1.Deep Learning-Accelerated Non-Contrast Abbreviated Liver MRI for Detecting Malignant Focal Hepatic Lesions: Dual-Center Validation
So Hyun PARK ; Moon Hyung CHOI ; Bohyun KIM ; Hyun-Soo LEE ; Sungjin YOON ; Young Joon LEE ; Dominik NICKEL ; Thomas BENKERT
Korean Journal of Radiology 2025;26(4):333-345
Objective:
To compare a deep learning (DL)-accelerated non-enhanced abbreviated MRI (AMRI DL) protocol with standard AMRI (AMRI STD) of the liver in terms of image quality and malignant focal lesion detection.
Materials and Methods:
This retrospective study included 155 consecutive patients (110 male; mean age 62.4 ± 11 years) from two sites who underwent standard liver MRI and additional AMRIDL sequences, specifically DL-accelerated single-shot fast-spin echo (SSFSE DL) and DL-accelerated diffusion-weighted imaging (DWIDL). Additional MRI phantom experiments assessed signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) values. Three reviewers evaluated AMRIDL and AMRI STD protocols for image quality using a five-point Likert scale and identified malignant hepatic lesions. Image quality scores and per-lesion sensitivities were compared between AMRIDL and AMRI STD using the Wilcoxon signed-rank test and logistic regression with generalized estimating equations, respectively.
Results:
Phantom experiments demonstrated comparable SNR and higher CNR for SSFSE DL compared to SSFSE STD, with similar ADC values for DWIDL and DWI STD. Among the 155 patients, 130 (83.9%) had chronic liver disease or a history of intra- or extrahepatic malignancy. Of 104 malignant focal lesions in 64 patients, 58 (55.8%) were hepatocellular carcinomas (HCCs), 38 (36.5%) were metastases, four (3.8%) were cholangiocarcinomas, and four (3.8%) were lymphomas. The pooled per-lesion sensitivity across three readers was 97.6% for AMRIDL, comparable to 97.6% for AMRI STD. Compared with AMRI STD, AMRIDL demonstrated superior image quality regarding structural sharpness, artifacts, and noise (all P < 0.001) and reduced the average scan time by approximately 50% (2 min 29 sec vs. 4 min 11 sec). In patients with chronic liver disease, AMRIDL achieved a 96.6% per-lesion sensitivity for HCC detection, similar to 96.5% for AMRI STD (P > 0.05).
Conclusion
The AMRIDL protocol offers comparable sensitivity for detecting malignant focal lesions, including HCC while significantly enhancing image quality and reducing scan time by approximately 50% compared to AMRI STD.
2.Radiofrequency Ablation for Recurrent Thyroid Cancers:2025 Korean Society of Thyroid Radiology Guideline
Eun Ju HA ; Min Kyoung LEE ; Jung Hwan BAEK ; Hyun Kyung LIM ; Hye Shin AHN ; Seon Mi BAEK ; Yoon Jung CHOI ; Sae Rom CHUNG ; Ji-hoon KIM ; Jae Ho SHIN ; Ji Ye LEE ; Min Ji HONG ; Hyun Jin KIM ; Leehi JOO ; Soo Yeon HAHN ; So Lyung JUNG ; Chang Yoon LEE ; Jeong Hyun LEE ; Young Hen LEE ; Jeong Seon PARK ; Jung Hee SHIN ; Jin Yong SUNG ; Miyoung CHOI ; Dong Gyu NA ;
Korean Journal of Radiology 2025;26(1):10-28
Radiofrequency ablation (RFA) is a minimally invasive treatment modality used as an alternative to surgery in patients with benign thyroid nodules, recurrent thyroid cancers (RTCs), and primary thyroid microcarcinomas. The Korean Society of Thyroid Radiology (KSThR) initially developed recommendations for the optimal use of RFA for thyroid tumors in 2009 and revised them in 2012 and 2017. As new meaningful evidence has accumulated since 2017 and in response to a growing global interest in the use of RFA for treating malignant thyroid lesions, the task force committee members of the KSThR decided to update the guidelines on the use of RFA for the management of RTCs based on a comprehensive analysis of current literature and expert consensus.
