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

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

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

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

OBJECTIVE: Data mining extracts meaningful information from large datasets. In this study, text mining techniques were used to extract keywords from the Journal of the Korean Society of Emergency Medicine, and the change trend was examined. METHODS: The rvest package in R was used to extract all papers published in the Journal of the Korean Society of Emergency Medicine from 2006 to 2016 that could be searched online. Among them, 3,952 keywords were extracted and studied. Using the selected keywords, the corpus was formed by refining keywords that did not correspond to MeSH (Medical Subject Headings) or were misspelled and had similar meanings based on agreement of researchers. Using the refined keywords, the frequencies of the keywords in the first and second halves of the studies were calculated and visualized. RESULTS: Word Cloud revealed that emergency medical service and cardiopulmonary resuscitation (CPR) were most frequently mentioned in both the first and second halves of the studies. In the first half, ultrasonography, stroke, poisoning, injury, and education were frequently mentioned, while in the second half, poisoning, injury, stroke, acute, and tomography were frequently mentioned. A pyramid graph revealed that the frequencies of emergency medical service and CPR were commonly high. CONCLUSION: Core keywords of the Journal of the Korean Society of Emergency Medicine were analyzed for correlations and trends. Changes in study topics according to key topics of interest and period were visually identified.
Cardiopulmonary Resuscitation ; Data Mining ; Dataset ; Education ; Emergencies ; Emergency Medical Services ; Emergency Medicine ; Poisoning ; Stroke ; Ultrasonography

PURPOSE: To determine impacts of symptom clusters, performance and emotional status on the quality of life of gynecologic cancer patients. METHODS: Subjects completed questionnaires consisting of four measurements: symptom cluster, performance and emotional status, and the quality of life. A total of 104 completed data sets were analyzed by descriptive statistics, t-test, ANOVA, Pearson's correlation coefficient, and a multiple regression analysis using the SPSS 21.0 program. RESULTS: Fatigue was identified as the most prevalent symptom (77.9%) and sweating (2.08) as the most severe and uncomfortable symptom (1.80). Six symptom clusters, performance status, anxiety and depression were negatively correlated with quality of life. Four symptom clusters were positively correlated with performance status, and six symptom clusters were positively correlated with anxiety and depression. Factors affecting quality of life were abdominal discomfort cluster (β=−0.23, p=0.005), performance status (β=−0.20, p=0.020), and depression (β=−0.42, p < 0.001). The model was statistically significant explaining 42.5% of variance (F=26.369, p < 0.001). CONCLUSION: The findings supported that symptom clusters and depression negatively influence the quality of life and need to be addressed as we are caring for patients and promoting quality of life.
Anxiety ; Dataset ; Depression ; Fatigue ; Humans ; Quality of Life ; Sweat ; Sweating

This study evaluated the feasibility of using texture analysis and machine learning to distinguish radiographic lung patterns. A total of 1200 regions of interest (ROIs) including four specific lung patterns (normal, alveolar, bronchial, and unstructured interstitial) were obtained from 512 thoracic radiographs of 252 dogs and 65 cats. Forty-four texture parameters based on eight methods of texture analysis (first-order statistics, spatial gray-level-dependence matrices, gray-level-difference statistics, gray-level run length image statistics, neighborhood gray-tone difference matrices, fractal dimension texture analysis, Fourier power spectrum, and Law's texture energy measures) were used to extract textural features from the ROIs. The texture parameters of each lung pattern were compared and used for training and testing of artificial neural networks. Classification performance was evaluated by calculating accuracy and the area under the receiver operating characteristic curve (AUC). Forty texture parameters showed significant differences between the lung patterns. The accuracy of lung pattern classification was 99.1% in the training dataset and 91.9% in the testing dataset. The AUCs were above 0.98 in the training set and above 0.92 in the testing dataset. Texture analysis and machine learning algorithms may potentially facilitate the evaluation of medical images.
Animals ; Area Under Curve ; Cats ; Classification ; Dataset ; Dogs ; Fourier Analysis ; Fractals ; Lung ; Machine Learning ; Neural Networks (Computer) ; Pattern Recognition, Visual ; Radiography, Thoracic ; Residence Characteristics ; ROC Curve

Brain disorder recognition has becoming a promising area of study. In reality, some disorders share similar features and signs, making the task of diagnosis and treatment challenging. This paper presents a rigorous and robust computer aided diagnosis system for the detection of multiple brain abnormalities which can assist physicians in the diagnosis and treatment of brain diseases. In this system, we used energy of wavelet sub bands, textural features of gray level co-occurrence matrix and intensity feature of MR brain images. These features are ranked using Wilcoxon test. The composite features are classifi ed using back propagation neural network. Bayesian regulation is adopted to fi nd the optimal weights of neural network. The experimentation is carried out on datasets DS-90 and DS-310 of Harvard Medical School. To enhance the generalization capability of the network, fi vefold stratifi ed cross validation technique is used. The proposed system yields multi class disease classifi cation accuracy of 100% in diff erentiating 90 MR brain images into 18 classes and 97.81% in diff erentiating 310 MR brain images into 6 classes. The experimental results reveal that the composite features along with BPNN classifi er create a competent and reliable system for the identifi cation of multiple brain disorders which can be used in clinical applications. The Wilcoxon test outcome demonstrates that standard deviation feature along with energies of approximate and vertical sub bands of level 7 contribute the most in achieving enhanced multi class classifi cation performance results.
Brain Diseases ; Brain ; Dataset ; Diagnosis ; Generalization (Psychology) ; Magnetic Resonance Imaging ; Schools, Medical ; Weights and Measures