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Objective. Image texture information was extracted from enhanced magnetic resonance imaging (MRI) and pathological hematoxylin and eosin- (HE-) stained images of female breast cancer patients. We established models individually, and then, we combine the two kinds of data to establish model. Through this method, we verified whether sufficient information could be obtained from enhanced MRI and pathological slides to assist in the determination of epidermal growth factor receptor (EGFR) mutation status in patients. Methods. We obtained enhanced MRI data from patients with breast cancer before treatment and selected diffusion-weighted imaging (DWI), T1 fast-spin echo (T1 FSE), and T2 fast-spin echo (T2 FSE) as the data sources for extracting texture information. Imaging physicians manually outlined the 3D regions of interest (ROIs) and extracted texture features according to the gray level cooccurrence matrix (GLCM) of the images. For the HE staining images of the patients, we adopted a specific normalization algorithm to simulate the images dyed with only hematoxylin or eosin and extracted textures. We extracted texture features to predict the expression of EGFR. After evaluating the predictive power of each model, the models from the two data sources were combined for remodeling. Results. For enhanced MRI data, the modeling of texture information of T1 FSE had a good predictive effect for EGFR mutation status. For pathological images, eosin-stained images can achieve a better prediction effect. We selected these two classifiers as the weak classifiers of the final model and obtained good results (training group: AUC, 0.983; 95% CI, 0.95-1.00; accuracy, 0.962; specificity, 0.936; and sensitivity, 0.979; test group: AUC, 0.983; 95% CI, 0.94-1.00; accuracy, 0.943; specificity, 1.00; and sensitivity, 0.905). Conclusion. The EGFR mutation status of patients with breast cancer can be well predicted based on enhanced MRI data and pathological data. This helps hospitals that do not test the EGFR mutation status of patients with breast cancer. The technology gives clinicians more information about breast cancer, which helps them make accurate diagnoses and select suitable treatments. [ABSTRACT FROM AUTHOR]

Correlation of parametrized image texture features (ITF) analyses conducted in different regions of interest (ROIs) overcomes limitations and reliably reflects image quality. The aim of this study is to propose a nonparametrical method and classify the quality of a magnetic resonance (MR) image that has undergone controlled degradation by using textural features in the image. Images of 41 patients, 17 women and 24 men, aged between 23 and 56 years were analyzed. T2-weighted sagittal sequences of the lumbar spine, cervical spine, and knee and T2-weighted coronal sequences of the shoulder and wrist were generated. The implementation of parallel imaging with the use of GRAPPA2, GRAPPA3, and GRAPPA4 led to a substantial reduction in the scanning time but also degraded image quality. The number of degraded image textural features was correlated with the scanning time. Longer scan times correlated with markedly higher ITF image persistence in comparison with images computed with reduced scan times. Higher ITF preservation was observed in images of bones in the spine and femur as compared to images of soft tissues, i.e., tendons and muscles. Finally, a nonparametrized image quality assessment based on an analysis of the ITF, computed for different tissues, correlating with the changes in acquisition time of the MR images, was successfully developed. The correlation between acquisition time and the number of reproducible features present in an MR image was found to yield the necessary assumptions to calculate the quality index. [ABSTRACT FROM AUTHOR]

Purpose. We want to develop a model for predicting lymph node status based on positron emission computed tomography (PET) images of untreated ovarian cancer patients. We use the feature map formed by wavelet transform and the parameters obtained by image segmentation to build the model. The model is expected to help clinicians and provide additional information about what to do with first-visit patients. Materials and Methods. Our study included 224 patients with ovarian cancer. We have chosen two main methods to extract information from images. On the one hand, we segmented the image to extract the parameters to evaluate the clustering effect. On the other hand, we used wavelet transform to extract the image's texture information to form the image's feature map. Based on the above two kinds of information, we used residual neural network and support vector machine for modeling. Results. We established a model to predict lymph node metastasis in patients with primary ovarian cancer using PET images. On the training set, our accuracy was 0.8854, AUC: 0.9472, CI: 0.9098-0.9752, sensitivity was 0.9865, and specificity was 0.7952. On the test set, our accuracy was 0.9104, AUC: 0.9259, CI: 0.8417-0.9889, sensitivity was 0.8125, and specificity was 1.0000. Conclusions. We used wavelet transform to process the preoperative medical images of ovarian cancer patients, and the residual neural network can effectively predict the lymph node metastasis of ovarian cancer patients, which is undoubted of great significance for patients' staging and treatment options. [ABSTRACT FROM AUTHOR]

