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Zhou S, Zhou G, Shen Y, Xia T, Zhao B, Sun Z, Gao L, Li B, Wang W, Zhang S, Opara NC, Chen X, Ju S, Wang YC. LI-RADS Nonradiation Treatment Response Algorithm Version 2024: Diagnostic Performance and Impact of Ancillary Features. AJR Am J Roentgenol 2025; 224:e2432035. [PMID: 39535775 DOI: 10.2214/ajr.24.32035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2024]
Abstract
BACKGROUND. LI-RADS Treatment Response Algorithm (TRA) version 2024 (v2024) introduced separate algorithms for detecting hepatocellular carcinoma (HCC) viability after radiation and nonradiation locoregional therapies (LRTs). The nonradiation algorithm incorporated MRI-based ancillary features to optionally upgrade lesions from LR-TR Equivocal to LR-TR Viable. OBJECTIVE. The purpose of this study was to compare the diagnostic performance of LI-RADS Nonradiation TRA v2024 with that of LI-RADS TRA version 2017 (v2017) and modified RECIST (mRECIST) for evaluating HCC response to LRT on MRI, with attention given to the impact of ancillary features. METHODS. This retrospective study included 231 patients (198 men and 33 women; median age, 56 years) who underwent LRT for HCC followed by liver resection or transplant between January 2017 and December 2022. Two radiologists (reader 1 and reader 2) independently evaluated treated lesions (n = 306) using LI-RADS Nonradiation TRA v2024, LI-RADS TRA v2017, and mRECIST. Lesions were classified as showing pathologic viability (n = 249) or complete pathologic necrosis (n = 57) based on curative surgery pathology. The diagnostic performance for pathologic viability was compared using Bonferroni-adjusted McNemar tests, with LR-TR Equivocal assessments classified as test negative. RESULTS. The sensitivity, specificity, and accuracy for LI-RADS Nonradiation TRA v2024 with ancillary features were 85.5%, 75.4%, and 83.7%, respectively, for reader 1 and 87.2%, 63.2%, and 82.7%, respectively, for reader 2; for LI-RADS Nonradiation TRA v2024 without ancillary features, they were 81.1%, 78.9%, and 80.7%, respectively, for reader 1 and 80.3%, 78.9%, and 80.1%, respectively, for reader 2; for LI-RADS TRA v2017, they were 79.9%, 82.5%, and 80.4%, respectively, for reader 1 and 79.1%, 79.0%, and 79.1%, respectively, for reader 2; and for mRECIST, they were 83.9%, 54.4%, and 78.4%, respectively, for reader 1 and 87.2%, 40.4%, and 78.4%, respectively, for reader 2. LI-RADS Nonradiation TRA v2024 with ancillary features showed higher sensitivity and accuracy than LI-RADS Nonradiation v2024 without ancillary features (both readers), higher sensitivity than LI-RADS TRA v2017 (both readers), higher specificity than mRECIST (both readers), and higher accuracy than LI-RADS TRA v2017 (reader 2) (p < .008); remaining comparisons between LI-RADS Nonradiation TRA v2024 with ancillary features and other systems were not significant (p > .008). CONCLUSION. LI-RADS Nonradiation TRA v2024 showed good diagnostic performance in detecting pathologic viability. Ancillary features yielded improved sensitivity and accuracy without a significant change in specificity. CLINICAL IMPACT. Use of LI-RADS Nonradiation TRA v2024 with ancillary features is recommended for guiding prognostic assessments and treatment decisions after LRT.
