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.

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.

Pulmonary hamartoma: Feasibility of dual-energy CT detection of intranodular fat

We have reported 2 cases of pulmonary hamartoma focusing on detecting intranodular fat, which is one of CT features suggestive of pulmonary hamartoma, using dual-energy CT analyses. For patient 1, a 73-year-old man was pointed out to have a nodular opacity on chest radiograph of pretreatment workup for retinal detachment. In patient 2, a 66-year-old woman with uterine carcinoma admitted for preoperative assessment. Both patients underwent dual-energy CT examination and the pulmonary lesions exhibited a downward-sloping curve at lower X-ray energies on attenuation curve of virtual monochromatic images, which suggested fatty tissue. Dual-energy CT analysis can help diagnose pulmonary hamartoma with detection of intralesional fatty tissue.

Exploring serum metabolic markers for the discrimination of ccRCC from renal angiomyolipoma by metabolomics

Aim: The discrimination of renal cell carcinoma from renal angiomyolipoma (RAML) is crucial for the effective treatment of each. Materials & methods: Serum samples were analyzed by nuclear magnetic resonance spectroscopy-based metabolomics and a number of metabolites were further quantified by HPLC-UV. Results: Clear-cell renal carcinoma (ccRCC) was characterized by drastic disruptions in energy, amino acids, creatinine and uric acid metabolic pathways. A logistic model for the differential diagnosis of RAML from ccRCC was established using the combination of serum levels of uric acid, the ratio of uric acid to hypoxanthine and the ratio of hypoxanthine to creatinine as variables with area under the curve of the receiver operating characteristic curve value of 0.907. Conclusion: Alterations in serum purine metabolites may be used as potential metabolic markers for the differential diagnosis of ccRCC and RAML.

Radiomics of computed tomography and magnetic resonance imaging in renal cell carcinoma-a systematic review and meta-analysis

OBJECTIVES

(1) To assess the methodological quality of radiomics studies investigating histological subtypes, therapy response, and survival in patients with renal cell carcinoma (RCC) and (2) to determine the risk of bias in these radiomics studies.

METHODS

In this systematic review, literature published since 2000 on radiomics in RCC was included and assessed for methodological quality using the Radiomics Quality Score. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool and a meta-analysis of radiomics studies focusing on differentiating between angiomyolipoma without visible fat and RCC was performed.

RESULTS

Fifty-seven studies investigating the use of radiomics in renal cancer were identified, including 4590 patients in total. The average Radiomics Quality Score was 3.41 (9.4% of total) with good inter-rater agreement (ICC 0.96, 95% CI 0.93-0.98). Three studies validated results with an independent dataset, one used a publically available validation dataset. None of the studies shared the code, images, or regions of interest. The meta-analysis showed moderate heterogeneity among the included studies and an odds ratio of 6.24 (95% CI 4.27-9.12; p < 0.001) for the differentiation of angiomyolipoma without visible fat from RCC.

CONCLUSIONS

Radiomics algorithms show promise for answering clinical questions where subjective interpretation is challenging or not established. However, the generalizability of findings to prospective cohorts needs to be demonstrated in future trials for progression towards clinical translation. Improved sharing of methods including code and images could facilitate independent validation of radiomics signatures.

KEY POINTS

• Studies achieved an average Radiomics Quality Score of 10.8%. Common reasons for low Radiomics Quality Scores were unvalidated results, retrospective study design, absence of open science, and insufficient control for multiple comparisons. • A previous training phase allowed reaching almost perfect inter-rater agreement in the application of the Radiomics Quality Score. • Meta-analysis of radiomics studies distinguishing angiomyolipoma without visible fat from renal cell carcinoma show moderate diagnostic odds ratios of 6.24 and moderate methodological diversity.

Radiomics of small renal masses on multiphasic CT: accuracy of machine learning-based classification models for the differentiation of renal cell carcinoma and angiomyolipoma without visible fat

OBJECTIVE

To investigate the discriminative capabilities of different machine learning-based classification models on the differentiation of small (< 4 cm) renal angiomyolipoma without visible fat (AMLwvf) and renal cell carcinoma (RCC).

