Gray-level Co-occurrence Matrix Entropy Research Articles

Dried blood drops on vertical surfaces

The analysis of structures in dried droplets has made it possible to detect the presence and conformational state of macromolecules in relevant biofluids. Therefore, the implementation of novel drying strategies for pattern formation could facilitate the identification of biomarkers for the diagnosis of pathologies. We present an experimental study of patterns formed by evaporating water-diluted blood droplets on a vertical surface. Three significant morphological features were observed in vertical droplet deposits: (1) The highest concentration of non-volatile molecules is consistently deposited in the lower part of the droplet, regardless of erythrocyte concentration. (2) The central region of deposits decreases rapidly with hematocrit; (3) At high erythrocyte concentrations (36–40% HCT), a broad coating of blood serum is produced in the upper part of the deposit. These findings are supported by the radial intensity profile, the relative thickness of the crown, the aspect ratio of the deformation, the relative area of the central region, and the Entropy of the Gray Level Co-occurrence Matrix Entropy (GLCM). Moreover, we explore the pattern formation during the drying of vertical blood drops. We found that hematocrit concentration has a significant impact on droplet drying dynamics. Finally, we conducted a proof-of-concept test to investigate the impact of vertical droplet evaporation on blood droplets with varying lipid concentrations. The results revealed that it is possible to differentiate between deposits with normal, slightly elevated, and moderately elevated lipid levels using only the naked eye.

Texture feature analysis of MRI-ADC images to differentiate glioma grades using machine learning techniques

Apparent diffusion coefficient (ADC) of magnetic resonance imaging (MRI) is an indispensable imaging technique in clinical neuroimaging that quantitatively assesses the diffusivity of water molecules within tissues using diffusion-weighted imaging (DWI). This study focuses on developing a robust machine learning (ML) model to predict the aggressiveness of gliomas according to World Health Organization (WHO) grading by analyzing patients’ demographics, higher-order moments, and grey level co-occurrence matrix (GLCM) texture features of ADC. A population of 722 labeled MRI-ADC brain image slices from 88 human subjects was selected, where gliomas are labeled as glioblastoma multiforme (WHO-IV), high-grade glioma (WHO-III), and low-grade glioma (WHO I-II). Images were acquired using 3T-MR systems and a region of interest (ROI) was delineated manually over tumor areas. Skewness, kurtosis, and statistical texture features of GLCM (mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence, and shade) were calculated using ADC values within ROI. The ANOVA f-test was utilized to select the best features to train an ML model. The data set was split into training (70%) and testing (30%) sets. The train set was fed into several ML algorithms and selected most promising ML algorithm using K-fold cross-validation. The hyper-parameters of the selected algorithm were optimized using random grid search technique. Finally, the performance of the developed model was assessed by calculating accuracy, precision, recall, and F1 values reported for the test set. According to the ANOVA f-test, three attributes; patient gender (1.48), GLCM energy (9.48), and correlation (13.86) that performed minimum scores were excluded from the dataset. Among the tested algorithms, the random forest classifier(0.8772 ± 0.0237) performed the highest mean-cross-validation score and selected to build the ML model which was able to predict tumor categories with an accuracy of 88.14% over the test set. The study concludes that the developed ML model using the above features except for patient gender, GLCM energy, and correlation, has high prediction accuracy in glioma grading. Therefore, the outcomes of this study enable to development of advanced tumor classification applications that assist in the decision-making process in a real-time clinical environment.

