Junghoan Park
Overview
Explore the profile of Junghoan Park including associated specialties, affiliations and a list of published articles.
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Articles
23
Citations
162
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0
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Recent Articles
1.
Park J, Kim J, Ryu R, Hwang S
Eur Radiol
. 2025 Feb;
PMID: 39971792
Objectives: To assess significant radiological and clinicopathological risk factors for post-surgery recurrence in patients with intraductal papillary mucinous neoplasm (IPMN). Materials And Methods: Patients with IPMNs who underwent surgery from...
2.
Ryu R, Kim J, Park J, Hwang S
Abdom Radiol (NY)
. 2025 Feb;
PMID: 39948224
Purpose: To assess features of small pancreatic ductal adenocarcinoma (s-PDA, ≤ 2 cm) according to extrapancreatic extension (EPE) and predictors for recurrence. Methods: This retrospective study included patients diagnosed with...
3.
Park J, Joo I, Jeon S, Kim J, Park S, Yoon S
Abdom Radiol (NY)
. 2024 Sep;
50(3):1448-1456.
PMID: 39299987
Purpose: To develop fully-automated abdominal organ segmentation algorithms from non-enhanced abdominal CT and low-dose chest CT and assess their feasibility for automated CT volumetry and 3D radiomics analysis of abdominal...
4.
Park J, Kim J, Bae J, Kang H, Choi S
Eur Radiol
. 2024 Aug;
35(2):700-711.
PMID: 39112752
Objectives: To develop and validate imaging-based models for predicting the malignancy risk of intraductal papillary mucinous neoplasm (IPMN). Materials And Methods: We retrospectively analyzed data from 241 IPMN patients who...
5.
Han S, Kim J, Park J, Kim S, Park S, Cho J, et al.
Sci Rep
. 2024 Jul;
14(1):17635.
PMID: 39085456
Our objective was to develop and evaluate the clinical feasibility of deep-learning-based synthetic contrast-enhanced computed tomography (DL-SynCCT) in patients designated for nonenhanced CT (NECT). We proposed a weakly supervised learning...
6.
Jeon S, Joo I, Park J, Yoo J
Radiol Med
. 2024 Jun;
129(7):967-976.
PMID: 38869829
Purpose: To evaluate the efficacy of volumetric CT attenuation-based parameters obtained through automated 3D organ segmentation on virtual non-contrast (VNC) images from dual-energy CT (DECT) for assessing hepatic steatosis. Materials...
7.
Yoo J, Joo I, Jeon S, Park J, Yoon S
Eur Radiol
. 2024 Feb;
34(9):6205-6213.
PMID: 38393403
Objectives: To investigate the clinical utility of fully-automated 3D organ segmentation in assessing hepatic steatosis on pre-contrast and post-contrast CT images using magnetic resonance spectroscopy (MRS)-proton density fat fraction (PDFF)...
8.
Jeon S, Joo I, Park J, Kim J, Park S, Yoon S
Sci Rep
. 2024 Feb;
14(1):4378.
PMID: 38388824
A novel 3D nnU-Net-based of algorithm was developed for fully-automated multi-organ segmentation in abdominal CT, applicable to both non-contrast and post-contrast images. The algorithm was trained using dual-energy CT (DECT)-obtained...
9.
Kim J, Yoon J, Kim S, Park J, Bae S, Lee J
Abdom Radiol (NY)
. 2023 Dec;
49(3):738-747.
PMID: 38095685
Purpose: To evaluate the efficacy of a vendor-specific deep learning reconstruction algorithm (DLRA) in enhancing image quality and focal lesion detection using three-dimensional T1-weighted gradient-echo images in gadoxetic acid-enhanced liver...
10.
Park J, Bae J, Kim J, Witanto J, Park S, Lee J
Abdom Radiol (NY)
. 2023 May;
48(8):2547-2556.
PMID: 37222771
Purpose: Liver Imaging Reporting and Data System (LI-RADS) is limited by interreader variability. Thus, our study aimed to develop a deep-learning model for classifying LI-RADS major features using subtraction images...