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

MR Virtual Biopsy of Solid Renal Masses: An Algorithmic Approach

Between 1983 and 2002, the incidence of solid renal tumors increased from 7.1 to 10.8 cases per 100,000. This is in large part due to the increase in the volume of ultrasound and cross-sectional imaging, although a majority of solid renal tumors are still found incidentally. Ultrasound and computed tomography (CT) have been the mainstay of renal mass screening and diagnosis but recent advances in magnetic resonance (MR) technology have made this the optimal choice when diagnosing and staging renal tumors. Our purpose in writing this review is to survey the modern MR imaging approach to benign and malignant solid renal tumors, consolidate the various imaging findings into an easy-to-read reference, and provide an imaging-based, algorithmic approach to renal mass characterization for clinicians. MR is at the forefront of renal mass characterization, surpassing ultrasound and CT in its ability to describe multiple tissue parameters and predict tumor biology. Cutting-edge MR protocols and the integration of diagnostic algorithms can improve patient outcomes, allowing the imager to narrow the differential and better guide oncologic and surgical management.

Minimal Fat Content in Papillary Renal Cell Carcinoma Diagnosed with Dual-Layer Dual-Energy CT

A 56-year-old man with a previous right nephrectomy for multiple papillary renal cell carcinomas (pRCC) underwent a follow-up CT scan. Using a dual-layer dual-energy CT (dlDECT), we demonstrated the presence of a small amount of fat in a 2.5 cm pRCC that mimicked the diagnosis of angiomyolipoma (AML). Histological examination demonstrated the absence of macroscopic intratumoral adipose tissue, showing a fair amount of enlarged foam macrophages loaded with intracytoplasmic lipids. The presence of fat density in an RCC is an extremely rare occurrence in the literature. To our knowledge, this is the first description using dlDECT of a minimal amount of fat tissue in a small RCC due to the presence of tumor-associated foam macrophages. Radiologists should be aware of this possibility when characterizing a renal mass with DECT. The option of RCCs must be considered, especially in the case of masses with an aggressive character or a positive history of RCC.

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.

Sporadic Renal Angiomyolipoma: Can We Adopt a Uniform Management Protocol?

Renal angiomyolipomas (AMLs), formerly known as PEComas (tumors showing perivascular epithelioid cell differentiation) are common benign renal masses composed of a varying ratio of fat, blood vessels, and smooth muscles. They are largely asymptomatic and diagnosed incidentally on imaging.

The adipose tissue content is the factor that gives AMLs their characteristic appearance on imaging and makes them easily identifiable. However, the fat-poor or fat-invisible varieties, which are difficult to differentiate radiologically from renal cell carcinomas (RCCs), present a diagnostic challenge. It is thus essential to establish the diagnosis and identify the atypical and hereditary cases as they require more intense surveillance and management due to their potential for malignant transformation.

Multiple management options are available, ranging from conservative approach to embolization and to the more radical option of nephrectomy. While the indications for intervention are relatively clear and aimed at a rather small cohort, the protocol for follow-up of the remainder of the cohort forming the majority of cases is not well established. The surveillance and discharge policies therefore vary between institutions and even between individual practitioners. We have reviewed the literature to establish an optimum management pathway focusing on the typical AMLs.

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.

Mixed epithelial and stromal tumor of the kidney composed mainly of solid components: A case report

Mixed epithelial and stromal tumor (MEST) is a relatively rare lesion of mixed epithelial and mesenchymal origin, consisting of epithelial components that form cysts and stromal cells that are positive for estrogen and progesterone receptors. The present case was a 54-year-old female who presented with hematuria. Abdominal ultrasonography revealed a 41 x 30 mm tumor in the right kidney, with the tumor protruding outward in the direction of the renal pelvis. Dynamic contrast-enhanced computed tomography and magnetic resonance imaging confirmed a solid tumor in the right kidney that showed gradual contrast enhancement and contained a central non-enhancing area with the appearance of a cystic component. Based on the imaging findings, the provisional diagnosis was papillary renal cell carcinoma or angiomyolipoma with epithelial cysts. Right nephrectomy was performed and the tumor was confirmed histopathologically as MEST. We report a very rare case of MEST that was composed mainly of solid components.

Tuberous Sclerosis: Current Update

Tuberous sclerosis complex (TSC) is a relatively rare autosomal dominant neurocutaneous disorder secondary to mutations in the TSC1 or TSC2 tumor suppressor genes. Although manifestation of the classic triad of seizures, intellectual disability, and facial angiofibromas may facilitate timely diagnosis of TSC, the multisystem features that may indicate TSC in the absence of these manifestations remain highly variable. In addition, patients with TSC are at risk of developing multiple benign and malignant tumors in various organ systems, resulting in increased morbidity and mortality. Thus, imaging plays a critical role in diagnosis, surveillance, and management of patients with TSC. It is crucial that radiologists be familiar with TSC and the various associated imaging features to avoid a delayed or incorrect diagnosis. Key manifestations include cortical dysplasias, subependymal nodules, subependymal giant cell astrocytomas, cardiac rhabdomyomas, lymphangioleiomyomatosis, and angiomyolipomas. Renal angiomyolipomas in particular can manifest with imaging features that mimic renal malignancy and pose a diagnostic dilemma. Other manifestations include dermatologic and ophthalmic manifestations, renal cysts, renal cell carcinomas, multifocal micronodular pneumocyte hyperplasia, splenic hamartomas, and other rare tumors such as perivascular epithelioid tumors. In addition to using imaging and clinical features to confirm the diagnosis, genetic testing can be performed. In this article, the molecular pathogenesis, clinical manifestations, and imaging features of TSC are reviewed. Current recommendations for management and surveillance of TSC are discussed as well. ^©RSNA, 2021.