Image Processing Techniques based Feature Extraction for Insect Damage Areas

Monitoring of forests is important for the diagnosis of insect damage to vegetation. Detection and monitoring of damaged areas facilitates the control of pests for practitioners. For this purpose, Unmanned Aerial Vehicles (UAVs) have been recently used to detect damaged areas. In order to distinguish damage areas from healthy areas on UAV images, it is necessary to extract the feature parameters of the images. Therefore, feature extraction is an important step in Computer Aided Diagnosis of insect damage monitored with UAV images. By reducing the size of the UAV image data, it is possible to distinguish between damaged and healthy areas from the extracted features. The accuracy of the classification algorithm depends on the segmentation method and the extracted features. The Grey-Level Co-occurrence Matrix (GLCM) characterizes areas texture based on the number of pixel pairs with specific intensity values arranged in specific spatial relationships. In this paper, texture characteristics of insect damage areas were extracted from UAV images using with GLCM. The 3000*4000 resolution UAV images containing damaged and healthy larch trees were analyzed using Definiens Developer (e-Cognition software) for multiresolution segmentation to detect the damaged areas. In this analysis, scale parameters were applied as 500, shape 0.1, color 0.9 and compactness 0.5. As a result of segmentation, GLCM homogeneity, GLCM contrast and GLCM entropy texture parameters were calculated for each segment. When calculating the texturing parameters, neighborhoods in different angular directions (0,45,90,135) are taken into account. As a result of the calculations made by considering all directions, it was found that GLCM homogeneity values ranged between 0.08 - 0.2, GLCM contrast values ranged between 82.86 - 303.58 and GLCM entropy values ranged between 7.81 - 8.51. On the other hand, GLCM homogeneity for healthy areas varies between 0.05 - 0.08, GLCM contrast between 441.70 - 888.80 and GLCM entropy between 8.93 - 9.40. The study demonstrated that GLCM technique can be a reliable method to detection of insect damage areas from UAV imagery.

Predicting the Recurrence of Gastric Cancer Using the Textural Features of Perigastric Adipose Tissue on [18F]FDG PET/CT.

This study aimed to assess the relationship between the histopathological and textural features of perigastric adipose tissue (AT) on 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) positron emission tomography/computed tomography (PET/CT) and to evaluate the prognostic significance of perigastric AT textural features in predicting recurrence-free survival (RFS) in patients with gastric cancer. Sixty-nine patients with gastric cancer who underwent staging [18F]FDG PET/CT and subsequent curative surgery were retrospectively reviewed. Textural features of perigastric AT were extracted from PET images. On histopathological analysis, CD4, CD8, and CD163 cell infiltration and matrix metalloproteinase-11 and interleukin-6 (IL-6) expression in perigastric AT were graded. The degree of CD163 cell infiltration in perigastric AT was significantly correlated with the mean standardized uptake value (SUV), SUV histogram entropy, grey-level co-occurrence matrix (GLCM) energy, and GLCM entropy of perigastric AT. The degree of IL-6 expression in the perigastric AT was significantly correlated with the mean and median SUVs of perigastric AT. In multivariate survival analysis, GLCM entropy, GLCM dissimilarity, and GLCM homogeneity of perigastric AT were significant predictors of RFS. The textural features of perigastric AT on [18F]FDG PET/CT significantly correlated with inflammatory response in perigastric AT and were significant prognostic factors for predicting RFS in patients with gastric cancer.

Measuring spatio-temporal heterogeneity and interior characteristics of green spaces in urban neighborhoods: A new approach using gray level co-occurrence matrix

Urban green space (UGS) is a complex and highly dynamic interface between people and nature. The existing methods of quantifying and evaluating UGS are mainly implemented on the surface features at a landscape scale, and most of them are insufficient to thoroughly reflect the spatial-temporal relationships, especially the internal characteristics changes at a small scale and the neighborhood spatial relationship of UGS. This paper thus proposes a method to evaluate the internal dynamics and neighborhood heterogeneity of different types of UGS in Leipzig using the gray level co-occurrence matrix (GLCM) index. We choose GLCM variance, contrast, and entropy to analyze five main types of UGS through a holistic description of their vegetation growth, spatial heterogeneity, and internal orderliness. The results show that different types of UGS have distinct characteristics due to the changes of surrounding buildings and the distance to the built-up area. Within a one-year period, seasonal changes in UGS far away from built-up areas are more obvious. As for the larger and dense urban forests, they have the lowest spatial heterogeneity and internal order. On the contrary, the garden areas present the highest heterogeneity. In this study, the GLCM index depicts the seasonal alternation of UGS on the temporal scale and shows the spatial form of each UGS, being in line with local urban planning contexts. The correlation analysis of indices also proves that each type of UGS has its distinct temporal and spatial characteristics. The GLCM is valid in assessing the internal characteristics and relationships of various UGS at the neighborhood scales, and using the methodology developed in our study, more studies and field experiments could be fulfilled to investigate the assessment accuracy of our GLCM index approach and to further enhance the scientific understanding on the internal features and ecological functions of UGS.