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Affiliation(s)
- Shuwei Zhou
- Department of Radiology, Zhongda Hospital, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, School of Medicine, Southeast University, 87 Dingjiaqiao Rd, Gulou District, Nanjing 210009, China
| | - Guofeng Zhou
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Institute of Medical Imaging, Shanghai, China
| | - Yang Shen
- Department of Radiology, The Peoples Hospital of Xuyi County, Huaian, China
| | - Tianyi Xia
- Department of Radiology, Zhongda Hospital, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, School of Medicine, Southeast University, 87 Dingjiaqiao Rd, Gulou District, Nanjing 210009, China
| | - Ben Zhao
- Department of Radiology, Zhongda Hospital, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, School of Medicine, Southeast University, 87 Dingjiaqiao Rd, Gulou District, Nanjing 210009, China
| | - Ziying Sun
- Department of Radiology, Zhongda Hospital, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, School of Medicine, Southeast University, 87 Dingjiaqiao Rd, Gulou District, Nanjing 210009, China
| | - Lei Gao
- Department of Radiology, Zhongda Hospital, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, School of Medicine, Southeast University, 87 Dingjiaqiao Rd, Gulou District, Nanjing 210009, China
| | - Binrong Li
- Department of Radiology, Zhongda Hospital, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, School of Medicine, Southeast University, 87 Dingjiaqiao Rd, Gulou District, Nanjing 210009, China
| | - Weilang Wang
- Department of Radiology, Zhongda Hospital, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, School of Medicine, Southeast University, 87 Dingjiaqiao Rd, Gulou District, Nanjing 210009, China
| | - Shuhang Zhang
- Department of Radiology, Zhongda Hospital, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, School of Medicine, Southeast University, 87 Dingjiaqiao Rd, Gulou District, Nanjing 210009, China
| | - Noble C Opara
- Department of Radiology, Zhongda Hospital, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, School of Medicine, Southeast University, 87 Dingjiaqiao Rd, Gulou District, Nanjing 210009, China
| | - Xunjun Chen
- Department of Radiology, The Peoples Hospital of Xuyi County, Huaian, China
| | - Shenghong Ju
- Department of Radiology, Zhongda Hospital, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, School of Medicine, Southeast University, 87 Dingjiaqiao Rd, Gulou District, Nanjing 210009, China
| | - Yuan-Cheng Wang
- Department of Radiology, Zhongda Hospital, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, School of Medicine, Southeast University, 87 Dingjiaqiao Rd, Gulou District, Nanjing 210009, China
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Khurana A, Chai N, Gibson A, Owen J, Sobieh A, Hawk G, Lee J. Association of LR treatment response category with outcome of patients with hepatocellular carcinoma on explant pathology. Abdom Radiol (NY) 2025:10.1007/s00261-025-04811-4. [PMID: 39863701 DOI: 10.1007/s00261-025-04811-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2024] [Revised: 01/15/2025] [Accepted: 01/17/2025] [Indexed: 01/27/2025]
Abstract
OBJECTIVES Liver transplant (LT) is an effective treatment for hepatocellular carcinoma (HCC) in appropriately selected patients. Locoregional therapy (LRT) is often performed to extend a patient's eligibility for LT. Imaging has a modest sensitivity of approximately 40-77% for detecting pathologically viable HCC in post-LRT patients. The impact on overall survival (OS) and disease-free survival (DFS) is unclear. We hypothesize that Liver Imaging Reporting & Data Systems Treatment Response (LI-RADS TR) category is equivalently correlated with long-term survival and overall disease-free progression when compared to explant pathology findings. We additionally hypothesize that neoadjuvant LRT can improve OS and DFS in LT patients initially within MC. METHODS Patients found to have HCC on explant between January 2005 and December 2021 were included. A total of 167 patients were divided into treatment (any pre-LT LRT except for Y-90 therapy) and control (no pre-LT LRT) groups. Of the patients who received pre-LT LRT, imaging studies were reviewed by two abdominal radiologists using 2018 LI-RADS criteria. Statistical analysis was performed using Kaplan-Meier survival curves and Cox proportional hazard models to assess OS and DFS. RESULTS No statistically significant difference in OS or DFS (p = 0.23 and p = 0.22 respectively) was initially found. Given significant difference in age between the groups (p < 0.0001), Cox proportional hazard models were used to adjust for age with statistical significance reached for better OS and DFS in the treatment group (p = 0.05 and p = 0.05 respectively). Contrary to our hypothesis, there was no difference between treatment response groups regarding overall survival or disease-free survival, presumably because of low number of HCC recurrences in our patient population (4%). CONCLUSION Despite not reaching statistical significance, LI-RADS TR categorization demonstrates a good interreader agreement (Kappa 0.6), helping radiologists feel comfortable that modest sensitivity of the LI-RADS TR treatment response category for detecting pathologically active malignancy does not confer a negative clinical outcome.
