Evidence map›Paper›PMID 42640784›Full record

SynthesisTechnology in cancer research & treatment

Ultrasound Radiomics for Differential Diagnosis of Renal Masses: A Systematic Review and Meta-Analysis.

Ying Xu, Shuhua Zhang, Yihua Wang, Hongzhao Lin, Wenli Liu, Xinrui Lu, Xue Zhang, Yang Liu, Ning Li

Abstract readSystematic ReviewMeta-Analysis
In one paragraph

Synthesis in Technology in cancer research & treatment. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Ying XuDepartment of Ultrasound, North China University of Science and Technology Affiliated Hospital, Tangshan, Hebei, China.ORCID 0009-0008-4824-8941
Shuhua ZhangDepartment of Ultrasound, North China University of Science and Technology Affiliated Hospital, Tangshan, Hebei, China.
Yihua WangDepartment of Ultrasound, North China University of Science and Technology Affiliated Hospital, Tangshan, Hebei, China.
Hongzhao LinDepartment of Ultrasound, North China University of Science and Technology Affiliated Hospital, Tangshan, Hebei, China.
Wenli LiuDepartment of Ultrasound, North China University of Science and Technology Affiliated Hospital, Tangshan, Hebei, China.
Xinrui LuDepartment of Ultrasound, North China University of Science and Technology Affiliated Hospital, Tangshan, Hebei, China.
Xue ZhangDepartment of Ultrasound, North China University of Science and Technology Affiliated Hospital, Tangshan, Hebei, China.
Yang LiuDepartment of Ultrasound, North China University of Science and Technology Affiliated Hospital, Tangshan, Hebei, China.
Ning LiDepartment of Ultrasound, North China University of Science and Technology Affiliated Hospital, Tangshan, Hebei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

IntroductionGiven the importance of early renal cancer diagnosis and the limitations of conventional imaging assessment, ultrasound radiomics has emerged as a promising non-invasive technique for differentiating benign from malignant renal masses. This meta-analysis is aimed to assess the ability of ultrasound radiomics in differentiating benign and malignant renal masses.Materials and MethodsLiterature was systematically retrieved from the inception of the databases to April 27, 2025. Meta-analysis was conducted using Review Manager, Meta-Disc, and STATA software to calculate pooled diagnostic accuracy metrics. Sensitivity analysis and subgroup analysis were performed to explore the sources of heterogeneity. Deeks' funnel plot was adopted to evaluate publication bias, and the Fagan nomogram was applied to assess clinical utility.ResultsThis study included 7 articles comprising a total of 1,864 cases (1,166 malignant and 698 benign). The pooled sensitivity, specificity, and AUC of ultrasound radiomics for differential diagnosis of benign and malignant renal masses were 0.83 (95% CI, 0.81-0.86), 0.79 (95% CI, 0.69-0.87), and 0.84(95% CI: 0.81-0.87). Significant heterogeneity was observed in pooled specificity among the 7 included studies. Subgroup analysis revealed that mass size and feature extraction software type contributed to the heterogeneity of specificity. Accordingly, 5 studies of simple small renal masses were further analyzed. The pooled sensitivity, specificity, and AUC of ultrasound radiomics for differential diagnosing of benign and malignant small renal masses were 0.84 (95% CI, 0.80-0.87), 0.72 (95% CI, 0.64-0.79), and 0.85(95% CI, 0.82-0.88). Fagan nomogram demonstrated a 30% increase in post-test probability for patients with positive pre-test results and a 15% decrease for those with negative pre-test results, indicating moderate clinical utility of radiomics.ConclusionUltrasound radiomics has favorable value in the differential diagnosis of benign and malignant renal masses, and is expected to serve as a reliable auxiliary tool for clinical preoperative diagnosis.

Indexed as

Kidney NeoplasmsDiagnosis, DifferentialHumansRadiomicsUltrasonographydiagnosisrenal massesrenal tumorssmall renal massesultrasound radiomics

Identifiers

PMID42640784
PMCPMC13519207

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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.