3.Establishment of Local Diagnostic Reference Levels for Pediatric Neck CT at Nine University Hospitals in South Korea
Jisun HWANG ; Hee Mang YOON ; Jae-Yeon HWANG ; Young Hun CHOI ; Yun Young LEE ; So Mi LEE ; Young Jin RYU ; Sun Kyoung YOU ; Ji Eun PARK ; Seok Kee LEE
Korean Journal of Radiology 2025;26(1):65-74
Objective:
To establish local diagnostic reference levels (DRLs) for pediatric neck CT based on age, weight, and water-equivalent diameter (WED) across multiple university hospitals in South Korea.
Materials and Methods:
This retrospective study analyzed pediatric neck CT examinations from nine university hospitals, involving patients aged 0–18 years. Data were categorized by age, weight, and WED, and radiation dose metrics, including volume CT dose index (CTDIvol) and dose length product, were recorded. Data retrieval and analysis were conducted using a commercially available dose-management system (Radimetrics, Bayer Healthcare). Local DRLs were established following the International Commission on Radiological Protection guidelines, using the 75th percentile as the reference value.
Results:
A total of 1159 CT examinations were analyzed, including 169 scans from Institution 1, 132 from Institution 2, 126 from Institution 3, 129 from Institution 4, 128 from Institution 5, 105 from Institution 6, 162 from Institution 7, 127 from Institution 8, and 81 from Institution 9. Radiation dose metrics increased with age, weight, and WED, showing significant variability both within and across institutions. For patients weighing less than 10 kg, the DRL for CTDIvol was 5.2 mGy. In the 10–19 kg group, the DRL was 5.8 mGy; in the 20–39 kg group, 7.6 mGy; in the 40–59 kg group, 11.0 mGy; and for patients weighing 60 kg or more, 16.2 mGy. DRLs for CTDIvol by age groups were as follows: 5.3 mGy for infants under 1 year, 5.7 mGy for children aged 1–4 years, 7.6 mGy for ages 5–9 years, 11.2 mGy for ages 10–14 years, and 15.6 mGy for patients 15 years or older.
Conclusion
Local DRLs for pediatric neck CT were established based on age, weight, and WED across nine university hospitals in South Korea.
4.Deep Learning-Accelerated Non-Contrast Abbreviated Liver MRI for Detecting Malignant Focal Hepatic Lesions: Dual-Center Validation
So Hyun PARK ; Moon Hyung CHOI ; Bohyun KIM ; Hyun-Soo LEE ; Sungjin YOON ; Young Joon LEE ; Dominik NICKEL ; Thomas BENKERT
Korean Journal of Radiology 2025;26(4):333-345
Objective:
To compare a deep learning (DL)-accelerated non-enhanced abbreviated MRI (AMRI DL) protocol with standard AMRI (AMRI STD) of the liver in terms of image quality and malignant focal lesion detection.
Materials and Methods:
This retrospective study included 155 consecutive patients (110 male; mean age 62.4 ± 11 years) from two sites who underwent standard liver MRI and additional AMRIDL sequences, specifically DL-accelerated single-shot fast-spin echo (SSFSE DL) and DL-accelerated diffusion-weighted imaging (DWIDL). Additional MRI phantom experiments assessed signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) values. Three reviewers evaluated AMRIDL and AMRI STD protocols for image quality using a five-point Likert scale and identified malignant hepatic lesions. Image quality scores and per-lesion sensitivities were compared between AMRIDL and AMRI STD using the Wilcoxon signed-rank test and logistic regression with generalized estimating equations, respectively.
Results:
Phantom experiments demonstrated comparable SNR and higher CNR for SSFSE DL compared to SSFSE STD, with similar ADC values for DWIDL and DWI STD. Among the 155 patients, 130 (83.9%) had chronic liver disease or a history of intra- or extrahepatic malignancy. Of 104 malignant focal lesions in 64 patients, 58 (55.8%) were hepatocellular carcinomas (HCCs), 38 (36.5%) were metastases, four (3.8%) were cholangiocarcinomas, and four (3.8%) were lymphomas. The pooled per-lesion sensitivity across three readers was 97.6% for AMRIDL, comparable to 97.6% for AMRI STD. Compared with AMRI STD, AMRIDL demonstrated superior image quality regarding structural sharpness, artifacts, and noise (all P < 0.001) and reduced the average scan time by approximately 50% (2 min 29 sec vs. 4 min 11 sec). In patients with chronic liver disease, AMRIDL achieved a 96.6% per-lesion sensitivity for HCC detection, similar to 96.5% for AMRI STD (P > 0.05).