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Affiliation(s)
| | | | | | | | | | | | - James Lee
- University of Kentucky, Lexington, USA
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Wang D, Zhang Y, Lyu R, Jia K, Xu PJ. LI-RADS version 2018 treatment response algorithm on extracellular contrast-enhanced MRI in patients treated with transarterial chemoembolization for hepatocellular carcinoma: diagnostic performance and the added value of ancillary features. Abdom Radiol (NY) 2024; 49:3045-3055. [PMID: 38605217 DOI: 10.1007/s00261-024-04275-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 03/03/2024] [Accepted: 03/03/2024] [Indexed: 04/13/2024]
Abstract
BACKGROUND The Liver Imaging Reporting and Data System (LI-RADS) Treatment Response Algorithm (TRA) (LI-RADS TRA) is used for assessing response of HCC to locoregional therapy (LRT), however, the value of ancillary features (AFs) for TACE-treated HCCs has not been extensively investigated on extracellular agent MRI (ECA-MRI). PURPOSE To evaluate the diagnostic performance of LI-RADS v2018 TRA on ECA-MRI for HCC treated with transarterial chemoembolization (TACE) and the value of ancillary features. METHODS This retrospective study included patients who underwent TACE for HCC and then followed by hepatic surgery between January 2019 and June 2023 with both pre- and post-TACE contrast-enhanced MRI available. Two radiologists independently evaluated the post-treated lesions on MRI using LI-RADS treatment response (TR) (LR-TR) algorithm and modified LR-TR (mLR-TR) algorithm in which ancillary features (restricted diffusion and intermediate T2-weighted hyperintensity) were added, respectively. Lesions were categorized as complete pathologic necrosis (100%, CPN) and non-complete pathologic necrosis (< 100%, non-CPN) on the basis of surgical pathology. The diagnostic performance in predicting viable and non-viable tumors based on LR-TR and mLR-TR algorithms was compared using the McNemar test. Interreader agreement was calculated by using Cohen's weighted and unweighted κ. RESULTS A total of 61 patients [mean age 59 years ± 10 (standard deviation); 47 men] with 79 lesions (57 pathologically viable) were included. For non-CPN prediction, the sensitivity, specificity of LR-TR viable and mLR-TR viable category were 75% (43 of 57), 82% (18 of 22) and 88% (50 of 57), 77% (17 of 22), respectively, the sensitivity of mLR-TR was significantly higher than that of LR-TR (P = 0.016) without difference in specificity (P = 1.000). Interreader agreement for LR-TR and mLR-TR category was moderate (k = 0.50, 95% confidence interval 0.33, 0.67, k = 0.42, 95% confidence interval 0.20, 0.63). The sensitivity of both LR-TR and mLR-TR algorithms in predicting viable tumors between conventional TACE (cTACE) and drug-eluting beads TACE (DEB-TACE) did not have significant difference (cTACE: 76%, 89% vs. DEB-TACE: 73%, 82%). CONCLUSIONS On ECA-MRI, applying ancillary features to LI-RADS v2018 TRA can improve the sensitivity in predicting pathologic tumor viability in patients treated with TACE for hepatocellular carcinoma with no significant difference in specificity.
