scholarly journals Clinical Value of Information Entropy Compared with Deep Learning for Ultrasound Grading of Hepatic Steatosis

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 1006 ◽  
Author(s):  
Jheng-Ru Chen ◽  
Yi-Ping Chao ◽  
Yu-Wei Tsai ◽  
Hsien-Jung Chan ◽  
Yung-Liang Wan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Hepatic Steatosis ◽  
Information Entropy ◽  
Tissue Characterization ◽  
Fatty Infiltration ◽  
Quantitative Measure ◽  
Roc Curves ◽  
Clinical Value ◽  
Roc Curve Analysis ◽  
Ultrasound Tissue Characterization

Entropy is a quantitative measure of signal uncertainty and has been widely applied to ultrasound tissue characterization. Ultrasound assessment of hepatic steatosis typically involves a backscattered statistical analysis of signals based on information entropy. Deep learning extracts features for classification without any physical assumptions or considerations in acoustics. In this study, we assessed clinical values of information entropy and deep learning in the grading of hepatic steatosis. A total of 205 participants underwent ultrasound examinations. The image raw data were used for Shannon entropy imaging and for training and testing by the pretrained VGG-16 model, which has been employed for medical data analysis. The entropy imaging and VGG-16 model predictions were compared with histological examinations. The diagnostic performances in grading hepatic steatosis were evaluated using receiver operating characteristic (ROC) curve analysis and the DeLong test. The areas under the ROC curves when using the VGG-16 model to grade mild, moderate, and severe hepatic steatosis were 0.71, 0.75, and 0.88, respectively; those for entropy imaging were 0.68, 0.85, and 0.9, respectively. Ultrasound entropy, which varies with fatty infiltration in the liver, outperformed VGG-16 in identifying participants with moderate or severe hepatic steatosis (p < 0.05). The results indicated that physics-based information entropy for backscattering statistics analysis can be recommended for ultrasound diagnosis of hepatic steatosis, providing not only improved performance in grading but also clinical interpretations of hepatic steatosis.

Symtosis: A liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm

2018 ◽  
Vol 155 ◽  
pp. 165-177 ◽  
Author(s):  
Mainak Biswas ◽  
Venkatanareshbabu Kuppili ◽  
Damodar Reddy Edla ◽  
Harman S. Suri ◽  
Luca Saba ◽  
...  
Keyword(s):  
Deep Learning ◽  
Risk Stratification ◽  
Tissue Characterization ◽  
Learning Paradigm ◽  
Ultrasound Tissue Characterization ◽  
Liver Ultrasound

scholarly journals Circulating LncRNAs as Novel, Non-Invasive Biomarkers for Prenatal Detection of Fetal Congenital Heart Defects

2016 ◽  
Vol 38 (4) ◽  
pp. 1459-1471 ◽  
Author(s):  
Meng Gu ◽  
Aibin Zheng ◽  
Wenjuan Tu ◽  
Jing Zhao ◽  
Lin Li ◽  
...  
Keyword(s):  
Pregnant Women ◽  
Congenital Heart Defects ◽  
Congenital Heart ◽  
Heart Defects ◽  
Roc Curves ◽  
Differentially Expressed ◽  
Rt Pcr ◽  
Clinical Value ◽  
Roc Curve Analysis ◽  
Novel Biomarkers

Objectives: To explore the clinical value of circulating long non-coding RNAs (lncRNAs) as biomarkers to predict fetal congenital heart defects (CHD) in pregnant women. Methods: Differential expression of lncRNAs isolated from the plasma of pregnant women with typical fetal CHD or healthy controls was analyzed by microarray. Gene ontology (GO), pathway and network analysis were performed to study the function of the lncRNAs. Differentially expressed lncRNAs were validated in plasma samples from 62 pregnant women with typical CHD and 62 matched controls by RT-PCR. The sensitivity and specificity of each lncRNA in the diagnosis of fetal CHD was determined by ROC curve analysis. Results: Microarray analysis identified 3694 up-regulated and 3919 down-regulated (fold change ≥2.0) lncRNAs. The top ten significantly differentially expressed, CHD-associated lncRNAs were validated by RT-PCR. Five significantly up-regulated or down-regulated lncRNAs were identified: ENST00000436681, ENST00000422826, AA584040, AA709223 and BX478947 with the AUC of ROC curves calculated as 0.892, 0.817, 0.755, 0.882 and 0.886, respectively. Conclusions: Specific lncRNAs aberrantly expressed in the plasma of pregnant women with typical fetal CHD may play a key role in the development of CHD and may be used as novel biomarkers for prenatal diagnosis of fetal CHD.

