Biopsy ; Classification ; Dataset ; Sensitivity and Specificity ; Thyroid Gland ; Thyroid Neoplasms ; Thyroid Nodule ; Ultrasonography

dataset of 883 CT scans from 467 subjects. Axial CT images obtained at the inferior endplate level of the 3rd lumbar vertebra were used for the analysis. Manually drawn segmentation maps of the skeletal muscle, visceral fat, and subcutaneous fat were created to serve as ground truth data. The performance of the fully convolutional network-based segmentation system was evaluated using the Dice similarity coefficient and cross-sectional area error, for both a separate internal validation dataset (426 CT scans from 308 subjects) and an external validation dataset (171 CT scans from 171 subjects from two outside hospitals).RESULTS: The mean Dice similarity coefficients for muscle, subcutaneous fat, and visceral fat were high for both the internal (0.96, 0.97, and 0.97, respectively) and external (0.97, 0.97, and 0.97, respectively) validation datasets, while the mean cross-sectional area errors for muscle, subcutaneous fat, and visceral fat were low for both internal (2.1%, 3.8%, and 1.8%, respectively) and external (2.7%, 4.6%, and 2.3%, respectively) validation datasets.CONCLUSION: The fully convolutional network-based segmentation system exhibited high performance and accuracy in the automatic segmentation of abdominal muscle and fat on CT images.]]>
Abdominal Muscles ; Adipose Tissue ; Artificial Intelligence ; Dataset ; Intra-Abdominal Fat ; Learning ; Muscle, Skeletal ; Muscles ; Sarcopenia ; Spine ; Subcutaneous Fat ; Tomography, X-Ray Computed

dataset from patient-derived xenograft model for TKI-treated ccRCC (GSE76068) was retrieved. Commonly altered pathways between the datasets were investigated by Ingenuity Pathway Analysis using commonly regulated differently expressed genes (DEGs). The significance of candidate DEG on intrinsic TKI resistance was assessed through immunohistochemistry in a separate cohort of 101 TKI-treated ccRCC cases.RESULTS: TNFRSF1A gene expression and tumor necrosis factor (TNF)-α pathway were upregulated in ccRCCs with acquired TKI resistance in both microarray datasets. Also, high expression (> 10% of labeled tumor cells) of TNF receptor 1 (TNFR1), the protein product of TNFRSF1A gene, was correlated with sarcomatoid dedifferentiation and was an independent predictive factor of clinically unfavorable response and shorter survivals in separated TKI-treated ccRCC cohort.CONCLUSION: TNF-α signaling may play a role in TKI resistance, and TNFR1 expression may serve as a predictive biomarker for clinically unfavorable TKI responses in ccRCC.]]>
Biomarkers ; Carcinoma, Renal Cell ; Cohort Studies ; Dataset ; Drug Resistance ; Gene Expression ; Gene Expression Profiling ; Heterografts ; Humans ; Immunohistochemistry ; Protein-Tyrosine Kinases ; Receptors, Tumor Necrosis Factor ; Receptors, Tumor Necrosis Factor, Type I ; Tumor Necrosis Factor-alpha

Although microscopic analysis of tissue slides has been the basis for disease diagnosis for decades, intra- and inter-observer variabilities remain issues to be resolved. The recent introduction of digital scanners has allowed for using deep learning in the analysis of tissue images because many whole slide images (WSIs) are accessible to researchers. In the present study, we investigated the possibility of a deep learning-based, fully automated, computer-aided diagnosis system with WSIs from a stomach adenocarcinoma dataset. Three different convolutional neural network architectures were tested to determine the better architecture for tissue classifier. Each network was trained to classify small tissue patches into normal or tumor. Based on the patch-level classification, tumor probability heatmaps can be overlaid on tissue images. We observed three different tissue patterns, including clear normal, clear tumor and ambiguous cases. We suggest that longer inspection time can be assigned to ambiguous cases compared to clear normal cases, increasing the accuracy and efficiency of histopathologic diagnosis by pre-evaluating the status of the WSIs. When the classifier was tested with completely different WSI dataset, the performance was not optimal because of the different tissue preparation quality. By including a small amount of data from the new dataset for training, the performance for the new dataset was much enhanced. These results indicated that WSI dataset should include tissues prepared from many different preparation conditions to construct a generalized tissue classifier. Thus, multi-national/multi-center dataset should be built for the application of deep learning in the real world medical practice.
Adenocarcinoma ; Classification ; Dataset ; Diagnosis ; Learning ; Observer Variation ; Stomach

