAC) in 204 patients with suspicious breast lesions. Subsequently, texture analysis techniques were used to generate texture maps from parametric images to quantify heterogeneities of QUS parametric images. Further, a second-pass texture analysis was applied to obtain texture-derivate features. QUS parameters, texture-parameters and texture-derivate parameters were determined from both tumor core and a 5-mm tumor margin and were used in comparison to histopathological analysis in order to develop a diagnostic model for classifying breast lesions as either benign or malignant. Both leave-one-out and hold-out cross-validations were used to evaluate the performance of the diagnostic model. Three standard classification algorithms including a linear discriminant analysis (LDA), k-nearest neighbors (KNN), and support vector machines-radial basis function (SVM-RBF) were evaluated.ResultsCore and margin information using the SVM-RBF attained the best classification performance of 90% sensitivity, 92% specificity, 91% accuracy, and 0.93 AUC utilizing QUS parameters and their texture derivatives, evaluated using leave-one-out cross-validation. Implementation of hold-out cross-validation using combination of both core and margin information and SVM-RBF achieved average accuracy and AUC of 88% and 0.92, respectively.ConclusionsQUS-based framework and derivative texture methods enable accurate classification of breast lesions. Evaluation of the proposed technique on a large cohort using hold-out cross-validation demonstrates its robustness and its generalization.

Outcomes assessment in intrahepatic cholangiocarcinoma using qualitative and quantitative imaging features

BackgroundTo assess the performance of imaging features, including radiomics texture features, in predicting histopathologic tumor grade, AJCC stage, and outcomes [time to recurrence (TTR) and overall survival (OS)] in patients with intrahepatic cholangiocarcinoma (ICC).MethodsSeventy-three patients (26 M/47F, mean age 63y) with pre-operative imaging (CT, n = 37; MRI, n = 21; CT and MRI, n = 15] within 6 months of resection were included in this retrospective study. Qualitative imaging traits were assessed by 2 observers. A 3rd observer measured tumor apparent diffusion coefficient (ADC), enhancement ratios (ERs), and Haralick texture features. Blood biomarkers and imaging features were compared with histopathology (tumor grade and AJCC stage) and outcomes (TTR and OS) using log-rank, generalized Wilcoxon, Cox proportional hazards regression, and Fisher exact tests.ResultsMedian TTR and OS were 53.9 and 79.7 months. ICC recurred in 64.4% (47/73) of patients and 46.6% (34/73) of patients died. There was fair accuracy for some qualitative imaging features in the prediction of worse tumor grade (maximal AUC of 0.68 for biliary obstruction on MRI, p = 0.032, observer 1) and higher AJCC stage (maximal AUC of 0.73 for biliary obstruction on CT, p = 0.002, observer 2; and AUC of 0.73 for vascular involvement on MRI, p = 0.01, observer 2). Cox proportional hazards regression analysis showed that CA 19–9 [hazard ratio (HR) 2.44/95% confidence interval (CI) 1.31–4.57/p = 0.005)] and tumor size on imaging (HR 1.13/95% CI 1.04–1.22/p = 0.003) were significant predictors of TTR, while CA 19–9 (HR 4.08/95% CI 1.75–9.56, p = 0.001) and presence of metastatic lymph nodes at histopathology (HR 2.86/95% CI 1.35–6.07/p = 0.006) were significant predictors of OS. On multivariable analysis, satellite lesions on CT (HR 2.79/95%CI 1.01–7.15/p = 0.032, observer 2), vascular involvement on MRI (HR 0.10/95% CI 0.01–0.85/p = 0.032, observer 1), and texture feature MRI variance (HR 0.55/95% CI 0.31–0.97, p = 0.040) predicted TTR once adjusted for the independent predictors CA 19–9 and tumor size on imaging. Several qualitative and quantitative features demonstrated associations with TTR, OS, and AJCC stage at univariable analysis (range: HR 0.35–19; p < 0.001–0.045), however none were predictive of OS at multivariable analysis when adjusted for CA 19–9 and metastatic lymph nodes (p > 0.088).ConclusionsThere was reasonable accuracy in predicting tumor grade and higher AJCC stage in ICC utilizing certain qualitative and quantitative imaging traits. Serum CA 19–9, tumor size, presence of metastatic lymph nodes, and qualitative imaging traits of satellite lesions and vascular involvement are predictors of patient outcomes, along with a promising predictive ability of certain quantitative texture features.

