SynthesisBMJ open2021
Accuracy of CT texture analysis for differentiating low-grade and high-grade renal cell carcinoma: systematic review and meta-analysis.
Synthesis in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- UCSF RMaC: University of California San Francisco 3D Multi-Phase Renal Mass CT Dataset with Tumor Segmentations.medRxiv : the preprint server for health sciences · 2026Article
- Renal Cell Carcinoma Texture Analysis With the KiTS23 Dataset: A Retrospective Study.Advances in urology · 2026Article
- Radiomics-Based Classification of Clear Cell Renal Cell Carcinoma ISUP Grade: A Machine Learning Approach with SHAP-Enhanced Explainability.Diagnostics (Basel, Switzerland) · 2025Article
- Tumor therapeutics in the era of "RECIST": past, current insights, and future prospects.Oncology reviews · 2024Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study aimed to assess the accuracy of CT texture analysis (CTTA) for differentiating low-grade and high-grade renal cell carcinoma (RCC).
designSystematic review and meta-analysis. DATA SOURCES: PubMed, Cochrane Library, Embase, Web of Science, OVID Medline, Science Direct and Springer were searched to identify the included studies. ELIGIBILITY CRITERIA FOR INCLUDING STUDIES: Clinical studies that report about the accuracy of CTTA in differentiating low-grade and high-grade RCC.
methodsMultiple databases were searched to identify studies from their inception to 20 October 2021. Two radiologists independently extracted data from the primary studies. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR) and diagnostic OR (DOR) were calculated to assess CTTA performance. The summary receiver operating characteristic (SROC) curve was plotted, and the area under the curve (AUC) was calculated to evaluate the accuracy of CTTA in grading RCC.
resultsThis meta-analysis included 11 studies, with 1603 lesions observed in 1601 patients. Values of the pooled sensitivity, specificity, PLR, NLR, DOR were 0.79 (95% CI 0.73 to 0.84), 0.84 (95% CI 0.81 to 0.87), 5.1 (95% CI 4.0 to 6.4), 0.24 (95% CI 0.19 to 0.32) and 21 (95% CI 13 to 33), respectively. The SROC curve showed that the AUC was 0.88 (95% CI 0.84 to 0.90). Deeks' test found no significant publication bias among the studies (p=0.42).
conclusionsThe findings of this meta-analysis suggest that CTTA has a high accuracy in differentiating low-grade and high-grade RCC. A standardised methodology and large sample-based study are necessary to certain the diagnostic accuracy of CTTA in RCC grading for clinical decision making.
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