Image Texture Analysis Research Articles

Single-use commercial bio-based plastics under environmental degradation conditions: Is their biodegradability and compostability a fact?

Evaluating compostability is increasingly essential for proving commercial bio-based cutlery or packaging since these materials must biodegrade under controlled conditions quickly. Utensils for eating represent Mexico's most popular consumer single-use materials, and Mexican regulations based on biodegradation or compostability are still vague and lack scientific evaluations. This study analyzed three bio-based polymeric materials (bags, dishes, and forks) from commercial brands following Mexican regulations and using various analytical techniques to verify their biodegradability and compostability. First, weight loss measurements, stress-strain tests, and topographic imaging were applied for preliminary observations at the macro scale up to 90 days of compostability. Besides, spectroscopy, microscopy, and thermal techniques indicate changes and behavior of the bio-based materials depending on the composition. The results suggest that bags exhibited the highest decomposition rate (80 %) compared to dishes and forks. Similarly, mechanical resistance indicates a reduction of 62 % for bags, 30 % for dishes, and almost none for forks. Texture image analysis revealed that the complexity and roughness of the materials increased over time, correlating with the physical changes observed. These results indicate minimal surface topography changes and higher stiffness for dishes and forks, indicating low biodegradability. SEM images supported these findings, showing surface degradation in bags and dishes but not in forks. FTIR and XRD analyses confirmed the presence of polyamide (bags) and polypropylene (dishes and forks). These results reduce biodegradation and differ from the claims made by manufacturers. The thermal analysis found similar results, indicating that the materials' thermal stability decreased after degradation, which is related to lower biodegradability and compostability. Overall, the study concluded only bags meet the criteria for compostability in national regulations. However, dishes and forks made of petroleum-derived polymers have higher resistance to natural and microbial degradation.

Texture-Based Classification of Fetal Growth Restriction From Intrauterine Neurosonographic Image.

Fetal growth restriction (FGR) is a condition where fetuses fail to reach their genetic potential for growth, posing a significant health challenge for newborns. The aim of this research was to explore the efficacy of texture-based analysis of neurosonographic images in identifying FGR in fetuses, which may provide a promising tool for early assessment of FGR. A retrospective analysis collected 100 intrauterine neurosonographic images from 50 FGR and 50 gestational age-appropriate fetuses. Using MaZda software, approximately 300 texture features were extracted from occipital white matter (OWM) and cerebellum of intrauterine neurosonographic images, respectively. Then 10 optimal features were separately selected by 3 algorithms, including the Fisher coefficient method, the method of minimizing classification error probability and average correlation coefficients, and the mutual information coefficient method. Further, the 10 statistically most significant features were selected from these sets to form the mixed feature set. After nonlinear discriminant analysis was performed to reduce feature dimensionality, the artificial neural network (ANN) classifier was conducted, respectively. For OWM and cerebellum, a total of 11 and 14 statistically significant features were selected. When the mixed feature sets of OWM and cerebellum were applied to ANN classifier, classification accuracy were 90.00% (κ = 0.800; P < .001) and 93.00% (κ = 0.860; P < .001), and the receiver operating characteristic curve for identifying FGR showed an area under the curve of 0.82 and 0.87. Texture analysis of fetal intrauterine neurosonographic images is a feasible and noninvasive strategy for evaluating FGR fetuses.

Image texture analysis of pellets made of lignocellulosic materials

This experiment aimed to find the relation between image texture features of pellets made of various lignocellulosic materials (wood, wheat straw, hay, giant miscanthus, prairie spartina, and giant knotweed) and their physico-mechanical properties (density, compressive energy, maximum compressive strength, modulus of elasticity). Using the Kruskal-Wallis's test, the effect of materials on these properties was examined. Texture features were derived from the grey-level co-occurrence matrix, grey-level run-length matrix, absolute gradient matrix, autoregressive model, and wavelet decomposition, resulting in 86 features, later reduced to 8 factors via explanatory factor analysis. These factors were used as predictors in regression models for physico-mechanical properties. The models for modulus of elasticity achieved R2adj values of 0.91–0.99 (except for hay and wood), compressive stress models achieved 0.65–0.99 (excluding hay and wood), compressive energy models ranged from 0.60 to 0.97 (excluding hay), and density models ranged from 0.56 to 0.97 (excluding wood). The study confirmed a significant correlation between material type, texture parameters and compression resistance, suggesting this method could monitor pellet quality in production.