OBJECTIVE: To investigate pathologic discrepancies between colposcopy-directed biopsy (CDB) of the cervix and loop electrosurgical excision procedure (LEEP) in women with cytologic high-grade squamous intraepithelial lesions (HSILs).METHODS: We retrospectively identified 297 patients who underwent both CDB and LEEP for HSILs in cervical cytology between 2015 and 2018, and compared their pathologic results. Considering the LEEP to be the gold standard, we evaluated the diagnostic performance of CDB for identifying cervical intraepithelial neoplasia (CIN) grades 2 and 3, adenocarcinoma in situ, and cancer (HSIL+). We also performed age subgroup analyses.RESULTS: Among the study population, 90.9% (270/297) had pathologic HSIL+ using the LEEP. The diagnostic performance of CDB for identifying HSIL+ was as follows: sensitivity, 87.8%; specificity, 59.3%; balanced accuracy, 73.6%; positive predictive value, 95.6%; and negative predictive value, 32.7%. Thirty-three false negative cases of CDB included CIN2,3 (n=29) and cervical cancer (n=4). The pathologic HSIL+ rate in patients with HSIL− by CDB was 67.3% (33/49). CDB exhibited a significant difference in the diagnosis of HSIL+ compared to LEEP in all patients (p<0.001). In age subgroup analyses, age groups <35 years and 35–50 years showed good agreement with the entire data set (p=0.496 and p=0.406, respectively), while age group ≥50 years did not (p=0.036).CONCLUSION: A significant pathologic discrepancy was observed between CDB and LEEP results in women with cytologic HSILs. The diagnostic inaccuracy of CDB increased in those ≥50 years of age.
Adenocarcinoma in Situ ; Biopsy ; Cervical Intraepithelial Neoplasia ; Cervix Uteri ; Colposcopy ; Conization ; Dataset ; Diagnosis ; Early Detection of Cancer ; Female ; Humans ; Papanicolaou Test ; Retrospective Studies ; Sensitivity and Specificity ; Squamous Intraepithelial Lesions of the Cervix ; Uterine Cervical Neoplasms

PURPOSE: We determined whether elevated serum alkaline phosphatase (ALP) was related to prevalence, location, type, length, and recurrence of pterygium in a population from the Republic of Korea.METHODS: A nationwide cross-sectional dataset, the Korean National Health and Nutrition Examination Survey (2008–2011), was used in this study. All participants were > 30 years of age and underwent the ALP test and ophthalmic evaluation (n = 22,359). One-way analysis of variance, the chi-square test, and Fisher's exact test were used to compare characteristics and outcomes among participants. Multivariable logistic regression was used to examine the possible associations between serum ALP levels and various types of pterygium. Data were adjusted for known risk factors for development of pterygium and ALP elevation (age, sex, residence, sunlight exposure, drinking, smoking, hypertension, diabetes, BMI, AST, ALT, vitamin D, and HDL).RESULTS: The overall prevalence of pterygium was 8.1%, and participants with pterygium had higher levels of serum ALP (p < 0.001). Participants with higher serum ALP had a significantly higher prevalence of all types of pterygium than those in the lower serum ALP quartiles. After adjusting for potential confounding factors, multivariate logistic regression analysis revealed that ALP was associated with the prevalence of pterygium (odds ratio [OR], 1.001; p = 0.038). Trend analysis between the OR and ALP quartiles revealed a linear trend in overall prevalence and in the intermediate type of pterygium. Subgroup analysis revealed a stronger correlation in participants > 50 years of age. One-way analysis of variance revealed an association between the size of pterygium and serum ALP quartile levels. Serum ALP was not associated with recurrence of pterygium.CONCLUSIONS: Increased serum ALP was associated with the prevalence and size of pterygium.
Alkaline Phosphatase ; Cross-Sectional Studies ; Dataset ; Drinking ; Hypertension ; Korea ; Logistic Models ; Nutrition Surveys ; Prevalence ; Pterygium ; Recurrence ; Republic of Korea ; Risk Factors ; Smoke ; Smoking ; Sunlight ; Vitamin D

Manually reviewing electroencephalograms (EEGs) is labor-intensive and demands automated seizure detection systems. To construct an efficient and robust event detector for experimental seizures from continuous EEG monitoring, we combined spectral analysis and deep neural networks. A deep neural network was trained to discriminate periodograms of 5-sec EEG segments from annotated convulsive seizures and the pre- and post-EEG segments. To use the entire EEG for training, a second network was trained with non-seizure EEGs that were misclassified as seizures by the first network. By sequentially applying the dual deep neural networks and simple pre- and post-processing, our autodetector identified all seizure events in 4,272 h of test EEG traces, with only 6 false positive events, corresponding to 100% sensitivity and 98% positive predictive value. Moreover, with pre-processing to reduce the computational burden, scanning and classifying 8,977 h of training and test EEG datasets took only 2.28 h with a personal computer. These results demonstrate that combining a basic feature extractor with dual deep neural networks and rule-based pre- and post-processing can detect convulsive seizures with great accuracy and low computational burden, highlighting the feasibility of our automated seizure detection algorithm.
Animals ; Dataset ; Electroencephalography ; Epilepsy ; Mice ; Microcomputers ; Seizures*

