Evidence map›Paper›PMID 39871201›Full record

SynthesisBMC cancer2025

Diagnostic performance of artificial intelligence in detection of renal cell carcinoma: a systematic review and meta-analysis.

Mahdi Gouravani, Mohammad Shahrabi Farahani, Mohammad Amin Salehi, Shayan Shojaei, Sina Mirakhori, Hamid Harandi, Soheil Mohammadi, Ramy R Saleh

Abstract readSystematic ReviewMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. 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

8 authors.

Mahdi Gouravani *Musculoskeletal Imaging Research Center (MIRC), Tehran University of Medical Sciences, Tehran, Iran.
Mohammad Shahrabi Farahani *Medical Students Research Committee, Shahed University, Tehran, Iran.
Mohammad Amin Salehi *School of Medicine, Tehran University of Medical Sciences, Dr. Qarib St, Keshavarz Blvd, Tehran, 14194, Iran. mohamsa@gmail.com.
Shayan ShojaeiSchool of Medicine, Tehran University of Medical Sciences, Dr. Qarib St, Keshavarz Blvd, Tehran, 14194, Iran.
Sina MirakhoriFaculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran.
Hamid HarandiSchool of Medicine, Tehran University of Medical Sciences, Dr. Qarib St, Keshavarz Blvd, Tehran, 14194, Iran.
Soheil MohammadiMallinckrodt Institute of Radiology, Washington University in St. Louis, Saint Louis, USA.
Ramy R SalehDepartment of Oncology, McGill University, Montreal, QC, H3A 0G4, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe detection of renal cell carcinoma (RCC) tumors in the earlier stages is of great importance for more effective treatment. Encouraged by the key role of imaging in the management of RCC, we conducted a systematic review and meta-analysis of the studies that made use of artificial intelligence (AI) for the detection of RCC to quantitatively determine the performance of AI for distinguishing related renal lesions. MATERIALS AND

methodsPubMed, Scopus, CENTRAL, and Embase electronic databases were systematically searched in November 2024 to identify studies that applied AI for the detection or classification of RCC. We conducted a meta-analysis to evaluate the diagnostic performance of utilized algorithms. Moreover, meta-regression was conducted over suspected covariates to evaluate potential sources of inter-study heterogeneity. Publication bias and quality assessment were also done for the included studies.

resultsSixty-four studies were included in this systematic review, of which 31 studies were selected for meta-analysis. The studies assessing algorithms' performance on internal validation showed pooled sensitivity and specificity of 85% (95% confidence interval [CI], 82 to 87) and 76% (95% CI, 70 to 80), respectively. Moreover, externally validated Al algorithms had a pooled sensitivity and specificity of 80% (95% CI, 73 to 84) and 90% (95% CI, 84 to 93), respectively. Studies that performed internal validation for clinician performance had a pooled sensitivity of 79% (95% CI, 72 to 85) and specificity of 60% (95% CI, 49 to 70).

conclusionThe findings of the present study validate the acceptable performance of AI algorithms when contrasted with medical professionals in the identification and categorization of RCC. Nevertheless, the presence of heterogeneity between studies and the absence of coherence in the results underscore the necessity for the cautious interpretation of these results and additional prospective studies.

Indexed as

Artificial IntelligenceCarcinoma, Renal CellKidney NeoplasmsAlgorithmsHumansSensitivity and SpecificityArtificial intelligenceDeep learningImagingMeta-analysisRenal cell carcinoma

Identifiers

PMID39871201
PMCPMC11773916

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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.