Computed tomography texture analysis for the prediction of lateral pelvic lymph node metastasis of rectal cancer

BackgroundThis study aimed to investigate the usefulness of computed tomography (CT) texture analysis in the diagnosis of lateral pelvic lymph node (LPLN) metastasis of rectal cancer.MethodsThis was a retrospective cohort study of 45 patients with rectal cancer who underwent surgery with LPLN dissection at Tokushima University Hospital from January 2017 to December 2021. The texture analysis of the LPLNs was performed on preoperative CT images, and 18 parameters were calculated. The correlation between each parameter and pathological LPLN metastasis was evaluated. The texture parameters were compared between pathologically metastasis-positive LPLNs and metastasis-negative LPLNs.ResultsA total of 40 LPLNs were extracted from 25 patients by preoperative CT scans. No LPLNs could be identified in the remaining 19 patients. Eight of the 25 patients had pathologically positive LPLN metastasis. Extracted LPLNs were analyzed by the texture analysis. Pathologically metastasis-positive LPLNs had significantly lower mean Hounsfield unit, gray-level co-occurrence matrix (GLCM) energy, and GLCM Entropy_log2 values, and a significantly larger volume than pathologically metastasis-negative LPLNs. Multivariate analysis revealed that the independent predictive factors for LPLN metastasis were volume (a conventional parameter) (odds ratio 7.81, 95% confidence interval 1.42–43.1, p value 0.018) and GLCM Entropy_log2 (a texture parameter) (odds ratio 12.7, 95% confidence interval 1.28–126.0, p value 0.030). The combination of both parameters improved the diagnostic specificity while maintaining the sensitivity compared with each parameter alone.ConclusionCombining the CT texture analysis with conventional diagnostic imaging may increase the accuracy of the diagnosis of LPLN metastasis of rectal cancer.

Texture analysis of the microstructure of internal curing concrete based on image recognition technology

Mechanical properties of internal curing concrete are greatly affected by its physical properties such as water content, cementing material content, porosity, and saturation. At the micro- level, such impact is finally reflected in the surface texture of its materials. In this study, the image recognition technology was used to find that the internal curing concrete samples have significant micromorphology and texture features. A texture parameter–strength model was established based on the relationship between Tamura texture parameters, gray level co-occurrence matrix (GLCM) texture parameters, and the mechanical strength. Due to the sensitivity of material properties and parameters, in terms of Tamura texture characteristics, coarseness, regularity and directionality are effective parameters for predicting the compressive strength of internally curing concrete. In terms of the GLCM texture, energy, correlation, entropy, and contrast are effective parameters to predict the compressive strength of the internal curing concrete. Correlations between each texture parameter and compressive strength follow different laws.

The Usefulness of Machine Learning-Based Evaluation of Clinical and Pretreatment [18F]-FDG-PET/CT Radiomic Features for Predicting Prognosis in Hypopharyngeal Cancer.

To examine whether the machine learning (ML) analyses using clinical and pretreatment 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography ([18F]-FDG-PET)-based radiomic features were useful for predicting prognosis in patients with hypopharyngeal cancer. This retrospective study included 100 patients with hypopharyngeal cancer who underwent [18F]-FDG-PET/X-ray computed tomography (CT) before treatment, and these patients were allocated to the training (n=80) and validation (n=20) cohorts. Eight clinical (age, sex, histology, T stage, N stage, M stage, UICC stage, and treatment) and 40 [18F]-FDG-PET-based radiomic features were used to predict disease progression. A feature reduction procedure based on the decrease of the Gini impurity was applied. Six ML algorithms (random forest, neural network, k-nearest neighbors, naïve Bayes, logistic regression, and support vector machine) were compared using the area under the receiver operating characteristic curve (AUC). Progression-free survival (PFS) was assessed using Cox regression analysis. The five most important features for predicting disease progression were UICC stage, N stage, gray level co-occurrence matrix entropy (GLCM_Entropy), gray level run length matrix run length non-uniformity (GLRLM_RLNU), and T stage. Patients who experienced disease progression displayed significantly higher UICC stage, N stage, GLCM_Entropy, GLRLM_RLNU, and T stage than those without progression (each, p<0.001). In both cohorts, the logistic regression model constructed by these 5 features was the best performing classifier (training: AUC=0.860, accuracy=0.800; validation: AUC=0.803, accuracy=0.700). In the logistic regression model, 5-year PFS was significantly higher in patients with predicted non-progression than those with predicted progression (75.8% vs. 8.3%, p<0.001), and this model was only the independent factor for PFS in multivariate analysis (hazard ratio = 3.22; 95% confidence interval = 1.03-10.11; p=0.045). The logistic regression model constructed by UICC, T and N stages and pretreatment [18F]-FDG-PET-based radiomic features, GLCM_Entropy, and GLRLM_RLNU may be the most important predictor of prognosis in patients with hypopharyngeal cancer.

