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Pathology-MRI Correlation of Hepatocarcinogenesis: Recent Update
Huh, Jimi,Kim, Kyung Won,Kim, Jihun,Yu, Eunsil The Korean Society of Pathologists and the Korean 2015 Journal of Pathology and Translational Medicine Vol.49 No.3
<P>Understanding the important alterations during hepatocarcinogenesis as well as the characteristic magnetic resonance imaging (MRI) and histopathological features will be helpful for managing patients with chronic liver disease and hepatocellular carcinoma. Recent advances in MRI techniques, such as fat/iron quantification, diffusion-weighted images, and gadoxetic acid-enhanced MRI, have greatly enhanced our understanding of hepatocarcinogenesis.</P>
Kyung Won Kim,Jimi Huh,Bushra Urooj,Jeongjin Lee,Jinseok Lee,In-Seob Lee,Hyesun Park,Seongwon Na,Yousun Ko The Korean Gastric Cancer Association 2023 대한위암학회지 Vol.23 No.3
Gastric cancer remains a significant global health concern, coercing the need for advancements in imaging techniques for ensuring accurate diagnosis and effective treatment planning. Artificial intelligence (AI) has emerged as a potent tool for gastric-cancer imaging, particularly for diagnostic imaging and body morphometry. This review article offers a comprehensive overview of the recent developments and applications of AI in gastric cancer imaging. We investigated the role of AI imaging in gastric cancer diagnosis and staging, showcasing its potential to enhance the accuracy and efficiency of these crucial aspects of patient management. Additionally, we explored the application of AI body morphometry specifically for assessing the clinical impact of gastrectomy. This aspect of AI utilization holds significant promise for understanding postoperative changes and optimizing patient outcomes. Furthermore, we examine the current state of AI techniques for the prognosis of patients with gastric cancer. These prognostic models leverage AI algorithms to predict long-term survival outcomes and assist clinicians in making informed treatment decisions. However, the implementation of AI techniques for gastric cancer imaging has several limitations. As AI continues to evolve, we hope to witness the translation of cutting-edge technologies into routine clinical practice, ultimately improving patient care and outcomes in the fight against gastric cancer.
Kyung Won Kim,Jimi Huh,Bushra Urooj,Jeongjin Lee,Jinseok Lee,In-Seob Lee,Hyesun Park,Seongwon Na,Yousun Ko 대한위암학회 2023 Journal of gastric cancer Vol.23 No.3
Gastric cancer remains a significant global health concern, coercing the need for advancements in imaging techniques for ensuring accurate diagnosis and effective treatment planning. Artificial intelligence (AI) has emerged as a potent tool for gastric-cancer imaging, particularly for diagnostic imaging and body morphometry. This review article offers a comprehensive overview of the recent developments and applications of AI in gastric cancer imaging. We investigated the role of AI imaging in gastric cancer diagnosis and staging, showcasing its potential to enhance the accuracy and efficiency of these crucial aspects of patient management. Additionally, we explored the application of AI body morphometry specifically for assessing the clinical impact of gastrectomy. This aspect of AI utilization holds significant promise for understanding postoperative changes and optimizing patient outcomes. Furthermore, we examine the current state of AI techniques for the prognosis of patients with gastric cancer. These prognostic models leverage AI algorithms to predict long-term survival outcomes and assist clinicians in making informed treatment decisions. However, the implementation of AI techniques for gastric cancer imaging has several limitations. As AI continues to evolve, we hope to witness the translation of cutting-edge technologies into routine clinical practice, ultimately improving patient care and outcomes in the fight against gastric cancer.