METHODS

This study retrospectively collected 163 patients with pathologically proven small renal mass, including 118 RCC and 45 AMLwvf patients. Target region of interest (ROI) delineation, followed by texture feature extraction, was performed on a representative slice with the largest lesion area on each phase of the four-phase CT images. Fifteen concatenations of the four-phasic features were fed into 224 classification models (built with 8 classifiers and 28 feature selection methods), classification performances of the 3360 resultant discriminative models were compared, and the top-ranked features were analyzed.

RESULTS

Image features extracted from the unenhanced phase (UP) CT image demonstrated dominant classification performances over features from other three phases. The two discriminative models "SVM + t_score" and "SVM + relief" achieved the highest classification AUC of 0.90. The 10 top-ranked features from UP included 1 shape feature, 5 first-order statistics features, and 4 texture features, where the shape feature and the first-order statistics features showed superior discriminative capabilities in differentiating RCC vs. AMLwvf through the t-SNE visualization.

CONCLUSION

Image features extracted from UP are sufficient to generate accurate differentiation between AMLwvf and RCC using machine learning-based classification model.

KEY POINTS

• Radiomics extracted from unenhanced CT are sufficient to accurately differentiate angiomyolipoma without visible fat and renal cell carcinoma using machine learning-based classification model. • The highest discriminative models achieved an AUC of 0.90 and were based on the analysis of unenhanced CT, alone or in association with images obtained at the nephrographic phase. • Features related to shape and to histogram analysis (first-order statistics) showed superior discrimination compared with gray-level distribution of the image (second-order statistics, commonly called texture features).

A case report of giant hamartoma of both kidneys with spontaneous rupture and hemorrhage in a pregnant woman: A case report

The aim of the present study was to describe a case of renal angiomyolipoma (RAML) in a 31-year-old woman who presented with massive hemorrhage, shock, severe anemia (Hb 63 g-l) and multiple lesions, prior to delivery of a dead fetus. A 31-year-old woman was admitted due to left flank and abdominal pain at 20 weeks of gestation age, and diagnosed with RAML complicated with spontaneous rupture and hemorrhage of the left kidney, for which emergency exploratory laparotomy, left kidney resection and splenectomy were performed. The patient delivered a dead fetus 3 days following surgery and recovered well postoperatively. Hemorrhagic RAML during pregnancy is a rare and complex vascular surgical emergency, and should be managed in a multidisciplinary manner. Spontaneous rupture is a serious threat to the life and health of pregnant women and fetuses. The present case is a typical example of RAML in a pregnant woman complicated by spontaneous rupture and hemorrhage, which highlights the importance of determining the risk of acute hemorrhage in early stages of pregnancy, and the significance of time and proper management. However, in cases of shock caused by spontaneous rupture and hemorrhage, the only way to save the life of the patient is to resect the lesion without delay.

Use of Iodine Concentration in the Lipid-Poor Portion of the Renal Mass for Differentiation of Angiomyolipoma from Renal Cell Carcinoma

Purpose: This study is aimed to evaluate the iodine concentration in the lipid-poor portion of the renal mass as a potential tool for the differentiation between angiomyolipoma (AML) and renal cell carcinoma (RCC). Materials and Methods: There were eight cases of AML and eight cases of RCC. All patients received corticomedullary, nephrographic and excretory phase enhanced scanning. The regions of interest (ROI) were manually placed in the lipid-poor portion of the renal mass and in the abdominal aorta. Average iodine concentrations were obtained for the ROIs and abdominal aorta. Data were compared using repeated measures analysis with the Bonferroni correction for multiple comparisons. Results: At the unenhanced phase, the iodine concentration in the lipid-poor portion of the renal mass of RCC was not significantly different from that of AML (p = 0.298). At the three enhanced phases, the iodine concentrations in the renal mass of RCC were substantially elevated compared with those of AML. In addition, the CT values of the renal mass of RCC were significantly higher than those of AML at all the enhanced phases. Of note, there was a significant correlation between iodine concentrations and CT values (r = 0.919; p < 0.001) in the lipid-poor portion of the renal mass of RCC. Conclusions: Between RCC and AML there was significant difference in iodine concentrations in the lipid-poor portion of the renal masses. Iodine concentration holds promise as a diagnostic alternative to macroscopic fat for differentiation of AML from RCC.

