Jeong Whun Kim
Seoul National University · Medicine
About the Lab
Professor Jeong Whun Kim's research lab specializes in diagnostic radiology and medical imaging, focusing on improving the accuracy of non-invasive imaging techniques for early detection and characterization of gastrointestinal and abdominal malignancies. The lab investigates advanced cross-sectional imaging modalities—such as CT, MRI, and ultrasound—combined with quantitative imaging biomarkers like diffusion-weighted imaging and radiomics to enhance tumor grading, staging, and tissue characterization. Key research directions include optimizing multi-modal imaging protocols for pancreatic neuroendocrine tumors, gallbladder carcinoma, and salivary gland lesions, with an emphasis on predicting histopathological features and guiding clinical decision-making.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15Background Pancreatic neuroendocrine tumors (PNET) include heterogeneous tumors with a variable degree of inherent biologic aggressiveness represented by the histopathologic grade. Although several studies investigated the computed tomography (CT) characteristics which can predict the histopathologic grade of PNET, accurate prediction of the PNET grade by CT examination alone is still limited. Purpose To investigate the important CT findings and CT texture variables for prediction of grade of PN
PURPOSE: To determine the efficacy of the combined use of magnetic resonance (MR) imaging, MR cholangiography (MRC), and MR angiography (MRA) in the preoperative evaluation of gallbladder carcinoma. MATERIALS AND METHODS: During a 20-month period, 41 patients with proven gallbladder carcinomas were referred for MR examination, including MR imaging, MRC, and gadolinium-enhanced dual-phase MRA to determine the operability of their gallbladder carcinoma. Eighteen patients who underwent surgery with
OBJECTIVE. The purposes of this study were to compare staging accuracy of high-resolution sonography (HRUS) with combined low- and high-MHz transducers with that of conventional sonography for gallbladder cancer and to investigate the differences in the imaging findings of neoplastic and nonneoplastic gallbladder polyps. MATERIALS AND METHODS. Our study included 37 surgically proven gallbladder cancer (T1a = 7, T1b = 2, T2 = 22, T3 = 6), including 15 malignant neoplastic polyps and 73 surgically
Recent advances in computed tomographic (CT) technology, three-dimensional imaging software, and cheaper data storage capacity have made faster, simpler, and more accurate gastric imaging available. Two-dimensional multiplanar reformation and CT gastrography including virtual gastroscopy and transparency rendering allow multiplanar cross-sectional imaging, gastroscopic viewing, and upper gastrointestinal series imaging in the same data acquisition. Multi-detector row CT allows noninvasive assess
BACKGROUND: In this meta-analysis, we compared the risk of obtaining nondiagnostic results and the diagnostic accuracy for detection of salivary gland malignancy between core needle biopsy (CNB) and fine-needle aspiration (FNA). METHODS: All published English-language studies comparing CNB and FNA diagnostic accuracy for salivary gland masses through December 2019 were searched. Pooled risk ratios (RRs) of nondiagnostic results, sensitivities, and specificities of CNB and FNA for salivary gland
Pure diffusion coefficient (D) is possibly a better marker than ADC(total) is for differentiating grade 1 from grade 2 or 3 PNET and, combined with tumor size, can predict grade 1 PNET with a high specificity.
Ultrasonography (US) has been considered image of choice for gallbladder (GB) polyp, however, it had limitations in differentiating between nonneoplastic polyps and neoplastic polyps. We developed and investigated the usefulness of a deep learning-based decision support system (DL-DSS) for the differential diagnosis of GB polyps on US. We retrospectively collected 535 patients, and they were divided into the development dataset (n = 437) and test dataset (n = 98). The binary classification convo
Research Areas
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