scholarly journals CT Texture analysis and CT scores for characterization of fluid collections

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hans-Jonas Meyer ◽  
Benedikt Schnarkowski ◽  
Jakob Leonhardi ◽  
Matthias Mehdorn ◽  
Sebastian Ebel ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Texture Features ◽  
Imaging Features ◽  
Conventional Imaging ◽  
Imaging Findings ◽  
Ct Guided ◽  
Septic Focus ◽  
Fluid Collections ◽  
Better Than

Abstract Background Texture analysis derived from Computed tomography (CT) might be able to better characterize fluid collections undergoing CT-guided percutaneous drainage treatment. The present study tested, whether texture analysis can reflect microbiology results in fluid collections suspicious for septic focus. Methods Overall, 320 patients with 402 fluid collections were included into this retrospective study. All fluid collections underwent CT-guided drainage treatment and were microbiologically evaluated. Clinically, serologically parameters and conventional imaging findings as well as textures features were included into the analysis. A new CT score was calculated based upon imaging features alone. Established CT scores were used as a reference standard. Results The present score achieved a sensitivity of 0.78, a specificity of 0.69, area under curve (AUC 0.82). The present score and the score by Gnannt et al. (AUC 0.81) were both statistically better than the score by Radosa et al. (AUC 0.75). Several texture features were statistically significant between infected fluid collections and sterile fluid collections, but these features were not significantly better compared with conventional imaging findings. Conclusions Texture analysis is not superior to conventional imaging findings for characterizing fluid collections. A novel score was calculated based upon imaging parameters alone with similar diagnostic accuracy compared to established scores using imaging and clinical features.

Mr imaging of primary non-Hodgkin's breast lymphoma

1997 ◽  
Vol 38 (1) ◽  
pp. 104-107
Author(s):  
S. Mussurakis ◽  
P. J. Carleton ◽  
L. W. Turnbull
Keyword(s):  
Mr Imaging ◽  
Large Mass ◽  
Invasive Cancer ◽  
Imaging Features ◽  
Conventional Imaging ◽  
Imaging Findings ◽  
Dynamic Data ◽  
Breast Lymphoma ◽  
The Masses ◽  
Tumour Differentiation

In this report we describe the MR imaging findings, including dynamic data, in a patient with primary non-Hodgkin's lymphoma of the breast. The precontrast T1-weighted sequence showed several hypointense, ill-defined, non-spiculated masses. In the T2-weighted images the masses showed a hyperintense halo. In the dynamic and postcontrast sequences all lesions enhanced markedly, and a further large mass was discovered. In comparison to mammography and sonography, only MR imaging identified the multicentric extent of the tumour. Differentiation from invasive cancer, based on either MR or conventional imaging features, was not possible.

scholarly journals Pancreatic Serous Cystic Neoplasms and Mucinous Cystic Neoplasms: Differential Diagnosis by Combining Imaging Features and Enhanced CT Texture Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Hai-Yan Chen ◽  
Xue-Ying Deng ◽  
Yao Pan ◽  
Jie-Yu Chen ◽  
Yun-Ying Liu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Texture Analysis ◽  
Validation Cohort ◽  
Texture Features ◽  
Imaging Features ◽  
Cystic Neoplasms ◽  
Radiological Features ◽  
Model Combining ◽  
Enhanced Ct ◽  
Ct Texture Analysis

