Evidence map›Paper›PMID 39686586›Full record

SynthesisAmerican journal of rhinology & allergy2025

Radiomics of the Paranasal Sinuses: A Systematic Review of Computer-Assisted Techniques to Assess Computed Tomography Radiological Data.

Rhea Darbari Kaul, Peta-Lee Sacks, Cedric Thiel, Janet Rimmer, Larry Kalish, Raewyn Gay Campbell, Raymond Sacks, Antonio Di Ieva, Richard John Harvey

Abstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in American journal of rhinology & allergy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Review
  5. Review
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

9 authors.

Rhea Darbari KaulRhinology and Skull Base Research Group, Applied Medical Research Centre, University of New South Wales, Sydney, Australia.ORCID 0000-0002-2945-6090
Peta-Lee SacksRhinology and Skull Base Research Group, Applied Medical Research Centre, University of New South Wales, Sydney, Australia.
Cedric ThielRhinology and Skull Base Research Group, Applied Medical Research Centre, University of New South Wales, Sydney, Australia.
Janet RimmerRhinology and Skull Base Research Group, Applied Medical Research Centre, University of New South Wales, Sydney, Australia.
Larry KalishRhinology and Skull Base Research Group, Applied Medical Research Centre, University of New South Wales, Sydney, Australia.
Raewyn Gay CampbellRhinology and Skull Base Research Group, Applied Medical Research Centre, University of New South Wales, Sydney, Australia.
Raymond SacksMacquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia.
Antonio Di IevaComputational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Human and Health Sciences, Macquarie University, Sydney, Australia.
Richard John HarveyRhinology and Skull Base Research Group, Applied Medical Research Centre, University of New South Wales, Sydney, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRadiomics is a quantitative approach to medical imaging, aimed to extract features into large datasets. By using artificial intelligence (AI) methodologies, large radiomic data can be analysed and translated into meaningful clinical applications. In rhinology, there is heavy reliance on computed tomography (CT) imaging of the paranasal sinus for diagnostics and assessment of treatment outcomes. Currently, there is an emergence of literature detailing radiomics use in rhinology.

objectiveThis systematic review aims to assess the current techniques used to analyze radiomic data from paranasal sinus CT imaging.

methodsA systematic search was performed using Ovid MEDLINE and EMBASE databases from January 1, 2019 until March 16, 2024 using the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) checklist and Cochrane Library Systematic Reviews for Diagnostic and Prognostic Studies. The QUADAS-2 and PROBAST tools were utilized to assess risk of bias.

resultsOur search generated 1456 articles with 10 articles meeting eligibility criteria. Articles were divided into 2 categories, diagnostic (n = 7) and prognostic studies (n = 3). The number of radiomic features extracted ranged 4 to 1409, with analysis including non-AI-based statistical analyses (n = 3) or machine learning algorithms (n = 7). The diagnostic or prognostic utility of radiomics analyses were rated as excellent (n = 3), very good (n = 2), good (n = 2), or not reported (n = 3) based upon area under the curve receiver operating characteristic (AUC-ROC) or accuracy. The average radiomics quality score was 36.95%.

conclusionRadiomics is an evolving field which can augment our understanding of rhinology diseases, however there are currently only minimal quality studies with limited clinical utility.

Indexed as

Image Processing, Computer-AssistedParanasal Sinus DiseasesParanasal SinusesTomography, X-Ray ComputedArtificial IntelligenceHumansPrognosisRadiomicsartificial intelligencechronic rhinosinusitiscomputed tomographydiagnosticsmachine learningnasopharyngeal carcinomaprognosticsradiomicsrhinologysinus

Identifiers

PMID39686586
PMCPMC11796290

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.