This study aims to explore the diagnosis in patients with Alzheimer's disease (AD) based on magnetic resonance (MR) images, and to compare the differences of bilateral hippocampus in classification and recognition. MR images were obtained from 25 AD patients and 25 normal controls (NC) respectively. Three-dimensional texture features were extracted from bilateral hippocampus of each subject. The texture features that existed significant differences between AD and NC were used as the features in a classification procedure. Back propagation (BP) neural network model was built to classify AD patients from healthy controls. The classification accuracy of three methods, which were principal components analysis, linear discriminant analysis and non-linear discriminant analysis, was obtained and compared. The correlations between bilateral hippocampal texture parameters and Mini-Mental State Examination (MMSE) scores were calculated. The classification accuracy of nonlinear discriminant analysis with a neural network model was the highest, and the classification accuracy of right hippocampus was higher than that of the left. The bilateral hippocampal texture features were correlated to MMSE scores, and the relative of right hippocampus was higher than that of the left. The neural network model with three-dimensional texture features could recognize AD patients and NC, and right hippocampus might be more helpful to AD diagnosis.
Objective To determine feasibility of texture analysis of non-enhanced CT scan for differential diagnosis of liver cancer and hepatic hemangioma. Methods Fifty-six patients with liver cancer or hepatic hemangioma confirmed by pathology were enrolled in this retrospective study. After exclusion of images of 4 patients with artifacts and lesion diameter less than 1.0 cm, images of 52 patients (57 lesions) were available to further analyze. Texture features derived from the gray-level histogram, co-occurrence and run-length matrix, absolute gradient, autoregressive model, and wavelet transform were calculated. Fisher, probability of classification error and average correlation (POE+ACC), and mutual information coefficients (MI) were used to extract 10 optimized texture features. The texture characteristics were analyzed by using linear discriminant analysis (LDA) and nonlinear discriminant analysis (NDA) provided by B11 module in the Mazda software, the minimum error probability of differential diagnosis of liver cancer and hepatic hemangioma was calculated. Most discriminating features (MDF) of LDA was applied to K nearest neighbor classification (KNN); NDA to extract the data used in artificial neural network (ANN) for differential diagnosis. Results The NDA/ANN-POE+ACC was the best for identifying liver cancer and hepatic hemangioma, and the minimum error probability was the lowest as compared with the LDA/KNN-Fisher, LDA/KNN-POE+ACC, LDA/KNN-MI, NDA/ANN-Fisher, and NDA/ANN-MI respectively, the differences were statistically significant (χ2=4.56, 4.26, 3.14, 3.14, 3.33;P=0.020, 0.018, 0.026, 0.026, 0.022). Conclusions The minimum error probability is low for different texture feature selection methods and different analysis methods of Mazda texture analysis software in identifying liver cancer and hepatic hemangioma, and NDA/ANN-POE+ACC method is best. So it is feasible to use texture analysis of non-enhanced CT images to identify liver cancer and hepatic hemangioma.
Objective To determine feasibility of texture analysis of CT images for the discrimination of hepatic epithelioid hemangioendothelioma (HEHE) and liver metastases of colon cancer. Methods CT images of 9 patients with 19 pathologically proved HEHEs and 18 patients with 38 liver metastases of colon cancer who received treatment in West China Hospital of Sichuan University from July 2012 to August 2016 were retrospectively analyzed. Results Thirty best texture parameters were automatically selected by the combination of Fisher coefficient (Fisher)+classification error probability combined with average correlation coefficients (PA)+mutual information (MI). The 30 texture parameters of arterial phase (AP) CT images were distributed in co-occurrence matrix (22 parameters), run-length matrix (1 parameter), histogram (4 parameters), gradient (1 parameter), and autoregressive model (2 parameters). The distribution of parameters in portal venous phase (PVP) were co-occurrence matrix (18 parameters), run-length matrix (2 parameters), histogram (7 parameters), gradient (2 parameters), and autoregressive model (1 parameter). In AP, the misclassification rates of raw data analysis (RDA)/K nearest neighbor classification (KNN), principal component analysis (PCA)/KNN, linear discriminant analysis (LDA)/KNN, and nonlinear discriminant analysis, and nonlinear discriminant analysis (NDA)/artificial neural network (ANN) was 38.60% (22/57), 42.11% (24/57), 8.77% (5/57), and 7.02% (4/57), respectively. In PVP, the misclassification rates of RDA/KNN, PCA/KNN, LDA/KNN, and NDA/ANN was 26.32% (15/57), 28.07% (16/57), 15.79% (9/57), and 10.53% (6/57), respectively. The misclassification rates of AP and PVP images had no statistical significance on the misclassification rates of RDA/KNN, PCA/KNN, LDA/KNN, and NDA/ANN between AP and PVP (P>0.05). Conclusion The texture analysis of CT images is feasible to identify HEHE and liver metastases of colon cancer.
