Differential diagnosis of hypervascular ultra-small renal cell carcinoma and renal angiomyolipoma with minimal fat in early stage by using thin-section multidetector computed tomography

Wang, Xu; Song, Guangtao; Sun, Jihong; Shao, Guoliang

doi:10.1007/s00261-020-02542-2

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…This study shows that demographic data and morphological features are also valuable in differential diagnosis, especially gender was identified as the main factor in all prediction models. AML.wovf is significantly more common in females [ 15 ], cystic degeneration and pseudocapsule are rare, and angular interface reported previously is common [ 14 , 25 ], all of which are helpful for differentiation [ 38 ]. Our study also shows that enhanced scanning phase is more valuable than PCP in differential diagnosis, especially CMP.…”

Section: Discussionmentioning

confidence: 99%

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

2021

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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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