Conclusion
The AMRIDL protocol offers comparable sensitivity for detecting malignant focal lesions, including HCC while significantly enhancing image quality and reducing scan time by approximately 50% compared to AMRI STD.
5.Radiofrequency Ablation for Recurrent Thyroid Cancers:2025 Korean Society of Thyroid Radiology Guideline
Eun Ju HA ; Min Kyoung LEE ; Jung Hwan BAEK ; Hyun Kyung LIM ; Hye Shin AHN ; Seon Mi BAEK ; Yoon Jung CHOI ; Sae Rom CHUNG ; Ji-hoon KIM ; Jae Ho SHIN ; Ji Ye LEE ; Min Ji HONG ; Hyun Jin KIM ; Leehi JOO ; Soo Yeon HAHN ; So Lyung JUNG ; Chang Yoon LEE ; Jeong Hyun LEE ; Young Hen LEE ; Jeong Seon PARK ; Jung Hee SHIN ; Jin Yong SUNG ; Miyoung CHOI ; Dong Gyu NA ;
Korean Journal of Radiology 2025;26(1):10-28
Radiofrequency ablation (RFA) is a minimally invasive treatment modality used as an alternative to surgery in patients with benign thyroid nodules, recurrent thyroid cancers (RTCs), and primary thyroid microcarcinomas. The Korean Society of Thyroid Radiology (KSThR) initially developed recommendations for the optimal use of RFA for thyroid tumors in 2009 and revised them in 2012 and 2017. As new meaningful evidence has accumulated since 2017 and in response to a growing global interest in the use of RFA for treating malignant thyroid lesions, the task force committee members of the KSThR decided to update the guidelines on the use of RFA for the management of RTCs based on a comprehensive analysis of current literature and expert consensus.
6.Establishment of Local Diagnostic Reference Levels for Pediatric Neck CT at Nine University Hospitals in South Korea
Jisun HWANG ; Hee Mang YOON ; Jae-Yeon HWANG ; Young Hun CHOI ; Yun Young LEE ; So Mi LEE ; Young Jin RYU ; Sun Kyoung YOU ; Ji Eun PARK ; Seok Kee LEE
Korean Journal of Radiology 2025;26(1):65-74
Objective:
To establish local diagnostic reference levels (DRLs) for pediatric neck CT based on age, weight, and water-equivalent diameter (WED) across multiple university hospitals in South Korea.
Materials and Methods:
This retrospective study analyzed pediatric neck CT examinations from nine university hospitals, involving patients aged 0–18 years. Data were categorized by age, weight, and WED, and radiation dose metrics, including volume CT dose index (CTDIvol) and dose length product, were recorded. Data retrieval and analysis were conducted using a commercially available dose-management system (Radimetrics, Bayer Healthcare). Local DRLs were established following the International Commission on Radiological Protection guidelines, using the 75th percentile as the reference value.
Results:
A total of 1159 CT examinations were analyzed, including 169 scans from Institution 1, 132 from Institution 2, 126 from Institution 3, 129 from Institution 4, 128 from Institution 5, 105 from Institution 6, 162 from Institution 7, 127 from Institution 8, and 81 from Institution 9. Radiation dose metrics increased with age, weight, and WED, showing significant variability both within and across institutions. For patients weighing less than 10 kg, the DRL for CTDIvol was 5.2 mGy. In the 10–19 kg group, the DRL was 5.8 mGy; in the 20–39 kg group, 7.6 mGy; in the 40–59 kg group, 11.0 mGy; and for patients weighing 60 kg or more, 16.2 mGy. DRLs for CTDIvol by age groups were as follows: 5.3 mGy for infants under 1 year, 5.7 mGy for children aged 1–4 years, 7.6 mGy for ages 5–9 years, 11.2 mGy for ages 10–14 years, and 15.6 mGy for patients 15 years or older.
Conclusion
Local DRLs for pediatric neck CT were established based on age, weight, and WED across nine university hospitals in South Korea.