Purpose. To noninvasively assess liver fibrosis using combined-contrast-enhanced (CCE) magnetic resonance imaging (MRI) and texture analysis. Materials and Methods. In this IRB-approved, HIPAA-compliant prospective study, 46 adults with newly diagnosed HCV infection and recent liver biopsy underwent CCE liver MRI following intravenous administration of superparamagnetic iron oxides (ferumoxides) and gadolinium DTPA (gadopentetate dimeglumine). The image texture of the liver was quantified in regions-of-interest by calculating 165 texture features. Liver biopsy specimens were stained with Masson trichrome and assessed qualitatively (METAVIR fibrosis score) and quantitatively (% collagen stained area). Using L 1 regularization path algorithm, two texture-based multivariate linear models were constructed, one for quantitative and the other for quantitative histology prediction. The prediction performance of each model was assessed using receiver operating characteristics (ROC) and correlation analyses. Results. The texture-based predicted fibrosis score significantly correlated with qualitative (r = 0.698, P < 0.001) and quantitative (r = 0.757, P < 0.001) histology. The prediction model for qualitative histology had 0.814-0.976 areas under the curve (AUC), 0.659-1.000 sensitivity, 0.778-0.930 specificity, and 0.674-0.935 accuracy, depending on the binary classification threshold. The prediction model for quantitative histology had 0.742-0.950 AUC, 0.688-1.000 sensitivity, 0.679-0.857 specificity, and 0.696-0.848 accuracy, depending on the binary classification threshold. Conclusion. CCE MRI and texture analysis may permit noninvasive assessment of liver fibrosis. [ABSTRACT FROM AUTHOR]

Microsatellites are small, repetitive sequences found all across the human genome. Microsatellite instability is the phenomenon of variations in the length of microsatellites induced by the insertion or deletion of repeat units in tumor tissue (MSI). MSI-type stomach malignancy has distinct genetic phenotypes and clinic pathological characteristics, and the stability of microsatellites influences whether or not patients with gastric mesothelioma react to immunotherapy. As a result, determining MSI status prior to surgery is critical for developing treatment options for individuals with gastric cancer. Traditional MSI detection approaches need immunological histochemistry and genetic analysis, which adds to the expense and makes it difficult to apply to every patient in clinical practice. In this study, to predict the MSI status of gastric cancer patients, researchers used image feature extraction technology and a machine learning algorithm to evaluate high-resolution histopathology pictures of patients. 279 cases of raw data were obtained from the TCGA database, 442 samples were obtained after preprocessing and upsampling, and 445 quantitative image features, including first-order statistics of impressions, texture features, and wavelet features, were extracted from the histopathological images of each sample. To filter the characteristics and provide a prediction label (risk score) for MSI status of gastric cancer, Lasso regression was utilized. The predictive label's classification performance was evaluated using a logistic classification model, which was then coupled with the clinical data of each patient to create a customized nomogram for MSI status prediction using multivariate analysis. [ABSTRACT FROM AUTHOR]

