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Lee S, Kim YY, Shin J, Shin H, Sirlin CB, Chernyak V. Performance of LI-RADS category 5 vs combined categories 4 and 5: a systemic review and meta-analysis. Eur Radiol 2024:10.1007/s00330-024-10813-5. [PMID: 38809263 DOI: 10.1007/s00330-024-10813-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 04/10/2024] [Accepted: 04/16/2024] [Indexed: 05/30/2024]
Abstract
OBJECTIVE Computed tomography (CT)/magnetic resonance imaging (MRI) Liver Imaging Reporting and Data System (LI-RADS, LR) category 5 has high specificity and modest sensitivity for diagnosis of hepatocellular carcinoma (HCC). The purpose of this study was to compare the diagnostic performance of LR-5 vs combined LR-4 and LR-5 (LR-4/5) for HCC diagnosis. METHODS MEDLINE and EMBASE databases through January 03, 2023 were searched for studies reporting the performance of LR-5 and combined LR-4/5 for HCC diagnosis, using CT/MRI LI-RADS version 2014, 2017, or 2018. A bivariate random-effects model was used to calculate the pooled per-observation diagnostic performance. Subgroup analysis was performed based on imaging modalities and type of MRI contrast material. RESULTS Sixty-nine studies (15,108 observations, 9928 (65.7%) HCCs) were included. Compared to LR-5, combined LR-4/5 showed significantly higher pooled sensitivity (83.0% (95% CI [80.3-85.8%]) vs 65.7% (95% CI [62.4-69.1%]); p < 0.001), lower pooled specificity (75.0% (95% CI [70.5-79.6%]) vs 91.7% (95% CI [90.2-93.1%]); p < 0.001), lower pooled positive likelihood ratio (3.60 (95% CI [3.06-4.23]) vs 6.18 (95% CI [5.35-7.14]); p < 0.001), and lower pooled negative likelihood ratio (0.22 (95% CI [0.19-0.25]) vs 0.38 (95% CI [0.35-0.41]) vs; p < 0.001). Similar results were seen in all subgroups. CONCLUSIONS Our meta-analysis showed that combining LR-4 and LR-5 would increase sensitivity but decrease specificity, positive likelihood ratio, and negative likelihood ratio. These findings may inform management guidelines and individualized management. CLINICAL RELEVANCE STATEMENT This meta-analysis estimated the magnitude of changes in the sensitivity and specificity of imaging criteria when LI-RADS categories 4 and 5 were combined; these findings can inform management guidelines and individualized management. KEY POINTS There is no single worldwide reporting system for liver imaging, partly due to regional needs. Combining LI-RADS categories 4 and 5 increased sensitivity and decreased specificity and positive and negative likelihood ratios. Changes in the sensitivity and specificity of imaging criteria can inform management guidelines and individualized management.
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Affiliation(s)
- Sunyoung Lee
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Yeun-Yoon Kim
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jaeseung Shin
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hyejung Shin
- Biostatistics Collaboration Unit, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Claude B Sirlin
- Liver Imaging Group, Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Victoria Chernyak
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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Guo D, Wan W, Bai X, Wen R, Peng J, Lin P, Liao W, Huang W, Liu D, Peng Y, Kang T, Yang H, He Y. Intra-individual comparison of Sonazoid contrast-enhanced ultrasound and SonoVue contrast-enhanced ultrasound in diagnosing hepatocellular carcinoma. Abdom Radiol (NY) 2024; 49:1432-1443. [PMID: 38584190 DOI: 10.1007/s00261-024-04250-7] [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: 11/10/2023] [Revised: 02/10/2024] [Accepted: 02/13/2024] [Indexed: 04/09/2024]
Abstract
PURPOSE To assess whether the diagnostic performance of Sonazoid contrast-enhanced ultrasound (SZUS) is non-inferior to that of SonoVue contrast-enhanced ultrasound (SVUS) in diagnosing hepatocellular carcinoma (HCC) in individuals with high risk. MATERIALS AND METHODS This prospective study was conducted from October 2020 to May 2022 and included participants with a high risk of HCC who underwent SZUS and SVUS. All lesions were confirmed by clinical or pathological diagnosis. Each nodule was classified according to the Contrast-Enhanced Ultrasound Liver Imaging Reporting and Data System version 2017 (CEUS LI-RADS v2017) for SVUS and SZUS and the modified CEUS LI-RADS (using Kupffer phase defect instead of late and mild washout) for SZUS. The diagnostic performance of both two modalities for all observations was compared. Analysis of the vascular phase and Kupffer phase imaging characteristics of CEUS was performed. RESULTS One hundred and fifteen focal liver lesions from 113 patients (94 HCCs, 12 non-HCC malignancies, and 9 benign lesions) were analysed. According to CEUS LI-RADS (v2017), SVUS and SZUS showed similar sensitivity (71.3% vs. 72.3%) and specificity (85.7% vs. 81.0%) in HCC diagnosis. However, the modified CEUS LI-RADS did not significantly improve the diagnostic efficacy of Sonazoid compared to CEUS LI-RADS v2017, having equivalent sensitivity (73.4% vs. 72.3%) and specificity (81.0% vs. 81.0%). The agreement between SVUS and SZUS for all observations was 0.610 (95% CI 0.475, 0.745), while for HCCs it was 0.452 (95% CI 0.257, 0.647). CONCLUSION Using LI-RADS v2017, SZUS and SVUS showed non-inferior efficacy in evaluating HCC lesions. In addition, adding Kupffer phase defects to SZUS does not notably improve its diagnostic efficacy.
