Morphologic analysis with computed tomography may help differentiate fat-poor angiomyolipoma from renal cell carcinoma: a retrospective study with 602 patients

Predictors of Benignity for Small Endophytic Echogenic Renal Masses

OBJECTIVES

To evaluate for distinguishing demographic and sonographic features of small (<3 cm) endophytic angiomyolipomas (AMLs) that differentiate them from endophytic renal cell carcinomas (RCCs).

METHODS

This is a Health Insurance Portablitiy and Accountablity Act (HIPAA)-compliant retrospective review of the demographics and ultrasound features of endophytic renal AMLs compared to a group of endophytic RCCs. AMLs were confirmed by identifying macroscopic fat on computed tomography (CT) or magnetic resonance imaging (MRI), while RCCs were pathologically proven. Statistical analysis was used to compare findings in the 2 groups.

RESULTS

There were a total of 66 patients with 66 AMLs, and 28 patients with 28 RCCs. Of the AMLs, 57 of 66 were in females, while 10 of the 28 RCC cases were in females (P < .0001). The mean AML long and short diameters were 11.0 × 9.3 mm and were statistically significantly smaller (P < .0001) than the diameters of the RCCs (23.4 × 22.1 mm). Likewise, the ratio of the long axis to the short axis measurement was statistically significantly different between the 2 groups (P < .0001). Of the studied sonographic features, statistically different features between AMLs and RCCs included an oval versus a round shape (P < .001), respectively, and the presence versus absence of an echogenic margin, respectively. Location of the mass, mass homogeneity, mass lobulation, and presence of cystic components were not distinguishing features using P < .01 levels.

CONCLUSION

For an endophytic echogenic mass in a female patient, a small size with an oval shape and an echogenic margin is statistically more likely to be an AML than an RCC, which may be helpful with management decisions.

Are There Ultrasound Features to Distinguish Small (<3 cm) Peripheral Renal Angiomyolipomas From Renal Cell Carcinomas?

BACKGROUND

Small echogenic renal masses are usually angiomyolipomas (AMLs), but some renal cell carcinomas (RCCs) can be echogenic and confused with an AML.

OBJECTIVES

This is a study to evaluate any distinguishing demographic and sonographic features of small (<3 cm) peripheral AMLs versus peripheral RCCs.

METHODS

This is a HIPAA-compliant retrospective review of the demographics and ultrasound features of peripheral renal AMLs compared with a group of peripheral RCCs. All AMLs had confirmation of macroscopic fat as noted on thin-cut CT or fat-saturation MRI sequence images. All RCCs were pathologically proven. Statistical analysis was used to compare findings in the two groups.

RESULTS

There were a total of 52 patients with 56 AMLs, compared with 42 patients with 42 RCCs. There were 42 females in the AML group versus 10 females in the RCC group (P < .0001). The AML diameters (15.7 mm × 12.0 mm) were statistically significantly smaller (Plargest  = .0085, Psmallest  < .001) than the diameters of the RCCs (19.9 mm × 18.5 mm). Ultrasound features found to be statistically different between the two groups were the ratio of the largest dimension to the smallest dimension (P < .001), a lobulated versus smooth margin of the AML (26 vs 30) compared with the RCC group (3 vs 39) (P = .0012), and an "unusual" versus a round shape (P < .001) of the AML group (45 vs 11) compared with the RCC group (9 vs 33). In the multivariable model, the patient sex, margin, and mass shape were predictive of AML, with an area under the receiver operating characteristic curve of 0.92.

CONCLUSION

For a small (<3 cm) peripheral echogenic mass in a female patient, a lobulated lesion with an unusual shape is highly predictive of being an AML.

Distinguishing lipid-poor angiomyolipoma from renal carcinoma using tumor shape

OBJECTIVES

To validate the "overflowing beer sign" (OBS) for distinguishing between lipid-poor angiomyolipoma (AML) and renal cell carcinoma, and to determine whether it improves the detection of lipid-poor AML when added to the angular interface sign, a previously-validated morphologic feature associated with AML.