In this paper, we develop a detection module with strong training testing to develop a dense convolutional neural network model. The model is designed in such a way that it is trained with necessary features for optimal modelling of the cancer detection. The method involves preprocessing of computerized tomography (CT) images for optimal classification at the testing stages. A 10-fold cross-validation is conducted to test the reliability of the model for cancer detection. The experimental validation is conducted in python to validate the effectiveness of the model. The result shows that the model offers robust detection of cancer instances that novel approaches on large image datasets. The simulation result shows that the proposed method provides analyzes with 94% accuracy than other methods. Also, it helps to reduce the detection errors while classifying the cancer instances than other methods the several existing methods. [ABSTRACT FROM AUTHOR]

Dense convolutional network (DenseNet) is a hot topic in deep learning research in recent years, which has good applications in medical image analysis. In this paper, DenseNet is summarized from the following aspects. First, the basic principle of DenseNet is introduced; second, the development of DenseNet is summarized and analyzed from five aspects: broaden DenseNet structure, lightweight DenseNet structure, dense unit, dense connection mode, and attention mechanism; finally, the application research of DenseNet in the field of medical image analysis is summarized from three aspects: pattern recognition, image segmentation, and object detection. The network structures of DenseNet are systematically summarized in this paper, which has certain positive significance for the research and development of DenseNet. [ABSTRACT FROM AUTHOR]

Protein is the material foundation of living things, and it directly takes part in and runs the process of living things itself. Predicting protein complexes helps us understand the structure and function of complexes, and it is an important foundation for studying how cells work. Genome-wide protein interaction (PPI) data is growing as high-throughput experiments become more common. The aim of this research is that it provides a dual-tree complex wavelet transform which is used to find out about the structure of proteins. It also identifies the secondary structure of protein network. Many computer-based methods for predicting protein complexes have also been developed in the field. Identifying the secondary structure of a protein is very important when you are studying protein characteristics and properties. This is how the protein sequence is added to the distance matrix. The scope of this research is that it can confidently predict certain protein complexes rapidly, which compensates for shortcomings in biological research. The three-dimensional coordinates of C atom are used to do this. According to the texture information in the distance matrix, the matrix is broken down into four levels by the double-tree complex wavelet transform because it has four levels. The subband energy and standard deviation in different directions are taken, and then, the two-dimensional feature vector is used to show the secondary structure features of the protein in a way that is easy to understand. Then, the KNN and SVM classifiers are used to classify the features that were found. Experiments show that a new feature called a dual-tree complex wavelet can improve the texture granularity and directionality of the traditional feature extraction method, which is called secondary structure. [ABSTRACT FROM AUTHOR]

Background. Accurate identification of ovarian tumors as benign or malignant is highly crucial. Radiomics is a new branch of imaging that has emerged in recent years to replace the traditional naked eye qualitative diagnosis. Objective. This study is aimed at exploring the difference in the application potential of two- (2D) and three-dimensional (3D) radiomics models based on CT plain scan in differentiating benign from malignant ovarian tumors. Method. A retrospective analysis was performed on 140 patients with ovarian tumors confirmed by surgery and pathology in our hospital from July 2017 to August 2020. These 140 patients were divided into benign group and malignant group according to the pathological results. The ITK-SNAP software was used to outline the regions-of-interest (ROI) of 2D or 3D tumors on the CT plain scan image of each patient; the texture features were extracted through analysis kit (AK), and the cases were randomly divided into training groups (n = 99) and validation group (n = 41) in a ratio of 7 : 3. The least absolute shrinkage and selection operator (LASSO) algorithm was used to perform dimensionality reduction, followed by the construction of the radiomics nomogram model using the logistic regression method. The receiver operating characteristic (ROC) curve was drawn, and the calibration curve and decision curve analysis (DCA) were used to evaluate and verify the results of the radiomics nomogram and compare the differences between 2D and 3D diagnostic performance. Results. There were 396 quantitative radiomics feature parameters extracted from 2D group and the 3D group, respectively. The area under the curve (AUC) of the radiomics nomogram of the 2D training group and the validation group were 0.96 and 0.97, respectively. The accuracy, specificity, and sensitivity of the training set were 92.9%, 88.9%, and 96.3%, respectively, and those of the validation set were 90.2%, 82.6%, and 100.0%, respectively. The AUCs of the radiomics nomogram of the 3D training group and validation group were 0.96% and 0.99%, respectively. The accuracy, sensitivity, and specificity of the training set were 92.9%, 96.3%, and 88.9%, respectively, and those of the validation set were 97.6%, 95.7%, and 100.0%, respectively. DeLong's test indicated that there was no statistical significance between the two sets (P > 0.05). Conclusions. For the differential diagnosis of benign and malignant ovarian tumors, the 2D and 3D radiomics nomogram models exhibited comparable diagnostic performance. Considering that the 2D model was cost-effective and time-efficient, it was more recommended to use 2D features in future research. [ABSTRACT FROM AUTHOR]