Enhancing Pléiades-based crop mapping with multi-temporal and texture information

Accurate crop mapping using satellite imagery is crucial for improving the monitoring of agricultural landscapes. Very high resolution (VHR) satellite imagery offers unique capabilities in this respect, allowing for even small fields to be discerned and image texture analysis to be performed. Additionally, satellite imagery has greater efficiency than unmanned aerial vehicles due to its extensive coverage. Moreover, the operation flexibility of VHR satellites means that timely image acquisition is possible several times during the growing season. This study investigates the potential of VHR Pléiades images and the random forest classifier for accurate crop mapping. Four images acquired on April 9th, May 12th, May 31st, and June 20th were used to test 16 classification scenarios, including single-date and multi-temporal combinations of spectral bands, texture features, and vegetation Indices (VIs). The classification using the spectral bands from all four images achieved the highest overall accuracy, 93.9% and 96.3% at field and pixel levels, respectively. The bitemporal classifications had lower accuracy. Nevertheless, the combination of the May 12th and June 20th spectral bands had 90% accuracy, which indicated that two images may be sufficient for reliable mapping if the periods with phenological differences between crops are considered. Adding texture features to the spectral bands significantly enhanced the accuracy (up to 8%) of single-date classifications, making it highly recommended when only one image is available. However, the impact of texture was more pronounced on the later dates. It showed the most marked benefit for vineyards and alfalfa, with minimal or no improvement observed for other classes like winter barley. An additional increase in overall accuracy was achieved in three of the four dates by supplementing the spectral and texture bands with VIs. This study highlights the importance of considering image acquisition dates and crop types when designing satellite-based crop mapping strategies for optimal accuracy.

Texture-based speciation of otitis media-related bacterial biofilms from optical coherence tomography images using supervised classification.

Otitis media (OM), a highly prevalent inflammatory middle-ear disease in children worldwide, is commonly caused by an infection, and can lead to antibiotic-resistant bacterial biofilms in recurrent/chronic OM cases. A biofilm related to OM typically contains one or multiple bacterial species. OCT has been used clinically to visualize the presence of bacterial biofilms in the middle ear. This study used OCT to compare microstructural image texture features from bacterial biofilms. The proposed method applied supervised machine-learning-based frameworks (SVM, random forest, and XGBoost) to classify multiple species bacterial biofilms from in vitro cultures and clinically-obtained in vivo images from human subjects. Our findings show that optimized SVM-RBF and XGBoost classifiers achieved more than 95% of AUC, detecting each biofilm class. These results demonstrate the potential for differentiating OM-causing bacterial biofilms through texture analysis of OCT images and a machine-learning framework, offering valuable insights for real-time in vivo characterization of ear infections.

Difference in Laterality of the Dorsal Striatum in Schizoaffective Disorder.