METHODS

Retrospective nested case-control study of all 134 AMLs in an institutional renal mass database matched 1:2 with 268 malignant renal masses from the same database. Cross-sectional imaging from each mass was reviewed and the presence of each sign was identified. A random selection of 60 masses (30 AML and 30 benign) was used to measure interobserver agreement.

RESULTS

Both signs were strongly associated with AML in the total population (OBS: OR 17.4 95% CI 8.0-42.5, p < 0.001; angular interface: OR 12.6, 95% CI 5.9-29.7, p < 0.001) and the population of patients excluding those with visible macroscopic fat (OBS: OR 11.2, 95% CI 4.8-28.7, p < 0.001; angular interface: 8.5, 95% CI 3.7-21.1, p < 0.001). In the lipid-poor population, the specificity of both signs was excellent (OBS: 95.6%, 95% CI 91.9%-98%; angular interface: 95.1%, 95% CI 91.3%-97.6%). Sensitivity was low for both signs (OBS: 31.4%, 95% CI 24.0-45.4%; angular interface: 30.5%, 95% CI 20.8%-41.6%). Both signs showed high levels of inter-rater agreement (OBS 90.0% 95% CI 80.5 - 95.9; angular interface 88.6, 95% CI 78.7-94.9) Testing for AML using the presence of either sign in this population improved sensitivity (39.0%, 95% CI 28.4%-50.4%, p = 0.023) without significantly reducing specificity (94.2%, 95% CI 90%-97%, p = 0.2) relative to the angular interface sign alone.

CONCLUSIONS

Recognition of the OBS increases the sensitivity of detection of lipid-poor AML without significantly reducing specificity.

Diagnostic performance of the "drooping" sign in CT diagnosis of exophytic renal angiomyolipoma

OBJECTIVE

To evaluate the prevalence of angular interface and the "drooping" sign in exophytic renal angiomyolipomas (AMLs) and the diagnostic performance in differentiating exophytic lipid-poor AMLs from other solid renal masses.

METHODS

This IRB-approved, two-center study included 185 patients with 188 exophytic solid renal masses < 4 cm with histopathology and pre-operative CT within 30 days of surgical resection or biopsy. Images were reviewed for the presence of angular interface and the "drooping" sign qualitatively by three readers blinded to the final diagnosis, with majority rules applied. Both features were assessed quantitatively by cohort creators (who are not readers) independently. Free-marginal kappa was used to assess inter-reader agreement and agreement between two methods assessing each feature. Fisher's exact test, Mann-Whitney test, and multivariable logistic regression with two-tailed p < 0.05 were used to determine statistical significance. Diagnostic performance was assessed.

RESULTS

Ninety-four patients had 96 AMLs, and 91 patients had 92 non-AMLs. Seventy-four (77%) of AMLs were lipid-poor based on quantitative assessment on CT. The presence of angular interface and the "drooping" sign by both qualitative and quantitative assessment were statistically significantly associated with AMLs (39% (qualitative) and 45% (quantitative) vs 15% (qualitative) and 13% (quantitative), and 48% (qualitative) and 43% (quantitative) vs 4% (qualitative) and 1% (quantitative), respectively, all p < 0.001) in univariable analysis. In multivariable analysis, only the "drooping" sign in either qualitative or quantitative assessment was a statistically significant predictor of AMLs (both p < 0.001). Inter-reader agreement for the "drooping" sign was moderate (k = 0.55) and for angular interface was fair (k = 0.33). Agreement between the two methods of assessing the "drooping" sign was substantial (k = 0.84) and of assessing the angular interface was moderate (k = 0.59). The "drooping" sign both qualitatively and quantitatively, alone or in combination of angular interface, had very high specificity (96-100%) and positive predictive value (PPV) (89-100%), moderate negative predictive value (62-68%), but limited sensitivity (23-49%) for lipid-poor AMLs.

