The role of iodinated contrast media in computed tomography structured Reporting and Data Systems (RADS): a narrative review.

Computed tomography (CT) Reporting & Data Systems (RADS) iodinated contrast media (ICM) radiology

Journal

Quantitative imaging in medicine and surgery
ISSN: 2223-4292
Titre abrégé: Quant Imaging Med Surg
Pays: China
ID NLM: 101577942

Informations de publication

Date de publication:
01 Nov 2023
Historique:
received: 02 05 2023
accepted: 31 07 2023
medline: 16 11 2023
pubmed: 16 11 2023
entrez: 16 11 2023
Statut: ppublish

Résumé

In recent years, there has been a large-scale dissemination of guidelines in radiology in the form of Reporting & Data Systems (RADS). The use of iodinated contrast media (ICM) has a fundamental role in enhancing the diagnostic capabilities of computed tomography (CT) but poses certain risks. The scope of the present review is to summarize the current role of ICM only in clinical reporting guidelines for CT that have adopted the "RADS" approach, focusing on three specific questions per each RADS: (I) what is the scope of the scoring system; (II) how is ICM used in the scoring system; (III) what is the impact of ICM enhancement on the scoring. We analyzed the original articles for each of the latest versions of RADS that can be used in CT [PubMed articles between January, 2005 and March, 2023 in English and American College of Radiology (ACR) official website]. We found 14 RADS suitable for use in CT out of 28 RADS described in the literature. Four RADS were validated by the ACR: Colonography-RADS (C-RADS), Liver Imaging-RADS (LI-RADS), Lung CT Screening-RADS (Lung-RADS), and Neck Imaging-RADS (NI-RADS). One RADS was validated by the ACR in collaboration with other cardiovascular scientific societies: Coronary Artery Disease-RADS 2.0 (CAD-RADS). Nine RADS were proposed by other scientific groups: Bone Tumor Imaging-RADS (BTI-RADS), Bone‑RADS, Coronary Artery Calcium Data & Reporting System (CAC-DRS), Coronavirus Disease 2019 Imaging-RADS (COVID-RADS), COVID-19-RADS (CO-RADS), Interstitial Lung Fibrosis Imaging-RADS (ILF-RADS), Lung-RADS (LU-RADS), Node-RADS, and Viral Pneumonia Imaging-RADS (VP-RADS). This overview suggests that ICM is not strictly necessary for the study of bones and calcifications (CAC-DRS, BTI-RADS, Bone-RADS), lung parenchyma (Lung-RADS, LU-RADS, COVID-RADS, CO-RADS, VP-RADS and ILF-RADS), and in CT colonography (C-RADS). On the other hand, ICM plays a key role in CT angiography (CAD-RADS), in the study of liver parenchyma (LI-RADS), and in the evaluation of soft tissues and lymph nodes (NI-RADS, Node-RADS). Future studies are needed in order to evaluate the impact of the new iodinated and non-iodinate contrast media, artificial intelligence tools and dual energy CT in the assignment of RADS scores.

Sections du résumé

Background and Objective UNASSIGNED
In recent years, there has been a large-scale dissemination of guidelines in radiology in the form of Reporting & Data Systems (RADS). The use of iodinated contrast media (ICM) has a fundamental role in enhancing the diagnostic capabilities of computed tomography (CT) but poses certain risks. The scope of the present review is to summarize the current role of ICM only in clinical reporting guidelines for CT that have adopted the "RADS" approach, focusing on three specific questions per each RADS: (I) what is the scope of the scoring system; (II) how is ICM used in the scoring system; (III) what is the impact of ICM enhancement on the scoring.
Methods UNASSIGNED
We analyzed the original articles for each of the latest versions of RADS that can be used in CT [PubMed articles between January, 2005 and March, 2023 in English and American College of Radiology (ACR) official website].
Key Content and Findings UNASSIGNED
We found 14 RADS suitable for use in CT out of 28 RADS described in the literature. Four RADS were validated by the ACR: Colonography-RADS (C-RADS), Liver Imaging-RADS (LI-RADS), Lung CT Screening-RADS (Lung-RADS), and Neck Imaging-RADS (NI-RADS). One RADS was validated by the ACR in collaboration with other cardiovascular scientific societies: Coronary Artery Disease-RADS 2.0 (CAD-RADS). Nine RADS were proposed by other scientific groups: Bone Tumor Imaging-RADS (BTI-RADS), Bone‑RADS, Coronary Artery Calcium Data & Reporting System (CAC-DRS), Coronavirus Disease 2019 Imaging-RADS (COVID-RADS), COVID-19-RADS (CO-RADS), Interstitial Lung Fibrosis Imaging-RADS (ILF-RADS), Lung-RADS (LU-RADS), Node-RADS, and Viral Pneumonia Imaging-RADS (VP-RADS).
Conclusions UNASSIGNED
This overview suggests that ICM is not strictly necessary for the study of bones and calcifications (CAC-DRS, BTI-RADS, Bone-RADS), lung parenchyma (Lung-RADS, LU-RADS, COVID-RADS, CO-RADS, VP-RADS and ILF-RADS), and in CT colonography (C-RADS). On the other hand, ICM plays a key role in CT angiography (CAD-RADS), in the study of liver parenchyma (LI-RADS), and in the evaluation of soft tissues and lymph nodes (NI-RADS, Node-RADS). Future studies are needed in order to evaluate the impact of the new iodinated and non-iodinate contrast media, artificial intelligence tools and dual energy CT in the assignment of RADS scores.

