Evidence map›Paper›PMID 38535772›Full record

ReviewTomography (Ann Arbor, Mich.)2024

Musculoskeletal Pitfalls on Molecular Imaging Studies of Oncologic Patients: How to Stay Out of Trouble.

Brooke Sarna, Ty Subhawong, Efrosyni Sfakianaki, Richard Wang, Anna Christodoulou-Vega, Fabiano N Cardoso

Abstract readReview
In one paragraph

Review in Tomography (Ann Arbor, Mich.), 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

6 authors.

Brooke SarnaDepartment of Radiology, University of Miami Miller School of Medicine, Miami, FL 33136, USA.ORCID 0000-0003-3328-1964
Ty SubhawongDepartment of Radiology, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
Efrosyni SfakianakiDepartment of Nuclear Medicine, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
Richard WangDepartment of Radiology, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
Anna Christodoulou-VegaDepartment of Radiology, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
Fabiano N CardosoDepartment of Radiology, University of Miami Miller School of Medicine, Miami, FL 33136, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An increasing amount of molecular imaging studies are ordered each year for an oncologic population that continues to expand and increase in age. The importance of these studies in dictating further care for oncologic patients underscores the necessity of differentiating benign from malignant findings, particularly for a population in whom incidental findings are common. The aim of this review is to provide pictorial examples of benign musculoskeletal pathologies which may be found on molecular imaging and which may be mistaken for malignant processes. Imaging examples are provided in the form of radiographs, bone scintigraphy, computed tomography, and fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) scans. Special attention is paid to specific features that help narrow the differential diagnosis and distinguish benign from malignant processes, with the goal of avoiding unnecessary invasive procedures.

Indexed as

Molecular ImagingPositron Emission Tomography Computed TomographyFluorodeoxyglucose F18HumansTomography, X-Ray ComputedFluorodeoxyglucose F18benign tumorsCT scanincidental findingsmolecular imagingMR imagingmusculoskeletalnuclear medicineoncologyPET scanpitfallsradiography

Identifiers

PMID38535772
PMCPMC10975531

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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.