Recent research has demonstrated that the dorsal striatum is directly associated with the integration of cognitive, sensory-motor, and motivational/emotional data. Disruptions in the corticostriatal circuit have been implicated in the pathophysiology of psychosis. The dorsal striatum was reported to show lateralized pathology in psychotic disorders. In this study, we aimed to analyze the laterality of the dorsal striatum with texture analysis of T2-weighted magnetic resonance imaging (MRI) images from schizoaffective disorder (SAD) patients. Twenty SAD patients, met the inclusion criteria and had available cranial MRI data were assigned as the patient group. Twenty healthy individuals were determined as the control group. Texture analysis values were obtained from striatum region of interests (ROI) generated from T2-weighted MRI images. Data are presented as mean and standard deviation. The suitability of the data for normal distribution was analyzed with the Kolmogorov-Smirnov test. Analysis of variance (ANOVA) test (Post Hoc TUKEY) was employed to compare the group data based on test findings. There was no significant difference between the groups in terms of gender and age. There were differences in the values of texture analysis parameters of both caudate and putamen nuclei in comparison to controls. We identified differences in the left dorsal striatum nuclei in SAD. The differences in the putamen were more and more pronounced than in the caudate. Texture analyses suggest that the left dorsal striatum nuclei may be different in SAD patients. Further studies are needed to determine the pathophysiology of SAD and how it may affect disease treatment.

Using texture analysis of ultrasonography images of neck lymph nodes to differentiate metastasis to non-metastasis in oral maxillary gingival squamous cell carcinoma

ObjectTo differentiate between metastatic neck nodes and non-metastatic neck nodes in oral maxillary gingival squamous cell carcinoma, textural analysis of these lymph nodes in ultrasound images was performed in this study. MethodsTwenty five metastatic neck nodes and 28 non-metastatic neck nodes were enrolled in this study. Seventy eight texture characteristics were retrieved from the US images using the LIFEx software.The Mann Whitney U test was measurably utilized to survey on the off chance that there was a measurably noteworthy distinction within the textural characteristics between metastatic neck nodes and non-metastatic neck nodes. The capacity of the surface highlights to recognize between metastatic neck nodes and non-metastatic neck nodes was illustrated utilizing the Receiver Operating Characteristic analysis curves (ROC). Youden's J statistic was used to determine the cut-off positions in each ROC curve that maximized sensitivity and specificity. ResultsZone size non uniformity (ZSNU) highlight appeared the foremost noteworthy contrast between these nodes (p < 0.001).Strength had Area Under the Curve (AUC) of 0.811, specificity of 0.821 and sensitivity of 0.8, when measured at the cutoff value of 896.344. ConclusionsOur results come about uncovered that quality highlight may be the finest surface highlight to distinguish from non-metastatic neck nodes and to anticipate metastatic neck nodes in oral maxillary gingival squamous cell carcinoma.

Study on the EC prediction of cracked soda saline-alkali soil based on texture analysis of high-resolution images from ground-based observation and machine learning methods

The rapid and accurate measurement of soil salinity is essential for assessing the level of soil salinization. In natural conditions, the surface of cohesive soda saline-alkali soil experiences shrinkage and cracking phenomena during water evaporation. The propagation and development of these desiccation cracks can be effectively described using texture features due to their statistical randomness. This study aims to establish a relationship between electrical conductivity (EC) values and texture features derived from both gray-level co-occurrence matrix (GLCM) and wavelet decomposition, and to develop a prediction model for EC accordingly. To achieve these objectives, crack images on the surface of 200 soil samples with varying levels of salinity were obtained in the Songnen Plain field. GLCM was computed and wavelet decomposition was performed to extract texture features in different directions and scales. The results demonstrate a significant correlation between texture features and soil EC values. Among the various texture features, 12 GLCM texture features with a grayscale level of 2 and a step size of 1 pixel, and wavelet texture features derived from a 4th level orthogonal decomposition based on the coiflet-1 function were selected as the optimal parameters for establishing and comparing the predictive effects of two machine learning models on soil EC values. In comparison to the testing results of the BP neural network model (R2=0.83, RPD=1.64, RMSE=0.32 dS/m, MAE=0.18 dS/m), the random forest model exhibited higher accuracy and stability (R2=0.95, RPD=3.48, RMSE=0.21 dS/m, MAE=0.07 dS/m), indicating that although both machine learning models demonstrated rapid and nondestructive capabilities, the random forest method was better suited for EC prediction in soda saline-alkali soil due to its superior accuracy.