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Affiliation(s)
- Danxia Guo
- Department of Medical Ultrasound, First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Guangxi Zhuang Autonomous Region, Nanning, 530021, China
| | - Weijun Wan
- Department of Medical Ultrasound, First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Guangxi Zhuang Autonomous Region, Nanning, 530021, China
| | - Xiumei Bai
- Department of Medical Ultrasound, First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Guangxi Zhuang Autonomous Region, Nanning, 530021, China
| | - Rong Wen
- Department of Medical Ultrasound, First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Guangxi Zhuang Autonomous Region, Nanning, 530021, China
| | - Jinbo Peng
- Department of Medical Ultrasound, First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Guangxi Zhuang Autonomous Region, Nanning, 530021, China
| | - Peng Lin
- Department of Medical Ultrasound, First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Guangxi Zhuang Autonomous Region, Nanning, 530021, China
| | - Wei Liao
- Department of Medical Ultrasound, First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Guangxi Zhuang Autonomous Region, Nanning, 530021, China
| | - Weiche Huang
- Department of Medical Ultrasound, First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Guangxi Zhuang Autonomous Region, Nanning, 530021, China
| | - Dun Liu
- Department of Medical Ultrasound, First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Guangxi Zhuang Autonomous Region, Nanning, 530021, China
| | - Yuye Peng
- Department of Medical Ultrasound, First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Guangxi Zhuang Autonomous Region, Nanning, 530021, China
| | - Tong Kang
- Department of Medical Ultrasound, First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Guangxi Zhuang Autonomous Region, Nanning, 530021, China
| | - Hong Yang
- Department of Medical Ultrasound, First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Guangxi Zhuang Autonomous Region, Nanning, 530021, China
| | - Yun He
- Department of Medical Ultrasound, First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Guangxi Zhuang Autonomous Region, Nanning, 530021, China.
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Zhang R, Li D, Chen Y, Xu W, Zhou W, Lin M, Xie X, Xu M. Development and Comparison of Prediction Models Based on Sonovue- and Sonazoid-Enhanced Ultrasound for Pathologic Grade and Microvascular Invasion in Hepatocellular Carcinoma. ULTRASOUND IN MEDICINE & BIOLOGY 2024; 50:414-424. [PMID: 38155069 DOI: 10.1016/j.ultrasmedbio.2023.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Revised: 10/31/2023] [Accepted: 12/01/2023] [Indexed: 12/30/2023]
Abstract
OBJECTIVE This study was aimed at developing and comparing prediction models based on Sonovue and Sonazoid contrast-enhanced ultrasound (CEUS) in predicting pathologic grade and microvascular invasion (MVI) of hepatocellular carcinoma (HCC). Also investigated was whether Kupffer phase images have additional predictive value for the above pathologic features. METHODS Ninety patients diagnosed with primary HCC who had undergone curative hepatectomy were prospectively enrolled. All patients underwent conventional ultrasound (CUS), Sonovue-CEUS and Sonazoid-CEUS examinations pre-operatively. Clinical, radiologic and pathologic features including pathologic grade, MVI and CD68 expression were collected. We developed prediction models comprising clinical, CUS and CEUS (Sonovue and Sonazoid, respectively) features for pathologic grade and MVI with both the logistic regression and machine learning (ML) methods. RESULTS Forty-one patients (45.6%) had poorly differentiated HCC (p-HCC) and 37 (41.1%) were MVI positive. For pathologic grade, the logistic model based on Sonazoid-CEUS had significantly better performance than that based on Sonovue-CEUS (area under the curve [AUC], 0.929 vs. 0.848, p = 0.035), whereas for MVI, these two models had similar accuracy (AUC, 0.810 vs. 0.786, p = 0.068). Meanwhile, we found that well-differentiated HCC tended to have a higher enhancement ratio in 6-12 min during the Kupffer phase of Sonazoid-CEUS, as well as higher CD68 expression compared with p-HCC. In addition, all of these models can effectively predict the risk of recurrence (p < 0.05). CONCLUSION Sonovue-CEUS and Sonazoid-CEUS were comparably excellent in predicting MVI, while Sonazoid-CEUS was superior to Sonovue-CEUS in predicting pathologic grade because of the Kupffer phase. The enhancement ratio in the Kupffer phase has additional predictive value for pathologic grade prediction.