Diagnostic Imaging for Solid Renal Tumors: A Pictorial Review

The prognosis of renal tumors depends on histologic subtype. The increased use of abdominal imaging has resulted in an increase in the number of small renal incidentaloma in recent decades. Of these incidentally discovered tumors, 20% are benign lesions warranting conservative management, but most are renal cell carcinomas that warrant a more aggressive therapeutic approach due to their malignant potential. Dedicated diagnostic renal imaging is important for characterization of renal tumors to facilitate treatment planning. This review discusses the ability to detect and differentiate renal cell carcinoma subtypes, angiomyolipoma and oncocytoma based on ultrasound imaging, computed tomography, multiparametric magnetic resonance, and nuclear imaging.

Differentiating solid, non-macroscopic fat containing, enhancing renal masses using fast Fourier transform analysis of multiphase CT

OBJECTIVE

To test the feasibility of two-dimensional fast Fourier transforms (FFT)-based imaging metrics in differentiating solid, non-macroscopic fat containing, enhancing renal masses using contrast-enhanced CT images. We quantify image-based intratumoral textural variations (indicator of tumor heterogeneity) using frequency-based (FFT) imaging metrics.

METHODS

In this Institutional Review Board approved, Health Insurance Portability and Accountability Act -compliant, retrospective case-control study, we evaluated 156 patients with predominantly solid, non-macroscopic fat containing, enhancing renal masses identified between June 2009 and June 2016. 110 cases (70%) were malignant RCC, including clear cell, papillary and chromophobe subtypes and, 46 cases (30%) were benign renal masses: oncocytoma and lipid-poor angiomyolipoma. Whole lesions were manually segmented using Synapse 3D (Fujifilm, CT) and co-registered from the multiphase CT acquisitions for each tumor. Pathological diagnosis of all tumors was obtained following surgical resection. Matlab function, FFT2 was used to perform the image to frequency transformation.

RESULTS

A Wilcoxon rank sum test showed that FFT-based metrics were significantly (p < 0.005) different between 1. benign vs malignant renal masses, 2. oncocytoma vs clear cell renal cell carcinoma and 3. oncocytoma vs lipid-poor angiomyolipoma. Receiver operator characteristics analysis revealed reasonable discrimination (area under the curve >0.7, p < 0.05) within these three groups of comparisons.

CONCLUSION

In combination with other metrics, FFT-metrics may improve patient management and potentially help differentiate other renal tumors. Advances in knowledge: We report for the first time that FFT-based metrics can differentiate between some solid, non-macroscopic fat containing, enhancing renal masses using their contrast-enhanced CT data.

CT and MRI of small renal masses

Small renal masses are increasingly detected incidentally at 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 article will summarize the current evidence of imaging features that correlate with the biology of small solid renal masses, and discuss key approaches in imaging characterization of these masses using CT and MRI.

Fat poor angiomyolipoma differentiation from renal cell carcinoma at 320-slice dynamic volume CT perfusion

PURPOSE

To compare various CT perfusion features of fat poor angiomyolipoma (AML) with those of size-matched renal cell carcinoma (RCC).

METHODS

One hundred and seventy-four patients [16 with fat poor AML (mean diameter, 3.1 cm; range, 1.5-5.5 cm) and 158 with RCC (mean diameter, 3.2 cm; range, 2.4-5.4 cm)] who had undergone 320-slice dynamic volume CT perfusion were evaluated. Equivalent blood volume (BV _Equiv), permeability surface-area product (PS), and blood flow (BF) of tumor were measured and analyzed. Fat poor AML was compared with each subtype of RCC (132 clear cell, 9 papillary, and 17 chromophobe). Receiver operating characteristic (ROC) curve analysis was performed for the comparison of fat poor AML and RCC. ROC curve analysis was not performed for the papillary RCC subtype because of the small number of masses of this subtype.

