Esophageal leiomyoma is a benign tumor that accounts for two-thirds of esophageal benign tumors. In general, patients with esophageal leiomyoma are asymptomatic; however, as the tumor grows in size, symptoms such as dysphagia, chest discomfort, obstruction, or bleeding can appear. A 65-year-old man visited our clinic to receive treatment for a subepithelial tumor in the lower esophagus, which was incidentally found during endoscopy for work-up of chest discomfort and indigestion. On endoscopic ultrasonography (EUS), the tumor was 2.6×1.9 cm in size, with a cystic lesion located in the submucosal and proper muscle layers and thick cystic wall. Therefore, it was highly likely to be a duplication cyst. As the patient had symptoms such as chest discomfort, surgical resection was performed. The tumor was finally diagnosed as a leiomyoma with cystic degeneration. Leiomyoma with cystic change that has similar EUS features to duplication cyst is rare. Thus, we report the case with a literature review.

Esophageal leiomyoma is a benign tumor that accounts for two-thirds of esophageal benign tumors. In general, patients with esophageal leiomyoma are asymptomatic; however, as the tumor grows in size, symptoms such as dysphagia, chest discomfort, obstruction, or bleeding can appear. A 65-year-old man visited our clinic to receive treatment for a subepithelial tumor in the lower esophagus, which was incidentally found during endoscopy for work-up of chest discomfort and indigestion. On endoscopic ultrasonography (EUS), the tumor was 2.6×1.9 cm in size, with a cystic lesion located in the submucosal and proper muscle layers and thick cystic wall. Therefore, it was highly likely to be a duplication cyst. As the patient had symptoms such as chest discomfort, surgical resection was performed. The tumor was finally diagnosed as a leiomyoma with cystic degeneration. Leiomyoma with cystic change that has similar EUS features to duplication cyst is rare. Thus, we report the case with a literature review.

OBJECTIVES: The neutrophil gelatinase-associated lipocalin (NGAL) level following non cardiac surgery is useful for predicting acute kidney damage. However, there is insufficient conclusive evidence as to whether NGAL can be used to predict subclinical AKI following non-cardiac surgery. METHODS: We measured serum NGAL and creatinine levels in 41 patients following non-cardiac surgery, and the increase of these variables was used to predict acute decreases in kidney function. RESULTS: The study included a total of 41 patients. The mean age was 64.65 ± 17.09 years. The serum creatinine concentration was increased 12 hours after surgery. The mean SD serum NGAL decreased after 4hours after surgery and continued to decrease after 12 hours after surgery. The incidence of subclinical AKI determined by the 4 hour serum NGAL level was 10(24.4%), and the incidence of serum creatinine elevation was 0(0.0%). The incidence of subclinical AKI determined by the 12 hour serum NGAL level was 4(9.8%), and the incidence of subclinical AKI determined by serum creatinine was 4(9.8%). The elevation of NGAL was more rapid than the serum creatinine 4 hours after surgery. CONCLUSIONS: We verified the usefulness of the serum NGAL level as a predictive factor for subclinical AKI after non-cardiac surgery.
Acute Kidney Injury* ; Creatinine ; Humans ; Incidence ; Kidney ; Lipocalins ; Neutrophils* ; Prognosis* ; Thoracic Surgery

PURPOSE: Absorbable retaining thread (ART) needle localization utilizes a guiding needle with a thread; this technique was invented to reduce patient discomfort and wire migration. We investigated the feasibility of ultrasound (US)-guided ART needle localization for nonpalpable breast lesions. METHODS: ART needle localization was performed for 26 nonpalpable breast lesions in 26 patients who were scheduled to undergo surgical excision the day after localization. Seventeen breast lesions were initially diagnosed as invasive ductal carcinoma, six as ductal carcinomas in situ, and one as fibrocystic change. The other two cases without an initial pathologic diagnosis had suspicious US features, and excision was planned concomitantly with contralateral breast cancer surgery. The primary outcome was the technical success rate of ART needle localization confirmed by US immediately after the procedure, and the secondary outcomes were the percentage of clear margins on pathology and the complication rate of ART needle localization. RESULTS: The technical success rate of ART needle localization was 96.2% (25 of 26 patients), and the ART was located 1 cm away from the mass in one patient (3.8%). The lesions were successfully removed with clear margins in all 26 patients. No significant complications related to ART needle localization were observed. CONCLUSION: ART needle localization can be an alternative to wire needle localization for nonpalpable breast lesions.
Breast Neoplasms ; Breast ; Carcinoma, Ductal ; Diagnosis ; Humans ; Needles ; Pathology ; Surgery, Computer-Assisted ; Ultrasonography

Objectives: This paper proposes a method for computer-assisted diagnosis of coronavirus disease 2019 (COVID-19) through chest X-ray imaging using a deep learning model without writing a single line of code using the Konstanz Information Miner (KNIME) analytics platform. Methods: We obtained 155 samples of posteroanterior chest X-ray images from COVID-19 open dataset repositories to develop a classification model using a simple convolutional neural network (CNN). All of the images contained diagnostic information for COVID-19 and other diseases. The model would classify whether a patient was infected with COVID-19 or not. Eighty percent of the images were used for model training, and the rest were used for testing. The graphic user interface-based programming in the KNIME enabled class label annotation, data preprocessing, CNN model training and testing, performance evaluation, and so on. Results: 1,000 epochs training were performed to test the simple CNN model. The lower and upper bounds of positive predictive value (precision), sensitivity (recall), specificity, and f-measure are 92.3% and 94.4%. Both bounds of the model’s accuracies were equal to 93.5% and 96.6% of the area under the receiver operating characteristic curve for the test set. Conclusions In this study, a researcher who does not have basic knowledge of python programming successfully performed deep learning analysis of chest x-ray image dataset using the KNIME independently. The KNIME will reduce the time spent and lower the threshold for deep learning research applied to healthcare.