Texture differences of microchambers and macrochambers in heel pads between the elderly with and without diabetes

ObjectiveThis study aims to use the texture analysis of ultrasound images to distinguish the features of microchambers (a superficial thinner layer) and macrochambers (a deep thicker layer) in heel pads between the elderly with and without diabetes, so as to preliminarily explore whether texture analysis can identify the potential injury characteristics of deep tissue under the influence of diabetes before the obvious injury signs can be detected in clinical management. MethodsUltrasound images were obtained from the right heel (dominant leg) of eleven elderly people with diabetes (DM group) and eleven elderly people without diabetes (Non-DM group). The TekScan system was used to measure the peak plantar pressure (PPP) of each participant. Six gray-level co-occurrence matrix (GLCM) features including contrast, correlation, dissimilarity, energy, entropy, homogeneity were used to quantify texture changes in microchambers and macrochambers of heel pads. ResultsSignificant differences in GLCM features (correlation, energy and entropy) of macrochambers were found between the two groups, while no significant differences in all GLCM features of microchambers were found between the two groups. No significant differences in PPP and tissue thickness in the heel region were observed between the two groups. ConclusionsIn the elderly with diabetes who showed no significant differences in PPP and plantar tissue thickness compared to those without diabetes, several texture features of ultrasound images were found to be significantly different. Our finding indicates that texture features (correlation, energy and entropy) of macrochambers could be used for early detection of soft tissue damage associated with diabetes.

Novel method of image analysis that combines Radon and Fourier image transformations for the purpose of differentiating between malignant and benign colonic biopsies.

Colorectal cancer is the third most common type of cancer. It develops slowly as a polyp that can turn into a cancerous tumor. This study aimed to develop a decision-making algorithm of microscopic images using texture analysis that is orientation free, to be used for automated classification of normal and neoplastic (malignant or premalignant) colonic biopsies. Forty-nine colonic adenocarcinomas, 41 adenomas and a control group of adjacent normal colonic mucosa were included in the texture analysis. Radon transform followed by the Fast Fourier transform were applied to the images. Subsequently, the gray level co-occurrence matrix (GLCM) transform was applied allowing the extraction of four textural variables (homogeneity, contrast, correlation, and entropy). For classification and prediction of the diagnosis, a statistical multivariate regression model and a neural network (NNET) model were used and compared. The statistical model provided a sensitivity of 71.3% and a specificity of 50% (Area under the ROC curve: 0.67) for classifying the neoplastic and the normal images, respectively. The NNET model was superior to the statistical model and produced a sensitivity of 97.9% and specificity of 88% (Area under the ROC curve: 0.92). To our knowledge, this is the first study that used a combination of Radon, FFT, and GLCM transformations in order to overcome the tissue orientation problem in texture analysis of microscopic images of colonic biopsies. The NNET classifier trained by the extracted textural features proved to be superior to the statistical classifier, thus predicting colonic neoplasia with high accuracy. RESEARCH HIGHLIGHTS: We propose a novel decision-making algorithm of orientation invariant image texture analysis, fast and easily implemented for automated differentiation between benign and neoplastic epithelial tumors of the colon. This method can reduce the turnaround time allowing to prioritize the biopsies during their examination and diagnosis by the pathologist.

Machine learning and magnetic resonance image texture analysis predicts accelerated lung function decline in ex-smokers with and without chronic obstructive pulmonary disease.