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Affiliation(s)
- Di Wang
- Department of Radiology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, Tianjin Institute of Hepatobiliary Disease, No. 83 Jintang Road, Hedong District, Tianjin, 300170, China
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Yang Zhang
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
- Department of Radiology, Dongying People's Hospital Shandong, Dongying, China
| | - Rong Lyu
- Department of Radiology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, Tianjin Institute of Hepatobiliary Disease, No. 83 Jintang Road, Hedong District, Tianjin, 300170, China
| | - Kefeng Jia
- Department of Radiology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, Tianjin Institute of Hepatobiliary Disease, No. 83 Jintang Road, Hedong District, Tianjin, 300170, China
| | - Peng-Ju Xu
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China.
- Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China.
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Patel R, Aslam A, Parikh ND, Mervak B, Mubarak E, Higgins L, Lala K, Conner JF, Khaykin V, Bashir M, Do RKG, Burke LMB, Smith EN, Kim CY, Shampain KL, Owen D, Mendiratta-Lala M. Updates on LI-RADS Treatment Response Criteria for Hepatocellular Carcinoma: Focusing on MRI. J Magn Reson Imaging 2023; 57:1641-1654. [PMID: 36872608 PMCID: PMC11078141 DOI: 10.1002/jmri.28659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 02/08/2023] [Accepted: 02/09/2023] [Indexed: 03/07/2023] Open
Abstract
As the incidence of hepatocellular carcinoma (HCC) and subsequent treatments with liver-directed therapies rise, the complexity of assessing lesion response has also increased. The Liver Imaging Reporting and Data Systems (LI-RADS) treatment response algorithm (LI-RADS TRA) was created to standardize the assessment of response after locoregional therapy (LRT) on contrast-enhanced CT or MRI. Originally created based on expert opinion, these guidelines are currently undergoing revision based on emerging evidence. While many studies support the use of LR-TRA for evaluation of HCC response after thermal ablation and intra-arterial embolic therapy, data suggest a need for refinements to improve assessment after radiation therapy. In this manuscript, we review expected MR imaging findings after different forms of LRT, clarify how to apply the current LI-RADS TRA by type of LRT, explore emerging literature on LI-RADS TRA, and highlight future updates to the algorithm. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Richa Patel
- Department of Radiology, Stanford, California, USA
| | - Anum Aslam
- Department of Radiology, University of Michigan Medicine, Ann Arbor, Michigan, USA
| | - Neehar D Parikh
- Department of Internal Medicine, University of Michigan Medicine, Ann Arbor, Michigan, USA
| | - Benjamin Mervak
- Department of Radiology, University of Michigan Medicine, Ann Arbor, Michigan, USA
| | - Eman Mubarak
- Department of Radiology, University of Michigan Medicine, Ann Arbor, Michigan, USA
| | - Lily Higgins
- Department of Radiology, University of Michigan Medicine, Ann Arbor, Michigan, USA
| | - Kayli Lala
- Department of Radiology, University of Michigan Medicine, Ann Arbor, Michigan, USA
| | - Jack F Conner
- Department of Radiology, University of Toledo Medical Center, Toledo, Ohio, USA
| | - Valerie Khaykin
- Department of Radiology and Hepatology, University of Michigan Medicine, Michigan, USA
| | - Mustafa Bashir
- Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Richard Kinh Gian Do
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Lauren M B Burke
- Department of Radiology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina, USA
| | - Elainea N Smith
- Department of Radiology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Charles Y Kim
- Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Kimberly L Shampain
- Department of Radiology, University of Michigan Medicine, Ann Arbor, Michigan, USA
| | - Dawn Owen
- Department of Radiation Oncology, Mayo Clinic Rochester, Rochester, Minnesota, USA
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Ippolito D, Maino C, Gatti M, Marra P, Faletti R, Cortese F, Inchingolo R, Sironi S. Radiological findings in non-surgical recurrent hepatocellular carcinoma: From locoregional treatments to immunotherapy. World J Gastroenterol 2023; 29:1669-1684. [PMID: 37077517 PMCID: PMC10107213 DOI: 10.3748/wjg.v29.i11.1669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/10/2023] [Accepted: 03/02/2023] [Indexed: 03/17/2023] Open
Abstract
Since hepatocellular carcinoma (HCC) represents an important cause of mortality and morbidity all over the world. Currently, it is fundamental not only to achieve a curative treatment but also to manage in the best way any possible recurrence. Even if the latest update of the Barcelona Clinic Liver Cancer guidelines for HCC treatment has introduced new locoregional techniques and confirmed others as well-established clinical practices, there is still no consensus about the treatment of recurrent HCC (RHCC). Locoregional treatments and medical therapy represent two of the most widely accepted approaches for disease control, especially in the advanced stage of liver disease. Different medical treatments are now approved, and others are under investigation. On this basis, radiology plays a central role in the diagnosis of RHCC and the assessment of response to locoregional treatments and medical therapy for RHCC. This review summarized the actual clinical practice by underlining the importance of the radiological approach both in the diagnosis and treatment of RHCC.
