Evidence map›Paper›PMID 33495866›Full record

ReviewWorld journal of urology2021

Radiomics to better characterize small renal masses.

Teele Kuusk, Joana B Neves, Maxine Tran, Axel Bex

Registry-linked trialAbstract readReview
PubMed Publisher
In one paragraph

Review in World journal of urology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05782400 (Multiomics Approach for Patients Stratification and Novel Target Identification in Metastatic Clear Renal Cell Carcnoma), which is not on this map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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.

NCT05782400 active not recruitingnot on this mapstarted 2023, after this paper: background citation

Multiomics Approach for Patients Stratification and Novel Target Identification in Metastatic Clear Renal Cell Carcnoma

TypeobservationalSponsorFondazione IRCCS Istituto Nazionale dei Tumori, MilanoRan2023 to 2027Enrolled100ConditionsMetastatic Clear Cell Renal CarcinomaArmsCT scan, Plasma collection
3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. The Use of Radiomic Tools in Renal Mass Characterization.Diagnostics (Basel, Switzerland) · 2023
    Review
  11. Radiomics analysis of bone marrow biopsy locations in [Physical and engineering sciences in medicine · 2023
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Renal cancer: overdiagnosis and overtreatment.World journal of urology · 2021
    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

4 authors.

Teele KuuskUrology Department, Darent Valley Hospital, Dartford and Gravesham NHS Trust, Dartford, UK.
Joana B NevesSpecialist Centre for Kidney Cancer, Royal Free London NHS Foundation Trust, London, UK.
Maxine TranSpecialist Centre for Kidney Cancer, Royal Free London NHS Foundation Trust, London, UK.
Axel BexSpecialist Centre for Kidney Cancer, Royal Free London NHS Foundation Trust, London, UK. a.bex@nki.nl.

Funding

Medical Research Council MR/S00209X/1
6 · The paper itself

Abstract

purposeRadiomics is a specific field of medical research that uses programmable recognition tools to extract objective information from standard images to combine with clinical data, with the aim of improving diagnostic, prognostic, and predictive accuracy beyond standard visual interpretation. We performed a narrative review of radiomic applications that may support improved characterization of small renal masses (SRM). The main focus of the review was to identify and discuss methods which may accurately differentiate benign from malignant renal masses, specifically between renal cell carcinoma (RCC) subtypes and from angiomyolipoma without visible fat (fat-poor AML) and oncocytoma. Furthermore, prediction of grade, sarcomatoid features, and gene mutations would be of importance in terms of potential clinical utility in prognostic stratification and selecting personalised patient management strategies.

methodsA detailed search of original articles was performed using the PubMed-MEDLINE database until 20 September 2020 to identify the English literature relevant to radiomics applications in renal tumour assessment. In total, 42 articles were included in the analysis in 3 main categories related to SRM: prediction of benign versus malignant SRM, subtypes, and nuclear grade, and other features of aggressiveness.

conclusionOverall, studies reported the superiority of radiomics over expert radiological assessment, but were mainly of retrospective design and therefore of low-quality evidence. However, it is clear that radiomics is an attractive modality that has the potential to improve the non-invasive diagnostic accuracy of SRM imaging and prediction of its natural behaviour. Further prospective validation studies of radiomics are needed to augment management algorithms of SRM.

Indexed as

Artificial IntelligenceKidney NeoplasmsPrecision MedicineRadiologyDecision Support Systems, ClinicalDiagnosis, DifferentialHumansNeoplasm StagingTumor BurdenArtificial intelligenceImagingRadiomicsRenal cell carcinomaSmall renal mass

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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.