Evidence map›Paper›PMID 37218935›Full record

ReviewTomography (Ann Arbor, Mich.)2023

Computed Tomography Urography: State of the Art and Beyond.

Michaela Cellina, Maurizio Cè, Nicolo' Rossini, Laura Maria Cacioppa, Velio Ascenti, Gianpaolo Carrafiello, Chiara Floridi

Abstract readReview
In one paragraph

Review in Tomography (Ann Arbor, Mich.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Michaela CellinaRadiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Piazza Principessa Clotilde 3, 20121 Milan, Italy.ORCID 0000-0002-7401-1971
Maurizio CèPostgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122 Milan, Italy.ORCID 0000-0002-8906-5665
Nicolo' RossiniDepartment of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy.
Laura Maria CacioppaDivision of Interventional Radiology, Department of Radiological Sciences, University Politecnica delle Marche, 60126 Ancona, Italy.
Velio AscentiPostgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122 Milan, Italy.
Gianpaolo CarrafielloRadiology Department, Policlinico di Milano Ospedale Maggiore|Fondazione IRCCS Ca' Granda, Via Francesco Sforza 35, 20122 Milan, Italy.
Chiara FloridiDivision of Interventional Radiology, Department of Radiological Sciences, University Politecnica delle Marche, 60126 Ancona, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Computed Tomography Urography (CTU) is a multiphase CT examination optimized for imaging kidneys, ureters, and bladder, complemented by post-contrast excretory phase imaging. Different protocols are available for contrast administration and image acquisition and timing, with different strengths and limits, mainly related to kidney enhancement, ureters distension and opacification, and radiation exposure. The availability of new reconstruction algorithms, such as iterative and deep-learning-based reconstruction has dramatically improved the image quality and reducing radiation exposure at the same time. Dual-Energy Computed Tomography also has an important role in this type of examination, with the possibility of renal stone characterization, the availability of synthetic unenhanced phases to reduce radiation dose, and the availability of iodine maps for a better interpretation of renal masses. We also describe the new artificial intelligence applications for CTU, focusing on radiomics to predict tumor grading and patients' outcome for a personalized therapeutic approach. In this narrative review, we provide a comprehensive overview of CTU from the traditional to the newest acquisition techniques and reconstruction algorithms, and the possibility of advanced imaging interpretation to provide an up-to-date guide for radiologists who want to better comprehend this technique.

Indexed as

KidneyTomography, X-Ray ComputedUreterUrinary BladderUrographyAlgorithmsArtificial IntelligenceHumansImage Processing, Computer-AssistedKidney NeoplasmsAI-based reconstruction algorithmsartificial intelligenceComputed TomographyCT urographyDual-Energy Computed Tomographyrenal cancer imaging

Identifiers

PMID37218935
PMCPMC10204399

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.