scholarly journals Hepatic Steatosis Assessment Using Quantitative Ultrasound Parametric Imaging Based on Backscatter Envelope Statistics

2019 ◽  
Vol 9 (4) ◽  
pp. 661 ◽  
Author(s):  
Zhuhuang Zhou ◽  
Qiyu Zhang ◽  
Weiwei Wu ◽  
Shuicai Wu ◽  
Po-Hsiang Tsui
Keyword(s):  
Liver Biopsy ◽  
Hepatic Steatosis ◽  
Quantitative Ultrasound ◽  
Tissue Characterization ◽  
Imaging Techniques ◽  
Parametric Imaging ◽  
Sampling Errors ◽  
Alcoholic Fatty Liver ◽  
Ultrasound Tissue Characterization ◽  
Model Based

Hepatic steatosis is a key manifestation of non-alcoholic fatty liver disease (NAFLD). Early detection of hepatic steatosis is of critical importance. Currently, liver biopsy is the clinical golden standard for hepatic steatosis assessment. However, liver biopsy is invasive and associated with sampling errors. Ultrasound has been recommended as a first-line diagnostic test for the management of NAFLD. However, B-mode ultrasound is qualitative and can be affected by factors including image post-processing parameters. Quantitative ultrasound (QUS) aims to extract quantified acoustic parameters from the ultrasound backscattered signals for ultrasound tissue characterization and can be a complement to conventional B-mode ultrasound. QUS envelope statistics techniques, both statistical model-based and non-model-based, have shown potential for hepatic steatosis characterization. However, a state-of-the-art review of hepatic steatosis assessment using envelope statistics techniques is still lacking. In this paper, envelope statistics-based QUS parametric imaging techniques for characterizing hepatic steatosis are reviewed and discussed. The reviewed ultrasound envelope statistics parametric imaging techniques include acoustic structure quantification imaging, ultrasound Nakagami imaging, homodyned-K imaging, kurtosis imaging, and entropy imaging. Future developments are suggested.

scholarly journals Small-window parametric imaging based on information entropy for ultrasound tissue characterization

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Po-Hsiang Tsui ◽  
Chin-Kuo Chen ◽  
Wen-Hung Kuo ◽  
King-Jen Chang ◽  
Jui Fang ◽  
...  
Keyword(s):  
Information Entropy ◽  
Tissue Characterization ◽  
Parametric Imaging ◽  
Ultrasound Tissue Characterization ◽  
Small Window

scholarly journals Introduction of the Grayscale Median for Ultrasound Tissue Characterization of the Transplanted Kidney

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 390
Author(s):  
Camilo G. Sotomayor ◽  
Stan Benjamens ◽  
Hildebrand Dijkstra ◽  
Derya Yakar ◽  
Cyril Moers ◽  
...  
Keyword(s):  
Kidney Transplant ◽  
Tissue Characterization ◽  
Kidney Transplant Recipients ◽  
Systematic Assessment ◽  
Ultrasound Tissue Characterization ◽  
Adult Kidney ◽  
Mersenne Twister ◽  
History Of

Ultrasound examination is advised for early post-kidney transplant assessment. Grayscale median (GSM) quantification is novel in the kidney transplant field, with no systematic assessment previously reported. In this prospective cohort study, we measured the post-operative GSM in a large cohort of adult kidney transplant recipients (KTR) who consecutively underwent Doppler ultrasound directly after transplantation (within 24 h), compared it with GSM in nontransplanted patients, and investigated its association with baseline and follow-up characteristics. B-mode images were used to calculate the GSM in KTR and compared with GSM data in nontransplanted patients, as simulated from summary statistics of the literature using a Mersenne twister algorithm. The association of GSM with baseline and 1-year follow-up characteristics were studied by means of linear regression analyses. In 282 KTR (54 ± 15 years old, 60% male), the median (IQR) GSM was 55 (45–69), ranging from 22 to 124 (coefficient of variation = 7.4%), without differences by type of donation (p = 0.28). GSM in KTR was significantly higher than in nontransplanted patients (p < 0.001), and associated with systolic blood pressure, history of cardiovascular disease, and donor age (std. β = 0.12, −0.20, and 0.13, respectively; p < 0.05 for all). Higher early post-kidney transplant GSM was not associated with 1-year post-kidney transplant function parameters (e.g., measured and estimated glomerular filtration rate). The data provided in this study could be used as first step for further research on the application of early postoperative ultrasound in KTR.

