Evidence map›Paper›PMID 36459898›Full record

SynthesisClinical imaging2023

CT radiomics for differentiating oncocytoma from renal cell carcinomas: Systematic review and meta-analysis.

Fatemeh Dehghani Firouzabadi, Nikhil Gopal, Fatemeh Homayounieh, Pouria Yazdian Anari, Xiaobai Li, Mark W Ball, Elizabeth C Jones, Safa Samimi, Evrim Turkbey, Ashkan A Malayeri

Abstract readMeta-AnalysisSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Clinical imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 2 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

17 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. SNMMI/EANM/ACNM Procedure Standard/Procedure Guideline on the Use of Molecular Imaging for Renal Mass Characterization.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2025
    Guideline
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  7. State of the art review of AI in renal imaging.Abdominal radiology (New York) · 2025
    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

10 authors.

Fatemeh Dehghani FirouzabadiRadiology Department, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA.
Nikhil GopalUrology Department, Clinical Center, National Cancer Institutes (NCI), National Institutes of Health, Bethesda, MD, USA.
Fatemeh HomayouniehRadiology Department, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA.
Pouria Yazdian AnariRadiology Department, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA.
Xiaobai LiBiostatistics and Clinical Epidemiology Service, NIH Clinical Center, Bethesda, MD, USA.
Mark W BallUrology Department, Clinical Center, National Cancer Institutes (NCI), National Institutes of Health, Bethesda, MD, USA.
Elizabeth C JonesRadiology Department, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA.
Safa SamimiRadiology Department, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA.
Evrim TurkbeyRadiology Department, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA.
Ashkan A MalayeriRadiology Department, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA. Electronic address: ashkan.malayeri@nih.gov.

Funding

Intramural NIH HHS Z99 CL999999
6 · The paper itself

Abstract

backgroundRadiomics is a type of quantitative analysis that provides a more objective approach to detecting tumor subtypes using medical imaging. The goal of this paper is to conduct a comprehensive assessment of the literature on computed tomography (CT) radiomics for distinguishing renal cell carcinomas (RCCs) from oncocytoma.

methodsFrom February 15th 2012 to 2022, we conducted a broad search of the current literature using the PubMed/MEDLINE, Google scholar, Cochrane Library, Embase, and Web of Science. A meta-analysis of radiomics studies concentrating on discriminating between oncocytoma and RCCs was performed, and the risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies method. The pooled sensitivity, specificity, and diagnostic odds ratio were evaluated via a random-effects model, which was applied for the meta-analysis. This study is registered with PROSPERO (CRD42022311575).

resultsAfter screening the search results, we identified 6 studies that utilized radiomics to distinguish oncocytoma from other renal tumors; there were a total of 1064 lesions in 1049 patients (288 oncocytoma lesions vs 776 RCCs lesions). The meta-analysis found substantial heterogeneity among the included studies, with pooled sensitivity and specificity of 0.818 [0.619-0.926] and 0.808 [0.537-0.938], for detecting different subtypes of RCCs (clear cell RCC, chromophobe RCC, and papillary RCC) from oncocytoma. Also, a pooled sensitivity and specificity of 0.83 [0.498-0.960] and 0.92 [0.825-0.965], respectively, was found in detecting oncocytoma from chromophobe RCC specifically.

conclusionsAccording to this study, CT radiomics has a high degree of accuracy in distinguishing RCCs from RO, including chromophobe RCCs from RO. Radiomics algorithms have the potential to improve diagnosis in scenarios that have traditionally been ambiguous. However, in order for this modality to be implemented in the clinical setting, standardization of image acquisition and segmentation protocols as well as inter-institutional sharing of software is warranted.

Indexed as

Adenoma, OxyphilicCarcinoma, Renal CellKidney NeoplasmsDiagnosis, DifferentialHumansSensitivity and SpecificityTomography, X-Ray ComputedChromophobe RCCClear cell RCCMachine learningOncocytomaPapillary RCCRenal cell carcinomaSystematic review

Identifiers

PMID36459898
PMCPMC9812928

What OpenQuestion holds

Textmetadata
LicenceTDM
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 OpenQuestion graph.