Fault enhancement using probabilistic neural networks and Laplacian of a Gaussian filter: A case study in the Great South Basin, New Zealand

Fault identification is critical in defining the structural framework for exploration and reservoir characterization studies. Interpreters routinely use edge-sensitive attributes to accelerate the manual picking process, in which the actual choice of a particular edge-sensitive attribute varies with the seismic data quality and with the reflectivity response of the faulted geologic formations. The cyan-magenta-yellow (CMY) color blending provides an effective way to combine the information content of two or three edge-sensitive attributes when more than one attribute is sensitive to faults. We have evaluated whether combining the information content of more than three attributes using probabilistic neural networks (PNNs) provides any additional uplift. We use training data consisting of manually picked faults on a coarse grid of 3D seismic lines, and then, we use an exhaustive search PNN to identify the optimal set of attributes to create a fault probability volume for a 3D survey acquired over the Great South Basin, New Zealand. We construct a suite of candidate attributes using our understanding of the attribute response to faults seen in the data and examples extracted from the published literature to use the list as the analyzed attributes. Using a subset of picked faults as training data, we evaluate which suite of attributes and hyperparameters exhibits the highest validation on the remaining training data. When used together, we find that volume aberrancy magnitude, gray-level cooccurrence matrix (GLCM) homogeneity, GLCM entropy, Sobel filter similarity, and envelope best predict the faults for this data set. The PNN supervised classification creates a seismic image volume that exhibits fault probabilities providing a simple combination of multiple seismic attributes. We also find that applying a directional Laplacian of a Gaussian and skeletonization filters to the PNN fault volumes provides a superior result to simple CMY blending techniques.

Radiomic Analysis of Tumour Heterogeneity Using MRI in Head and Neck Cancer Following Chemoradiotherapy: A Feasibility Study.

ObjectivesTo evaluate interval changes in heterogeneity on diffusion-weighted apparent diffusion coefficient (ADC) maps and T1-weighted post-gadolinium (T1w post gad) MRI in head and neck carcinoma (HNSCC), with and without chemo-radiotherapy (CRT) response.MethodsThis prospective observational cohort study included 24 participants (20 men, age 62.9 ± 8.8 years) with stage III and IV HNSCC. The primary tumour (n = 23) and largest lymph node (n = 22) dimensions, histogram parameters and grey-level co-occurrence matrix (GLCM) parameters were measured on ADC maps and T1w post gad sequences, performed pretreatment and 6 and 12 weeks post CRT. The 2-year treatment response at primary and nodal sites was recorded. The Wilcoxon signed-rank test was used to compare interval changes in parameters after stratifying for treatment response and failure (p < 0.001 statistical significance).Results23/23 primary tumours and 18/22 nodes responded to CRT at 2 years. Responding HNSCC demonstrated a significant interval change in ADC histogram parameters (kurtosis, coefficient of variation, entropy, energy for primary tumour; kurtosis for nodes) and T1w post gad GLCM (entropy and contrast in the primary tumour and nodes) by 6 weeks post CRT (p < 0.001). Lymph nodes with treatment failure did not demonstrate an interval alteration in heterogeneity parameters.ConclusionsADC maps and T1w post gad MRI demonstrate the evolution of heterogeneity parameters in successfully treated HNSCC by 6 weeks post CRT; however, this is not observed in lymph nodes failing treatment.Advances in KnowledgeEarly reduction in heterogeneity is demonstrated on MRI when HNSCC responds to CRT.

Clinical Significance of Peritumoral Adipose Tissue PET/CT Imaging Features for Predicting Axillary Lymph Node Metastasis in Patients with Breast Cancer.