RESULTS

BV _Equiv and BF were significantly lower in fat poor AML than in clear cell RCC (P < 0.05 for both). Fat poor AML had higher BV _Equiv, PS, and BF than papillary RCC (P < 0.05 for all). PS and BF in fat poor AML significantly exceeded those in chromophobe RCC (P < 0.05 for both). For differentiating fat poor AML from clear cell RCC, area under the ROC curve (AUC) of BV _Equiv and BF were 0.82 and 0.69. Using the optimal threshold value, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 0.82, 0.81, 0.35, 0.97 for BV _Equiv and 0.71, 0.75, 0.24, 0.96 for BF, respectively. For differentiating fat poor AML from chromophobe RCC, AUC of PS and BF were 0.77 and 0.79, respectively. The optimal sensitivity, specificity, PPV, and NPV were 0.77, 0.75, 0.75, 0.76 for PS and 0.71, 0.81, 0.72, 0.80 for BF, respectively.

CONCLUSIONS

Fat poor AML and subtypes of RCCs demonstrate different perfusion features at 320-slice dynamic volume CT, allowing their differentiations with BV _Equiv, PS, and BF being valuable perfusion parameters.

Angiomyolipoma of the Kidneys: Current Perspectives and Challenges in Diagnostic Imaging and Image-Guided Therapy

Angiomyolipomas (AML) are benign tumors of the kidneys frequently encountered in radiologic practice in large tertiary centers. In comparison to renal cell carcinomas (RCC), AML are seldom treated unless they are large, undergo malignant transformation or develop complications like acute hemorrhage. The common garden triphasic (classic) AML is an easy diagnosis, however, some variants lack macroscopic fat in which case the radiologic differentiation from RCC becomes challenging. Several imaging features, both qualitative and quantitative, have been described in differentiating the 2 entities. Although minimal fat AML is not entirely a radiologic diagnosis, the suspicion raised on imaging necessitates sampling and potentially avoids an unwanted surgery. Recently a new variant, epitheloid AML has been described which often has atypical imaging features and is at a higher risk for malignant transformation. Apart from the diagnosis, the radiologist also needs to convey information regarding nephrometric scores which help in surgical decision-making. Recently, more and more AMLs are managed with selective arterial embolization and percutaneous ablation, both of which are associated with less morbidity when compared to surgery. The purpose of this article is to review the imaging and pathologic features of classic AML as well as the differentiation of minimal fat AML from RCC. In addition, an overview of nephrometric scoring and image-guided interventions is also provided.

Chemical shift magnetic resonance imaging for distinguishing minimal-fat renal angiomyolipoma from renal cell carcinoma: a meta-analysis

OBJECTIVES

To determine the performance of chemical shift signal intensity index (CS-SII) values for distinguishing minimal-fat renal angiomyolipoma (mfAML) from renal cell carcinoma (RCC) and to assess RCC subtype characterisation.

METHODS

We identified eligible studies on CS magnetic resonance imaging (CS-MRI) of focal renal lesions via PubMed, Embase, and the Cochrane Library. CS-SII values were extracted by lesion type and evaluated using linear mixed model-based meta-regression. RCC subtypes were analysed. Two-sided p value <0.05 indicated statistical significance. Methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool.

RESULTS

Eleven articles involving 850 patients were included. Minimal-fat AML had significantly higher CS-SII value than RCC (p < 0.05); there were no significant differences between mfAML and clear cell RCC (cc-RCC) (p = 0.112). Clear cell RCC had a significantly higher CS-SII value than papillary RCC (p-RCC) (p < 0.001) and chromophobe RCC (ch-RCC) (p = 0.045). The methodological quality was relatively high, and Begg's test data points indicated no obvious publication bias.

CONCLUSIONS

The CS-SII value for differentiating mfAML from cc-RCC remains unproven, but is a promising method for differentiating cc-RCC from p-RCC and ch-RCC.

KEY POINTS

• RCC CS-SII values are significantly lower than those of mfAML overall. • CS-SII values cannot aid differentiation between mfAML and cc-RCC. • CS-SII values might help characterise RCC subtypes.

Diagnostic Performance of CT for Diagnosis of Fat-Poor Angiomyolipoma in Patients With Renal Masses: A Systematic Review and Meta-Analysis

OBJECTIVE

The purpose of this article is to systematically review and perform a meta-analysis of the diagnostic performance of CT for diagnosis of fat-poor angiomyolipoma (AML) in patients with renal masses.

MATERIALS AND METHODS

MEDLINE and EMBASE were systematically searched up to February 2, 2017. We included diagnostic accuracy studies that used CT for diagnosis of fat-poor AML in patients with renal masses, using pathologic examination as the reference standard. Two independent reviewers assessed the methodologic quality using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Sensitivity and specificity of included studies were calculated and were pooled and plotted in a hierarchic summary ROC plot. Sensitivity analyses using several clinically relevant covariates were performed to explore heterogeneity.

