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.

BACKGROUND

This study aimed to assess the use of morphological parameters, including the internal-to-external circle area ratio (IECR), in contrast-enhanced CT to distinguish small (< 3 cm) fat-poor angiomyolipoma (AML) from renal cell carcinoma (RCC).

METHODS

A total of 212 tumors (35 fat-poor AMLs and 177 RCCs) in the initial cohort were retrospectively evaluated using contrast-enhanced CT. Morphological characteristics (angular interface sign [AIS] score, overflowing beer sign [OBS] score, tumor diameter, circularity index, and IECR) were compared between RCC and fat-poor AML. The diagnostic performance of the significant parameters was evaluated via the area under the receiver operating characteristic curve (AUC) and compared via the DeLong test. Logistic regression was used to determine the main factors for distinguishing fat-poor AML from RCC. Three prediction models were constructed and evaluated: one omitting circularity index and IECR, one incorporating circularity index, and one incorporating IECR. The effectiveness of the prediction models was then confirmed through a validation cohort (19 fat-poor AMLs and 99 RCCs).

RESULTS

There were significant differences between RCC and fat-poor AML in both sex (P < 0.001) and all morphological parameters, including AIS score (P = 0.003), OBS score (P < 0.001), any sign for AML (P < 0.001), tumor diameter (P = 0.008), circularity index (P < 0.001), and IECR (P < 0.001), with AUC values ranging from 0.619 to 0.899. The diagnostic performance of IECR (AUC, 0.899) was significantly better than that of other parameters (Z range, 2.128-8.582; all P < 0.05). To distinguish fat-poor AML from RCC, the AUC values of the prediction model omitting circularity index and IECR, prediction model incorporating circularity index, and prediction model incorporating IECR were 0.873, 0.921, and 0.951 in the initial cohort, as well as 0.867, 0.891, and 0.933 in the validation cohort, respectively. The prediction model that used the IECR outperformed the models without the IECR.

CONCLUSIONS

The IECR can be used as a simple and practical quantitative morphological factor to distinguish fat-poor AML from RCC. Adding IECR can increase the diagnostic performance of prediction models on the basis of morphological characteristics in the differential diagnosis of fat-poor AML and RCC.

CLINICAL TRIAL NUMBER

Not applicable.