Objective To determine feasibility of texture analysis of CT images for the discrimination of nonhypervascular pancreatic neuroendocrine tumor (PNET) from pancreatic ductal adenocarcinoma (PDAC). Methods CT images of 15 pathologically proved as PNETs and 30 PDACs in West China Hospital of Sichuan University from January 2009 to January 2017 were retrospectively analyzed. Results Thirty best texture parameters were automatically selected by the combination of Fisher coefficient (Fisher)+classification error probability combined with average correlation coefficients (PA)+mutual information (MI). The 30 texture parameters of arterial phase (AP) CT images were distributed in co-occurrence matrix (18 parameters), run-length matrix (10 parameters), and autoregressive model (2 parameters). The distribution of parameters in portal venous phase (PVP) were co-occurrence matrix (15 parameters), run-length matrix (10 parameters), histogram (1 parameter), absolute gradient (1 parameter), and autoregressive model (3 parameters). In AP and PVP, the parameter with the highest diagnostic performance were both Teta2, and the area under curve (AUC) value was 0.829 and 0.740 (P<0.001,P=0.009), respectively. By the B11 of MaZda, the misclassification rate of raw data analysis (RDA)/K nearest neighbor classification (KNN), principal component analysis (PCA)/KNN, linear discriminant analysis (LDA)/KNN, and nonlinear discriminant analysis (NDA)/artificial neural network (ANN) was 28.89% (13/45), 28.89% (13/45), 0 (0/45), and 4.44% (2/45), respectively. In PVP, the misclassification rate of RDA/KNN, PCA/KNN, LDA/KNN, and NDA/ANN was 35.56% (16/45), 33.33% (15/45), 4.44% (2/45), and 11.11% (5/45), respectively. Conclusions CT texture analysis is feasible in the discrimination of nonhypervascular PNET and PDAC. Teta2 is the parameter with the highest diagnostic performance, and in AP, LDA/KNN modality has the lowest misclassification rate.
Objective To access the diagnostic performance of CT texture analysis to differentiate atypical pancreatic solid pseudopapillary tumor (SPT) from pancreatic ductal adenocarcinoma (PDAC). Methods CT images of 26 patients with pathologically proved atypical SPT and 52 patients with PDAC were analyzed. 3D regions of interest (ROIs) on arterial phase (AP) and portal venous phase (PVP) images were drawn by ITK-Snap software. A.K. software (GE company, USA) was used to extract texture features for the discrimination of atypical SPT and PDAC. After removing redundancy (by a correlation analysis through R software), texture features were selected by single-factor and multi-factor logistic regression, and logistic regression model was finally established. Receiver operating characteristic (ROC) analysis was performed to assess the diagnostic performance of single texture feature and logistic model. Results A total of 792 texture features ［396 of AP, 396 of PVP］ from AP and PVP CT images were obtained by A.K. software. Of these, 61 texture features (35 of AP, 26 of PVP) were selected by R software (result of correlation analysis showed that correlation coefficient >0.7). Two texture features, including MinIntensity and Correlation_AllDirection_offset1_SD, were selected to establish logistic model. The sensitivity and specificity of these 2 texture features were 71.15% and 76.92%, 63.46% and 76.92% respectively, the area under curve ( AUC) were 0.740 and 0.754 respectively. The model’s sensitivity and specificity were 73.08% and 80.77% respectively, the AUC value was 0.796. There was no significance among the model, MinIntensity, and Correlation_AllDirection_offset1_SD (P>0.05). Conclusions CT texture analysis of 3D ROI is a quantitative method for differential diagnosis of atypical SPT from PDAC.
This study aims to investigate the value of pre-treatment computed tomography (CT) texture analysis in predicting therapeutic response of liver metastasis from colorectal cancer after combined targeting chemotherapy. A total of 82 patients with colorectal cancer liver metastases who underwent chemotherapy combined with targeted therapy (cetuximab) between March 2011 and October 2017 comprised this retrospective study population. According to the RECIST1.1, the best curative effect evaluation of patients was recorded. Complete response (CR) and partial response (PR) were assigned to the response group, and the stable disease (SD) and progressive disease (PD) were assigned to the non-response group. The CT texture analysis was based on the Omini-Kinetics software, and the three-dimensional (3D) texture analysis was performed on the marked lesion on portal phase. The differences of texture parameters between the response group and the non-response group were compared. The receiver operating characteristic (ROC) curves were depicted on the parameters which with statistically difference, to characterize value in predicting the response to target-combined chemotherapy. The differences of Entropy, Energy, Variance, std. Deviation, Quantile95 and sumEntropy between the two groups in pre-treatment lesions were significant (P < 0.05). And lesions with higher Entropy, lower Energy, higher Variance, higher std Deviation and higher sumEntropy seemed to indicate a better therapeutic response. When sumEntropy > 0.867, good diagnostic efficiency could be obtained, with sensitivity of 60.5% and specificity of 79.5%, respectively. In conclusion, texture parameters derived from baseline CT images of colorectal cancer liver metastasis have the potential value acting as imaging biomarkers in predicting tumor response to combined target chemotherapy.