ObjectiveTo establish a diagnostic model by combining imaging features with enhanced CT texture analysis to differentiate pancreatic serous cystadenomas (SCNs) from pancreatic mucinous cystadenomas (MCNs).Materials and MethodsFifty-seven and 43 patients with pathology-confirmed SCNs and MCNs, respectively, from one center were analyzed and divided into a training cohort (n = 72) and an internal validation cohort (n = 28). An external validation cohort (n = 28) from another center was allocated. Demographic and radiological information were collected. The least absolute shrinkage and selection operator (LASSO) and recursive feature elimination linear support vector machine (RFE_LinearSVC) were implemented to select significant features. Multivariable logistic regression algorithms were conducted for model construction. Receiver operating characteristic (ROC) curves for the models were evaluated, and their prediction efficiency was quantified by the area under the curve (AUC), 95% confidence interval (95% CI), sensitivity and specificity.ResultsFollowing multivariable logistic regression analysis, the AUC was 0.932 and 0.887, the sensitivity was 87.5% and 90%, and the specificity was 82.4% and 84.6% with the training and validation cohorts, respectively, for the model combining radiological features and CT texture features. For the model based on radiological features alone, the AUC was 0.84 and 0.91, the sensitivity was 75% and 66.7%, and the specificity was 82.4% and 77% with the training and validation cohorts, respectively.ConclusionThis study showed that a logistic model combining radiological features and CT texture features is more effective in distinguishing SCNs from MCNs of the pancreas than a model based on radiological features alone.

Diagnostic yield and safety of percutaneous CT-guided biopsy of retroperitoneal lesions and analysis of imaging features

2021 ◽  
pp. 028418512199028
Author(s):  
Anil Kumar Singh ◽  
Zafar Neyaz ◽  
Ritu Verma ◽  
Anshul Gupta ◽  
Richa Mishra ◽  
...  
Keyword(s):  
Diagnostic Yield ◽  
Final Diagnosis ◽  
Microbiological Analysis ◽  
Imaging Features ◽  
Imaging Findings ◽  
Chi Square ◽  
Tissue Samples ◽  
Ct Guided ◽  
Guided Biopsy ◽  
Ct Guided Biopsy

Background Computed tomography (CT)-guided biopsy is emerging as a preferred method for obtaining tissue samples from retroperitoneal lesions due to clear visualization of needle and vessels. Purpose To assess diagnostic yield and safety of CT-guided biopsy of retroperitoneal lesions and compare CT findings in different disease categories. Material and Methods This retrospective analytical study included 86 patients with retroperitoneal lesions who underwent CT-guided biopsy from December 2010 to March 2020. All procedures were performed with co-axial technique and multiple cores were obtained and subjected to histopathology. Additional tests like immunohistochemistry or microbiological analysis were done depending on clinical suspicion. Diagnostic yield calculation and comparison of imaging findings was done by one-way ANOVA, chi-square, and Fisher’s exact tests. Results CT-guided biopsy was technically successful in all cases with a diagnostic yield of 91.9%. Minor complications in the form of small hematomas were seen in two patients. Major disease categories on final diagnosis were lymphoma, tuberculosis, and metastases. A variety of malignant and benign soft-tissue neoplasms were also noted less commonly. With help of immunohistochemistry, lymphoma subtype was established in 88.8% of cases. Addition of microbiological tests like the GeneXpert assay helped in the diagnosis of tuberculosis in some cases. A mass-like appearance and vascular encasement was common in metastatic group and lymphoma. Conclusion Percutaneous CT-guided biopsy is a safe method for the sampling of retroperitoneal lesions with high diagnostic yield. Imaging findings are mostly overlapping; however, some features are more common in a particular disease condition.

scholarly journals Retrospective CT/MRI Texture Analysis of Rapidly Progressive Hepatocellular Carcinoma

2020 ◽  
Vol 10 (3) ◽  
pp. 136
Author(s):  
Charissa Kim ◽  
Natasha Cigarroa ◽  
Venkateswar Surabhi ◽  
Balaji Ganeshan ◽  
Anil K. Pillai
Keyword(s):  
Hepatocellular Carcinoma ◽  
Texture Analysis ◽  
Visual Analysis ◽  
Intensity Variation ◽  
Texture Features ◽  
Composite Score ◽  
Imaging Features ◽  
Coarse Texture ◽  
Significant Difference ◽  
Magnetic Resonance Imaging Mri