Assessment of Clinical Stage IA Lung Adenocarcinoma with pN1/N2 Metastasis Using CT Quantitative Texture Analysis.

ObjectiveTo explore the application of texture analysis basing on computed tomography (CT) images in predicting lymph-node metastasis in patients with clinical stage IA lung adenocarcinoma.MethodsIn total, 256 patients with clinical stage IA lung adenocarcinoma who had underwentgone preoperative CT examinations were enrolled. A total of 25 texture features using MaZda (version 4.6) software and conventional radiological features were extracted from raw CT data sets. Based on surgical results, patients were stratified into lymph node metastasis–positive and –negative groups. Independent-sample t-tests and Mann–Whitney U tests were used to compare continuous variables between the groups. Continuity-correction and χ2 tests were used for categorical variable comparison. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of lymph-node metastasis.ResultsIn total, 256 clinical stage IA lung adenocarcinoma cases were proved by pathology: 39 (15.23%) cases with lymph-node metastasis (14 N1a, seven N1b, six N2a1, ten N2a2, and two N2b) and 217 (84.77%) cases without lymph-node metastasis. Univariate and multivariate logistic regression analyses demonstrated that total volume (OR 3.777, p=0.015), average CT value of whole tumor (OR 16.271, p<0.001), three texture parameters (mean OR 8.473, p<0.001; skewness OR 6.393, p=0.001; and entropy OR 0.343, p=0.049) were independent factors associated with lymph-node status. As such, early-stage lung adenocarcinoma with higher total volume (>4.05 cm3), average CT value of whole tumor (>–70 HU), mean (>133.79), entropy (>1.98), and lower skewness (≤0.02) pointed to positive lymph-node metastasis.ConclusionTexture parameters were independent factors associated with lymph-node status in clinical stage IA lung adenocarcinoma.

A Preliminary Study of CT Texture Analysis for Characterizing Epithelial Tumors of theParotid Gland.

ObjectiveThe aim of this study was to explore and validate the diagnostic performance of whole-volume CT texture features in differentiating the common benign and malignant epithelial tumors of the parotid gland.Materials and MethodsContrast-enhanced CT images of 83 patients with common benign and malignant epithelial tumors of the parotid gland confirmed by histopathology were retrospectively analyzed, including 50 patients with pleomorphic adenoma (PA) and 33 patients with malignant epithelial tumors. Quantitative texture features of tumors were extracted from CT images of arterial phase. The diagnostic performance of texture features was evaluated via receiver operating characteristic (ROC) curve and area under ROC curve (AUC). The specificity and sensitivity were respectively discussed by the maximum Youden’s index.ResultsAll the texture features were subject to normal distribution and homoscedasticity. Energy, mean, correlation, and sum entropy of epithelial malignancy group were significantly higher than those of PA group (P<0.05). There were no statistically significant differences between PA group and epithelial malignancy group in uniformity, entropy, skewness, kurtosis, contrast, and difference entropy (P>0.05). The AUC of each texture feature and joint diagnostic model was 0.887 (energy), 0.734 (mean), 0.739 (correlation), 0.623 (sum entropy), 0.888 (energy-mean), 0.883 (energy-correlation), 0.784 (mean-correlation). The diagnostic efficiency of energy-mean was the best. Based on the maximum Youden’s index, the specificity of energy-correlation was the highest (97%) and the sensitivity of energy was the highest (97%).ConclusionEnergy, mean, correlation, and sum entropy can be the effective quantitative texture features to differentiate the benign and malignant epithelial tumors of the parotid gland. With higher AUC, energy and energy-mean are superior to other indexes or joint diagnostic models in differentiating the benign and malignant epithelial tumors of the parotid gland. CT texture analysis can be used as a noninvasive and valuable means of preoperative assessment of parotid epithelial tumors without additional cost to the patients.

