Evidence map›Paper›PMID 39382764›Full record

ArticleInsights into imaging2024

Is there enough evidence supporting the clinical adoption of clear cell likelihood score (ccLS)? An updated systematic review and meta-analysis.

Jingyu Zhong, Yangfan Hu, Yue Xing, Xianwei Liu, Xiang Ge, Yibin Wang, Yuping Shi, Junjie Lu, Jiarui Yang, Yang Song and 5 more

Abstract read
In one paragraph

Article in Insights into imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. 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

15 authors.

Jingyu Zhong *Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
Yangfan Hu *Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
Yue Xing *Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
Xianwei LiuDepartment of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
Xiang GeDepartment of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
Yibin WangDepartment of Urology, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
Yuping ShiDepartment of Nephrology, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
Junjie LuDepartment of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, 94305, USA.
Jiarui YangDepartment of Biomedical Engineering, Boston University, Boston, MA, 02215, USA.
Yang SongMR Research Collaboration Team, Siemens Healthineers Ltd., Shanghai, 200126, China.
Minda LuMR Application, Siemens Healthineers Ltd., Shanghai, 200126, China.
Jingshen ChuDepartment of Science and Technology Development, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
Huan ZhangDepartment of Radiology, Ruijin Hospital, Shanghai Jiao Tong University of Medicine, Shanghai, 200025, China. huanzhangy@163.com.
Defang DingDepartment of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China. DDF4249@shtrhospital.com.
Weiwu YaoDepartment of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China. YWW4142@shtrhospital.com.ORCID http://orcid.org/0000-0002-6612-8520

Funding

Laboratory Open Fund of Key Technology and Materials in Minimally Invasive Spine Surgery 2024JZWC-YBA07Laboratory Open Fund of Key Technology and Materials in Minimally Invasive Spine Surgery 2024JZWC-ZDA03National Natural Science Foundation of China 82271934National Natural Science Foundation of China 82302183National Natural Science Foundation of China 82471935Research Found of Health Commission of Changing District, Shanghai Municipality 2023QN01Research Fund of Tongren Hospital, Shanghai Jiao Tong University School of Medicine TRGG202101Research Fund of Tongren Hospital, Shanghai Jiao Tong University School of Medicine TRKYRC-XX202204Research Fund of Tongren Hospital, Shanghai Jiao Tong University School of Medicine TRYJ2021JC06Research Fund of Tongren Hospital, Shanghai Jiao Tong University School of Medicine TRYXJH18Research Fund of Tongren Hospital, Shanghai Jiao Tong University School of Medicine TRYXJH28Yangfan Project of Science and Technology Commission of Shanghai Municipality 22YF1442400
6 · The paper itself

Abstract

objectiveTo review the evidence for clinical adoption of clear cell likelihood score (ccLS) for identifying clear cell renal cell carcinoma (ccRCC) from small renal masses (SRMs).

methodsWe distinguished the literature on ccLS for identifying ccRCC via systematic search using PubMed, Embase, Web of Science, China National Knowledge Infrastructure, and Wanfang Data until 31 March, 2024. The risk of bias and concern on application was assessed using the modified quality assessment of diagnostic accuracy studies (QUADAS-2) tool. The level of evidence supporting the clinical adoption of ccLS for identifying ccRCC was determined based on meta-analyses.

resultsEight MRI studies and three CT studies were included. The risk of bias and application were mainly related to the index test and flow and timing, due to incomplete imaging protocol, unclear rating process, and inappropriate interval between imaging and surgery. The diagnostic odds ratios (95% confidence intervals) of MRI and CT ccLS were 14.69 (9.71-22.22; 6 studies, 1429 SRM, 869 ccRCC), and 5.64 (3.34-9.54; 3 studies, 296 SRM, 147 ccRCC), respectively, for identifying ccRCC from SRM. The evidence level for clinical adoption of MRI and CT ccLS were both rated as weak. MRI ccLS version 2.0 potentially has better diagnostic performance than version 1.0 (1 study, 700 SRM, 509 ccRCC). Both T2-weighted-imaging with or without fat suppression might be suitable for MRI ccLS version 2.0 (1 study, 111 SRM, 82 ccRCC).

conclusionccLS shows promising diagnostic performance for identifying ccRCC from SRM, but the evidence for its adoption in clinical routine remains weak. CRITICAL RELEVANCE STATEMENT: Although clear cell likelihood score (ccLS) demonstrates promising performance for detecting clear cell renal cell carcinoma, additional evidence is crucial to support its routine use as a tool for both initial diagnosis and active surveillance of small renal masses. KEY POINTS: Clear cell likelihood score is designed for the evaluation of small renal masses. Both CT and MRI clear cell likelihood scores are accurate and efficient. More evidence is necessary for the clinical adoption of a clear cell likelihood score.

Indexed as

Carcinoma (renal cell)DiagnosisMagnetic resonance imagingTomography (X-ray computed)

Identifiers

PMID39382764
PMCPMC11464715

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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.