Ji-eun Byun

BACKGROUND: There appears to be less agreement in the identification of cancers in the transition zone (TZ), which is not as reliable as those in peripheral zone when using the Prostate Imaging Reporting and Data System (PI-RADS) version 2 (v2). In response to such shortcomings, the updated version 2.1 was introduced, which incorporated diffusion-weighted imaging (DWI) into category 2 and clarified lexicons. PURPOSE: To compare the diagnostic performance for the detection of clinically significa

To establish a diagnostic algorithm for predicting complicated appendicitis in children based on CT and clinical features. Methods: This retrospective study included 315 children (<18 years old) who were diagnosed with acute appendicitis and underwent appendectomy between January 2014 and December 2018. A decision tree algorithm was used to identify important features associated with the condition and to develop a diagnostic algorithm for predicting complicated appendicitis, including CT and

A complementary pattern (CP) for multiple antennas in order to reduce mutual coupling between antenna elements is proposed and investigated. The proposed CP is comprised of meandered transmission lines between antennas on FR4 (ɛr=4.6) substrate. The multiple antennas are a printed embedded type based on composite right/left handed (CRLH) metamaterial transmission lines. In order to validate the CP effects, simulation and measurement results for both the with-CP case and the without-CP case in me

Duodenal gastrointestinal stromal tumors (GISTs) are rare, and studies on their management are not sufficient. Owing to the complex anatomy of the duodenum and pancreatic head, GISTs can be misdiagnosed as pancreatic head tumors. Surgical resection is the first treatment for localized duodenal GISTs; thus, noninvasive imaging is important for the diagnosis and treatment of GISTs. Computed tomography, magnetic resonance imaging and endoscopic ultrasonography findings can be helpful for the diagno

BACKGROUND: Repeated transarterial chemoembolization (TACE) can be associated with loss of its efficacy and subsequent tumor progression. PURPOSE: To identify features of gadoxetic acid-enhanced magnetic resonance imaging (MRI) associated with TACE refractoriness and to develop a prediction model for estimating the risk of TACE refractoriness. MATERIAL AND METHODS: Among 1025 patients with intermediate-stage hepatocellular carcinoma (HCC) who underwent TACE as a first-line treatment during 2010-

The clinical-CT index is more accurate than CTL-S and clinical indices alone for the diagnosis of NAFLD and may be clinically useful in screening for NAFLD.

Background: Post-prostatectomy incontinence (PPI) is a major complication that can significantly decrease quality of life. Approximately 20% of patients experience consistent PPI as long as 1 year after radical prostatectomy (RP). This study develops a preoperative predictive model and compares its diagnostic performance with conventional tools. Methods: A total of 166 prostate cancer patients who underwent magnetic resonance imaging (MRI) and RP were evaluated. According to the date of the RP,

PURPOSE: The purpose of this study is to compare the visibility of microcalcifications of digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) using breast specimens. MATERIALS AND METHODS: Thirty-one specimens' DBT and FFDM were retrospectively reviewed by four readers. RESULTS: The image quality of microcalcifications of DBT was rated as superior or equivalent in 71.0% by reader 1, 67.8% by reader 2, 64.5% by reader 3, and 80.6% by reader 4. The Fleiss kappa statistic f

Background: Approximately 20–50% of prostate cancer patients experience biochemical recurrence (BCR) after radical prostatectomy (RP). Among them, cancer recurrence occurs in about 20–30%. Thus, we aim to reveal the utility of machine learning algorithms for the prediction of early BCR after RP. Methods: A total of 104 prostate cancer patients who underwent magnetic resonance imaging and RP were evaluated. Four well-known machine learning algorithms (i.e., k-nearest neighbors (KNN), multilayer p