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Purpose The aim of this study was to determine the accuracy of 3-dimensional (3D) pelvic floor ultrasonography and compare it with defecography in assessment of posterior pelvic disorders. Methods Eligible patients were consecutive women undergoing 3D pelvic floor ultrasonography at one hospital between August 2017 and February 2019. All 3D pelvic floor ultrasonography was performed by one examiner. A total of 167 patients with suspected posterior pelvic disorder was retrospectively enrolled in the study. The patients were divided into 3 groups according to the main symptoms. Results There were 82 rectoceles on defecography (55 barium trapping) and 84 on 3D pelvic floor ultrasonography. Each modality identified 6 enteroceles. There were 43 patients with pelvic floor dyssynergia on defecography and 41 on ultrasonography. There were 84 patients with intussusception on defecography and 41 on 3D pelvic floor ultrasonography. Agreement of the 2 diagnostic tests was confirmed using Cohen’s kappa value. Rectocele (kappa, 0.784) and enterocele (kappa, 0.654) both indicated good agreement between defecography and 3D pelvic floor ultrasonography. In addition, pelvic floor dyssynergia (kappa, 0.406) showed moderate agreement, while internal intussusception (kappa, 0.296) had fair agreement. Conclusion This study showed good agreement for detection of posterior pelvic disorders between defecography and 3D pelvic floor ultrasonography.

Purpose To investigate the correlation of the strain elasticity of breast cancer with histologic features, immunohistochemical markers and molecular subtypes that are known to be factors related to prognosis. Materials and Methods B-mode ultrasound and strain elastography were performed in 123 patients (mean age, 53.4; range, 28–82) with invasive ductal carcinoma (IDC) (mean size, 1.54 cm; range, 0.4–7.0 cm). Histologic grade, lymph node (LN) status, lymphovascular invasion, immunohistochemical biomarkers [estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2 (HER2), CK5/6, epidermal growth factor receptor, and Ki-67] and molecular subtypes were determined from surgical pathology reports. The relationships between these factors and elasticity scores were evaluated. Results LN involvement was associated with a higher elasticity score which was statistically significant (p = 0.042). The tumor size, lymphovascular invasion, histologic grades, immunohistochemical markers and molecular subtypes had no significant correlation with the elasticity score (p > 0.05 for all). However, the IDCs with larger size and a positive lymphovascular invasion tended to have higher elasticity scores. Furthermore, higher histologic grade cancers and the HER2 overexpression-type tended to have lower elasticity scores. Conclusion The elasticity score of IDC had a significant correlation with LN involvement but no statistically significant correlation with the histologic features, immunohistochemical markers or molecular subtypes.

Pancreatic panniculitis is a rare complication characterized by subcutaneous fat necrosis associated with pancreatic disease. It has been postulated that pancreatic panniculitis is caused by the systemic activity of pancreatic enzymes that lead to microcirculatory disturbances. We report a 41-year-old heavy alcoholic woman with pancreatic panniculitis that coexisted with acute and chronic pancreatitis. She was diagnosed with chronic pancreatitis and alcoholic liver cirrhosis 5 years ago. She presented with multiple, tender, erythematous, subcutaneous nodules with heat sensation on both lower legs. Laboratory evaluation revealed an increase in the serum blood amylase and lipase. Histopathologic findings showed fat necrosis with inflammation around the necrotic subcutaneous fat tissue. The lesions subsided gradually with an improvement of acute pancreatitis.

Abstract Background Since the development of sequencing technology, an enormous amount of genetic information has been generated, and human cancer analysis using this information is drawing attention. As the effects of variants on human cancer become known, it is important to find cancer-associated variants among countless variants. Results We propose a new filter-based feature selection method applicable for extracting cancer-associated somatic variants considering correlations of data. Both variants associated with the activation and deactivation of cancer’s characteristics are analyzed using dual correlation filters. The multiobjective optimization is utilized to consider two types of variants simultaneously without redundancy. To overcome high computational complexity problem, we calculate the correlation-based weight to select significant variants instead of directly searching for the optimal subset of variants. The proposed algorithm is applied to the identification of melanoma metastasis or breast cancer stage, and the classification results of the proposed method are compared with those of conventional single correlation filter-based method. Conclusions We verified that the proposed dual correlation filter-based method can extract cancer-associated variants related to the characteristics of human cancer.

Goal: Individual characteristics are determined through a genome consisting of a complex base combination. This base combination is reflected in the k-word profile, which represents the number of consecutive k bases. Therefore, it is important to analyze the genome-specific statistical specificity in the k-word profile to understand the characteristics of the genome. In this paper, we propose a new k-word-based method to analyze genome-specific properties. Methods: We define k-words consisting of the same number of bases as statistically identical k-words. The statistically identical k-words are estimated to appear at a similar frequency by statistical prediction. However, this may not be true in the genome because it is not a random list of bases. The ratio between frequencies of two statistically identical k-words can then be used to investigate the statistical specificity of the genome reflected in the k-word profile. In order to find important ratios representing genomic characteristics, a reference value is calculated that results in a minimum error when classifying data by ratio alone. Finally, we propose a genetic algorithm-based search algorithm to select a minimum set of ratios useful for classification. Results: The proposed method was applied to the full-length sequence of microorganisms for pathogenicity classification. The classification accuracy of the proposed algorithm was similar to that of conventional methods while using only a few features. Conclusions: We proposed a new method to investigate the genome-specific statistical specificity in the k-word profile which can be applied to find important properties of the genome and classify genome sequences.