Rapidly progressive hepatocellular carcinoma (RPHCC) is a subset of hepatocellular carcinoma that demonstrates accelerated growth, and the radiographic features of RPHCC versus non-RPHCC have not been determined. The purpose of this retrospective study was to use baseline radiologic features and texture analysis for the accurate detection of RPHCC and subsequent improvement of clinical outcomes. We conducted a qualitative visual analysis and texture analysis, which selectively extracted and enhanced imaging features of different sizes and intensity variation including mean gray-level intensity (mean), standard deviation (SD), entropy, mean of the positive pixels (MPP), skewness, and kurtosis at each spatial scaling factor (SSF) value of RPHCC and non-RPHCC tumors in a computed tomography (CT) cohort of n = 11 RPHCC and n = 11 non-RPHCC and a magnetic resonance imaging (MRI) cohort of n = 13 RPHCC and n = 10 non-RPHCC. There was a statistically significant difference across visual CT irregular margins p = 0.030 and CT texture features in SSF between RPHCC and non-RPHCC for SSF-6, coarse-texture scale, mean p = 0.023, SD p = 0.053, MPP p = 0.023. A composite score of mean SSF-6 binarized + SD SSF-6 binarized + MPP SSF-6 binarized + irregular margins was significantly different between RPHCC and non-RPHCC (p = 0.001). A composite score ≥3 identified RPHCC with a sensitivity of 81.8% and specificity of 81.8% (AUC = 0.884, p = 0.002). CT coarse-texture-scale features in combination with visually detected irregular margins were able to statistically differentiate between RPHCC and non-RPHCC. By developing an image-based, non-invasive diagnostic criterion, we created a composite score that can identify RPHCC patients at their early stages when they are still eligible for transplantation, improving the clinical course of patient care.

scholarly journals The Potential Value of Texture Analysis Based on Dynamic Contrast-Enhanced MR Images in the Grading of Breast Phyllode Tumors

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiaoguang Li ◽  
Hong Guo ◽  
Chao Cong ◽  
Huan Liu ◽  
Chunlai Zhang ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Roc Curve ◽  
Breast Imaging ◽  
Statistical Significance ◽  
Texture Features ◽  
Imaging Findings ◽  
Mr Images ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced ◽  
Occurrence Matrix

PurposeTo explore the value of texture analysis (TA) based on dynamic contrast-enhanced MR (DCE-MR) images in the differential diagnosis of benign phyllode tumors (BPTs) and borderline/malignant phyllode tumors (BMPTs).MethodsA total of 47 patients with histologically proven phyllode tumors (PTs) from November 2012 to March 2020, including 26 benign BPTs and 21 BMPTs, were enrolled in this retrospective study. The whole-tumor texture features based on DCE-MR images were calculated, and conventional imaging findings were evaluated according to the Breast Imaging Reporting and Data System (BI-RADS). The differences in the texture features and imaging findings between BPTs and BMPTs were compared; the variates with statistical significance were entered into logistic regression analysis. The receiver operating characteristic (ROC) curve was used to assess the diagnostic performance of models from image-based analysis, TA, and the combination of these two approaches.ResultsRegarding texture features, three features of the histogram, two features of the gray-level co-occurrence matrix (GLCM), and three features of the run-length matrix (RLM) showed significant differences between the two groups (all p < 0.05). Regarding imaging findings, however, only cystic wall morphology showed significant differences between the two groups (p = 0.014). The areas under the ROC curve (AUCs) of image-based analysis, TA, and the combination of these two approaches were 0.687 (95% CI, 0.518–0.825, p = 0.014), 0.886 (95% CI, 0.760–0.960, p < 0.0001), and 0.894 (95% CI, 0.754–0.970, p < 0.0001), respectively.ConclusionTA based on DCE-MR images has potential in differentiating BPTs and BMPTs.

scholarly journals Glioma Grading via Analysis of Digital Pathology Images Using Machine Learning

Cancers ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 578 ◽  
Author(s):  
Saima Rathore ◽  
Tamim Niazi ◽  
Muhammad Aksam Iftikhar ◽  
Ahmad Chaddad
Keyword(s):  
Machine Learning ◽  
Treatment Planning ◽  
Digital Pathology ◽  
Texture Features ◽  
Support Vector ◽  
Low Grade ◽  
Imaging Features ◽  
Conventional Imaging ◽  
Gray Level ◽  
Phenotypic Information