Lee, Chang Kyung,Seo, Nieun,Kim, Bohyun,Huh, Jimi,Kim, Jeong Kon,Lee, Seung Soo,Kim, In Seong,Nickel, Dominik,Kim, Kyung Won The Korean Society of Radiology 2017 KOREAN JOURNAL OF RADIOLOGY Vol.18 No.2
<P><B>Objective</B></P><P>To compare the breathing effects on dynamic contrast-enhanced (DCE)-MRI between controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA)-volumetric interpolated breath-hold examination (VIBE), radial VIBE with k-space-weighted image contrast view-sharing (radial-VIBE), and conventional VIBE (c-VIBE) sequences using a dedicated phantom experiment.</P><P><B>Materials and Methods</B></P><P>We developed a moving platform to simulate breathing motion. We conducted dynamic scanning on a 3T machine (MAGNETOM Skyra, Siemens Healthcare) using CAIPIRINHA-VIBE, radial-VIBE, and c-VIBE for six minutes per sequence. We acquired MRI images of the phantom in both static and moving modes, and we also obtained motion-corrected images for the motion mode. We compared the signal stability and signal-to-noise ratio (SNR) of each sequence according to motion state and used the coefficients of variation (CoV) to determine the degree of signal stability.</P><P><B>Results</B></P><P>With motion, CAIPIRINHA-VIBE showed the best image quality, and the motion correction aligned the images very well. The CoV (%) of CAIPIRINHA-VIBE in the moving mode (18.65) decreased significantly after the motion correction (2.56) (<I>p</I> < 0.001). In contrast, c-VIBE showed severe breathing motion artifacts that did not improve after motion correction. For radial-VIBE, the position of the phantom in the images did not change during motion, but streak artifacts significantly degraded image quality, also after motion correction. In addition, SNR increased in both CAIPIRINHA-VIBE (from 3.37 to 9.41, <I>p</I> < 0.001) and radial-VIBE (from 4.3 to 4.96, <I>p</I> < 0.001) after motion correction.</P><P><B>Conclusion</B></P><P>CAIPIRINHA-VIBE performed best for free-breathing DCE-MRI after motion correction, with excellent image quality.</P>
Seo, Nieun,Park, Seong J,Kim, Bohyun,Lee, Chang K,Huh, Jimi,Kim, Jeong K,Lee, Seung S,Kim, In S,Nickel, Dominik,Kim, Kyung W British Institute of Radiology 2016 The British journal of radiology Vol.89 No.1066
<P>Advances in knowledge: CAIPIRINHA-VIBE and KWIC-Radial- VIBE provide comparably better quality of freebreathing DCE-MRIs than c-VIBE.</P>
( Bohyun Kim ),( Soon Sun Kim ),( Sung Won Cho ),( Jae Youn Cheong ),( Jimi Huh ),( Jai Keun Kim ),( Jei Hee Lee ),( Hye Ri Ahn ),( Hyo Jung Cho ) 대한간학회 2020 춘·추계 학술대회 (KASL) Vol.2020 No.1
Aims: Liver stiffness (LS) is an emerging imaging-based prognostic biomarker for patients with chronic liver disease. We investigated whether LS quantified using magnetic resonance elastography (MRE) could predict the prognosis of advanced hepatocellular carcinoma (HCC) patients treated with sorafenib. Methods: We selected 50 sorafenib-treated advanced HCC patients who underwent MRE within 3 months before drug administration from a prospectively maintained cohort of chronic liver disease patients, according to the inclusion and exclusion criteria. Univariate and multivariate analyses were performed to evaluate the prognostic role of laboratory data, tumor characteristics, and MRE-assessed LS for overall survival (OS), progression-free survival (PFS), and significant liver injury (≥grade 3) after sorafenib administration. Results: High MRE-assessed LS was significantly associated with poor OS (kPa; hazard ratio [HR], 1.54; 95% confidence interval [CI], 1.23-1.92; P<0.001) as well as higher serum alpha-fetoprotein (AFP, ≥400 ng/mL) and advanced tumor stage (modified Union for International Cancer Control [mUICC] IVb). Higher MRE-assessed LS was also significantly associated with the development of significant liver injury after sorafenib administration (kPa; HR, 1.62; 95% CI, 1.21-2.17; P=0.001). PFS analysis identified higher serum AFP (≥400 ng/mL) and advanced tumor stage (modified UICC IVb) as significant risk factors for early disease progression, whereas LS was not associated with PFS. Conclusions: Higher MRE-assessed LS is a potential biomarker for predicting poor OS and significant liver injury in advanced HCC patients treated with sorafenib.