CONCLUSION

The "drooping" sign by both qualitative and quantitative assessment is highly specific for lipid-rich and lipid-poor AMLs. This feature alone or in combination with angular interface can aid in CT diagnosis of lipid-poor AMLs with very high specificity and PPV.

The Role of CT Imaging in Characterization of Small Renal Masses

Small renal masses (SRM) are increasingly detected incidentally during imaging. They vary widely in histology and aggressiveness, and include benign renal tumors and renal cell carcinomas that can be either indolent or aggressive. Imaging plays a key role in the characterization of these small renal masses. While a confident diagnosis can be made in many cases, some renal masses are indeterminate at imaging and can present as diagnostic dilemmas for both the radiologists and the referring clinicians. This review focuses on CT characterization of small renal masses, perhaps helping us understand small renal masses. The following aspects were considered for the review: (a) assessing the presence of fat, (b) assessing the enhancement, (c) differentiating renal tumor subtype, and (d) identifying valuable CT signs.

The value of CT features and demographic data in the differential diagnosis of type 2 papillary renal cell carcinoma from fat-poor angiomyolipoma and oncocytoma

PURPOSES

To determine the CT features and demographic data predictive of type 2 papillary renal cell carcinoma (PRCC) that can help distinguish this neoplasm from fat-poor angiomyolipoma (fpAML) and oncocytoma.

METHODS

Fifty-four patients with type 2 PRCC, 48 with fpAML, and 47 with oncocytoma in the kidney from multiple centers were retrospectively reviewed. The demographic data and CT features of type 2 PRCC were analyzed and compared with those of fpAML and oncocytoma by univariate analysis and multiple logistic regression analysis to determine the predictive factors for differential diagnosis. Then, receiver operating characteristic (ROC) curve analysis was performed to further assess the logistic regression model and set the threshold level values of the numerical parameters.

RESULTS

Older age (≥ 46.5 years), unenhanced lesion-to-renal cortex attenuation (RLRCA) < 1.21, corticomedullary ratio of lesion to renal cortex net enhancement (RLRCNE) < 0.32, and size ≥ 30.1 mm were independent predictors for distinguishing type 2 PRCC from fpAML (OR 14.155, 8.332, and 57.745, respectively, P < 0.05 for all). The area under the curve (AUC) of the multiple logistic regression model in the ROC curve analysis was 0.970. In the combined evaluation, the four independent predictors had a sensitivity and specificity of 0.896 and 0.889, respectively. A corticomedullary RLRCNE < 0.61, irregular shape, and male sex were independent predictors for the differential diagnosis of type 2 PRCC from oncocytoma (OR 15.714, 12.158, and 6.175, respectively, P < 0.05 for all). In the combined evaluation, the three independent predictors had a sensitivity and specificity of 0.889 and 0.979, respectively. The AUC of the multiple logistic regression model in the ROC curve analysis was 0.964.

CONCLUSION

The combined application of CT features and demographic data had good ability in distinguishing type 2 PRCC from fpAML and oncocytoma, respectively.

Etiology of Small Echogenic Renal Masses

OBJECTIVES

There has been controversy on how frequently small echogenic masses are angiomyolipomas (AMLs) versus renal cell carcinoma (RCC) and how best to manage these echogenic masses. We performed this study to determine the etiologies of echogenic renal masses and compare them with prior publications to reach possible management decisions.

METHODS

This is a retrospective chart review of all consecutive renal ultrasound examinations performed at our institution between January 2015 and December 2016, with an ultrasound report finding containing the wording "echogenic" and "mass." This yielded 6462 total examinations. A total of 107 echogenic lesions met inclusion and exclusion criteria with correlative computed tomography, pathology, or long-term (>5 years) follow-up ultrasound. These lesions were stratified into those that were ≤2 cm and those that were >2 cm.