We investigated whether textural parameters of peritumoral breast adipose tissue (AT) based on F-18 fluorodeoxyglucose (FDG) PET/CT could predict axillary lymph node metastasis in patients with breast cancer. A total of 326 breast cancer patients with preoperative FDG PET/CT were retrospectively enrolled. PET/CT images were visually assessed and the maximum FDG uptake of axillary lymph nodes (LN SUVmax) was measured. From peritumoral breast AT, 38 textural features of PET imaging were extracted. The diagnostic ability of PET based on visual analysis, LN SUVmax, and textural features of peritumoral breast AT for predicting axillary lymph node metastasis were assessed using the area under the receiver operating characteristic curve (AUC) values. Among the 38 peritumoral breast AT textural features, grey-level co-occurrence matrix (GLCM) entropy showed the highest AUC value (0.830) for predicting axillary lymph node metastasis. The value of GLCM entropy was higher than that of visual analysis (0.739; p < 0.05) and the AUC value was comparable to that of LN SUVmax (0.793; p > 0.05). In the subgroup analysis of patients with negative findings on visual analysis, GLCM entropy still showed a high diagnostic ability (AUC: 0.759) in predicting lymph node metastasis. The findings suggest a potential diagnostic role of PET/CT imaging features of peritumoral breast AT in predicting axillary lymph node metastasis in patients with breast cancer.

A Preclinical Study on Radiomics-Driven Brain Tumor Prediction Using Deep Convolution Neural Network

Radiomics is an exponentially increasing discipline that focuses on mapping the textural details found in various tissues for medical diagnosis. Nevertheless, high-end GPUs, the method of producing Radiomics artifacts is practically infeasible but can take a long time with radiological representation for some higher order functionality like Gray-level Co-occurrence Matrix (GLCM). Researchers created RadSynth, a deep Convolutional Neural Network (CNN) framework that constructs Radiomics images efficiently. For simulation of GLCM uncertainty artifacts through post-contrast DCE-MRI, RadSynth has been investigated on a prostate cancer therapeutics market of seventy patients. When compared to conventional GLCM entropy images, RadSynth offered great computational uncertainty images. We conclude from this evaluation that both spatial distribution and optimization influence psychic distance estimation, and experimental results are less resilient to varying image resolution rather than varied optimization frequency.

Additional Value of PET/CT-Based Radiomics to Metabolic Parameters in Diagnosing Lynch Syndrome and Predicting PD1 Expression in Endometrial Carcinoma.

PurposeWe aim to compare the radiomic features and parameters on 2-deoxy-2-[fluorine-18] fluoro-D-glucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) between patients with endometrial cancer with Lynch syndrome and those with endometrial cancer without Lynch syndrome. We also hope to explore the biologic significance of selected radiomic features.Materials and MethodsWe conducted a retrospective cohort study, first using the 18F-FDG PET/CT images and clinical data from 100 patients with endometrial cancer to construct a training group (70 patients) and a test group (30 patients). The metabolic parameters and radiomic features of each tumor were compared between patients with and without Lynch syndrome. An independent cohort of 23 patients with solid tumors was used to evaluate the value of selected radiomic features in predicting the expression of the programmed cell death 1 (PD1), using 18F-FDG PET/CT images and RNA-seq genomic data.ResultsThere was no statistically significant difference in the standardized uptake values on PET between patients with endometrial cancer with Lynch syndrome and those with endometrial cancer without Lynch syndrome. However, there were significant differences between the 2 groups in metabolic tumor volume and total lesion glycolysis (p < 0.005). There was a difference in the radiomic feature of gray level co-occurrence matrix entropy (GLCMEntropy; p < 0.001) between the groups: the area under the curve was 0.94 in the training group (sensitivity, 82.86%; specificity, 97.14%) and 0.893 in the test group (sensitivity, 80%; specificity, 93.33%). In the independent cohort of 23 patients, differences in GLCMEntropy were related to the expression of PD1 (rs =0.577; p < 0.001).ConclusionsIn patients with endometrial cancer, higher metabolic tumor volumes, total lesion glycolysis values, and GLCMEntropy values on 18F-FDG PET/CT could suggest a higher risk for Lynch syndrome. The radiomic feature of GLCMEntropy for tumors is a potential predictor of PD1 expression.