The definitive role of CEUS in an ambiguous case of renal cell carcinoma

Angiomyolipoma is a benign tumor consisting of abnormal vessels, smooth muscles, and fatty tissue. Renal cell carcinoma is an insidious neoplasm accounting for approximately 2% of global cancer diagnoses. Due to similar diagnostic features, the differentiation between the two types is sometimes difficult. We hereby present the case of a 60-year-old patient with no clinical symptoms and a focal lesion in the parenchymal layer of the left kidney incidentally detected on ultrasound examination. The putative diagnosis was angiomyolipoma, which was then confirmed by another ultrasound and computed tomography examinations. However, a further radiologic consultation revealed another probable diagnosis - renal cell carcinoma. Contrast-enhanced ultrasound was conducted, and the enhancement pattern was suggestive of cancer. To sum up, a thorough imaging examination plays an important role in the diagnostic work-up of neoplastic lesions in the kidney. Even then, however, the radiological image of the lesion may be misleading, so differential diagnosis is important for making a proper diagnosis.

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 convention-radiomics CT nomogram for differentiating fat-poor angiomyolipoma from clear cell renal cell carcinoma

This study aimed to construct convention-radiomics CT nomogram containing conventional CT characteristics and radiomics signature for distinguishing fat-poor angiomyolipoma (fp-AML) from clear-cell renal cell carcinoma (ccRCC). 29 fp-AML and 110 ccRCC patients were enrolled and underwent CT examinations in this study. The radiomics-only logistic model was constructed with selected radiomics features by the analysis of variance (ANOVA)/Mann–Whitney (MW), correlation analysis, and Least Absolute Shrinkage and Selection Operator (LASSO), and the radiomics score (rad-score) was computed. The convention-radiomics logistic model based on independent conventional CT risk factors and rad-score was constructed for differentiating. Then the relevant nomogram was developed. Receiver operation characteristic (ROC) curves were calculated to quantify the accuracy for distinguishing. The rad-score of ccRCC was smaller than that of fp-AML. The convention-radioimics logistic model was constructed containing variables of enhancement pattern, V_UP, and rad-score. To the entire cohort, the area under the curve (AUC) of convention-radiomics model (0.968 [95% CI 0.923–0.990]) was higher than that of radiomics-only model (0.958 [95% CI 0.910–0.985]). Our study indicated that convention-radiomics CT nomogram including conventional CT risk factors and radiomics signature exhibited better performance in distinguishing fp-AML from ccRCC.

Genetic Syndromes Affecting Both Children and Adults: A Practical Guide to Imaging-based Diagnosis, Management, and Screening Recommendations for General Radiologists

Genetic syndromes are an infrequently encountered but challenging group of conditions for both pediatric and adult radiologists given the multitude of possible findings and important complications associated with these syndromes. This article reviews characteristic syndromic imaging features, as well as discussing important complications and screening recommendations for a selected group of clinically relevant genetic syndromes affecting both pediatric and adult populations.

Quantitative Analysis of Multiphase Contrast-Enhanced CT Images: A Pilot Study of Preoperative Prediction of Fat-Poor Angiomyolipoma and Renal Cell Carcinoma

OBJECTIVE. The objective of our study was to preoperatively predict fat-poor angiomyolipoma (fp-AML) and renal cell carcinoma (RCC) by conducting quantitative analysis of contrast-enhanced CT images. MATERIALS AND METHODS. One hundred fifteen patients with a pathologic diagnosis of fp-AML or RCC from a single institution were randomly allocated into a train set (tumor size: mean ± SD, 4.50 ± 2.62 cm) and test set (tumor size: 4.32 ± 2.73 cm) after data augmentation. High-dimensional histogram-based features, texture-based features, and Laws features were first extracted from CT images and were then combined as different combinations sets to construct a logistic prediction model based on the least absolute shrinkage and selection operator procedure for the prediction of fp-AML and RCC. Prediction performances were assessed by classification accuracy, area under the ROC curve (AUC), positive predictive value, negative predictive value, true-positive rate, and false-positive rate (FPR). In addition, we also investigated the effects of different gray-scales of quantitative features on prediction performances. RESULTS. The following combination sets of features achieved satisfying performances in the test set: histogram-based features (mean AUC = 0.8492, mean classification accuracy = 91.01%); histogram-based features and texture-based features (mean AUC = 0.9244, mean classification accuracy = 91.81%); histogram-based features and Laws features (mean AUC = 0.8546, mean classification accuracy = 88.76%); and histogram-based features, texture-based features, and Laws features (mean AUC = 0.8925, mean classification accuracy = 90.36%). The different quantitative gray-scales did not have an obvious effect on prediction performances. CONCLUSION. The integration of histogram-based features with texture-based features and Laws features provided a potential biomarker for the preoperative diagnosis of fp-AML and RCC. The accurate diagnosis of benign or malignant renal masses would help to make the clinical decision for radical surgery or close follow-up.