Four-phase computed tomography helps differentiation of renal oncocytoma with central hypodense areas from clear cell renal cell carcinoma

PURPOSE: To explore the utility of four-phase computed tomography (CT) in distinguishing renal oncocytoma with central hypodense areas from clear cell renal cell carcinoma (ccRCC). METHODS: Eighteen patients with oncocytoma and 63 patients with ccRCC presenting with central hypodense areas were included in this study. All patients underwent four-phase CT imaging including the excretory phases later than 20 min after contrast injection. Two blinded experienced radiologists visually reviewed the enhancement features of the central hypodense areas in the excretory phase images and selected the area demonstrating the greatest degree of enhancement of the tumor in the corticomedullary phase images. Regions of interest (ROIs) were placed in the same location in each of the three contrast-enhanced imaging phases. Additionally, ROIs were placed in the adjacent normal renal cortex for normalization. The ratio of the lesion to cortex attenuation (L/C) for the three contrast-enhanced imaging phases and absolute de-enhancement were calculated. The receiver operating characteristic curve was used to obtain the cut-off values. RESULTS: Complete enhancement inversion of the central areas was observed in 12 oncocytomas (66.67%) and 16 ccRCCs (25.40%) (P = 0.003). Complete enhancement inversion combined with L/C in the corticomedullary phase lower than 1.0 (P < 0.001) or absolute de-enhancement lower than 42.5 HU (P < 0.001) provided 86.42% and 85.19% accuracy, 61.11% and 55.56% sensitivity, 93.65% and 93.65% specificity, 73.33% and 71.43% positive predictive value (PPV), and 89.39% and 88.06% negative predictive value (NPV), respectively, for the diagnosis of oncocytomas. Combined with complete enhancement inversion, L/C in the corticomedullary phase lower than 1.0 and absolute de-enhancement lower than 42.5 HU provided 87.65%, 55.56%, 96.83%, 83.33%, and 88.41% of accuracy, sensitivity, specificity, PPV, and NPV, respectively, for the diagnosis of oncocytomas. CONCLUSION: The combination of enhancement features of the central hypodense areas and the peripheral tumor parenchyma can help distinguish oncocytoma with central hypodense areas from ccRCC.

CT differentiation of the oncocytoma and renal cell carcinoma based on peripheral tumor parenchyma and central hypodense area characterisation.

BACKGROUND: Although the central scar is an essential imaging characteristic of renal oncocytoma (RO), its utility in distinguishing RO from renal cell carcinoma (RCC) has not been well explored. The study aimed to evaluate whether the combination of CT characteristics of the peripheral tumor parenchyma (PTP) and central hypodense area (CHA) can differentiate typical RO with CHA from RCC. METHODS: A total of 132 tumors on the initial dataset were retrospectively evaluated using four-phase CT. The excretory phases were performed more than 20 min after the contrast injection. In corticomedullary phase (CMP) images, all tumors had CHAs. These tumors were categorized into RO (n = 23), clear cell RCC (ccRCC) (n = 85), and non-ccRCC (n = 24) groups. The differences in these qualitative and quantitative CT features of CHA and PTP between ROs and ccRCCs/non-ccRCCs were statistically examined. Logistic regression filters the main factors for separating ROs from ccRCCs/non-ccRCCs. The prediction models omitting and incorporating CHA features were constructed and evaluated, respectively. The effectiveness of the prediction models including CHA characteristics was then confirmed through a validation dataset (8 ROs, 35 ccRCCs, and 10 non-ccRCCs). RESULTS: The findings indicate that for differentiating ROs from ccRCCs and non-ccRCCs, prediction models with CHA characteristics surpassed models without CHA, with the corresponding areas under the curve (AUC) being 0.962 and 0.914 versus 0.952 and 0.839 respectively. In the prediction models that included CHA parameters, the relative enhancement ratio (RER) in CMP and enhancement inversion, as well as RER in nephrographic phase and enhancement inversion were the primary drivers for differentiating ROs from ccRCCs and non-ccRCCs, respectively. The prediction models with CHA characteristics had the comparable diagnostic ability on the validation dataset, with respective AUC values of 0.936 and 0.938 for differentiating ROs from ccRCCs and non-ccRCCs. CONCLUSION: The prediction models with CHA characteristics can help better differentiate typical ROs from RCCs. When a mass with CHA is discovered, particularly if RO is suspected, EP images with longer delay scanning periods should be acquired to evaluate the enhancement inversion characteristics of CHA.

Use of specific contrast-enhanced CT regions of interest to differentiate renal oncocytomas from small clear cell and chromophobe renal cell carcinomas.

PURPOSE We aimed to examine the usefulness of utilizing a specific contrast-enhanced computed tomog raphy (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 nephro graphic 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) calcula tions. 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 vali date 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 dif ferentiate 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 differ entiate 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 particu lar ROIs can enable the efficient and reliable differentiation of RO from ccRCC and chRCC for use in routine patient differential diagnosis.