Prognostic value of 18F-FDG PET/CT with texture analysis in patients with rectal cancer treated by surgery.

The aim of this study was to evaluate the ability of texture analysis using pretreatment 18F-FDG PET/CT to predict prognosis in patients with surgically treated rectal cancer. We analyzed 94 patients with pathologically proven rectal cancer who underwent pretreatment 18F-FDG PET/CT and were subsequently treated with surgery. The volume of interest of the primary tumor was defined using a threshold of 40% of the maximum standardized uptake value (SUVmax), and conventional (SUVmax, metabolic tumor volume [MTV], total lesion glycolysis [TLG]) and textural PET features were extracted. Harmonization of PET features was performed with the ComBat method. The study endpoints were overall survival (OS) and progression-free survival (PFS), and the prognostic value of PET features was evaluated by Cox regression analysis. In the follow-up period (median 41.7 [interquartile range, 30.5-60.4] months), 21 (22.3%) and 30 (31.9%) patients had cancer-related death or disease progression, respectively. Univariate analysis revealed a significant association of (1) MTV, TLG, and gray-level co-occurrence matrix (GLCM) entropy with OS; and (2) SUVmax, MTV, TLG, and GLCM entropy with PFS. In multivariate analysis including clinical characteristics, GLCM entropy (≥ 2.13) was the only relevant prognostic PET feature for poor OS (hazard ratio [HR]: 4.16, p = 0.035) and PFS (HR: 2.70, p = 0.046). GLCM entropy, which indicates metabolic intratumoral heterogeneity, was an independent prognostic factor in patients with surgically treated rectal cancer. Compared with conventional PET features, GLCM entropy has better predictive value and shows potential to facilitate precision medicine.

Abstract PS5-42: Change in intra-lesion heterogeneity on CT predicts long-term survival following treatment with CDK4/6 inhibitors in hormone receptor-positive metastatic breast cancer (MBC)

Abstract Background: CDK 4/6 inhibitors are currently standard of care for patients CDK 4/6 inhibitors but currently lack validated predictive markers. We evaluated whether changes in intra-lesional heterogeneity, extracted computationally from routine computed tomography (CT) of MBC could predict survival following treatment with CDK 4/6 inhibitors. Methods: 33 patients with ER+ breast cancer metastasized to the liver who received palbociclib in combination with anti-estrogen therapy were identified from a registry. For each patient, CT exams were acquired prior to treatment and following initiation of treatment (median time between scans of 106 +/- 48.6 days). Liver metastases were manually delineated on all scans with consultation of radiology reports. On each scan, gray level co-occurrence matrix (GLCM) entropy, a computational measure of intra-lesional heterogeneity, was calculated and averaged across all metastatic lesions and its change over treatment was computed. For comparison, response was also assessed via RECIST v1.1: 65% of patients had objective response/stable disease, while 35% had progressive disease on scan following initiation of treatment. Imaging metrics were assessed individually and in a multivariate comparison including clinical features for association with overall survival (OS) via a cox proportional hazards model. Results: Change in intra-lesion heterogeneity was associated with OS with a hazard ratio [HR] of 2.82 (p=0.019). An increase in entropy between the pre- and treatment scan was associated with poorer viability on treatment including CDK inhibitors, indicating that a favorable response can be distinguished by a decrease in intra-lesion heterogeneity. RECIST response was also associated with OS (HR=0.24, p&amp;lt;0.001). In a multivariable comparison with clinical variables, change in GLCM entropy and response per RECIST criteria both demonstrated multivariate significance. Neither the heterogeneity measure on the pre-treatment scan (HR=0.56, p=0.23) nor the scan acquired during treatment (HR=1.51, p=0.27) treatment scans alone were significantly associated with overall survival. Conclusions: The change in lesion heterogeneity on clinical imaging following the initiation of treatment was found to offer independent value in distinguishing patients with favorable survival following CDK 4/6 inhibitor treatment. Multivariate association of imaging features and clinical variables with overall survivalHazard ratio (HR)p-valueChange in Heterogeneity4.670.019RECIST response0.160.003PR status1.010.99HER2 status1.130.92Age1.040.26Pathology0.770.39Line of Treatment1.360.12 Citation Format: Nathaniel Braman, Siddharth Kunte, Kaustav Bera, Jame Abraham, Albert Montero, Anant Madabhushi. Change in intra-lesion heterogeneity on CT predicts long-term survival following treatment with CDK4/6 inhibitors in hormone receptor-positive metastatic breast cancer (MBC) [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS5-42.