Background. The quantification of tumor heterogeneity with molecular images, by analyzing the local or global variation in the spatial arrangements of pixel intensity with texture analysis, possesses a great clinical potential for treatment planning and prognosis. To address the lack of available software for computing the tumor heterogeneity on the public domain, we develop a software package, namely, Chang-Gung Image Texture Analysis (CGITA) toolbox, and provide it to the research community as a free, open-source project. Methods. With a user-friendly graphical interface, CGITA provides users with an easy way to compute more than seventy heterogeneity indices. To test and demonstrate the usefulness of CGITA, we used a small cohort of eighteen locally advanced oral cavity (ORC) cancer patients treated with definitive radiotherapies. Results. In our case study of ORC data, we found that more than ten of the current implemented heterogeneity indices outper formed SUVmean for outcome prediction in the ROC analysis with a higher area under curve (AUC). Heterogeneity indices provide a better area under the curve up to 0.9 than the SUVmean and TLG (0.6 and 0.52, resp.). Conclusions. CGITA is a free and open-source software package to quantify tumor heterogeneity from molecular images. CGITA is available for free for academic use at http://code.google.com/p/cgita. [ABSTRACT FROM AUTHOR]

Identifying the interactions of the drug-target is central to the cognate areas including drug discovery and drug reposition. Although the high-throughput biotechnologies have made tremendous progress, the indispensable clinical trials remain to be expensive, laborious, and intricate. Therefore, a convenient and reliable computer-aided method has become the focus on inferring drug-target interactions (DTIs). In this research, we propose a novel computational model integrating a pyramid histogram of oriented gradients (PHOG), Position-Specific Scoring Matrix (PSSM), and rotation forest (RF) classifier for identifying DTIs. Specifically, protein primary sequences are first converted into PSSMs to describe the potential biological evolution information. After that, PHOG is employed to mine the highly representative features of PSSM from multiple pyramid levels, and the complete describers of drug-target pairs are generated by combining the molecular substructure fingerprints and PHOG features. Finally, we feed the complete describers into the RF classifier for effective prediction. The experiments of 5-fold Cross-Validations (CV) yield mean accuracies of 88.96%, 86.37%, 82.88%, and 76.92% on four golden standard data sets (enzyme, ion channel, G protein-coupled receptors (GPCRs), and nuclear receptor, respectively). Moreover, the paper also conducts the state-of-art light gradient boosting machine (LGBM) and support vector machine (SVM) to further verify the performance of the proposed model. The experimental outcomes substantiate that the established model is feasible and reliable to predict DTIs. There is an excellent prospect that our model is capable of predicting DTIs as an efficient tool on a large scale. [ABSTRACT FROM AUTHOR]