Sparing of Fatty Infiltration Around Focal Hepatic Lesions in Patients with Hepatic Steatosis: Sonographic Appearance with CT and MRI Correlation

2008 ◽  
Vol 190 (4) ◽  
pp. 1018-1027 ◽  
Author(s):  
Kyoung Won Kim ◽  
Min Ju Kim ◽  
Seung Soo Lee ◽  
Hyoung Jung Kim ◽  
Yong Moon Shin ◽  
...  
Keyword(s):  
Hepatic Steatosis ◽  
Fatty Infiltration ◽  
Sonographic Appearance ◽  
Focal Hepatic Lesions ◽  
Hepatic Lesions ◽  
Ct And Mri

scholarly journals Evaluation of Coagulation, Fibrinolysis and Endothelial Biomarkers in Cirrhotic Patients With or Without Portal Venous Thrombosis

2020 ◽  
Vol 26 ◽  
pp. 107602962098266
Author(s):  
Wenhua Ren ◽  
Jing Zhang ◽  
Yuying Chen ◽  
Meng Wen ◽  
Yu Su ◽  
...  
Keyword(s):  
Coagulation Factor ◽  
Predictive Performance ◽  
Plasminogen Activator Inhibitor ◽  
Roc Curves ◽  
Clinical Value ◽  
Inhibitor Complex ◽  
Vein Thrombosis ◽  
Cirrhotic Patients ◽  
Portal Venous Thrombosis ◽  
Von Willebrand

To evaluate variations in coagulation, fibrinolysis and endothelial marker expression in cirrhotic patients and to explore their clinical value and predictive performance in cirrhotic patients with or without portal vein thrombosis (PVT), we performed a case-control study with 175 cirrhotic patients and 50 healthy individuals. 99 patients had PVT and another 76 patients did not. All participants were evaluated for plasma levels of conventional hemostatic markers. Thrombin-antithrombin complex (TAT), plasmin-α2-plasmin inhibitor complex (PIC), thrombomodulin (TM), tissue plasminogen activator inhibitor complex (t-PAIC), von Willebrand factor antigen (vWF: Ag) and coagulation factor Ⅷ (FⅧ: c) were also assessed and the ratio of TAT/t-PAIC was calculated. We analyzed differences in these biomarkers among the three groups and constructed receiver operating characteristic (ROC) curves. Patients with PVT exhibited significantly higher TAT and TAT/t-PAIC than cirrhotic patients without PVT (both P < 0.001). Areas under the curve (AUC) of ROC analyses for TAT and TAT/t-PAIC were 0.68 and 0.66, the cut-off levels were 1.55 ng/ml and 0.46, with sensitivities and specificities of 78.79% and 51.32% regarding TAT, 39.8% and 90.79% regarding TAT/t-PAIC. Levels of FⅧ: c and vWF: Ag in patients with PVT were significantly lower than those without PVT (p = 0.026 and p = 0.027, respectively). The AUCROC, cut-off level, sensitivity and specificity of FⅧ: c were 0.64, 111.1%, 66.67% and 60%, respectively. For vWF: Ag they were 0.61, 429%, 89.66% and 38.71%, respectively. Cirrhotic patients have disorders of coagulation, fibrinolysis and the endothelial system. TAT, TAT/t-PAIC, FⅧ: c and vWF: Ag can be used as potential biomarkers for predicting PVT in cirrhotic patients.

scholarly journals Identification of differentially methylated genes as diagnostic and prognostic biomarkers of breast cancer

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Xiao-hong Mao ◽  
Qiang Ye ◽  
Guo-bing Zhang ◽  
Jin-ying Jiang ◽  
Hong-ying Zhao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
High Sensitivity ◽  
Breast Cancer Diagnosis ◽  
Diagnostic Value ◽  
Methylation Pattern ◽  
Clinical Value ◽  
Roc Curve Analysis ◽  
Differentially Methylated Genes ◽  
Sensitivity Specificity