ObjectiveTo determine value of texture analysis based on bi-phasic enhanced CT images in diagnosis of acute pancreatitis (AP) with acute renal injury (AKI).MethodsA total of 62 patients with clinically proven AP including 39 patients with AKI and 23 patients without AKI were analyzed retrospectively. The region of interest (ROI) was chosen at the axial CT-enhanced images of bilateral kidneys using the ITK-Snap software and the texture analysis was performed by the Analysis-Kinetics (A.K.) analysis software. Using the Analysis of Variance, Mann-Whitney U test, Spearman correlation analysis and LASSO regression to reduce the features dimension, and screening out the textures by the logistic regression. The receiver operating characteristic (ROC) curve was established to determine the diagnostic performance of the features.ResultsIn the total of 396 image histological features originally extracted from the texture analysis, 6 features were finally screened out through the dimensionality reduction, involving the Haralick correlation, Inertia, Mean value, Cluster prominence, Short run high grey level emphasis, and Surface area. The area under curve (AUC), threshold, sensitivity, specificity, and accuracy in diagnosing of AP with AKI respectively was 0.926, 0.619, 89.4%, 71.4% and 82.7% by the Haralick correlation; which respectively was 0.790, 0.665, 59.6%, 82.1%, 68.0% by the Inertia; which respectively was 0.983, 0.662, 89.4%, 100%, 93.3% by the Mean value; which respectively was 0.903, 0.696, 80.9%, 85.7%, 82.7% by the Cluster prominence; which respectively was 0.980, 0.778, 76.6%, 100%, 85.3% by the Short run high grey level emphasis; which respectively was 0.819, 0.604, 78.7%, 75.0%, 77.3% by the Surface area.ConclusionTextures of contrast-enhanced CT images have better resolving ability and higher accuracy in diagnosis of AP with AKI and diagnostic efficiency of Mean value is the best.
Transrectal contrast-enhanced ultrasound (CEUS) is an important examination for rectal tumors. The inhomogeneity of the CEUS images has important clinical significance. However, there is no objective method to evaluate this index. In this study, a method based on gray-level co-occurrence matrix (GLCM) is proposed to extract texture features of images and grade these images according the inhomogeneity. Specific processes include compressing the gray level of the image, calculating the texture statistics of gray level co-occurrence matrix, combining feature selection and principal component analysis (PCA) for dimensionality reduction, and training and validating quadratic discriminant analysis (QDA). After ten cross-validation, the overall accuracy rate of machine classification was 87.01%, and the accuracy of each level was as follows: Grade Ⅰ 52.94%, Grade Ⅱ 96.48% and Grade Ⅲ 92.35% respectively. The proposed method has high accuracy in judging grade Ⅱ and Ⅲ images, which can help to identify the grade of inhomogeneity of contrast-enhanced ultrasound images of rectal tumors, and may be used to assist clinical doctors in judging the grade of inhomogeneity of contrast-enhanced ultrasound of rectal tumors.
CT texture analysis (CTTA) can objectively evaluate the heterogeneity of tissues and their lesions beyond the ability of subjective visual interpretation by extracting the texture features of CT images, then performing analysis and quantitative and objective evaluation, reflecting the tissue micro environmental information. This article reviews the recent studies on the applications of CTTA in gastric cancers, in the aspects of identification of gastric tumors, prediction of stage, correlation with Lauren classification, prediction of occult peritoneal carcinomatosis, evaluation of efficacy and prognosis, and prediction of biomarkers. It is regarded that CTTA has a good application prospect in gastric cancers.
ObjectiveTo summarize the application of image texture analysis in the diagnosis and treatment of gastric cancer.MethodsReviewed the literatures on the application of image texture analysis related methods in the diagnosis and treatment of gastric cancer, and summarized the value of texture analysis in the diagnosis and treatment of gastric cancer in terms of diagnosis, staging, curative effect evaluation, and prognosis prediction.ResultsImage texture analysis had been widely used in diagnosis, staging, curative effect evaluation, prognosis prediction of gastric cancer, and other related diagnosis and treatment applications.ConclusionsImage texture analysis is an important part of the diagnosis and treatment of gastric cancer, which has a good development prospect.