ArticleAbdominal radiology (New York)2023
Performance of clear cell likelihood scores in characterizing solid renal masses at multiparametric MRI: an external validation study.
Article in Abdominal radiology (New York), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Combined radiological approach for characterization of solid renal masses: a proposed strategy for integrating the RENAL nephrometry score into the clear cell likelihood score.BMC medical imaging · 2026Article
- Renal CT algorithm for evaluating clear cell carcinoma in solid renal masses ≤ 4 cm: a retrospective chinese two-center validation study.Abdominal radiology (New York) · 2026Article
- Can multiparametric MRI clear cell likelihood scores differentiate fat-Poor AML from CcRCC in subcentimeter lesions?Abdominal radiology (New York) · 2025Article
- Clear cell likelihood score may improve diagnosis and management of renal masses.Abdominal radiology (New York) · 2024Review
- Is there enough evidence supporting the clinical adoption of clear cell likelihood score (ccLS)? An updated systematic review and meta-analysis.Insights into imaging · 2024Article
- Differentiation between renal epithelioid angiomyolipoma and clear cell renal cell carcinoma using clear cell likelihood score.Abdominal radiology (New York) · 2023Article
- Invited Commentary: MRI Clear Cell Likelihood Score for Indeterminate Solid Renal Masses: Is There a Path for Broad Clinical Adoption?Radiographics : a review publication of the Radiological Society of North America, Inc · 2023Article
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6 authors.
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Abstract
purposeThe purpose of this study is to evaluate the accuracy and interobserver agreement of ccLS in diagnosing clear cell renal cell carcinoma (ccRCC).
methodsThis retrospective single-center study evaluated consecutive patients with solid renal masses who underwent mpMRI followed by percutaneous biopsy and/or surgical excision between January 2010 and December 2020. Predominantly (> 75%) cystic masses, masses with macroscopic fat and infiltrative masses were excluded. Two abdominal radiologists independently scored each renal mass according to the proposed ccLS algorithm. The diagnostic performance of ccLS categories for ccRCC was calculated using logistic regression modeling. Diagnostic accuracy for predicting ccRCC was calculated using 2 × 2 contingency tables. Interobserver agreement for ccLS was evaluated with Cohen's k statistic.
resultsA total of 79 patients (mean age, 63 years ± 12 [SD], 50 men) with 81 renal masses were evaluated. The mean size was 36 mm ± 28 (range 10-160). Of the renal masses included, 44% (36/81) were ccRCC. The area under the receiver operating characteristic curve was 0.87 (95% CI 0.79-0.95). Using ccLS ≥ 4 to diagnose ccRCC, the sensitivity, specificity, and positive predictive value were 93% (95% CI 79, 99), 63% (95% CI 48, 77), and 67% (95% CI 58, 75), respectively. The negative predictive value of ccLS ≤ 2 was 93% (95% CI 64, 99). The proportion of ccRCC by ccLS category 1 to 5 were 10%, 0%, 10%, 57%, and 84%, respectively. Interobserver agreement was moderate (k = 0.47).
conclusionIn this study, clear cell likelihood score had moderate interobserver agreement and resulted in 96% negative predictive value in excluding ccRCC.
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