RESULTS

Almost all masses were benign, with the majority (79/107) being AMLs (73.8%); 64 of the 79 (81%) of the AMLs were in female patients. Two of the 107 masses were RCCs, and 1 mass was an oncocytic neoplasm. There were 77 of the masses that were ≤2 cm and these masses were benign except for one lesion of an oncocytic neoplasm. There were 30 of the 107 masses >2 cm, with 2 of the 30 (6.7%) being RCCs.

CONCLUSIONS

Incidental echogenic renal masses are most commonly AMLs. However, some masses may be RCCs. In comparing our results with the prior literature, we feel that small echogenic renal masses ≤1 cm usually require no further evaluation, while masses greater than that size require other imaging.

Differential diagnosis and prognosis of small renal masses: association with collateral vessels detected using contrast-enhanced computed tomography

Background

Active surveillance (AS) is one of the treatment methods for patients with small renal masses (SRMs; < 4 cm), including renal cell carcinomas (RCCs). However, some small RCCs may exhibit aggressive neoplastic behaviors and metastasize. Little is known about imaging biomarkers capable of identifying potentially aggressive small RCCs. Contrast-enhanced computed tomography (CECT) often detects collateral vessels arising from neoplastic angiogenesis in RCCs. Therefore, this study aimed to evaluate the association between SRM differential diagnoses and prognoses, and the detection of collateral vessels using CECT.

Methods

A total of 130 consecutive patients with pathologically confirmed non-metastatic SRMs (fat-poor angiomyolipomas [fpAMLs; n = 7] and RCCs [n = 123]) were retrospectively enrolled. Between 2011 and 2019, SRM diagnoses in these patients were confirmed after biopsy or surgical resection. All RCCs were surgically resected. Regardless of diameter, a collateral vessel (CV) was defined as any blood vessel connecting the tumor from around the kidney using CECT. First, we analyzed the role of CV-detection in differentiating between fpAML and RCC. Then, we evaluated the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of RCC diagnosis based on CV-detection using CECT. We also assessed the prognostic value of CV-detection using the Fisher exact test, and Kaplan-Meier method and the log-rank test.

Results

The sensitivity, specificity, PPV, NPV, and accuracy of CV-detection for the diagnosis of small RCCs was 48.5, 45.5, 100, 100, and 9.5% respectively. Five of 123 (4.1%) patients with RCC experienced recurrence. CV-detection using CECT was the only significant factor associated with recurrence (p = 0.0177). Recurrence-free survival (RFS) was significantly lower in patients with CV compared with in those without CV (5-year RFS 92.4% versus 100%, respectively; p = 0.005). In addition, critical review of the CT images revealed the CVs to be continuous with the venous vessels around the kidney.

Conclusions

The detection of CVs using CECT is useful for differentiating between small fpAMLs and RCCs. CV-detection may also be applied as a predictive parameter for small RCCs prone to recurrence after surgical resection. Moreover, AS could be suitable for small RCCs without CVs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09971-w.

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

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.

A Non-Invasive Scoring System to Differential Diagnosis of Clear Cell Renal Cell Carcinoma (ccRCC) From Renal Angiomyolipoma Without Visible Fat (RAML-wvf) Based on CT Features

Background

Renal angiomyolipoma without visible fat (RAML-wvf) and clear cell renal cell carcinoma (ccRCC) have many overlapping features on imaging, which poses a challenge to radiologists. This study aimed to create a scoring system to distinguish ccRCC from RAML-wvf using computed tomography imaging.

Methods

A total of 202 patients from 2011 to 2019 that were confirmed by pathology with ccRCC (n=123) or RAML (n=79) were retrospectively analyzed by dividing them randomly into a training cohort (n=142) and a validation cohort (n=60). A model was established using logistic regression and weighted to be a scoring system. ROC, AUC, cut-off point, and calibration analyses were performed. The scoring system was divided into three ranges for convenience in clinical evaluations, and the diagnostic probability of ccRCC was calculated.