Cancer pathology reflects disease progression (or regression) and associated molecular characteristics, and provides rich phenotypic information that is predictive of cancer grade and has potential implications in treatment planning and prognosis. According to the remarkable performance of computational approaches in the digital pathology domain, we hypothesized that machine learning can help to distinguish low-grade gliomas (LGG) from high-grade gliomas (HGG) by exploiting the rich phenotypic information that reflects the microvascular proliferation level, mitotic activity, presence of necrosis, and nuclear atypia present in digital pathology images. A set of 735 whole-slide digital pathology images of glioma patients (median age: 49.65 years, male: 427, female: 308, median survival: 761.26 days) were obtained from TCGA. Sub-images that contained a viable tumor area, showing sufficient histologic characteristics, and that did not have any staining artifact were extracted. Several clinical measures and imaging features, including conventional (intensity, morphology) and advanced textures features (gray-level co-occurrence matrix and gray-level run-length matrix), extracted from the sub-images were further used for training the support vector machine model with linear configuration. We sought to evaluate the combined effect of conventional imaging, clinical, and texture features by assessing the predictive value of each feature type and their combinations through a predictive classifier. The texture features were successfully validated on the glioma patients in 10-fold cross-validation (accuracy = 75.12%, AUC = 0.652). The addition of texture features to clinical and conventional imaging features improved grade prediction compared to the models trained on clinical and conventional imaging features alone (p = 0.045 and p = 0.032 for conventional imaging features and texture features, respectively). The integration of imaging, texture, and clinical features yielded a significant improvement in accuracy, supporting the synergistic value of these features in the predictive model. The findings suggest that the texture features, when combined with conventional imaging and clinical markers, may provide an objective, accurate, and integrated prediction of glioma grades. The proposed digital pathology imaging-based marker may help to (i) stratify patients into clinical trials, (ii) select patients for targeted therapies, and (iii) personalize treatment planning on an individual person basis.

scholarly journals Texture features from computed tomography correlate with markers of severity in acute alcohol-associated hepatitis

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Michele M. Tana ◽  
David McCoy ◽  
Briton Lee ◽  
Roshan Patel ◽  
Joseph Lin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Texture Analysis ◽  
Texture Features ◽  
Clinical Severity ◽  
Recursive Feature Elimination ◽  
Imaging Features ◽  
Learning Approaches ◽  
Quantitative Ct ◽  
Test Set ◽  
Ct Slices

Abstract The aim of this study was to use texture analysis to establish quantitative CT-based imaging features to predict clinical severity in patients with acute alcohol-associated hepatitis (AAH). A secondary aim was to compare the performance of texture analysis to deep learning. In this study, mathematical texture features were extracted from CT slices of the liver for 34 patients with a diagnosis of AAH and 35 control patients. Recursive feature elimination using random forest (RFE-RF) was used to identify the best combination of features to distinguish AAH from controls. These features were subsequently used as predictors to determine associated clinical values. To compare machine learning with deep learning approaches, a 2D dense convolutional neural network (CNN) was implemented and trained for the classification task of AAH. RFE-RF identified 23 top features used to classify AAH images, and the subsequent model demonstrated an accuracy of 82.4% in the test set. The deep learning CNN demonstrated an accuracy of 70% in the test set. We show that texture features of the liver are unique in AAH and are candidate quantitative biomarkers that can be used in prospective studies to predict the severity and outcomes of patients with AAH.

scholarly journals CT Texture Analysis for Differentiating Bronchiolar Adenoma, Adenocarcinoma In Situ, and Minimally Invasive Adenocarcinoma of the Lung

2021 ◽  
Vol 11 ◽  
Author(s):  
Jinju Sun ◽  
Kaijun Liu ◽  
Haipeng Tong ◽  
Huan Liu ◽  
Xiaoguang Li ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Thin Section ◽  
Texture Features ◽  
Diagnostic Value ◽  
Ct Imaging ◽  
Imaging Features ◽  
Invasive Adenocarcinoma ◽  
Unenhanced Ct ◽  
Minimally Invasive Adenocarcinoma

