LAUSR

PURPOSE We aimed to examine the usefulness of utilizing a specific contrast-enhanced computed tomography (CT) region of interest (ROI) to differentiate renal oncocytoma (RO) from small clear cell renal cell carcinoma (ccRCC) and chromophobe renal cell carcinoma (chRCC). METHODS A retrospective analysis of pre-contrast phase (PCP), corticomedullary phase (CMP), and nephrographic phase (NP) contrast-enhanced CT images of the histopathologically confirmed initial cohort (27 ROs, 74 ccRCCs, and 36 chRCCs) was conducted. Small, medium, large, and whole ROIs (S-ROI, M-ROI, L-ROI, and W-ROI, respectively) were utilized for CT attenuation value of tumor (AVT), lesion-to-cortex attenuation (L/C), and heterogeneous degree of tumor (HDT) calculations. Differences in these parameters were then compared between RO and ccRCC/chRCC, with receiver operating characteristic (ROC) curves being utilized to gauge the diagnostic utility of the statistically significant parameters. Logistic regression analyses were employed to identify key factors capable of differentiating RO and ccRCC/chRCC, with predictive models further being established. A validation cohort (6 ROs, 30 ccRCCs, and 12 chRCCs) was then employed to validate the performance of the predictive models. RESULTS Of the parameters evaluated using different ROIs, L/C-CMP (S-ROI) (0.88 ± 0.15 vs. 1.13 ± 0.25, P < .001) and HDT-CMP (W-ROI) (23.02 (12.00-51.21) vs. 37.81 (16.09-89.45), P < .001) were best suited to differentiating RO and ccRCC, yielding respective area under the curve (AUC) values of 0.803 and 0.834. AVT-NP (S-ROI) (122.85 ± 18.87 vs. 86.50 ± 18.65, P < .001) and AVT-NP (M-ROI) (119 (86-167) vs. 81.5 (53-142), P < .001) were better able to differentiate RO and chRCC, yielding respective AUC values of 0.918 and 0.906. Logistic regression analyses revealed that L/C-CMP (S-ROI) and HDT-PCP, as well as AVT-NP (S-ROI) and HDT-CMP, were the primary factors capable of differentiating RO from ccRCC and chRCC, respectively. The predictive model developed to differentiate between RO and ccRCC exhibited a sensitivity of 66.67% and 55.14% in the initial and validation cohorts, respectively, with corresponding specificity of 94.59% and 93.55%, accuracy of 87.13% and 86.84%, and AUC of 0.908 and 0.876. The predictive model developed to differentiate between RO and chRCC exhibited a sensitivity of 85.19% and 100.00% in the initial and validation cohorts, respectively, with corresponding specificity of 94.59% and 92.86%, accuracy of 87.30% and 95.24%, and AUC of 0.944 and 0.959. CONCLUSION These data demonstrate that a combination of quantitative parameters measured with particular ROIs can enable the efficient and reliable differentiation of RO from ccRCC and chRCC for use in routine patient differential diagnosis.