Background. Intracranial solitary fibrous tumor(SFT)/hemangiopericytoma (HPC) is an aggressive malignant tumor originating from the intracranial vasculature. Angiomatous meningioma (AM) is a benign tumor with a good prognosis. The imaging manifestations of the two are very similar. Thus, novel noninvasive diagnostic method is urgently needed in clinical practice. Texture analysis and model building through machine learning may have good prospects. Aim. To evaluate whether a 3D-MRI texture feature model could be used to differentiate malignant intracranial SFT/HPC from AM. Method. A total of 97 patients with SFT/HPC and 95 with AM were included in this study. Patients from each group were randomly divided into the train (70%) and test (30%) sets. ROIs were drawn along the edge of the tumor on each section of T1WI, T2WI, and contrasted T1WI using ITK-SNAP software. The segmented image was imported into the AK software for texture feature extraction, and the 3D ROI signal intensity histograms of T1WI, T2WI, and contrasted T1WI were automatically obtained along with all the parameters. Modeling was performed using the language R. Confusion matrix was used to analyze the accuracy of the model. ROC curve was constructed to assess the grading ability of the logistic regression model. Results. After Lasso dimension reduction, 5, 9, and 7 texture features were extracted from T1WI, T2WI, and contrasted T1WI, respectively; additional 8 texture features were extracted from the combined sequence for modeling. The ROC analyses on four models resulted in an area under the curve (AUC) of 0.885 (sensitivity 76.1%, specificity 87.9%) for T1WI model, 0.918 (73.1%, 95.5%) for T2WI model, 0.815 (55.2%, 93.9%) for contrasted T1WI model, and 0.959 (92.5%, 84.8%) for the combined sequence model and were enough to correctly distinguish the two groups in 71.2%, 81.4%, 69.5%, and 83.1% of cases in test set, respectively. Conclusions. The radiological model based on texture features could be used to differentiate SFT/HPC from AM. [ABSTRACT FROM AUTHOR]

Background. To evaluate the diagnostic performance of apparent diffusion coefficient (ADC) histogram parameters for differentiating the genetic subtypes in lower-grade diffuse gliomas and explore which segmentation method (ROI-1, the entire tumor ROI; ROI2, the tumor ROI excluding cystic and necrotic portions) performs better. Materials and Methods. We retrospectively evaluated 56 lower-grade diffuse gliomas and divided them into three categories: IDH-wild group (IDHwt, 16cases); IDH mutant with the intact 1p or 19q group (IDHmut/1p19q+, 18cases); and IDH mutant with the 1p/19q codeleted group (IDHmut/1p19q−, 22cases). Histogram parameters of ADC maps calculated with the two different ROI methods: ADCmean, min, max, mode, P5, P10, P25, P75, P90, P95, kurtosis, skewness, entropy, StDev, and inhomogenity were compared between these categories using the independent t test or Mann–Whitney U test. For statistically significant results, a receiver operating characteristic (ROC) curves were constructed, and the optimal cutoff value was determined by maximizing Youden's index. Area under the curve (AUC) results were compared using the method of Delong et al. Results. The inhomogenity from the two different ROI methods for distinguishing IDHwt gliomas from IDHmut gliomas both showed the biggest AUC (0.788, 0.930), the optimal cutoff value was 0.229 (sensitivity, 81.3%; specificity, 75.0%) for the ROI-1 and 0.186 (sensitivity, 93.8%; specificity, 82.5%) for the ROI-2, and the AUC of the inhomogenity from the ROI-2 was significantly larger than that from another segmentation, but no significant differences were identified between the AUCs of other same parameters from the two different ROI methods. For the differentiaiton of IDHmut/1p19q− tumors and IDHmut/1p19q+ tumors, with the ROI-1, the ADCmode showed the biggest AUC (AUC: 0.784; sensitivity, 61.1%; specificity, 90.9%), with the ROI-2, and the skewness performed best (AUC, 0.821; sensitivity, 81.8%; specificity, 77.8%), but no significant differences were identified between the AUCs of the same parameters from the two different ROI methods. Conclusion. ADC values analyzed by the histogram method could help to classify the genetic subtypes in lower-grade diffuse gliomas, no matter which ROI method was used. Extracting cystic and necrotic portions from the entire tumor lesions is preferable for evaluating the difference of the intratumoral heterogeneity and classifying IDH-wild tumors, but not significantly beneficial to predicting the 1p19q genotype in the lower-grade gliomas. [ABSTRACT FROM AUTHOR]