RESULTS

Fifteen studies (2258 patients) were included. Pooled sensitivity and specificity were 0.67 (95% CI, 0.48-0.81) and 0.97 (95% CI, 0.89-0.99), respectively. Substantial and considerable heterogeneity was present with regard to sensitivity and specificity (I² = 91.21% and 78.53%, respectively). At sensitivity analyses, the specificity estimates were comparable and consistently high across all subgroups (0.93-1.00), but sensitivity estimates showed significant variation (0.14-0.82). Studies using pixel distribution analysis (n = 3) showed substantially lower sensitivity estimates (0.14; 95% CI, 0.04-0.40) compared with the remaining 12 studies (0.81; 95% CI, 0.76-0.85).

CONCLUSION

CT shows moderate sensitivity and excellent specificity for diagnosis of fat-poor AML in patients with renal masses. When methods other than pixel distribution analysis are used, better sensitivity can be achieved.

Renal angiomyolipoma without visible fat: Can we make the diagnosis using CT and MRI?

Renal angiomyolipomas without visible fat (AML.wovf) are benign masses that are incidentally discovered mainly in women. AML.wovf are typically homogeneously hyperdense on unenhanced CT without calcification or haemorrhage. Unenhanced CT pixel analysis is not useful for diagnosis. AML.wovf are characteristically homogeneously hypointense on T2-weighted (T2W)-MRI and apparent diffusion coefficient (ADC) maps. Despite early reports, only a minority of AML.wovf show signal intensity drop on chemical-shift MRI due to microscopic fat. AML.wovf most commonly show avid early enhancement with washout kinetics at contrast-enhanced CT and MRI. The combination of homogeneously low T2W and/or ADC signal intensity with avid early enhancement and washout is highly accurate for diagnosis of AML.wovf.

KEY POINTS

• AML.wovf are small incidental benign renal masses occurring mainly in women. • AML.wovf are homogeneously hyperdense with low signal on T2W-MRI and ADC map. • AML.wovf typically show avid early enhancement with washout kinetics. • Combining features on CT/MRI is accurate for diagnosis of AML.wovf.

Renal Angiomyolipoma: Radiologic Classification and Imaging Features According to the Amount of Fat

OBJECTIVE

The purposes of this article are to introduce the radiologic classifications of renal angiomyolipoma (AML) and the clinical implications, to show the imaging features of each type of AML, and to describe which types of AML should be biopsied.

CONCLUSION

Renal AML can be classified according to amount of fat as fat rich, fat poor, or fat invisible. To detect fat, one needs to thoroughly evaluate the entire AML by controlling the size and shape of the ROI. Fat-invisible AML should be biopsied, and fat-poor AML requires further investigation to determine whether biopsy is necessary to differentiate it from renal cell carcinoma. If differentiation between AML and renal cell carcinoma is not clear with CT and MRI, percutaneous biopsy may be performed.

Active Surveillance of Nonfatty Renal Masses in Patients With Lymphangioleiomyomatosis: Use of CT Features and Patterns of Growth to Differentiate Angiomyolipoma From Renal Cancer

OBJECTIVE

The objective of this study was to report our experience with active surveillance of nonfatty renal masses in a large cohort of patients with lymphangioleiomyomatosis (LAM), correlate their CT features and patterns of growth with histopathology results, and provide guidelines for management.

SUBJECTS AND METHODS

Yearly CT examinations were performed of 367 women (age range, 21-75 years; mean age, 47 years). For the 31 patients with 37 nonfatty renal masses that were biopsied, excised, or followed for ≥ 5 years, CT enhancement characteristics and patterns of growth were compared with the histopathology results.