Purpose: This study aimed to investigate the potential of computed tomography (CT) imaging features and texture analysis to distinguish bronchiolar adenoma (BA) from adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA).Materials and Methods: Fifteen patients with BA, 38 patients with AIS, and 36 patients with MIA were included in this study. Clinical data and CT imaging features of the three lesions were evaluated. Texture features were extracted from the thin-section unenhanced CT images using Artificial Intelligence Kit software. Then, multivariate logistic regression analysis based on selected texture features was employed to distinguish BA from AIS/MIA. Receiver operating characteristics curves were performed to determine the diagnostic performance of the features.Results: By comparison with AIS/MIA, significantly different CT imaging features of BA included nodule type, tumor size, and pseudo-cavitation sign. Among them, pseudo-cavitation sign had a moderate diagnostic value for distinguishing BA and AIS/MIA (AUC: 0.741 and 0.708, respectively). Further, a total of 396 quantitative texture features were extracted. After comparation, the top six texture features showing the most significant difference between BA and AIS or MIA were chosen. The ROC results showed that these key texture features had a high diagnostic value for differentiating BA from AIS or MIA, among which the value of a comprehensive model with six selected texture features was the highest (AUC: 0.977 or 0.976, respectively) for BA and AIS or MIA. These results indicated that texture analyses can effectively improve the efficacy of thin-section unenhanced CT for discriminating BA from AIS/MIA.Conclusion: CT texture analysis can effectively improve the efficacy of thin-section unenhanced CT for discriminating BA from AIS/MIA, which has a potential clinical value and helps pathologist and clinicians to make diagnostic and therapeutic strategies.

Characterization of Rh/Si multilayer structure by HREM and HAADF

1992 ◽  
Vol 50 (1) ◽  
pp. 122-123
Author(s):  
Y. Cheng ◽  
J. Liu ◽  
M.B. Stearns ◽  
D.G. Steams
Keyword(s):  
Normal Incidence ◽  
High Resolution Electron Microscopy ◽  
X Rays ◽  
Beam Direction ◽  
Cross Sectional ◽  
Optical Elements ◽  
Resolution Electron ◽  
Point To Point ◽  
Better Than

The Rh/Si multilayer (ML) thin films are promising optical elements for soft x-rays since they have a calculated normal incidence reflectivity of ∼60% at a x-ray wavelength of ∼13 nm. However, a reflectivity of only 28% has been attained to date for ML fabricated by dc magnetron sputtering. In order to determine the cause of this degraded reflectivity the microstructure of this ML was examined on cross-sectional specimens with two high-resolution electron microscopy (HREM and HAADF) techniques.Cross-sectional specimens were made from an as-prepared ML sample and from the same ML annealed at 298 °C for 1 and 100 hours. The specimens were imaged using a JEM-4000EX TEM operating at 400 kV with a point-to-point resolution of better than 0.17 nm. The specimens were viewed along Si [110] projection of the substrate, with the (001) Si surface plane parallel to the beam direction.

Characterization of Pancreas at Diabetic Patients in CT images using Texture Analysis

2017 ◽  
Vol 7 (7) ◽  
pp. 8
Author(s):  
Mona E. Elbashier ◽  
Suhaib Alameen ◽  
Caroline Edward Ayad ◽  
Mohamed E. M. Gar-Elnabi
Keyword(s):  
Size Distribution ◽  
Texture Analysis ◽  
Classification Accuracy ◽  
Ct Images ◽  
Diabetic Patients ◽  
Gray Level ◽  
Grey Level ◽  
Run Length ◽  
Interactive Data

This study concern to characterize the pancreas areato head, body and tail using Gray Level Run Length Matrix (GLRLM) and extract classification features from CT images. The GLRLM techniques included eleven’s features. To find the gray level distribution in CT images it complements the GLRLM features extracted from CT images with runs of gray level in pixels and estimate the size distribution of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level distribution of images. The results show that the Gray Level Run Length Matrix and  features give classification accuracy of pancreashead 89.2%, body 93.6 and the tail classification accuracy 93.5%. The overall classification accuracy of pancreas area 92.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate pancreas area names.

Sign in / Sign up

Export Citation Format

Share Document