Evidence map›Paper›PMID 30741316›Full record

ArticlePediatric radiology2019

Computed tomography texture features can discriminate benign from malignant lymphadenopathy in pediatric patients: a preliminary study.

Alexis M Cahalane, Aoife Kilcoyne, Azadeh Tabari, Shaunagh McDermott, Michael S Gee

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Article in Pediatric radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.7field-weighted citation impact, top 29% of its field
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 8 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Alexis M CahalaneDepartment of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA, 02114, USA. acahalane@mgh.harvard.edu.ORCID 0000-0002-2380-5819
Aoife KilcoyneDepartment of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA, 02114, USA.
Azadeh TabariDepartment of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA, 02114, USA.
Shaunagh McDermottDepartment of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA, 02114, USA.
Michael S GeeDepartment of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA, 02114, USA.
Massachusetts General Hospital · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDifferentiation of benign from malignant lymphadenopathy remains challenging in pediatric radiology. Textural analysis (TA) quantitates heterogeneity of tissue signal intensities and has been applied to analysis of CT images.

objectiveThe purpose of this study was to establish whether CT textural analysis of enlarged lymph nodes visualized on pediatric CT can distinguish benign from malignant lymphadenopathy. MATERIALS AND

methodsWe retrospectively identified enlarged lymph nodes measuring 10-20 mm on contrast-enhanced CTs of patients age 18 years and younger that had been categorized as benign or malignant based on the known diagnoses. We placed regions of interest (ROIs) over lymph nodes of interest and performed textural analysis with and without feature size filtration. We then calculated test performance characteristics for TA features, along with multivariate logistic regression modeling using Akaike Information Criterion (AIC) minimization, to determine the optimal thresholds for distinguishing benign from malignant lymphadenopathy.

resultsWe identified 34 enlarged malignant nodes and 29 benign nodes from 63 patients within the 10- to 20-mm size range. Filtered image TA exhibited 82.4% sensitivity, 86.2% specificity and 84.1% accuracy for detecting malignant lymph nodes using mean and entropy parameters, whereas unfiltered TA exhibited 88.2% sensitivity, 72.4% specificity and 81.0% accuracy using mean and mean value of positive pixels parameters.

conclusionThis preliminary study demonstrates that the use of TA features improves the utility of pediatric CT to distinguish benign from malignant lymphadenopathy. The addition of TA to pediatric CT protocols has great potential to aid the characterization of indeterminate lymph nodes. If definitive differentiation between benign and malignant lymphadenopathy is possible by TA, it has the potential to reduce the need for follow-up imaging and tissue sampling, with reduced associated radiation exposure. However future studies are needed to confirm the clinical applicability of TA in distinguishing benign from malignant lymphadenopathy.

Indexed as

AdolescentChildDiagnosis, DifferentialFemaleHumansLymphadenopathyMaleRadiographic Image Interpretation, Computer-AssistedRetrospective StudiesSensitivity and SpecificityTomography, X-Ray ComputedChildrenComputed tomographyLymphadenopathyLymph nodesTextural analysisYoung adults

Identifiers

PMID30741316
OpenAlexW2912078419

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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.