Supersaturation Nucleation and Growth of Plagioclase: a numerical model of decompression-induced crystallization

Supersaturation Nucleation and Growth of Plagioclase (SNGPlag) is a numerical model that predicts the nucleation and growth of plagioclase crystals in a decompressing magma as a function of time. The model is written in Matlab, but is available as a standalone compiled program. SNGPlag uses the MELTS webservice to determine equilibrium plagioclase mode, for a user-defined magma composition, as a function of pressure and temperature. User inputs include decompression path, the presence and size distributions of antecrysts and phenocrysts, and crystal shape. At each time step, the model evaluates the difference between the calculated crystallinity and equilibrium crystallinity for a given pressure and temperature to determine the degree of supersaturation, which then sets plagioclase nucleation and growth rates. Growth rates are used to grow the existing crystals whereas nucleation adds new crystals. SNGPlag produces results that can be compared to quantitative textures in natural volcanic rocks, including total crystallinity, microlite number density, microlite crystal size distribution, the characteristic size of microlite crystals, as well as a time series of crystallinity. Model results are consistent with the established crystallization theory. As expected, microlite crystallinity increases as decompression rate slows. Decompression path greatly affects microlite textures. For the same average decompression rate, single-step paths have higher crystallinities and microlite number densities than multi-step decompressions, which are in turn more crystalline than continuous paths. Pre-existing crystals damp microlite crystallization, as these crystals provide a substrate to accommodate crystal growth and thus reduce supersaturation. The size distribution and volume fraction of these pre-existing crystals determines the magnitude of the damping. SNGPlag predicts that melt composition and temperature also exert important controls. Higher temperatures and higher silica contents both reduce microlite crystallization. In comparison with the previous studies of decompression rate based on microlite crystallization experiments, SNGPlag generally predicts minimum decompression rates that are up to three-to-four times slower. The difference is likely because those studies applied single- or multi-step decompression experiments to simulate natural magma ascent, which may be better represented by continuous decompression pathways or series of continuous decompression intervals punctuated with pauses. Previous studies also fail to account for the effects of phenocrysts or antecrysts on microlite nucleation and growth.

Correlation of radiomics of metastatic lesions in gastroesophageal adenocarcinoma (GEA) with tumoral DKK1 mRNA expression and other immune biomarkers in patients (pts) treated with DKN-01.

429 Background: Dickkopf-1 (DKK1) modulates Wnt and PI3K/AKT signaling pathways and contributes to an immune suppressive tumor microenvironment. Recently, anti-DKK1 antibody (DKN-01) [D] plus pembrolizumab (P) in GEA tumors has demonstrated clinical activity. Radiomic Quantitative Texture Analysis (QTA) is a non-invasive method for imaging biomarker discovery that can predict molecular drivers of cancer from CT scans obtained during clinical trials. QTA may be a useful tool for predicting DKK1 expression and other immune biomarkers in metastatic lesions from GEA pts treated with DKN-01. Methods: 13 pts with GEA (12 M; 1F; age 37-81) enrolled in D+P Phase I/II trial with high vs low DKK1 RNA Scope H-Scores (H Score &gt; 35; n = 6 vs H-Score &lt; 35; n = 7) were identified. Metastatic Target lesions (TLs) from baseline CTs with IV contrast underwent additional QTA analysis (TexRAD, CE mark, Essex, UK) after RECIST analysis. ROIs were placed on the TLs and histogram frequency curves (HFC) of the metastatic tumor pixels were generated. First order tumor HFC statistical results (i.e. mean, Standard Deviation-SD, Skewness, Kurtosis, MPP) were correlated with tumoral DKK1 mRNA expression, baseline plasma DKK1 levels and other biomarkers (PD-L1 CPS and MDSCs) along with tumor shrinkage using Pearson’s r correlation (significance p &lt; 0.05). Results: Tumor derived QTA parameter (QTA SD) of the largest metastatic TL from each subject was positively correlated with DKK1 RNAscope H-score (r = 0.661, p value = .014) meaning larger SD texture scores had greater DKK1 expression. Also, tumor derived QTA parameter (QTA MPP) was correlated with baseline plasma DKK1 (r = -0.461, p = 0.005). Finally, QTA SD was correlated with PD-L1 expression (r = 0.630, p = 0.028) and TL shrinkage (r = -0.590, p = 0.034). Conclusions: Radiomics of GEA tumor metastasis using QTA showed significant correlations between tumor texture, tumor DKK1 mRNA expression and baseline plasma DKK1 suggesting that tumor textures may provide a non-invasive tool for assessing DKK1. Although promising as a imaging biomarker, further studies are required to validate these findings. Clinical trial information: NCT02013154.