Results

Four independent risk factors are included in the system: 1) presence of a pseudocapsule, 2) a heterogeneous tumor parenchyma in pre-enhancement scanning, 3) a non-high CT attenuation in pre-enhancement scanning, and 4) a heterogeneous enhancement in CMP. The prediction accuracy had an ROC of 0.978 (95% CI, 0.956–0.999; P=0.011), similar to the primary model (ROC, 0.977; 95% CI, 0.954–1.000; P=0.012). A sensitivity of 91.4% and a specificity of 93.9% were achieved using 4.5 points as the cutoff value. Validation showed a good result (ROC, 0.922; 95% CI, 0.854–0.991, P=0.035). The number of patients with ccRCC in the three ranges (0 to <2 points; 2–4 points; >4 to ≤11 points) significantly increased with increasing scores.

Conclusion

This scoring system is convenient for distinguishing between ccRCC and RAML-wvf using four computed tomography features.

Circularity Index on Contrast-Enhanced Computed Tomography Helps Distinguish Fat-Poor Angiomyolipoma from Renal Cell Carcinoma: Retrospective Analyses of Histologically Proven 257 Small Renal Tumors Less Than 4 cm

OBJECTIVE

To evaluate circularity as a quantitative shape factor of small renal tumor on computed tomography (CT) in differentiating fat-poor angiomyolipoma (AML) from renal cell carcinoma (RCC).

MATERIALS AND METHODS

In 257 consecutive patients, 257 pathologically confirmed renal tumors (either AML or RCC less than 4 cm), which did not include visible fat on unenhanced CT, were retrospectively evaluated. A radiologist drew the tumor margin to measure the perimeter and area in all the contrast-enhanced axial CT images. In each image, a quantitative shape factor, circularity, was calculated using the following equation: 4 × π × (area ÷ perimeter²). The median circularity (circularity index) was adopted as a representative value in each tumor. The circularity index was compared between fat-poor AML and RCC, and the receiver operating characteristic (ROC) curve analysis was performed. Univariable and multivariable binary logistic regression analysis was performed to determine the independent predictor of fat-poor AML.

RESULTS

Of the 257 tumors, 26 were AMLs and 231 were RCCs (184 clear cell RCCs, 25 papillary RCCs, and 22 chromophobe RCCs). The mean circularity index of AML was significantly lower than that of RCC (0.86 ± 0.04 vs. 0.93 ± 0.02, p < 0.001). The mean circularity index was not different between the subtypes of RCCs (0.93 ± 0.02, 0.92 ± 0.02, and 0.92 ± 0.02 for clear cell, papillary, and chromophobe RCCs, respectively, p = 0.210). The area under the ROC curve of circularity index was 0.924 for differentiating fat-poor AML from RCC. The sensitivity and specificity were 88.5% and 90.9%, respectively (cut-off, 0.90). Lower circularity index (≤ 0.9) was an independent predictor (odds ratio, 41.0; p < 0.001) for predicting fat-poor AML on multivariable logistic regression analysis.

CONCLUSION

Circularity is a useful quantitative shape factor of small renal tumor for differentiating fat-poor AML from RCC.

Early dark cortical band sign on CT for differentiating clear cell renal cell carcinoma from fat poor angiomyolipoma and detecting peritumoral pseudocapsule

OBJECTIVES

To retrospectively evaluate whether the early dark cortical band (EDCB) on CT can be a predictor to differentiate clear cell renal cell carcinoma (ccRCC) from fat poor angiomyolipoma (Fp-AML) and to detect peritumoral pseudocapsules in ccRCC.

METHODS

The EDCBs, which are comprised of unenhanced thin lines at the tumor-renal cortex border in the corticomedullary phase, on the CT images of 342 patients who underwent partial nephrectomy were evaluated. Independent predictors among the clinical and CT findings for differentiating ccRCC from Fp-AML were identified using multivariate analyses. The diagnostic performance of the EDCB for diagnosing peritumoral pseudocapsule in ccRCC and differentiating ccRCC from Fp-AML was calculated.