Hyo Jung Park,Yongbin Shin,Jisuk Park,Hyosang Kim,In Seob Lee,Dong-Woo Seo,Jimi Huh,Tae Young Lee,박태용,Jeongjin Lee,김경원 대한영상의학회 2020 Korean Journal of Radiology Vol.21 No.1
Objective: We aimed to develop and validate a deep learning system for fully automated segmentation of abdominal muscle and fat areas on computed tomography (CT) images. Materials and Methods: A fully convolutional network-based segmentation system was developed using a training dataset of 883 CT scans from 467 subjects. Axial CT images obtained at the inferior endplate level of the 3rd lumbar vertebra were used for the analysis. Manually drawn segmentation maps of the skeletal muscle, visceral fat, and subcutaneous fat were created to serve as ground truth data. The performance of the fully convolutional network-based segmentation system was evaluated using the Dice similarity coefficient and cross-sectional area error, for both a separate internal validation dataset (426 CT scans from 308 subjects) and an external validation dataset (171 CT scans from 171 subjects from two outside hospitals). Results: The mean Dice similarity coefficients for muscle, subcutaneous fat, and visceral fat were high for both the internal (0.96, 0.97, and 0.97, respectively) and external (0.97, 0.97, and 0.97, respectively) validation datasets, while the mean cross-sectional area errors for muscle, subcutaneous fat, and visceral fat were low for both internal (2.1%, 3.8%, and 1.8%, respectively) and external (2.7%, 4.6%, and 2.3%, respectively) validation datasets. Conclusion: The fully convolutional network-based segmentation system exhibited high performance and accuracy in the automatic segmentation of abdominal muscle and fat on CT images.
Cho Hyo Jung,Ahn Young Hwan,Sim Min Suh,은정우,Kim Soon Sun,Kim Bong Wan,Huh Jimi,Lee Jei Hee,Kim Jai Keun,Lee Buil,Cheong Jae Youn,Kim Bohyun 거트앤리버 소화기연관학회협의회 2022 Gut and Liver Vol.16 No.2
Background/Aims: Posthepatectomy liver failure (PHLF) is a major complication that increases mortality in patients with hepatocellular carcinoma after surgical resection. The aim of this retrospective study was to evaluate the utility of magnetic resonance elastography-assessed liver stiffness (MRE-LS) for the prediction of PHLF and to develop an MRE-LS-based risk prediction model. Methods: A total of 160 hepatocellular carcinoma patients who underwent surgical resection with available preoperative MRE-LS data were enrolled. Clinical and laboratory parameters were collected from medical records. Logistic regression analyses were conducted to identify the risk factors for PHLF and develop a risk prediction model. Results: PHLF was present in 24 patients (15%). In the multivariate logistic analysis, high MRE-LS (kPa; odds ratio [OR] 1.49, 95% confidence interval [CI] 1.12 to 1.98, p=0.006), low serum albumin (≤3.8 g/dL; OR 15.89, 95% CI 2.41 to 104.82, p=0.004), major hepatic resection (OR 4.16, 95% CI 1.40 to 12.38, p=0.014), higher albumin-bilirubin score (>–0.55; OR 3.72, 95% CI 1.15 to 12.04, p=0.028), and higher serum α-fetoprotein (>100 ng/mL; OR 3.53, 95% CI 1.20 to 10.40, p=0.022) were identified as independent risk factors for PHLF. A risk prediction model for PHLF was established using the multivariate logistic regression equation. The area under the receiver operating characteristic curve (AUC) of the risk prediction model was 0.877 for predicting PHLF and 0.923 for predicting grade B and C PHLF. In leave-one-out cross-validation, the risk model showed good performance, with AUCs of 0.807 for all-grade PHLF and 0. 871 for grade B and C PHLF. Conclusions: Our novel MRE-LS-based risk model had excellent performance in predicting PHLF, especially grade B and C PHLF.
A Multicenter Study on Hepatocellular Adenomas in Korea: Clinicopathological and Imaging Features
( Subin Heo ),( Bohyun Kim ),( So Yeon Kim ),( Hyo Jeong Kang ),( In Hye Song ),( Sung Hak Lee ),( Jimi Huh ),( Seokhwi Kim ),( Seunghee Baek ),( Seung Soo Lee ),( Sang Hyun Choi ),( Jong Keon Jang ) 대한간학회 2024 춘·추계 학술대회 (The Liver Week) Vol.2024 No.1