Our objective was to train machine-learning algorithms on hyperpolarized magnetic resonance imaging (MRI) datasets to generate models of accelerated lung function decline in participants with and without chronic-obstructive-pulmonary-disease. We hypothesized that hyperpolarized gas MRI ventilation, machine-learning, and multivariate modeling could be combined to predict clinically-relevant changes in forced expiratory volume in 1s ( ) across 3 years. Hyperpolarized MRI was acquired using a coronal Cartesian fast gradient recalled echo sequence with a partial echo and segmented using a k-means clustering algorithm. A maximum entropy mask was used to generate a region-of-interest for texture feature extraction using a custom-developed algorithm and the PyRadiomics platform. The principal component and Boruta analyses were used for feature selection. Ensemble-based and single machine-learning classifiers were evaluated using area-under-the-receiver-operator-curve and sensitivity-specificity analysis. We evaluated 88 ex-smoker participants with months follow-up data, 57 of whom (22 females/35 males, years) had negligible changes in and 31 participants (7 females/24 males, years) with worsening . In addition, 3/88 ex-smokers reported a change in smoking status. We generated machine-learning models to predict decline using demographics, spirometry, and texture features, with the later yielding the highest classification accuracy of 81%. The combined model (trained on all available measurements) achieved the overall best classification accuracy of 82%; however, it was not significantly different from the model trained on MRI texture features alone. For the first time, we have employed hyperpolarized MRI ventilation texture features and machine-learning to identify ex-smokers with accelerated decline in with 82% accuracy.

Prognostic Significance of 18 F-FDG PET/CT Radiomics in Patients With Resectable Pancreatic Ductal Adenocarcinoma Undergoing Curative Surgery.

This study aimed to investigate the prognostic significance of PET/CT radiomics to predict overall survival (OS) in patients with resectable pancreatic ductal adenocarcinoma (PDAC). We enrolled 627 patients with resectable PDAC who underwent preoperative 18 F-FDG PET/CT and subsequent curative surgery. Radiomics analysis of the PET/CT images for the primary tumor was performed using the Chang-Gung Image Texture Analysis toolbox. Radiomics features were subjected to least absolute shrinkage and selection operator (LASSO) regression to select the most valuable imaging features of OS. The prognostic significance was evaluated by Cox proportional hazards regression analysis. Conventional PET parameters and LASSO score were assessed as predictive factors for OS by time-dependent receiver operating characteristic curve analysis. During a mean follow-up of 28.8 months, 378 patients (60.3%) died. In the multivariable Cox regression analysis, tumor differentiation, resection margin status, tumor stage, and LASSO score were independent prognostic factors for OS (HR, 1.753, 1.669, 2.655, and 2.946; all P < 0.001, respectively). The time-dependent receiver operating characteristic curve analysis showed that the LASSO score had better predictive performance for OS than conventional PET parameters. The LASSO score using the 18 F-FDG PET/CT radiomics of the primary tumor was the independent prognostic factor for predicting OS in patients with resectable PDAC and may be helpful in determining therapeutic and follow-up plans for these patients.

Multidimensional Outcome Parameters in a Cress Seedling-CuCl2 Crystallization Assay to Corroborate Specific Effects of Stannum metallicum 30x Compared to Lactose 30x.

Previously we developed a test system which yielded highly significant evidence for specific effects of a Stannum metallicum 30x preparation in a multi-center replication trial. This test system is based on cress seed germination in homeopathic or control samples, CuCl2 crystallization of the cress extract, and subsequent digital textural image analysis of the resulting crystallization patterns. The current study aimed to investigate whether three novel outcome parameters could further corroborate and possibly characterize the specific effects of Stannum metallicum 30x. To this end, (1) cress seedling length, (2) a second texture analysis parameter, entropy and (3) the local connected fractal dimension (LCFD) of crystallization patterns as a measure of complexity were considered. The stability of the experimental setup was monitored throughout the entire investigation with systematic negative control (SNC) experiments. Cress length and entropy revealed a time-modulated potency treatment effect, in the absence of a significant main treatment effect. This indicated that the effect of the potency treatment varied significantly across the different experimental days. LCFD yielded a highly significant potency treatment effect. In addition, a significant interaction of treatment with experimental day seems to indicate a modulation of this effect. No significant effects were observed in any of the evaluations of the SNC experiments, indicative of a stable experimental setup and a reliable and specific treatment effect. Neither significant nor strong correlations were found between the four parameters, indicating that they reflect different effects of Stannum metallicum 30x on the organism treated. This quadruple characterization of the biological effects of Stannum metallicum 30x provides an unprecedented opportunity for basic homeopathy research into, among others, the presumed specificity of homeopathic preparations.