RESULTS

The EDCB was observed in 157 of 254 (61.8%) ccRCCs, 4 of 31 (12.9%) chromophobe RCCs, 1 of 21 (4.8%) papillary RCCs, 3 of 11 (27.3%) clear cell papillary RCCs, 3 of 8 (37.5%) oncocytomas, and 0 of 17 (0%) Fp-AMLs. There was substantial interobserver agreement for the EDCB (k = 0.719). The EDCB was a significant predictor for differentiating ccRCC from Fp-AML (p < 0.001). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value of the EDCB for differentiating ccRCC from Fp-AML were 61.8%, 100%, 100%, and 14.9%, respectively, and those for detecting pseudocapsule in 236 ccRCCs were 62.3%, 68.8%, 96.5%, and 11.7%, respectively.

CONCLUSION

Although diagnostic accuracy of the EDCB for detecting peritumoral pseudocapsule in RCC is inadequate, it can be a predictor for differentiating ccRCC from Fp-AML with high specificity and PPV.

KEY POINTS

• The early dark cortical band (EDCB) sign is observed in nearly two-thirds of clear cell renal cell carcinoma (ccRCC) that are treated by partial nephrectomy and have substantial interobserver agreement. • The EDCB is a significant predictor for differentiating ccRCCs from fat poor angiomyolipomas, with a high specificity and positive predictive value. • Diagnostic accuracy of the EDCB for detecting peritumoral pseudocapsule in ccRCC is inadequate, though better than those in the nephrographic and excretory-phase images.

Review of Value of CT Texture Analysis and Machine Learning in Differentiating Fat-Poor Renal Angiomyolipoma from Renal Cell Carcinoma

The diagnosis of patients with suspected angiomyolipoma relies on the detection of abundant macroscopic intralesional fat, which is always of no use to differentiate fat-poor angiomyolipoma (fp-AML) from renal cell carcinoma and diagnosis of fp-AML excessively depends on individual experience. Texture analysis was proven to be a potentially useful biomarker for distinguishing between benign and malignant tumors because of its capability of providing objective and quantitative assessment of lesions by analyzing features that are not visible to the human eye. This review aimed to summarize the literature on the use of texture analysis to diagnose patients with fat-poor angiomyolipoma vs those with renal cell carcinoma and to evaluate its current application, limitations, and future challenges in order to avoid unnecessary surgical resection.

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

PURPOSE

The purpose of this study was to investigate the difference between imaging features of ultra-small renal cell carcinoma (usRCC) and angiomyolipoma with minimal fat (mfAML) whose enhancement were both hypervascular by using multidetector computed tomography (MDCT).

MATERIALS AND METHODS

Confirmed by pathology, 40 cases of hypervascular usRCC and 21 cases of hypervascular mfAML both with diameter of 2 cm or less were compared and analyzed retrospectively, including traditional imaging features and thin-section computed tomography (CT) dynamic enhanced parameters. Meanwhile, receiver operating characteristic (ROC) curves were constructed to evaluate the diagnostic efficacy of each significant parameter and the information with diagnostic value was selected to construct the prediction model.

RESULTS

Comparison of traditional imaging features: the features, included age, shape, location, central location of tumor, wedge sign, renal cortex lift sign, black star sign, enhanced homogeneity in cortical phase (CP) and enhancement pattern had no significant difference between usRCC and mfAML (P > 0.05); sex, cystic degeneration or necrosis, pseudocapsule sign, and enhanced homogeneity in nephrographic phase (NP) had significant differences between usRCC and mfAML (P < 0.05). Comparison of CT dynamic enhanced parameters: the CT value, NEV and REV of usRCC were all higher than mfAML in both CP and NP (P < 0.01). Respectively, the area under the ROC curve (AUC) were 0.74, 0.75, 0.78, 0.83, 0.81 and 0.78. The sensitivity and specificity for differentiating ucRCC from mfAML were 85.0% and 76.2% respectively when NEV_NP was 73.6 HU as the critical value. Multivariate analysis showed that male, cystic degeneration or necrosis, and NEV_NP higher than 73.6 HU as an independent risk factor for usRCC (P < 0.01). The AUC value of the prediction model constructed by the combination was 0.94, the accuracy was 86.89%, the sensitivity was 82.50%, and the specificity was 95.24%.