8.Development of automatic organ segmentation based on positron-emission tomography analysis system using Swin UNETR in breast cancer patients in Korea
Dong Hyeok CHOI ; Joonil HWANG ; Hai-Jeon YOON ; So Hyun AHN
The Ewha Medical Journal 2025;48(2):e30-
Purpose:
The standardized uptake value (SUV) is a key quantitative index in nuclear medicine imaging; however, variations in region‐of‐interest (ROI) determination exist across institutions. This study aims to standardize SUV evaluation by introducing a deep learning‐based quantitative analysis method that enhances diagnostic and prognostic accuracy.
Methods:
We used the Swin UNETR model to automatically segment key organs (breast, liver, spleen, and bone marrow) critical for breast cancer prognosis. Tumor segmentation was performed iteratively based on predefined SUV thresholds, and prognostic information was extracted from the liver, spleen, and bone marrow (reticuloendothelial system). The artificial intelligence training process employed 3 datasets: a test dataset (40 patients), a validation dataset (10 patients), and an independent test dataset (10 patients). To validate our approach, we compared the SUV values obtained using our method with those produced by commercial software.
Results:
In a dataset of 10 patients, our method achieved an auto‐segmentation accuracy of 0.9311 for all target organs. Comparison of maximum SUV and mean SUV values from our automated segmentation with those from traditional single‐ROI methods revealed differences of 0.19 and 0.16, respectively, demonstrating improved reliability and accuracy in whole‐organ SUV analysis.
Conclusion
This study successfully standardized SUV calculation in nuclear medicine imaging through deep learning‐based automated organ segmentation and SUV analysis, significantly enhancing accuracy in predicting breast cancer prognosis.
9.Harnessing Institutionally Developed Clinical Targeted Sequencing to Improve Patient Survival in Breast Cancer: A Seven-Year Experience
Jiwon KOH ; Jinyong KIM ; Go-Un WOO ; Hanbaek YI ; So Yean KWON ; Jeongmin SEO ; Jeong Mo BAE ; Jung Ho KIM ; Jae Kyung WON ; Han Suk RYU ; Yoon Kyung JEON ; Dae-Won LEE ; Miso KIM ; Tae-Yong KIM ; Kyung-Hun LEE ; Tae-You KIM ; Jee-Soo LEE ; Moon-Woo SEONG ; Sheehyun KIM ; Sungyoung LEE ; Hongseok YUN ; Myung Geun SONG ; Jaeyong CHOI ; Jong-Il KIM ; Seock-Ah IM
Cancer Research and Treatment 2025;57(2):443-456
Purpose:
Considering the high disease burden and unique features of Asian patients with breast cancer (BC), it is essential to have a comprehensive view of genetic characteristics in this population. An institutional targeted sequencing platform was developed through the Korea Research-Driven Hospitals project and was incorporated into clinical practice. This study explores the use of targeted next-generation sequencing (NGS) and its outcomes in patients with advanced/metastatic BC in the real world.
Materials and Methods:
We reviewed the results of NGS tests administered to BC patients using a customized sequencing platform—FiRST Cancer Panel (FCP)—over 7 years. We systematically described clinical translation of FCP for precise diagnostics, personalized therapeutic strategies, and unraveling disease pathogenesis.
Results:
NGS tests were conducted on 548 samples from 522 patients with BC. Ninety-seven point six percentage of tested samples harbored at least one pathogenic alteration. The common alterations included mutations in TP53 (56.2%), PIK3CA (31.2%), GATA3 (13.8%), BRCA2 (10.2%), and amplifications of CCND1 (10.8%), FGF19 (10.0%), and ERBB2 (9.5%). NGS analysis of ERBB2 amplification correlated well with human epidermal growth factor receptor 2 immunohistochemistry and in situ hybridization. RNA panel analyses found potentially actionable and prognostic fusion genes. FCP effectively screened for potentially germline pathogenic/likely pathogenic mutation. Ten point three percent of BC patients received matched therapy guided by NGS, resulting in a significant overall survival advantage (p=0.022), especially for metastatic BCs.
Conclusion
Clinical NGS provided multifaceted benefits, deepening our understanding of the disease, improving diagnostic precision, and paving the way for targeted therapies. The concrete advantages of FCP highlight the importance of multi-gene testing for BC, especially for metastatic conditions.

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