RESULTS

Four of 37 nonfatty renal masses were biopsied without follow-up CT examinations: Two were heterogeneous renal cell carcinomas (RCCs), one was a heterogeneous nonfatty angiomyolipoma (AML), and one was homogeneous nonfatty AML. In the remaining 33 nonfatty renal masses with multiple follow-up CT examinations, two growth patterns were identified. Four showed a continuous increase in size of > 0.5 cm/y in some years, and all four in this first group were heterogeneous and were biopsy-proven RCC. The second group was composed of the remaining 29 masses. These 29 masses showed yearly no change, increase, or decrease in diameter. Eight were heterogeneous, and 21 were homogeneous. Of the masses showing a yearly increase, the increase was < 0.5 cm/y in all except one. In the one exception, the increase followed a decrease. Nine of the 29 masses were biopsied, and all nine were nonfatty renal masses (five homogeneous, four heterogeneous).

CONCLUSION

Our data provide further evidence in a large prospective study with longterm follow-up that active surveillance is an appropriate strategy in the management of nonfatty renal masses in patients with LAM. Our analysis of the growth patterns reveals duration of growth in addition to growth rate as criteria for biopsy or excision. Biopsy should be reserved for nonfatty renal masses that show sustained growth or growth > 0.5 cm/y during follow-up.

CT imaging of solid renal masses: pitfalls and solutions

Computed tomography (CT) remains the first-line imaging test for the characterisation of renal masses; however, CT has inherent limitations, which if unrecognised, may result in errors. The purpose of this manuscript is to present 10 pitfalls in the CT evaluation of solid renal masses. Thin section non-contrast enhanced CT (NECT) is required to confirm the presence of macroscopic fat and diagnosis of angiomyolipoma (AML). Renal cell carcinoma (RCC) can mimic renal cysts at NECT when measuring <20 HU, but are usually heterogeneous with irregular margins. Haemorrhagic cysts (HC) may simulate solid lesions at NECT; however, a homogeneous lesion measuring >70 HU is essentially diagnostic of HC. Homogeneous lesions measuring 20-70 HU at NECT or >20 HU at contrast-enhanced (CE) CT, are indeterminate, requiring further evaluation. Dual-energy CT (DECT) can accurately characterise these lesions at baseline through virtual NECT, iodine overlay images, or quantitative iodine concentration analysis without recalling the patient. A minority of hypo-enhancing renal masses (most commonly papillary RCC) show indeterminate or absent enhancement at multiphase CT. Follow-up, CE ultrasound or magnetic resonance imaging (MRI) is required to further characterise these lesions. Small (<3 cm) endophytic cysts commonly show pseudo-enhancement, which may simulate RCC; this can be overcome with DECT or MRI. In small (<4 cm) solid renal masses, 20% of lesions are benign, chiefly AML without visible fat or oncocytoma. Low-dose techniques may simulate lesion heterogeneity due to increased image noise, which can be ameliorated through the appropriate use of iterative reconstruction algorithms.

Small (< 4 cm) Renal Tumors With Predominantly Low Signal Intensity on T2-Weighted Images: Differentiation of Minimal-Fat Angiomyolipoma From Renal Cell Carcinoma

OBJECTIVE

The purpose of this study was to retrospectively investigate the utility of multiparametric MRI in differentiating minimal-fat angiomyolipoma (AML) from renal cell carcinoma (RCC) in small renal tumors with predominantly low signal intensity on T2-weighted MR images.

MATERIALS AND METHODS

Fifty-six patients with pathologically identified renal tumors (1-4 cm) with predominantly low signal intensity on T2-weighted images without visible fat on unenhanced CT images were enrolled. Clinical and MRI variables (tumor-to-renal cortex signal intensity [SI] ratio on T2-weighted images [T2 ratio], apparent diffusion coefficient [ADC], and SI index) on chemical-shift images were evaluated.

RESULTS

The ADC was significantly lower in RCC than in minimal-fat AML (p = 0.001). The T2 ratio and signal intensity index were not significantly different between RCC (p = 0.31) and minimal-fat AML (p = 0.74). Multivariate analysis showed that ADC (odds ratio [OR], 0.01; p = 0.02) and male sex (OR, 46.7; p < 0.001) were the independent predictors of RCC. For differentiating minimal-fat AML from RCC, the ROC AUC of ADC was 0.781. When ADC and sex were combined, the AUC significantly increased to 0.937 with a cutoff value of 1.129 × 10^-3 mm²/s. For making the diagnosis of minimal-fat AML if the ADC was greater than the threshold, sensitivity was 89.7% and specificity was 88.2% (p = 0.02).