CONCLUSION

Morphological characteristics in traditional diagnosis of small renal carcinoma (diameter of 4 cm or less) have certain significance in differentiating hypervascular usRCC and mfAML in early stage, but the diagnostic efficacy was limited. Sex, cystic degeneration or necrosis, and quantitative parameters measured after enhancement play an important role in differential diagnosis of hypervascular usRCC and mfAML, and the prediction model constructed by the combination has a good diagnostic performance.

Current management of small renal masses

One of the consequences of the growing use of diagnostic imaging techniques is the notable growth in the detection of small renal masses presumably corresponding to localized tumors that are potentially curable with surgical treatment. When faced with the finding of a small renal mass, radiologists must determine whether it is benign or malignant, and if it is malignant, what subtype it belong to, and whether it should be managed with surgical treatment, with ablative techniques, or with watchful waiting with active surveillance. Small renal masses are now a clinical entity that require management different from the approaches used for classical renal cell carcinomas. In this scenario, radiologists are key because they are involved in all aspects of the management of these tumors, including in their diagnosis, treatment, and follow-up.

Role of Interface Sign and Diffusion-Weighted Magnetic Resonance Imaging in Differential Diagnosis of Exophytic Renal Masses

PURPOSE

We aimed to investigate the role of interfaces of exophytic solid and cystic renal masses on magnetic resonance imaging (MRI) and the added value of diffusion-weighted imaging in differentiating benign from malignant lesions.

METHODS

The Institutional Review Board approved this retrospective study, and informed consent was waived. A total of 265 patients (109 [41%] women and 156 [59%] men) with a mean age of 57 ± 12 (standard deviation) years were enrolled in this study. Preoperative MRI (n = 238) examinations of patients with solid or cystic renal masses and MRI (n = 27) examinations of patients with Bosniak IIF cysts without progression were reviewed. Solid/cystic pattern, interface types and apparent diffusion coefficient (ADC) values were recorded by 2 radiologists. The diagnostic performance of combining normalized ADC values with interface sign were evaluated.

RESULTS

Among 265 renal lesions (109 cystic and 156 solid), all malignant lesions (n = 192) had a round interface. No malignant lesions showed an angular interface. For prediction of benignity in cystic lesions, sensitivity (82.86% vs 56.16%), negative predictive value (92.50% vs 85.71%), and accuracy (94.50% vs 87.92%) ratios of angular interface were higher compared to all (solid plus cystic) lesions. The best normalized ADC cutoff values for predicting malignancy in lesions with round interface were as follows: for all (solid plus cystic), ≤ 0.75 (AUROC = 0.804); solid, ≤ 0.6 (AUROC = 0.819); and cystic, ≤ 0.8 (AUROC = 0.936).

CONCLUSIONS

Angular interface can be a predictor of benignity for especially cystic renal masses. The evaluation of interface type with normalized ADC value can be an important clue in differential diagnosis especially in patients avoiding contrast.

Renal Angiomyolipoma Based on New Classification: How to Differentiate It From Renal Cell Carcinoma

OBJECTIVE

The purpose of this article is to describe useful imaging features for differentiating angiomyolipoma (AML) subtypes from renal cell carcinoma subtypes.