Is the Caudate, Putamen, and Globus Pallidus the Delusional Disorder's Trio? A Texture Analysis Study.

The neurobiological basis of delusional disorder is less explored through neuroimaging techniques than in other psychotic disorders. This study aims to provide information about the neural origins of delusional disorder (DD) by examining the neuroanatomical features of some basal nuclei with magnetic resonance imaging (MRI) texture analysis. Twenty DD patients and 20 healthy individuals were included in the study. Globus pallidus, putamen, and caudate nuclei were selected individually with a region of interest (ROI) on the axial MRI images. The entire texture analysis algorithm applied to all selected ROIs was done with an in-house software. Nuclei on both sides were taken as separate samples. There were no significant differences between groups in terms of age and gender. The average "mean, median and maximum" values of all three nuclei were decreased in DD patients. The small putamen area and the differences detected in different tissue parameters for all three nuclei in delusional disorder patients indicate that they differ in delusional disorder from normal controls (p < 0.05). The differences detected in the texture parameters for all three nuclei indicate that there is something different in the DD from in the normal controls. Neuroimaging studies with larger samples and different techniques in the future may shed light on the etiology of delusional disorder.

Discriminating Glioblastoma from Normal Brain Tissue In Vivo Using Optical Coherence Tomography and Angiography: A Texture and Microvascular Analysis Approach

Brain tumors arise from abnormal cell growth in the brain. Glioblastoma, the most common and aggressive type, poses significant challenges for identification during surgery. The primary goal of this study is to identify and differentiate normal brain tissue from glioblastoma tissue using optical coherence tomography (OCT) and OCT angiography (OCTA). These techniques offer a non-invasive way to analyze the morphological and microvascular alternations associated with glioblastoma in an animal model. To monitor the changes in morphology and vascular distribution of brain tissue as glioblastoma tumors grow, time-series OCT and OCTA results were collected for comparison. Texture analysis of OCT images was proposed using the gray-level co-occurrence matrix (GLCM), from which homogeneity and variance were calculated as discriminative parameters. Additionally, OCTA was used to assess microvascular characteristics, including vessel diameter, density, and fractal dimension. The findings demonstrate that the proposed methods can effectively distinguish between normal and cancerous brain tissue in vivo.

Texture identification in liquid crystal-protein droplets using evaporative drying, generalized additive modeling, and K-means Clustering

Sessile drying droplets manifest distinct morphological patterns, encompassing diverse systems, viz., DNA, proteins, blood, and protein-liquid crystal (LC) complexes. This study employs an integrated methodology that combines drying droplet, image texture analysis (features from First Order Statistics, Gray Level Co-occurrence Matrix, Gray Level Run Length Matrix, Gray Level Size Zone Matrix, and Gray Level Dependence Matrix), and statistical data analysis (Generalized Additive Modeling and K-means clustering). It provides a comprehensive qualitative and quantitative exploration by examining LC-protein droplets at varying initial phosphate buffered concentrations (0x, 0.25x, 0.5x, 0.75x, and 1x) during the drying process under optical microscopy with crossed polarizing configuration. Notably, it unveils distinct LC-protein textures across three drying stages: initial, middle, and final. The Generalized Additive Modeling (GAM) reveals that all the features significantly contribute to differentiating LC-protein droplets. Integrating the K-means clustering method with GAM analysis elucidates how textures evolve through the three drying stages compared to the entire drying process. Notably, the final drying stage stands out with well-defined, non-overlapping clusters, supporting the visual observations of unique LC textures. Furthermore, this paper contributes valuable insights, showcasing the efficacy of drying droplets as a rapid and straightforward tool for characterizing and classifying dynamic LC textures.Graphical

Investigating cartilage-related diseases by polarization-resolved second harmonic generation (P-SHG) imaging.