Water Boundary Line Detection for Unmanned Surface Vehicles

Background: Unmanned Surface Vehicles (USV) can undertake risks or special tasks in marine independently and will be widely used in the future. In the autonomous navigation of USV equipped with vision camera, the water boundary line needs to be detected in real time and it is one of these key intelligent environment perception methods for USV. Methods: An efficient water boundary line detection method based on Gray Level Co-occurrence Matrix (GLCM) texture entropy is proposed. In image preprocessing, the high-brightness areas are eliminated to avoid the effects of water boundary line detection. Results: GLCM entropy is employed to segment water, land and air for water line regression. The proposed method is efficient for the images with high-brightness areas. Conclusion: The experimental results demonstrate that the proposed method is not only more accurate than the existing water boundary line detection method, but also has good real-time performance and is suitable for the application in USV.

Assessment of malignant potential in intraductal papillary mucinous neoplasms of the pancreas using MR findings and texture analysis.

To investigate the utility of MR findings and texture analysis for predicting the malignant potential of pancreatic intraductal papillary mucinous neoplasms (IPMNs). Two hundred forty-eight patients with surgically confirmed IPMNs (106 malignant [invasive carcinoma/high-grade dysplasia] and 142 benign [low/intermediate-grade dysplasia]) and who underwent magnetic resonance imaging (MRI) with MR cholangiopancreatography (MRCP) were included. Two reviewers independently analyzed MR findings as proposed by the 2017 international consensus guidelines. Texture analysis of MRCP was also performed. A multivariate logistic regression analysis was used to identify predictors for malignant IPMNs. Diagnostic performance was also analyzed using receiver operating curve analysis. Among MR findings, enhancing mural nodule size ≥ 5mm, main pancreatic duct (MPD) ≥ 10mm or MPD of 5 to 9mm, and abrupt change of MPD were significant predictors for malignant IPMNs (p< 0.05). Among texture variables, significant predictors were effective diameter, surface area, sphericity, compactness, entropy, and gray-level co-occurrence matrix entropy (p< 0.05). At multivariate analysis, enhancing mural nodule ≥ 5mm (odds ratios (ORs), 6.697 and 6.968, for reviewers 1 and 2, respectively), MPD ≥ 10mm or MPD of 5 to 9mm (ORs, 4.098 and 4.215, and 2.517 and 3.055, respectively), larger entropy (ORs, 1.485 and 1.515), and smaller compactness (ORs, 0.981 and 0.977) were significant predictors for malignant IPMNs (p< 0.05). When adding texture variable to MR findings, diagnostic performance for predicting malignant IPMNs improved from 0.80 and 0.78 to 0.85 and 0.85 in both reviewers (p< 0.05), respectively. MRCP-derived texture features are useful for predicting malignant IPMNs, and the addition of texture analysis to MR features may improve diagnostic performance for predicting malignant IPMNs. • Among the MR imaging findings, an enhancing mural nodule size ≥ 5mm and dilated main pancreatic ducts are independent predictors for malignant IPMNs. • Greater entropy and smaller compactness on MR texture analysis are independent predictors for malignant IPMNs. • The addition of MR texture analysis improved the diagnostic performance for predicting malignant IPMNs from 0.80 and 0.78 to 0.85 and 0.85, respectively.