CONCLUSION

In small renal tumors with predominantly low SI on T2-weighted images, ADC is useful for differentiating minimal-fat AML from RCC. Combining ADC with male sex increases the accuracy of RCC prediction.

Diagnosis of Renal Angiomyolipomas: Classic, Fat-Poor, and Epithelioid Types

With the increasing discovery of small renal masses with cross-sectional imaging, there has been the concomitant rise in their treatment. With the intent of early curative surgery for a presumed renal cell carcinoma, many renal masses are being resected at surgery without a confirmed diagnosis. Many of them are benign, and some are angiomyolipomas. The diagnosis of renal angiomyolipoma using imaging is, therefore, is as important as ever. Although most, if not all angiomyolipomas with abundant fat are diagnosed readily, some have too little fat to be detected with imaging. This article reviews the current classification, imaging pitfalls, and diagnosis of angiomyolipoma with an emphasis on the fat-poor types. Proper imaging technique, a thorough search for fat, and the appropriate use of percutanoeus biopsy are all needed to eliminate the unnecessary treatment of these benign neoplasms.

Comparison of Biexponential and Monoexponential Model of Diffusion-Weighted Imaging for Distinguishing between Common Renal Cell Carcinoma and Fat Poor Angiomyolipoma

OBJECTIVE

To compare the diagnostic accuracy of intravoxel incoherent motion (IVIM)-derived parameters and apparent diffusion coefficient (ADC) in distinguishing between renal cell carcinoma (RCC) and fat poor angiomyolipoma (AML).

MATERIALS AND METHODS

Eighty-three patients with pathologically confirmed renal tumors were included in the study. All patients underwent renal 1.5T MRI, including IVIM protocol with 8 b values (0-800 s/mm²). The ADC, diffusion coefficient (D), pseudodiffusion coefficient (D^*), and perfusion fraction (f) were calculated. One-way ANOVA was used for comparing ADC and IVIM-derived parameters among clear cell RCC (ccRCC), non-ccRCC and fat poor AML. The diagnostic performance of these parameters was evaluated by using receiver operating characteristic (ROC) analysis.

RESULTS

The ADC were significantly greater in ccRCCs than that of non-ccRCCs and fat poor AMLs (each p < 0.010, respectively). The D and D^* among the three groups were significantly different (all p < 0.050). The f of non-ccRCCs were less than that of ccRCCs and fat poor AMLs (each p < 0.050, respectively). In ROC analysis, ADC and D showed similar area under the ROC curve (AUC) values (AUC = 0.955 and 0.964, respectively, p = 0.589) in distinguishing between ccRCCs and fat poor AMLs. The combination of D > 0.97 × 10^-3 mm²/s, D^* < 28.03 × 10^-3 mm²/s, and f < 13.61% maximized the diagnostic sensitivity for distinguishing non-ccRCCs from fat poor AMLs. The final estimates of AUC (95% confidence interval), sensitivity, specificity, positive predictive value, negative predictive value and accuracy for the entire cohort were 0.875 (0.719-0.962), 100% (23/23), 75% (9/12), 88.5% (23/26), 100% (9/9), and 91.4% (32/35), respectively.

CONCLUSION

The ADC and D showed similar diagnostic accuracy in distinguishing between ccRCCs and fat poor AMLs. The IVIM-derived parameters were better than ADC in discriminating non-ccRCCs from fat poor AMLs.

Loss of intratumoral macroscopic fat in renal angiomyolipoma following chemoradiation therapy for pancreatic cancer

Angiomyolipoma (AML) is the most common benign mesenchymal tumour of the kidney. Classically, AML can readily be diagnosed by identifying the negatively attenuating intratumoral macroscopic fat component on non-enhanced CT scans. However, intratumoral macroscopic fat may not be visible on CT scans, mimicking renal cell carcinoma. We report a case of renal AML with CT scan evidence of macroscopic intratumoral fat that was not readily visible on subsequent CT or MRI, presumably owing to a generalized rapid loss of adipose tissue due to cachexia in a patient with pancreatic adenocarcinoma. Radiologists should be aware that AML may lose its intratumoral fat on follow-up imaging and may simulate renal cell carcinoma.

