Evidence map›Paper›PMID 39684226›Full record

ArticleInternational journal of molecular sciences2024

Radiogenomics Pilot Study: Association Between Radiomics and Single Nucleotide Polymorphism-Based Microarray Copy Number Variation in Diagnosing Renal Oncocytoma and Chromophobe Renal Cell Carcinoma.

Abeer J Alhussaini, Abirami Veluchamy, Adel Jawli, Neil Kernohan, Benjie Tang, Colin N A Palmer, J Douglas Steele, Ghulam Nabi

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of molecular sciences, 2024. 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
–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

2 citing papers in PubMed.

  1. Review
  2. Article
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

8 authors.

Abeer J AlhussainiDivision of Imaging Sciences and Technology, School of Medicine, Ninewells Hospital, University of Dundee, Dundee DD1 9SY, UK.ORCID 0000-0002-8020-8535
Abirami VeluchamyTayside Centre for Genomic Analysis, School of Medicine, University of Dundee, Dundee DD1 9SY, UK.ORCID 0000-0002-1089-7398
Adel JawliDivision of Imaging Sciences and Technology, School of Medicine, Ninewells Hospital, University of Dundee, Dundee DD1 9SY, UK.ORCID 0009-0005-2252-1258
Neil KernohanDepartment of Pathology, Ninewells Hospital, Dundee DD9 1SY, UK.
Benjie TangSurgical Skills Centre, Dundee Institute for Healthcare Simulation Respiratory Medicine and Gastroenterology, School of Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee DD1 9SY, UK.
Colin N A PalmerDivision of Population Pharmacogenetics, Population Health and Genomics, Biomedical Research Centre, Ninewells Hospital and Medical School, University of Dundee, Dundee DD1 9SY, UK.ORCID 0000-0002-6415-6560
J Douglas SteeleDivision of Imaging Sciences and Technology, School of Medicine, Ninewells Hospital, University of Dundee, Dundee DD1 9SY, UK.ORCID 0000-0002-9822-8753
Ghulam NabiDivision of Imaging Sciences and Technology, School of Medicine, Ninewells Hospital, University of Dundee, Dundee DD1 9SY, UK.ORCID 0000-0001-9406-5195

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

RO and ChRCC are kidney tumours with overlapping characteristics, making differentiation between them challenging. The objective of this research is to create a radiogenomics map by correlating radiomic features to molecular phenotypes in ChRCC and RO, using resection as the gold standard. Fourteen patients (6 RO and 8 ChRCC) were included in the prospective study. A total of 1,875 radiomic features were extracted from CT scans, alongside 632 cytobands containing 16,303 genes from the genomic data. Feature selection algorithms applied to the radiomic features resulted in 13 key features. From the genomic data, 24 cytobands highly correlated with histology were selected and cross-correlated with the radiomic features. The analysis identified four radiomic features that were strongly associated with seven genomic features. These findings demonstrate the potential of integrating radiomic and genomic data to enhance the differential diagnosis of RO and ChRCC, paving the way for more precise and non-invasive diagnostic tools in clinical practice.

Indexed as

Adenoma, OxyphilicCarcinoma, Renal CellDNA Copy Number VariationsKidney NeoplasmsPolymorphism, Single NucleotideAdultAgedDiagnosis, DifferentialFemaleGenomicsHumansMaleMiddle AgedPilot ProjectsProspective StudiesRadiomicschromophobecomputed tomographyoncocytomaradiogenomicsrenal masses

Identifiers

PMID39684226
PMCPMC11641663

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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.