ArticleCancer imaging : the official publication of the International Cancer Imaging Society2021
Differentiation of renal angiomyolipoma without visible fat from small clear cell renal cell carcinoma by using specific region of interest on contrast-enhanced CT: a new combination of quantitative tools.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic value of contrast-enhanced CT in clear cell renal cell carcinoma: a systematic review and meta-analysis.BMC urology · 2024Pooled it
- Discriminating papillary renal cell carcinoma from metanephric adenoma: a quantitative contrast-enhanced CT-based analysis.Abdominal radiology (New York) · 2026Article
- Differentiation of Fat-Poor Renal Angiomyolipoma From Clear Cell Renal Cell Carcinoma: Diagnostic Performance of a Novel Type of Color Contrast Enhanced Ultrasound.Cancer medicine · 2026Article
- Peritumoral Cortex Low-Enhancement Sign on Corticomedullary Phase CT: A Distinctive Indicator of Small Renal Malignancies.Korean journal of radiology · 2025Article
- Usefulness of the internal-to-external circle area ratio in contrast-enhanced CT to differentiate small (< 3 cm) fat-poor angiomyolipoma from renal cell carcinoma.BMC medical imaging · 2025Article
- Development of modified multi-parametric CT algorithms for diagnosing clear-cell renal cell carcinoma in small solid renal masses.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Dual-Stage AI Model for Enhanced CT Imaging: Precision Segmentation of Kidney and Tumors.Tomography (Ann Arbor, Mich.) · 2025Article
- Computed tomography and magnetic resonance imaging characteristics of renal cell carcinoma: Differences between subtypes and clinical evaluation.Journal of clinical imaging science · 2025Review
- Differential diagnosis of clear cell renal cell carcinoma with low signal intensity on T2WI from angiomyolipoma without visible fat on MR imaging.Frontiers in oncology · 2025Article
- The Use of Radiomic Tools in Renal Mass Characterization.Diagnostics (Basel, Switzerland) · 2023Review
- Alkaline phosphatase combines with CT factors for differentiating small (≤ 4 cm) fat-poor angiomyolipoma from renal cell carcinoma: a multiple quantitative tool.World journal of urology · 2023Article
- Four-phase computed tomography helps differentiation of renal oncocytoma with central hypodense areas from clear cell renal cell carcinomaDiagnostic and interventional radiology (Ankara, Turkey) · 2023Article
- Fat-poor renal angiomyolipoma with prominent cystic degeneration: A case report and review of the literature.World journal of clinical cases · 2023Article
- Artificial intelligence for early detection of renal cancer in computed tomography: A review.Cambridge prisms. Precision medicine · 2023Review
- Article
- Use of specific contrast-enhanced CT regions of interest to differentiate renal oncocytomas from small clear cell and chromophobe renal cell carcinomas.Diagnostic and interventional radiology (Ankara, Turkey) · 2022Article
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Abstract
backgroundTo investigate the value of using specific region of interest (ROI) on contrast-enhanced CT for differentiating renal angiomyolipoma without visible fat (AML.wovf) from small clear cell renal cell carcinoma (ccRCC).
methodsFour-phase (pre-contrast phase [PCP], corticomedullary phase [CMP], nephrographic phase [NP], and excretory phase [EP]) contrast-enhanced CT images of AML.wovf (n = 31) and ccRCC (n = 74) confirmed by histopathology were retrospectively analyzed. The CT attenuation value of tumor (AVT), net enhancement value (NEV), relative enhancement ratio (RER), heterogeneous degree of tumor (HDT) and standardized heterogeneous ratio (SHR) were obtained by using different ROIs [small: ROI (1), smaller: ROI (2), large: ROI (3)], and the differences of these quantitative data between AML.wovf and ccRCC were statistically analyzed. Multivariate regression was used to screen the main factors for differentiation in each scanning phase, and the prediction models were established and evaluated.
resultsAmong the quantitative parameters determined by different ROIs, the degree of enhancement measured by ROI (2) and the enhanced heterogeneity measured by ROI (3) performed better than ROI (1) in distinguishing AML.wovf from ccRCC. The receiver operating characteristic (ROC) curves showed that the area under the curve (AUC) of RER_CMP (2), RER_NP (2) measured by ROI (2) and HDT_CMP and SHR_CMP measured by ROI (3) were higher (AUC = 0.876, 0.849, 0.837 and 0.800). Prediction models that incorporated demographic data, morphological features and quantitative data derived from the enhanced phase were superior to quantitative data derived from the pre-contrast phase in differentiating between AML.wovf and ccRCC. Among them, the model in CMP was the best prediction model with the highest AUC (AUC = 0.986).
conclusionThe combination of quantitative data obtained by specific ROI in CMP can be used as a simple quantitative tool to distinguish AML.wovf from ccRCC, which has a high diagnostic value after combining demographic data and morphological features.
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