Evidence map›Paper›PMID 39325194›Full record

SynthesisWorld journal of urology2024

Clinical application of radiomics for the prediction of treatment outcome and survival in patients with renal cell carcinoma: a systematic review.

Zine-Eddine Khene, Isamu Tachibana, Theophile Bertail, Raphael Fleury, Raj Bhanvadia, Payal Kapur, Satwik Rajaram, Junyu Guo, Alana Christie, Ivan Pedrosa and 2 more

Abstract readSystematic Review
PubMed Publisher
In one paragraph

Synthesis in World journal of urology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Article
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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

12 authors.

Zine-Eddine KheneDepartment of Urology, University of Texas Southwestern Medical Center, 2001 Inwood Rd, WCB3, Floor 4, Dallas, TX, 75390, USA.
Isamu TachibanaDepartment of Urology, University of Texas Southwestern Medical Center, 2001 Inwood Rd, WCB3, Floor 4, Dallas, TX, 75390, USA.
Theophile BertailDepartment of Urology, University of Rennes, Rennes, France.
Raphael FleuryDepartment of Urology, University of Rennes, Rennes, France.
Raj BhanvadiaDepartment of Urology, University of Texas Southwestern Medical Center, 2001 Inwood Rd, WCB3, Floor 4, Dallas, TX, 75390, USA.
Payal KapurDepartment of Pathology, UT Southwestern Medical Center, Dallas, TX, USA.
Satwik RajaramDepartment of Pathology, UT Southwestern Medical Center, Dallas, TX, USA.
Junyu GuoDepartment of Radiology, UT Southwestern Medical Center, Dallas, TX, USA.
Alana ChristieSimmons Comprehensive Cancer Center Biostatistics, University of Texas, Southwestern Medical Center, Dallas, TX, USA.
Ivan PedrosaDepartment of Radiology, UT Southwestern Medical Center, Dallas, TX, USA.
Yair LotanDepartment of Urology, University of Texas Southwestern Medical Center, 2001 Inwood Rd, WCB3, Floor 4, Dallas, TX, 75390, USA.
Vitaly MargulisDepartment of Urology, University of Texas Southwestern Medical Center, 2001 Inwood Rd, WCB3, Floor 4, Dallas, TX, 75390, USA. vitaly.margulis@utsouthwestern.edu.ORCID http://orcid.org/0000-0002-5837-4561

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe management of renal cell carcinoma (RCC) relies on clinical and histopathological features for treatment decisions. Recently, radiomics, which involves the extraction and analysis of quantitative imaging features, has shown promise in improving RCC management. This review evaluates the current application and limitations of radiomics for predicting treatment and oncological outcomes in RCC.

methodsA systematic search was conducted in Medline, EMBASE, and Web of Science databases or studies that used radiomics to predict response to treatment and survival outcomes in patients with RCC. The study quality was assessed using the Radiomics Quality Score (RQS) tools.

resultsThe systematic review identified a total of 27 studies, examining 6,119 patients. The most used imaging modality was contrast-enhanced abdominal CT. The reviewed studies extracted between 19 and 3376 radiomics features, including Histogram, Texture, Filter, or transformation method. Radiomics-based risk stratification models provided valuable insights into treatment response and oncological outcomes. All developed signatures demonstrated at least modest accuracy (AUC range: 0.55-0.99). The studies included in this analysis reported heterogeneous results regarding radiomics methods. The range of Radiomics Quality Score (RQS) was from - 5 to 20, with a mean RQS total of 9.15 ± 7.95.

conclusionRadiomics has emerged as a promising tool in the management of RCC. It offers the potential for improved risk stratification and response assessment. However, future trials must demonstrate the generalizability of findings to prospective cohorts before progressing towards clinical translation.

Indexed as

Carcinoma, Renal CellKidney NeoplasmsHumansPredictive Value of TestsPrognosisRadiomicsSurvival RateTomography, X-Ray ComputedTreatment OutcomeArtificial intelligenceKidney cancerMachine learningRadiomicsRenal cell carcinomaSurvival

Identifiers

PMID39325194

What OpenQuestion holds

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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.