Establishing quantitative parameters for differentiating between healthy and diseased cartilage tissues by examining collagen fibril degradation patterns facilitates the understanding of tissue characteristics during disease progression. These findings could also complement existing clinical methods used to diagnose cartilage-related diseases. In this study, cartilage samples from normal, osteoarthritis (OA), and rheumatoid arthritis (RA) tissues were prepared and analyzed using polarization-resolved second harmonic generation (P-SHG) imaging and quantitative image texture analysis. The enhanced molecular contrast obtained from this approach is expected to aid in distinguishing between healthy and diseased cartilage tissues. P-SHG image analysis revealed distinct parameters in the cartilage samples, reflecting variations in collagen fibril arrangement and organization across different pathological states. Normal tissues exhibited distinct χ33/χ31 values compared with those of OA and RA, indicating collagen type transition and cartilage erosion with chondrocyte swelling, respectively. Compared with those of normal tissues, OA samples demonstrated a higher degree of linear polarization, suggesting increased tissue birefringence due to the deposition of type-I collagen in the extracellular matrix. The distribution of the planar orientation of collagen fibrils revealed a more directional orientation in the OA samples, associated with increased type-I collagen, while the RA samples exhibited a heterogeneous molecular orientation. This study revealed that the imaging technique, the quantitative analysis of the images, and the derived parameters presented in this study could be used as a reference for disease diagnostics, providing a clear understanding of collagen fibril degradation in cartilage.

The texture analysis of computed tomography studies in clear cell renal cell carcinoma: reproducibility of 2D and 3D segmentation

Background: Differentiation of tumor grade at the preoperative stage is of utmost importance for the modification of the treatment strategy and the extent of operation. However, the routine analysis of computed tomography (CT) data in clear cell renal cell carcinoma (ccRCC) does not allow for reliable determination of the tumor grade.&#x0D; Aim: To assess the reproducibility of the results of 2D and 3D segmentation of a kidney tumor in the cortico-medullary and nephrographic phases of CT studies, as well as the reproducibility of the first order texture parameters for 2D and 3D tumor segmentation in patients with verified ccRCC.&#x0D; Materials and methods: This retrospective study included the CT data of 50 patients with morphologically verified ccRCC obtained before their surgical treatment. The first patient group included the patients with the renal tumor size in the axial plane of ≥ 4 cm (28 patients, 29 CT studies), and the second patient group included those with the renal tumor size in axial plane of 4 cm (22 patients, 23 CT studies). Two radiologists independently performed segmentation of the renal tumor in the cortico-medullary and nephrographic phases of CT procedures done under a standard protocol with the bolus intravenous contrast enhancement. A two-dimensional region of interest (2D ROI) was selected by the investigators on a subjectively selected axial slice, where the tumor had the largest size. When forming a three-dimensional region of interest (3D ROI), the entire tumor volume was segmented. Next, the statistical analysis of the segmentation results and the results of calculation of the first order texture indices was performed with calculation of the intra-class correlation coefficient (ICC) to assess the strength of the data correlation. The ICC of ≥ 0.75 demonstrated the reproducibility of the segmentation results and the first order texture indices.&#x0D; Results: The 3D segmentation method for ccRCC demonstrated the best ROI reproducibility results, regardless of the tumor size and the phase of contrast enhancement, with the ICC values of 0.961 (95% confidence interval: 0.946–0.971) for the cortico-medullary phase and 0.969 (95% CI: 0.958–0.977) for the nephrographic phase. The 2D tumor segmentation method showed unsatisfactory ROI reproducibility, with the ICC values of ≤ 0.058; however, the unsatisfactory reproducibility of the segmentation results in the patients with ccRCC tumor size of ≥ 4 cm did not significantly affect the reproducibility of the Entropy and Energy texture indices (good to excellent correlation). With the 3D segmentation of ccRCC, most first-order texture metrics were reproducible, with the exception of the Kurtosis parameter. The Entropy and Energy scores in both patient groups demonstrated a high degree of reproducibility. In the 2D tumor segmentation, high reproducibility of the first order texture metrics was obtained for the Entropy and Energy indices.&#x0D; Conclusion: The 3D segmentation of the CT data for ccRCC has high reproducibility, the most first-order textural features were excellently reproducible when segmentations were performed in 3D. The 2D CT data segmentation method for ccRCC demonstrated low reproducibility; however, some of the first order texture indices were reproducible. Both segmentation methods can be used for the texture analysis of CT images.