Abstract Background Aberrant DNA methylation is significantly associated with breast cancer. Methods In this study, we aimed to determine novel methylation biomarkers using a bioinformatics analysis approach that could have clinical value for breast cancer diagnosis and prognosis. Firstly, differentially methylated DNA patterns were detected in breast cancer samples by comparing publicly available datasets (GSE72245 and GSE88883). Methylation levels in 7 selected methylation biomarkers were also estimated using the online tool UALCAN. Next, we evaluated the diagnostic value of these selected biomarkers in two independent cohorts, as well as in two mixed cohorts, through ROC curve analysis. Finally, prognostic value of the selected methylation biomarkers was evaluated breast cancer by the Kaplan-Meier plot analysis. Results In this study, a total of 23 significant differentially methylated sites, corresponding to 9 different genes, were identified in breast cancer datasets. Among the 9 identified genes, ADCY4, CPXM1, DNM3, GNG4, MAST1, mir129-2, PRDM14, and ZNF177 were hypermethylated. Importantly, individual value of each selected methylation gene was greater than 0.9, whereas predictive value for all genes combined was 0.9998. We also found the AUC for the combined signature of 7 genes (ADCY4, CPXM1, DNM3, GNG4, MAST1, PRDM14, ZNF177) was 0.9998 [95% CI 0.9994–1], and the AUC for the combined signature of 3 genes (MAST1, PRDM14, and ZNF177) was 0.9991 [95% CI 0.9976–1]. Results from additional validation analyses showed that MAST1, PRDM14, and ZNF177 had high sensitivity, specificity, and accuracy for breast cancer diagnosis. Lastly, patient survival analysis revealed that high expression of ADCY4, CPXM1, DNM3, PRDM14, PRKCB, and ZNF177 were significantly associated with better overall survival. Conclusions Methylation pattern of MAST1, PRDM14, and ZNF177 may represent new diagnostic biomarkers for breast cancer, while methylation of ADCY4, CPXM1, DNM3, PRDM14, PRKCB, and ZNF177 may hold prognostic potential for breast cancer.

Diagnostic performance of distinct metabolic features in the plasma of patients with silicosis

2020 ◽  
Author(s):  
Changjiang Xue ◽  
Na Wu ◽  
Yali Fan ◽  
Jing Ma ◽  
Qiao Ye
Keyword(s):  
Pilot Study ◽  
Validation Study ◽  
High Performance ◽  
Interstitial Fibrosis ◽  
Roc Curves ◽  
Sphingolipid Metabolism ◽  
Control Group ◽  
Plasma Samples ◽  
Proline Metabolism ◽  
Roc Curve Analysis

Abstract Background Silicosis is a progressive pneumoconiosis characterized by interstitial fibrosis following exposure to silica dust. This study aimed to identify potential noninvasive metabolic biomarkers for the diagnosis and monitoring of this condition by pilot and validation analyses of patients with silicosis in metabolomics studies.Methods Patients with silicosis, dust-exposed workers (DEWs) without silicosis and age-matched healthy controls were recruited in a case-control study. Plasma samples were collected, and metabolomics analyses by ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS) were conducted. Distinct metabolic features (DMFs) among the groups were identified in the pilot study and were validated in the validation study. The enriched signalling pathways of these DMFs were determined. The ability of DMFs to discriminate among the groups in the validation study was analysed through receiver operating characteristic (ROC) curves. The correlations between DMFs and clinical features were also explored.Results Twenty-nine DMFs and 9 DMFs were detected in the plasma of the DEW and silicosis groups, respectively, compared with the control group; these features showed the same trend in the pilot study and the validation study. Sphingolipid metabolism was the major metabolic pathway in the DEWs, and arginine and proline metabolism was associated with silicosis. Twenty DMFs in the DEWs and 3 DMFs in the patients with silicosis showed a discriminatory ability with ROC curve analysis. The abundance of kynurenine was higher in Stage III silicosis than in Stage I or Stage II silicosis. L-arginine and kynurenine were both negatively correlated with the percentage of forced vital capacity predicted in silicosis.Conclusions Distinct metabolic features of plasma samples related to sphingolipid metabolism and arginine and proline metabolism were identified in the DEW and silicosis groups, respectively. L-arginine and kynurenine may have a predictive role in the diagnosis and severity of silicosis.

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