Evidence map›Paper›PMID 39101023›Full record

ArticleRadiology case reports2024

Innovative imaging approaches for neuroendocrine tumor characterization: Combined dual energy CT and perfusion protocol implementation.

Mariana Sofia Madrid Lewis, Abel Enrique Manjarres Guevara, Jaime Alfonso Madrid Jaramillo, Carlos Martín Campana Granda

Abstract readCase Reports
In one paragraph

Article in Radiology case reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Mariana Sofia Madrid LewisDepartment of Radiology, Centro Especializado En Radiología e Imágenes Diagnosticas (Cerid), Barranquilla, Colombia.
Abel Enrique Manjarres GuevaraDepartment of Radiology, Centro Especializado En Radiología e Imágenes Diagnosticas (Cerid), Barranquilla, Colombia.
Jaime Alfonso Madrid JaramilloDepartment of Radiology, Centro Especializado En Radiología e Imágenes Diagnosticas (Cerid), Barranquilla, Colombia.
Carlos Martín Campana GrandaDepartment of Radiology, Centro Especializado En Radiología e Imágenes Diagnosticas (Cerid), Barranquilla, Colombia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The article addresses the diagnostic value of the combined use of computed tomography (CT) perfusion and dual-energy CT (DECT) in patients with neuroendocrine tumors. It emphasizes the heterogeneity and complexity of these neoplasms, primarily affecting the gastrointestinal tract, bronchopulmonary system, and pancreas. While conventional CT is widely employed in their diagnosis, the combination of CT perfusion and dual-energy CT offers greater precision, particularly in detecting synchronous tumors and characterizing their vascularization. A clinical case of a patient with chronic abdominal symptoms, whose diagnosis was facilitated using both combined techniques, is presented. The discussion explores how CT perfusion assesses tumor vascularization and how dual-energy CT improves soft tissue differentiation, resulting in increased diagnostic accuracy. It is highlighted that this approach not only enhances detection rates but also positively impacts clinical management and healthcare costs. Therefore, the importance of considering these advanced tools in the diagnosis of neuroendocrine tumors to improve diagnostic precision and efficiency in patient care is underscored.

Indexed as

AdenomasComputed tomography perfusionDiagnostic accuracyDual-energy CTNeuroendocrine tumorsTechnique combinations

Identifiers

PMID39101023
PMCPMC11295452

What OpenQuestion holds

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Registered trials

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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 OpenQuestion graph.