Texture analysis on bi-parametric MRI for evaluation of aggressiveness in patients with prostate cancer

To evaluate the association between texture parameters based on bi-parametric MRI and Gleason score (GS) in patients with prostate cancer (PCa) and to evaluate diagnostic performance of any significant parameter for discriminating clinically significant cancer (CSC, GS ≥ 7) from non-CSC. A total of 116 patients who had been confirmed as prostate adenocarcinoma by radical prostatectomy or biopsy were divided into a training (n = 65) and a validation dataset (n = 51). All of the patients underwent preoperative 3T-MRI. Texture analysis was performed on axial T2WI and ADC maps (generated from b values, 0 and 1000s/mm2) using dedicated software to cover the whole tumor volume. The correlation coefficient was calculated to evaluate the association between texture parameters and GS, and subsequent multiple regression analyses were applied for the significant parameters. To extract an optimal cut-off value for prediction of CSC, ROC curve analysis was performed. In the training dataset, gray-level co-occurrence matrix (GLCM) entropy on ADC map was the only significant indicator for GS (coefficient of determination R2, 0.4227, P = 0.0034). The AUC of GLCM entropy on ADC map was 0.825 (95% CI 0.711-0.907) with a maximum accuracy of 82%, a sensitivity of 86%, a specificity of 71%. When a cut-off value of 2.92 was applied to the validation dataset, it showed an accuracy of 92%, a sensitivity of 98%, and a specificity of 70%. GLCM entropy on ADC map was associated with GS in patients with PCa and its estimated accuracy for discriminating CSC from non-CSC was 82%.

Extraction of Sea Ice Cover by Sentinel-1 SAR Based on Support Vector Machine With Unsupervised Generation of Training Data

In this article, we focus on developing a novel method to extract sea ice cover (i.e., discrimination/classification of sea ice and open water) using Sentinel-1 (S1) cross-polarization [vertical-horizontal (VH) or horizontal-vertical (HV)] data in extra-wide (EW) swath mode based on the support vector machine (SVM) method. The classification basis includes the S1 radar backscatter and texture features, which are calculated from S1 data using the gray level co-occurrence matrix (GLCM). Different from previous methods where appropriate samples are manually selected to train the SVM to classify sea ice and open water, we proposed a method of unsupervised generation of the training samples based on two GLCM texture features, i.e., entropy and homogeneity, that have contrasting characteristics on sea ice and open water. We eliminate the most uncertainty of selecting training samples in machine learning and achieve automatic classification of sea ice and open water by using S1 EW data. The comparisons based on a few cases show good agreements between the synthetic aperture radar (SAR)-derived sea ice cover using the proposed method and visual inspections, of which the accuracy reaches approximately 90%-95%. Besides this, compared with the analyzed sea ice cover data Ice Mapping System (IMS) based on 728 S1 EW images, the accuracy of the extracted sea ice cover by using S1 data is more than 80%.

Baseline Computed Tomography Radiomic and Genomic Assessment of Head and Neck Squamous Cell Carcinoma.

To determine the relationship between computed tomography (CT) radiomic features and gene expression levels in head and neck squamous cell carcinoma (HNSCC). This retrospective study included 66 patients with HNSCC primary lesions (36 oropharyngeal, 6 hypopharyngeal, 10 laryngeal, 14 oral cavity). Gene expression information for 6 targetable genes (fibroblast growth factor receptor [FGFR]1, epidermal growth factor receptor [EGFR], FGFR2, FGFR3, EPHA2, PIK3CA) was obtained via Agilent microarrays from samples collected between 1997 and 2010. Pretreatment contrast-enhanced soft tissue neck CT scans were reviewed, and 142 radiomics features were derived. R was used to calculate Pearson correlation coefficients were calculated between gene expression levels and each radiomic feature. P values were adjusted using the false discovery rate (FDR) method. There were significant correlations between FGFR1 and 5 gray level cooccurrence matrix (GLCM) features with FDR-adjusted P values less than 0.05: inertia (r = 0.366, FDR-adjusted P = 0.006), absolute value (r = 0.31, FDR-adjusted P = 0.024), contrast (r = 0.366, FDR-adjusted P = 0.006), difference average (r = 0.31, FDR-adjusted P = 0.024), and difference variance (r = 0.37, FDR-adjusted P = 0.005). There was 1 correlated feature for FGFR2 with an FDR-adjusted P value less than 0.05: fractal dimension box-coarse (r = 0.33, FDR-adjusted P = 0.018). There was 1 correlated feature for EPHA2 with an FDR-adjusted P value less than 0.05: GLCM entropy (r = -0.28, FDR-adjusted P = 0.049). Six of the 7 features that showed significant correlation belonged to the GLCM class of features. The CT radiomic features demonstrate correlations with FGFR1 status in HNSCC and should be further investigated for their potential to predict FGFR1 status.