Effects of Tetrahydrolipstatin on Glioblastoma in Mice: MRI-Based Morphologic and Texture Analysis Correlated with Histopathology and Immunochemistry Findings-A Pilot Study.

This study aimed to investigate the effects of tetrahydrolipstatin (orlistat) on heterotopic glioblastoma in mice by applying MRI and correlating the results with histopathology and immunochemistry. Human glioblastoma cells were injected subcutaneously into the groins of immunodeficient mice. After tumor growth of >150 mm3, the animals were assigned into a treatment group (n = 6), which received daily intraperitoneal injections of orlistat, and a control group (n = 7). MRI was performed at the time of randomization and before euthanizing the animals. Tumor volumes were calculated, and signal intensities were analyzed. The internal tumor structure was evaluated visually and with texture analysis. Western blotting and protein expression analysis were performed. At histology, all tumors showed high mitotic and proliferative activity (Ki67 ≥ 10%). Reduced fatty acid synthetase expression was measured in the orlistat group (p < 0.05). Based on the results of morphologic MRI-based analysis, tumor growth remained concentric in the control group and changed to eccentric in the treatment group (p < 0.05). The largest area under the receiver operating curve of the predictors derived from the texture analysis of T2w images was for wavelet transform parameters WavEnHL_s3 and WavEnLH_s4 at 0.96 and 1.00, respectively. Orlistat showed effects on heterotopically implanted glioblastoma multiforme in MRI studies of mice based on morphologic and texture analysis.

Application of texture signatures based on multiparameter-magnetic resonance imaging for predicting microvascular invasion in hepatocellular carcinoma: Retrospective study.

Despite continuous changes in treatment methods, the survival rate for advanced hepatocellular carcinoma (HCC) patients remains low, highlighting the importance of diagnostic methods for HCC. To explore the efficacy of texture analysis based on multi-parametric magnetic resonance (MR) imaging (MRI) in predicting microvascular invasion (MVI) in preoperative HCC. This study included 105 patients with pathologically confirmed HCC, categorized into MVI-positive and MVI-negative groups. We employed Original Data Analysis, Principal Component Analysis, Linear Discriminant Analysis (LDA), and Non-LDA (NDA) for texture analysis using multi-parametric MR images to predict preoperative MVI. The effectiveness of texture analysis was determined using the B11 program of the MaZda4.6 software, with results expressed as the misjudgment rate (MCR). Texture analysis using multi-parametric MRI, particularly the MI + PA + F dimensionality reduction method combined with NDA discrimination, demonstrated the most effective prediction of MVI in HCC. Prediction accuracy in the pulse and equilibrium phases was 83.81%. MCRs for the combination of T2-weighted imaging (T2WI), arterial phase, portal venous phase, and equilibrium phase were 22.86%, 16.19%, 20.95%, and 20.95%, respectively. The area under the curve for predicting MVI positivity was 0.844, with a sensitivity of 77.19% and specificity of 91.67%. Texture analysis of arterial phase images demonstrated superior predictive efficacy for MVI in HCC compared to T2WI, portal venous, and equilibrium phases. This study provides an objective, non-invasive method for preoperative prediction of MVI, offering a theoretical foundation for the selection of clinical therapy.