Methylation of the O6-methylguanine methyltransferase (MGMT) gene promoter is correlated with the effectiveness of the current standard of care in glioblastoma patients. In this study, a deep learning pipeline is designed for automatic prediction of MGMT status in 87 glioblastoma patients with contrast-enhanced T1W images and 66 with fluid-attenuated inversion recovery(FLAIR) images. The end-to-end pipeline completes both tumor segmentation and status classification. The better tumor segmentation performance comes from FLAIR images (Dice score, 0.897 ± 0.007) compared to contrast-enhanced T1WI (Dice score, 0.828 ± 0.108), and the better status prediction is also from the FLAIR images (accuracy, 0.827 ± 0.056 ; recall, 0.852 ± 0.080 ; precision, 0.821 ± 0.022 ; and F 1 score, 0.836 ± 0.072). This proposed pipeline not only saves the time in tumor annotation and avoids interrater variability in glioma segmentation but also achieves good prediction of MGMT methylation status. It would help find molecular biomarkers from routine medical images and further facilitate treatment planning. [ABSTRACT FROM AUTHOR]

Purpose. In the clinical management of hypopharyngeal squamous cell carcinoma (HSCC), preoperative identification of early recurrence (≤2 years) after curative resection is essential. Thus, we aimed to develop a CT-based radiomic signature to predict early recurrence in HSCC patients preoperatively. Methods. In total, 167 HSCC patients who underwent partial surgery were enrolled in this retrospective study and divided into two groups, i.e., the training cohort (n = 133) and the validation cohort (n = 34). Each individual was followed up for at least for 2 years. Radiomic features were extracted from CT images, and the radiomic signature was built with the least absolute shrinkage and selection operator (LASSO) logistic regression (LR) model. The associations of preoperative clinical factors with early recurrence were evaluated. A radiomic signature-combined model was built, and the area under the curve (AUC) was used to explore their performance in discriminating early recurrence. Results. Among the 1415 features, 335 of them were selected using the variance threshold method. Then, the SelectKBest method was further used for the selection of 31 candidate features. Finally, 11 out of 31 optimal features were identified with the LASSO algorithm. In the LR classifier, the AUCs of the training and validation sets in discriminating early recurrence were 0.83 (95% CI: 0.76-0.90) (sensitivity 0.8 and specificity 0.83) and 0.83 (95% CI: 0.67-0.99) (sensitivity 0.69 and specificity 0.71), respectively. Conclusions. Using the radiomic signature, we developed a radiomic signature to preoperatively predict early recurrence in patients with HSCC, which may serve as a potential noninvasive tool to guide personalized treatment. [ABSTRACT FROM AUTHOR]

This study was aimed at building a computed tomography- (CT-) based radiomics approach for the differentiation of sarcomatoid renal cell carcinoma (SRCC) and clear cell renal cell carcinoma (CCRCC). It involved 29 SRCC and 99 CCRCC patient cases, and to each case, 1029 features were collected from each of the corticomedullary phase (CMP) and nephrographic phase (NP) image. Then, features were selected by using the least absolute shrinkage and selection operator regression method and the selected features of the two phases were explored to build three radiomics approaches for SRCC and CCRCC classification. Meanwhile, subjective CT findings were filtered by univariate analysis to construct a radiomics model and further selected by Akaike information criterion for integrating with the selected image features to build the fifth model. Finally, the radiomics models utilized the multivariate logistic regression method for classification and the performance was assessed with receiver operating characteristic curve (ROC) and DeLong test. The radiomics models based on the CMP, the NP, the CMP and NP, the subjective findings, and the combined features achieved the AUC (area under the curve) value of 0.772, 0.938, 0.966, 0.792, and 0.974, respectively. Significant difference was found in AUC values between each of the CMP radiomics model (0.0001 ≤ p ≤ 0.0051) and the subjective findings model (0.0006 ≤ p ≤ 0.0079) and each of the NP radiomics model, the CMP and NP radiomics model, and the combined model. Sarcomatoid change is a common pathway of dedifferentiation likely occurring in all subtypes of renal cell carcinoma, and the CT-based radiomics approaches in this study show the potential for SRCC from CCRCC differentiation. [ABSTRACT FROM AUTHOR]

