Why the study?
The study was conducted to investigate the value of using specific regions of interest on contrast-enhanced CT to differentiate renal angiomyolipoma without visible fat from small clear cell renal cell carcinoma.
Does the use of specific regions of interest (ROI) on contrast-enhanced CT improve the differentiation of renal angiomyolipoma without visible fat from small clear cell renal cell carcinoma?
Does the use of specific regions of interest (ROI) on contrast-enhanced CT improve the differentiation of renal angiomyolipoma without visible fat from small clear cell renal cell carcinoma?
A prediction model combining demographic data, morphological features, and specific ROI-derived quantitative data from the corticomedullary phase of contrast-enhanced CT can highly accurately differentiate AML.wovf from small ccRCC.
Specific ROI on multiphase CT may aid AML.wovf vs ccRCC differentiation; leaves open prospective validation before clinical adoption.
BACKGROUND: To 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). METHODS: Four-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. RESULTS: Among 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). CONCLUSION: The 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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Wang et al. (2021) studied this question.
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