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Affiliation(s)
- Rui Zhang
- Department of Medical Ultrasound, Division of Interventional Ultrasound, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Di Li
- Department of Medical Ultrasound, Division of Interventional Ultrasound, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yanlin Chen
- Department of Medical Ultrasound, Division of Interventional Ultrasound, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Wenxin Xu
- Department of Medical Ultrasound, Division of Interventional Ultrasound, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Wenwen Zhou
- Department of Medical Ultrasound, Division of Interventional Ultrasound, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Manxia Lin
- Department of Medical Ultrasound, Division of Interventional Ultrasound, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiaoyan Xie
- Department of Medical Ultrasound, Division of Interventional Ultrasound, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Ming Xu
- Department of Medical Ultrasound, Division of Interventional Ultrasound, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
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Liao W, Que Q, Wen R, Lin P, Chen Y, Pang J, Guo D, Wen D, Yang H, He Y. Comparison of the Feasibility and Diagnostic Performance of ACR CEUS LI-RADS and a Modified CEUS LI-RADS for HCC in Examinations Using Sonazoid. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2023; 42:2501-2511. [PMID: 37269244 DOI: 10.1002/jum.16282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 03/07/2023] [Accepted: 05/02/2023] [Indexed: 06/05/2023]
Abstract
OBJECTIVES The present study aimed to determine the feasibility of the American College of Radiology's (ACR) contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS) (version 2017) in examinations using Sonazoid and compare its diagnostic performance with that of modified LI-RADS in patients at high risk of hepatocellular carcinoma (HCC). METHODS This retrospective study's sample population consisted of 137 participants with a total of 140 nodules who underwent CEUS with Sonazoid and pathological confirmation via surgery or biopsy from January 2020 to February 2022. The lesions were evaluated and classified based on the reference standards (ie, ACR CEUS LI-RADS and modified LI-RADS). The overall diagnostic capabilities of the two systems were evaluated in terms of accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with 95% confidence intervals (CIs). RESULTS The participants had a median age of 51 years and an interquartile range of 43-58 years. Regarding LR-5 as a predictor of HCC, the accuracy results of the ACR LI-RADS and modified LI-RADS algorithms were 72.9 and 71.4%, respectively (P = .50). The sensitivity of both systems was the same (69.7%; 95% CI: 60.7-77.8%). Regarding LR-M as a predictor of non-HCC malignancy, the diagnostic performance of the algorithms was the same, with accuracy and sensitivity results of 76.4 and 73.3%, respectively (95% CI: 44.9-92.2%). CONCLUSION The findings indicate that modified LI-RADS had a moderate level of diagnostic performance for HCC in examinations using Sonazoid, which was comparable to ACR LI-RADS.
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Affiliation(s)
- Wei Liao
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Qiao Que
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Rong Wen
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Peng Lin
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yuji Chen
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jinshu Pang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Danxia Guo
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Dongyue Wen
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Hong Yang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
- Guangxi Zhuang Autonomous Region Engineering Research Center for Artificial Intelligence Analysis of Multimodal Tumor Images, Nanning, China
| | - Yun He
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
- Guangxi Zhuang Autonomous Region Engineering Research Center for Artificial Intelligence Analysis of Multimodal Tumor Images, Nanning, China
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Hirooka M. Liver imaging reporting and data with perfluorobutane microbubbles-Is there hope? Hepatol Res 2022; 52:663-664. [PMID: 35930327 DOI: 10.1111/hepr.13803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Masashi Hirooka
- Department of Gastroenterology and Metabology, Ehime University Graduate School of Medicine, Touon, Ehime, Japan
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De Muzio F, Grassi F, Dell’Aversana F, Fusco R, Danti G, Flammia F, Chiti G, Valeri T, Agostini A, Palumbo P, Bruno F, Cutolo C, Grassi R, Simonetti I, Giovagnoni A, Miele V, Barile A, Granata V. A Narrative Review on LI-RADS Algorithm in Liver Tumors: Prospects and Pitfalls. Diagnostics (Basel) 2022; 12:diagnostics12071655. [PMID: 35885561 PMCID: PMC9319674 DOI: 10.3390/diagnostics12071655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 06/27/2022] [Accepted: 07/05/2022] [Indexed: 11/16/2022] Open