Corneal cross-linking (CXL) is a surgical procedure able to modify corneal biomechanics and stabilize keratoconus progression. Although it is known that CXL produces changes in corneal collagen distribution, these are still a topic of discussion. Here we quantitatively compare the corneal stroma architecture between two animal models four weeks after in vivo conventional CXL treatment, with second harmonic generation (SHG) imaging microscopy and the structure tensor (ST). The healing stage and the stroma recovery were also analyzed by means of histological sections. Results show that the CXL effects depend on the initial arrangement of the corneal collagen. While the treatment increases the order in corneas with a low level of initial organization, corneas presenting a fairly regular pattern are hardly affected. Histological samples showed active keratocytes in anterior and middle stroma, what means that the recovery is still in progress. The combination of SHG imaging and the ST is able to objectively discriminate the changes suffered by the collagen arrangement after the CXL treatment, whose effectiveness depends on the initial organization of the collagen fibers within the corneal stroma. [ABSTRACT FROM AUTHOR]

Purpose. To evaluate the relationship between extension or texture features of late gadolinium enhancement (LGE) and ventricular tachyarrhythmias in hypertrophic cardiomyopathy (HCM). Materials and Methods. Twenty-three patients with HCM were enrolled in this IRB-approved study. The extension of LGE was determined based on the American Heart Association segments model. Texture analysis was performed for 43 myocardial LGE using an open-access software (MaZda, Technical University of Lodz, Institute of Electronics, Poland). The relationship between the extension or texture features of LGE and ventricular tachyarrhythmias was evaluated using unpaired test and receiver-operating characteristic (ROC) analysis. Results. Six of 23 patients had a history of ventricular tachyarrhythmias, and 16 patients had LGE. All of the 6 patients with the arrhythmias had more than 4 LGE segments and more LGE segments than those without (p < 0.01). Among 4 texture features, entropy LL was the only discriminator between the 2 patient groups (p < 0.01; threshold, 19624; area under the curve [AUC], 0.72). An ROC analysis gave the number of segments showing LGE a better result (AUC, 0.96) for identification of HCM patients with ventricular tachyarrhythmias than the entropy LL of LGE. Conclusion. Patients with HCM and a history of ventricular tachyarrhythmias had a wider extension of LGE, and their entropy LL of LGE was significantly lower than those without. The extension of LGE and texture analysis may provide information about LGE related to ventricular tachyarrhythmias in HCM. [ABSTRACT FROM AUTHOR]

Although paraquat (PQ) concentrations are the most reliable prognosis predictors of PQ poisoning, these laboratory tests are not readily available in all hospitals. In this study, we proposed an imaging related parameter, that is, the ratio of injured lung volume fraction, for the prognosis evaluation of acute PQ poisoning based on the correlation between disease progress and lung imaging features. An artificial neural network was trained and then used to classify the injured and normal lung regions. The ratio of injured lung volume fraction was calculated from the injured lung volume fractions in the first and second CT scans after three-dimensional reconstruction. Parameters of blood tests were collected. A significant difference was observed with respect to the ratio of injured lung volume fraction between survivors and nonsurvivors (0.73 ± 0.17 versus 0.40 ± 0.14, P < 0.001). No patients survived when the ratio of injured lung volume fraction was less than 0.3, while all patients survived as the ratio of injured lung volume fraction was greater than 0.8. Thus, the ratio of injured lung volume fraction may provide an alternative and informative measure for the prognosis of acute PQ poisoning. [ABSTRACT FROM AUTHOR]