BACKGROUND: Venous thromboembolism is a common complication in patients with glioma. The clotting factor von Willebrand factor (VWF) is a highly adhesive procoagulant molecule that mediates platelet adhesion to endothelial and subendothelial surfaces. In the current analysis, we examined The Cancer Genome Atlas (TCGA) data to assess the VWF gene in patients with lower grade gliomas. METHODS: For newly diagnosed gliomas, we evaluated the association between VWF and overall survival in the Genomic Data Commons TCGA Lower Grade Glioma (LGG) dataset in TCGA. Simple statistics were calculated to identify patterns of mutual exclusivity or co-occurrence of VWF mutations. For each pair of query genes an odds ratio was calculated that indicates the likelihood that the mutations in the two genes are mutually exclusive or co-occurrent across the selected cases. To determine whether the identified relationship was significant for a gene pair, Fisher's exact test was performed. RESULTS: Lower grade gliomas with less VWF gene expression had significantly better survival than those with more VWF gene expression (hazard ratio 0.64, 95% confidence interval 0.44 to 0.92, p=0.015 log rank test). When we analyzed the data with Cox regression, VWF expression had a significant effect on survival (p=0.02) that was unrelated to the effect of IDH1 expression (p=0.062), TP53 expression (p=0.135), independent of ATRX expression (p=0.021) and histology (astrocytoma versus oligoastrocytoma and oligodendroglioma, p=0.002). VWF mutations significantly co-occur with mutations in TP53 and ATRX (p<0.001). CONCLUSION: The deleterious prognostic effect of VWF expression and its co-occurrent mutations with TP53 and ATRX in lower grade gliomas are not surprising, given VWF's role in other cancers. Therefore, VWF gene expression may be a clinically important risk marker in lower grade glioma.
Adhesives ; Blood Platelets ; Dataset ; Gene Expression ; Genes, vif ; Genome ; Glioblastoma ; Glioma ; Humans ; Odds Ratio ; Oligodendroglioma ; Venous Thromboembolism ; von Willebrand Factor

BACKGROUND: The prevalence and incidence of type 1 diabetes mellitus (T1DM) in all age groups and the prevalence of metabolic syndrome in patients with T1DM in Korea were estimated. METHODS: The incidence and prevalence of T1DM between 2007 and 2013 were calculated using the Korean National Health Insurance Service (NHIS) datasets of claims. Clinical characteristics and prevalence of metabolic syndrome in individuals with T1DM between 2009 and 2013 were determined using the database of NHIS preventive health checkups. RESULTS: The prevalence of T1DM in Korea between 2007 and 2013 was 0.041% to 0.047%. The annual incidence rate of T1DM in Korea in 2007 to 2013 was 2.73 to 5.02/100,000 people. Although the incidence rate of typical T1DM was highest in teenagers, it remained steady in adults over 30 years of age. In contrast, the incidence rate of atypical T1DM in 2013 was higher in people aged 40 years or older than in younger age groups. Age- and sex-adjusted prevalence of metabolic syndrome in patients with T1DM was 51.65% to 55.06% between 2009 and 2013. CONCLUSION: T1DM may be more common in Korean adults than previously believed. Metabolic syndrome may be a frequent finding in individuals with T1DM in Korea.
Adolescent ; Adult ; Dataset ; Diabetes Mellitus, Type 1 ; Humans ; Incidence ; Korea ; National Health Programs ; Prevalence

PURPOSE: It has been proposed that using new prediction methods, such as neural networks based on dental data, could improve age estimation. This study aimed to assess the possibility of exploiting neural networks for estimating age by means of the pulp-to-tooth ratio in canines as a non-destructive, non-expensive, and accurate method. In addition, the predictive performance of neural networks was compared with that of a linear regression model. MATERIALS AND METHODS: Three hundred subjects whose age ranged from 14 to 60 years and were well distributed among various age groups were included in the study. Two statistical software programs, SPSS 21 (IBM Corp., Armonk, NY, USA) and R, were used for statistical analyses. RESULTS: The results indicated that the neural network model generally performed better than the regression model for estimation of age with pulp-to-tooth ratio data. The prediction errors of the developed neural network model were acceptable, with a root mean square error (RMSE) of 4.40 years and a mean absolute error (MAE) of 4.12 years for the unseen dataset. The prediction errors of the regression model were higher than those of the neural network, with an RMSE of 10.26 years and a MAE of 8.17 years for the test dataset. CONCLUSION: The neural network method showed relatively acceptable performance, with an MAE of 4.12 years. The application of neural networks creates new opportunities to obtain more accurate estimations of age in forensic research.
Cone-Beam Computed Tomography ; Dataset ; Forensic Dentistry ; Humans ; Linear Models ; Methods ; Neural Networks (Computer)