Abstract
Liver cancer is the sixth most detected tumor and the third leading cause of tumor death worldwide. Hepatocellular carcinoma (HCC) is the most common primary liver malignancy with specific risk factors and a targeted population. Imaging plays a major role in the management of HCC from screening to post-therapy follow-up. In order to optimize the diagnostic-therapeutic management and using a universal report, which allows more effective communication among the multidisciplinary team, several classification systems have been proposed over time, and LI-RADS is the most utilized. Currently, LI-RADS comprises four algorithms addressing screening and surveillance, diagnosis on computed tomography (CT)/magnetic resonance imaging (MRI), diagnosis on contrast-enhanced ultrasound (CEUS) and treatment response on CT/MRI. The algorithm allows guiding the radiologist through a stepwise process of assigning a category to a liver observation, recognizing both major and ancillary features. This process allows for characterizing liver lesions and assessing treatment. In this review, we highlighted both major and ancillary features that could define HCC. The distinctive dynamic vascular pattern of arterial hyperenhancement followed by washout in the portal-venous phase is the key hallmark of HCC, with a specificity value close to 100%. However, the sensitivity value of these combined criteria is inadequate. Recent evidence has proven that liver-specific contrast could be an important tool not only in increasing sensitivity but also in diagnosis as a major criterion. Although LI-RADS emerges as an essential instrument to support the management of liver tumors, still many improvements are needed to overcome the current limitations. In particular, features that may clearly distinguish HCC from cholangiocarcinoma (CCA) and combined HCC-CCA lesions and the assessment after locoregional radiation-based therapy are still fields of research.
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Affiliation(s)
- Federica De Muzio
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100 Campobasso, Italy;
| | - Francesca Grassi
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 81100 Naples, Italy; (F.G.); (F.D.); (R.G.)
| | - Federica Dell’Aversana
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 81100 Naples, Italy; (F.G.); (F.D.); (R.G.)
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy
- Correspondence:
| | - Ginevra Danti
- Division of Radiology, Azienda Ospedaliera Universitaria Careggi, 50134 Florence, Italy; (G.D.); (F.F.); (G.C.); (V.M.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (P.P.); (F.B.)
| | - Federica Flammia
- Division of Radiology, Azienda Ospedaliera Universitaria Careggi, 50134 Florence, Italy; (G.D.); (F.F.); (G.C.); (V.M.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (P.P.); (F.B.)
| | - Giuditta Chiti
- Division of Radiology, Azienda Ospedaliera Universitaria Careggi, 50134 Florence, Italy; (G.D.); (F.F.); (G.C.); (V.M.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (P.P.); (F.B.)
| | - Tommaso Valeri
- Department of Clinical Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy; (T.V.); (A.A.); (A.G.)
- Department of Radiological Sciences, University Hospital Ospedali Riuniti, Via Tronto 10/a, 60126 Torrette, Italy
| | - Andrea Agostini
- Department of Clinical Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy; (T.V.); (A.A.); (A.G.)
- Department of Radiological Sciences, University Hospital Ospedali Riuniti, Via Tronto 10/a, 60126 Torrette, Italy
| | - Pierpaolo Palumbo
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (P.P.); (F.B.)
- Area of Cardiovascular and Interventional Imaging, Department of Diagnostic Imaging, Abruzzo Health Unit 1, 67100 L’Aquila, Italy
| | - Federico Bruno
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (P.P.); (F.B.)
- Emergency Radiology, San Salvatore Hospital, Via Lorenzo Natali 1, 67100 L’Aquila, Italy;
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84084 Fisciano, Italy;
| | - Roberta Grassi
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 81100 Naples, Italy; (F.G.); (F.D.); (R.G.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (P.P.); (F.B.)
| | - Igino Simonetti
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, Via Mariano Semmola, 80131 Naples, Italy; (I.S.); (V.G.)
| | - Andrea Giovagnoni
- Department of Clinical Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy; (T.V.); (A.A.); (A.G.)
- Department of Radiological Sciences, University Hospital Ospedali Riuniti, Via Tronto 10/a, 60126 Torrette, Italy
| | - Vittorio Miele
- Division of Radiology, Azienda Ospedaliera Universitaria Careggi, 50134 Florence, Italy; (G.D.); (F.F.); (G.C.); (V.M.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (P.P.); (F.B.)
| | - Antonio Barile
- Emergency Radiology, San Salvatore Hospital, Via Lorenzo Natali 1, 67100 L’Aquila, Italy;
| | - Vincenza Granata
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, Via Mariano Semmola, 80131 Naples, Italy; (I.S.); (V.G.)
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