CONCLUSION

A newer radiologic classification of renal AML consists of fat-rich AML (≤ -10 HU), fat-poor AML (> -10 HU; tumor-to-spleen ratio < 0.71; signal intensity index, > 16.5%), and fat-invisible AML (> -10 HU; tumor-to-spleen ratio, > 0.71; signal intensity index, < 16.5%). Each subtype must be differentiated from the renal cell carcinoma subtype because of overlapping imaging features.

Are growth patterns on MRI in small (< 4 cm) solid renal masses useful for predicting benign histology?

PURPOSE

To evaluate previously described growth patterns in < 4 cm solid renal masses.

MATERIALS AND METHODS

With IRB approval, 63 renal cell carcinomas (RCC; clear cell n = 22, papillary n = 28, chromophobe n = 13) and 36 benign masses [minimal-fat (mf) angiomyolipoma (AML) n = 13, oncocytoma n = 23) from a single institution were independently evaluated by two blinded radiologists (R1/R2) using T2-weighted MRI for (1) the angular interface sign (AIS), (2) bubble-over sign (BOS), (3) percentage (%) exophytic growth and (4) long-to-short axis ratio. Comparisons were performed using ANOVA, chi-square and multi-variate regression.

RESULTS

AIS was present in 11.1% (7/63) -9.5% (6/63) R1/R2 RCC compared to 13.9% (5/36) -19.4% (7/36) R1/R2 benign masses (p = 0.68 and 0.16). BOS was present in 11.1% (7/63) -3.2% (2/63) R1/R2 RCC compared to 16.7% (6/36) -8.3% (3/36) R1/R2 benign masses (p = 0.432 and 0.261). Agreement was moderate (K = 0.50 and 0.55). mf-AML [66 ± 32% (range 0-100%)] and oncocytoma [53 ± 26% (0-90%)] had larger % exophytic growth compared to RCC [32 ± 23% (0-80%)] (p < 0.001). No RCC had 90-100% exophytic growth, present in 38.5% (5/13) mf-AMLs and 17.4% (4/23) oncocytomas. The long-to-short axis did not differ between groups (p = 0.053).

CONCLUSIONS

Benign masses show greater % exophytic growth whereas other growth patterns are not useful. Future studies evaluating % exophytic growth using multi-variate MR analysis in renal masses are required.

KEY POINTS

• Greater exophytic growth is associated with benignity among solid renal masses. • Only minimal fat AMLs and oncocytomas had 90-100% exophytic growth. • The angular interface sign was not useful to differentiate benign masses from RCC. • The bubble-over sign was not useful to differentiate benign masses from RCC. • Subjective analysis of growth patterns had fair-to-moderate agreement.

The Risks of Renal Angiomyolipoma: Reviewing the Evidence

Renal angiomyolipoma (RAML), though a rare benign tumor, may impose a significant morbidity or even mortality due to its unique characteristics and the complications subsequent to its treatment. The classic tumor variant is composed of smooth muscular, vascular, and fatty components. The most straightforward diagnosis is when the fat component is abundant and gives a characteristic appearance on different imaging studies. In fat-poor lesions, however, the diagnosis is difficult and presumed a renal cell carcinoma. Yet, some variants of RAML, though rare, express an aggressive behavior leading to metastasis and mortality. The challenge lies in the early detection of benign variants and identifying aggressive lesions for proper management. Another challenge is when the vascular tissue component predominates and poses a risk of hemorrhage that may extend to the retroperitoneum in a massive life-threatening condition. The predicament here is to identify the characteristics of tumors at risk of bleeding and provide a prophylactic treatment. According to the clinical presentation, different treatment modalities, prophylactic or therapeutic, are available that span the spectrum of observation, embolization, or surgery. Renal impairment may result from extensive tumor burden or as a complication of the management itself. Improvement of diagnostic techniques, super-selective embolization, nephron-sparing surgery, and late treatment with the mammalian target of rapamycin inhibitors have provided more effective and safe management strategies. In this review, we examine the evidence pertaining to the risks imposed by RAML to the patients and identify merits and hazards associated with different treatment modalities.