Hyoung Jin Kim
Seoul National University · 医学
研究室紹介
Professor Hyoung Jin Kim's research lab specializes in radiological and medical imaging innovation, focusing on advancing diagnostic accuracy and prognostic modeling in thoracic diseases. The lab integrates deep learning and radiomics with multidetector CT to improve the assessment of lung cancer, particularly adenocarcinoma and subsolid nodules, by extracting quantitative imaging features from preoperative scans. Research also emphasizes optimizing image reconstruction techniques to reduce variability and enhance diagnostic reliability, while exploring the clinical utility of advanced MRI sequences such as reduced field-of-view diffusion-weighted imaging. The lab's work bridges medical imaging, artificial intelligence, and clinical oncology to support personalized patient management.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15Background Recent studies have suggested that chest CT scans could be used as a primary screening or diagnostic tool for coronavirus disease 2019 (COVID-19) in epidemic areas. Purpose To perform a meta-analysis to evaluate diagnostic performance measures, including predictive values of chest CT and initial reverse transcriptase polymerase chain reaction (RT-PCR). Materials and Methods Medline and Embase were searched from January 1, 2020, to April 3, 2020, for studies on COVID-19 that reported t
PURPOSE: To identify the impact of reconstruction algorithms on CT radiomic features of pulmonary tumors and to reveal and compare the intra- and inter-reader and inter-reconstruction algorithm variability of each feature. METHODS: Forty-two patients (M:F = 19:23; mean age, 60.43±10.56 years) with 42 pulmonary tumors (22.56±8.51mm) underwent contrast-enhanced CT scans, which were reconstructed with filtered back projection and commercial iterative reconstruction algorithm (level 3 and 5). Two re
Background Deep learning models have the potential for lung cancer prognostication, but model output as an independent prognostic factor must be validated with clinical risk factors. Purpose To develop and validate a preoperative CT-based deep learning model for predicting disease-free survival in patients with lung adenocarcinoma. Materials and Methods In this retrospective study, a deep learning model was trained to extract prognostic information from preoperative CT examinations. Data set 1 f
Mass measurement of GGNs showed measurement variability from -17.7% to 18.6% and may be a useful method in the follow-up of GGNs with solid portions less than or equal to 5 mm.
Pulmonary subsolid nodules (SSNs) refer to pulmonary nodules with pure ground-glass nodules and part-solid ground-glass nodules. SSNs are frequently encountered in the clinical setting, such as screening chest computed tomography (CT). The main concern regarding pulmonary SSNs, particularly when they are persistent, has been lung adenocarcinoma and its precursors. The CT manifestations of SSNs help radiologists and clinicians manage these lesions. However, the management plan for SSNs has not pr
Reduced FOV DWI of the pancreas provides better overall IQ including better anatomic detail, lesion conspicuity and subjective clinical utility.
BackgroundPathologic visceral pleural invasion (pVPI) leads to upstaging from T1 to T2. However, it is unclear whether the CT features for pVPI can be reliably used as a clinical T2 descriptor for preoperative staging.PurposeTo validate the diagnostic accuracy and analyze the prognostic value of CT findings for the prediction of pVPI in patients with resected node-negative lung adenocarcinoma.Materials and MethodsThis retrospective cohort study included clinical T1N0M0 adenocarcinomas resected b
Background Low-dose CT screening for lung cancer in a tuberculosis-endemic country may be less effective because of false-positive results caused by tuberculosis sequelae. Purpose To evaluate the impact of tuberculosis sequelae at CT screening according to the American College of Radiology Lung CT Screening Reporting and Data System (Lung-RADS) using data from the Korean Lung Cancer Screening Project (K-LUCAS). Materials and Methods This is a secondary analysis of K-LUCAS (ClinicalTrials.gov ide
Tumor diameter has traditionally been used as a standard metric in terms of diagnosis and prognosis prediction of lung cancer. However, recent advances in imaging techniques and data analyses have enabled novel quantitative imaging biomarkers that can characterize disease status more comprehensively and/or predict tumor behavior more precisely. The most widely used imaging modality for lung tumor assessment is computed tomography. Therefore, we focused on computed tomography imaging biomarkers s
Cumulative histogram analysis of nCBV using 3 T MRI can be a useful method for preoperative glioma grading. The nCBV C99 value is helpful in distinguishing high- from low-grade gliomas and grade IV from III gliomas.