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Affiliation(s)
- Davide Ippolito
- Department of Radiology, IRCCS San Gerardo dei Tintori, Monza 20900, Italy
- School of Medicine and Surgery, University of Milano-Bicocca, Milano 20121, Italy
| | - Cesare Maino
- Department of Radiology, IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Marco Gatti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Paolo Marra
- Department of Diagnostic and Interventional Radiology, Papa Giovanni XXIII Hospital, Bergamo 24127, Italy
| | - Riccardo Faletti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Francesco Cortese
- Interventional Radiology Unit, “F. Miulli” Regional General Hospital, Bari 70121, Italy
| | - Riccardo Inchingolo
- Interventional Radiology Unit, “F. Miulli” Regional General Hospital, Bari 70121, Italy
| | - Sandro Sironi
- School of Medicine and Surgery, University of Milano-Bicocca, Milano 20121, Italy
- Department of Diagnostic and Interventional Radiology, Papa Giovanni XXIII Hospital, Bergamo 24127, Italy
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Kim DH, Kim B, Choi JI, Oh SN, Rha SE. LI-RADS Treatment Response versus Modified RECIST for Diagnosing Viable Hepatocellular Carcinoma after Locoregional Therapy: A Systematic Review and Meta-Analysis of Comparative Studies. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2022; 83:331-343. [PMID: 36237934 PMCID: PMC9514432 DOI: 10.3348/jksr.2021.0173] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 12/19/2021] [Accepted: 01/12/2022] [Indexed: 11/23/2022]
Abstract
Purpose To systematically compare the performance of liver imaging reporting and data system treatment response (LR-TR) with the modified Response Evaluation Criteria in Solid Tumors (mRECIST) for diagnosing viable hepatocellular carcinoma (HCC) treated with locoregional therapy (LRT). Materials and Methods Original studies of intra-individual comparisons between the diagnostic performance of LR-TR and mRECIST using dynamic contrast-enhanced CT or MRI were searched in MEDLINE and EMBASE, up to August 25, 2021. The reference standard for tumor viability was surgical pathology. The meta-analytic pooled sensitivity and specificity of the viable category using each criterion were calculated using a bivariate random-effects model and compared using bivariate meta-regression. Results For five eligible studies (430 patients with 631 treated observations), the pooled per-lesion sensitivities and specificities were 58% (95% confidence interval [CI], 45%–70%) and 93% (95% CI, 88%–96%) for the LR-TR viable category and 56% (95% CI, 42%–69%) and 86% (95% CI, 72%–94%) for the mRECIST viable category, respectively. The LR-TR viable category provided significantly higher pooled specificity (p < 0.01) than the mRECIST but comparable pooled sensitivity (p = 0.53). Conclusion The LR-TR algorithm demonstrated better specificity than mRECIST, without a significant difference in sensitivity for the diagnosis of pathologically viable HCC after LRT.
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Affiliation(s)
- Dong Hwan Kim
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Bohyun Kim
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Joon-Il Choi
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Soon Nam Oh
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Sung Eun Rha
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
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