Evidence map›Paper›PMID 35296413›Full record

ArticleAcademic radiology2022

Ultrasound Features to Differentiate COVID-19 Vaccine-Induced Benign Adenopathy from Breast Cancer Related Malignant Adenopathy.

Chung Hl, Whitman Gj, Leung Jwt, Sun J, Middleton Lp, Le-Petross Ht

Open access · bronzeAbstract read
In one paragraph

Article in Academic radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.5field-weighted citation impact, top 19% of its field
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

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Review
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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

6 authors at 1 institution in 1 country.

Chung HlDepartment of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd Unit 1350, CPB5.3201, Houston, TX, 77030. Electronic address: HLChung@mdanderson.org.
Whitman GjDepartment of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd Unit 1350, CPB5.3201, Houston, TX, 77030.
Leung JwtDepartment of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd Unit 1350, CPB5.3201, Houston, TX, 77030.
Sun JDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX.
Middleton LpDepartment of Anatomical Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX.
Le-Petross HtDepartment of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd Unit 1350, CPB5.3201, Houston, TX, 77030.
The University of Texas MD Anderson Cancer Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

RATIONALE AND

objectiveTo identify nodal features used to distinguish coronavirus disease 2019 (COVID-19) vaccine-Induced benign reactive adenopathy from malignant adenopathy. MATERIALS AND

methodsThis IRB-approved, single-institution, retrospective study compared features of 77 consecutive patients with benign adenopathy secondary to a messenger RNA COVID-19 vaccine with 76 patients with biopsy-proven malignant adenopathy from breast cancer. Patient demographics and nodal features were compared between the two groups using univariate and multivariate logistic regression models. A receiver operating characteristic analysis with the maximum value of Youden's index was performed for the cutoff value of cortical thickness for predicting nodal status.

resultsThe mean cortical thickness was 5.1 mm ± 2.8 mm among benign nodes and 8.9 mm ± 4.5 mm among malignant nodes (p < 0.001). A cortical thickness ≥3.0 mm had a sensitivity of 100% and a specificity of 21% (area under the curve [AUC] = 0.60, 95% confidence interval [CI]: 0.52-0.68). When the cutoff for cortical thickness was increased to ≥5.4 mm, the sensitivity decreased to 74%, while the specificity increased to 69% (AUC = 0.77, 95% CI: 0.70-0.84).Cortical thickness correlated with nodal morphology type (r

conclusionCortical thickness and morphology are predictive of malignancy. Cortical thickness cutoff of ≥5.4 mm demonstrates higher specificity and improved accuracy for detecting malignant adenopathy than a cutoff of ≥3.0 mm.

Indexed as

Breast NeoplasmsCOVID-19LymphadenopathyAxillaCOVID-19 VaccinesFemaleHumansLymph NodesRetrospective StudiesSensitivity and SpecificityCOVID-19 Vaccinescortical thickness, ultrasoundCOVID-19lymphadenopathyvaccine

Identifiers

PMID35296413
PMCPMC8858693
OpenAlexW4213223082

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.