Identifiants

pubmed: 37969632
doi: 10.21037/qims-23-603
pii: qims-13-11-7621
pmc: PMC10644138
doi:

Types de publication

Journal Article Review

Langues

eng

Pagination

7621-7631

Informations de copyright

2023 Quantitative Imaging in Medicine and Surgery. All rights reserved.

Déclaration de conflit d'intérêts

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-23-603/coif). CAM serves as an unpaid editorial board member of Quantitative Imaging in Medicine and Surgery. The other authors have no conflicts of interest to declare.

Références

Eur Radiol. 2021 Aug;31(8):6116-6124
pubmed: 33585994
Invest Radiol. 2020 Sep;55(9):598-600
pubmed: 32452883
Quant Imaging Med Surg. 2020 Jul;10(7):1428-1440
pubmed: 32676362
J Am Coll Radiol. 2016 Aug;13(8):931-5
pubmed: 27260486
Insights Imaging. 2018 Feb;9(1):1-7
pubmed: 29460129
Invest Radiol. 2020 Sep;55(9):592-597
pubmed: 32701620
Acta Radiol. 2015 May;56(5):581-6
pubmed: 24895062
Radiographics. 2019 Sep-Oct;39(5):1435-1436
pubmed: 31498744
Radiology. 2020 Aug;296(2):E97-E104
pubmed: 32339082
Diagn Interv Imaging. 2022 Jun;103(6):316-323
pubmed: 35090845
Can Assoc Radiol J. 2014 May;65(2):121-34
pubmed: 24758919
Int J Comput Assist Radiol Surg. 2023 Oct;18(10):1903-1914
pubmed: 36947337
Vasc Endovascular Surg. 2022 Jun 10;:15385744221108040
pubmed: 35688795
Jpn J Radiol. 2022 Jun;40(6):547-559
pubmed: 34981319
Atherosclerosis. 2020 Feb;294:25-32
pubmed: 31945615
J Cardiovasc Comput Tomogr. 2022 Nov-Dec;16(6):536-557
pubmed: 35864070
Acta Radiol. 2015 Jun;56(6):702-8
pubmed: 24938661
J Am Coll Radiol. 2018 Aug;15(8):1097-1108
pubmed: 29983244
Eur Radiol. 2022 Apr;32(4):2837-2854
pubmed: 34652520
Eur J Radiol. 2022 May;150:110251
pubmed: 35303556
Quant Imaging Med Surg. 2020 Feb;10(2):537-540
pubmed: 32190581
Skeletal Radiol. 2022 Sep;51(9):1743-1764
pubmed: 35344076
Quant Imaging Med Surg. 2021 Sep;11(9):4028-4041
pubmed: 34476187
Quant Imaging Med Surg. 2021 Feb;11(2):876-878
pubmed: 33532287
Radiology. 2005 Jul;236(1):3-9
pubmed: 15987959
Acta Radiol. 2010 Nov;51(9):1014-20
pubmed: 20849319
AJNR Am J Neuroradiol. 2020 Jun;41(6):944-946
pubmed: 32381539
Emerg Radiol. 2020 Apr;27(2):115-126
pubmed: 31925592
Eur Radiol. 2020 Sep;30(9):4930-4942
pubmed: 32346790
Radiology. 2015 Sep;276(3):637-53
pubmed: 26302388
Eur Radiol. 2021 Oct;31(10):7637-7652
pubmed: 33765161
Radiography (Lond). 2023 Jan;29(1):8-13
pubmed: 36179410
J Vasc Surg Venous Lymphat Disord. 2023 May;11(3):659-660
pubmed: 37080689
Indian J Otolaryngol Head Neck Surg. 2023 Sep;75(3):2257-2259
pubmed: 37636734
J Cardiovasc Comput Tomogr. 2018 May - Jun;12(3):185-191
pubmed: 29793848
Quant Imaging Med Surg. 2020 Sep;10(9):1891-1893
pubmed: 32879867
J Comput Assist Tomogr. 2020 Sep/Oct;44(5):656-666
pubmed: 32842067
Quant Imaging Med Surg. 2021 Jun;11(6):2486-2498
pubmed: 34079718
J Cardiovasc Comput Tomogr. 2021 Nov-Dec;15(6):470-476
pubmed: 34127407

Auteurs

Marco Parillo (M)

Fondazione Policlinico Universitario Campus Bio-Medico, Roma, Italy.
Research Unit of Diagnostic Imaging and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Roma, Italy.

Aart J van der Molen (AJ)

Department of Radiology, C-2S, Leiden University Medical Center, Leiden, The Netherlands.

Patrick Asbach (P)

Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Department of Radiology, Campus Benjamin Franklin, Berlin, Germany.

Fabian Henry Jürgen Elsholtz (FHJ)

Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Department of Radiology, Campus Benjamin Franklin, Berlin, Germany.

Andrea Laghi (A)

Department of Medical Surgical Sciences and Translational Medicine, Faculty of Medicine and Psychology-Sapienza University of Rome, Roma, Italy.

Maxime Ronot (M)

Service de Radiologie, Hôpital Beaujon, AP-HP Nord, Clichy, France.
Université Paris Cité, CRI UMR1148, Paris, France.

Jim S Wu (JS)

Department of Radiology, Beth Israel Deaconess Medical Center, Boston, MA, USA.

Carlo Augusto Mallio (CA)

Fondazione Policlinico Universitario Campus Bio-Medico, Roma, Italy.
Research Unit of Diagnostic Imaging and Interventional Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Roma, Italy.

Carlo Cosimo Quattrocchi (CC)

Centre for Medical Sciences-CISMed, University of Trento, Trento, Italy.

Classifications MeSH