CT negative attenuation pixel distribution and texture analysis for detection of fat in small angiomyolipoma on unenhanced CT

PURPOSE

The purpose of the paper is to evaluate if CT pixel distribution and texture analysis can identify fat in angiomyolipoma (AML) on unenhanced CT.

METHODS

Thirty-seven patients with 38 AMLs and 75 patients with 83 renal cell carcinomas (RCCs) were evaluated. Region of interest (ROI) was manually placed over renal mass on unenhanced CT. In-house software generated multiple overlapping small-ROIs of various sizes within whole-lesion-ROI. Maximal number of pixels under cutoff attenuation values in the multiple small-ROIs was calculated. Skewness of CT attenuation histogram was calculated from whole-lesion-ROI. Presence of fat in renal mass was also evaluated subjectively. Performance of subjective evaluation and objective methods for identifying fat was compared using McNemar test.

RESULTS

Macroscopic fat was identified in 15/38 AMLs and 1/83 RCCs by both subjective evaluation and by CT negative pixel distribution analysis (p = 1.0). Optimal threshold was ≥6 pixels below -30 HU within 13-pixel-ROI. Skewness of < -0.4 in whole-lesion-ROI identified fat in 10/38 AMLs and 0/83 RCCs. By combining CT negative pixel distribution analysis and skewness, fat was identified in 20/38 AMLs and 1/83 RCCs, but the difference to the subjective method was not statistically significant (p = 0.07).

CONCLUSION

CT negative attenuation pixel distribution analysis does not identify fat in AML beyond subjective evaluation. Addition of skewness by texture analysis may help improve identifying fat in AML.

Lipid-poor renal angiomyolipoma: Differentiation from clear cell renal cell carcinoma using wash-in and washout characteristics on contrast-enhanced computed tomography

In the present study, a total of 82 patients (42 men and 40 women; age range, 24-84 years), including 34 patients with lipid-poor renal angiomyolipoma (AML) and 49 with clear cell renal cell carcinoma (RCC), who had undergone multiphase contrast-enhanced computed tomography (CT) (i.e., CT with unenhanced, corticomedullary, nephrographic and 5-min delay phase scanning) were evaluated. The peak enhancement attenuation value, net enhancement attenuation value, enhancement ratio, washout value and washout ratio were calculated to compare the enhancement characteristics between the two diseases. The results revealed that the lipid-poor AMLs had a significantly higher mean attenuation value compared with that of CCRCCs on unenhanced CT scans (37.8 vs. 30.9 HU; Mann-Whitney U test, P=0.003). In addition, significant differences were found between lipid-poor AMLs and CCRCCs with regard to wash-in (Mann-Whitney U test, P=0.001) and enhancement ratios (Mann-Whitney U test, P=0.010) on contrast-enhanced CT scans. A receiver operating characteristic (ROC) analysis revealed an area under the curve (AUC) of 0.722 using wash-in for differentiation between CCRCCs and lipid-poor AMLs. Lipid-poor AMLs exhibited a reduced washout of contrast enhancement (35.8±32.7 HU washout value; 29.4±0.187% washout ratio) compared with that of CCRCCs (48.3±28.4 HU washout value; 35.7±0.148% washout ratio; Mann-Whitney U test, P=0.037 and P=0.204, respectively). The ROC analysis result yielded an AUC of 0.639 for the use of washout to differentiate CCRCCs from lipid-poor AMLs. In summary, a larger wash-in and washout of contrast enhancement is a predictor that a lesion is CCRCC.

Renal Masses: Imaging Evaluation

This article illustrates the imaging characteristics of cystic and solid renal masses, along with a summary of identified imaging criteria that may be of use to differentiate masses that are more likely to be benign from those that are more likely to be malignant. In addition, important features of known or suspected renal cancers that should be identified before treatment are summarized, including staging of renal cancer and RENAL nephrometry. Finally, the imaging appearance of patients following treatment of renal cancer, including after partial or total nephrectomy, thermal ablation, or chemotherapy for metastatic disease, is reviewed.

Angiomyolipoma with minimal fat: differentiation from clear cell renal cell carcinoma and papillary renal cell carcinoma by texture analysis on CT images

RATIONALE AND OBJECTIVES

To retrospectively evaluate the diagnostic performance of texture analysis (TA) for the discrimination of angiomyolipoma (AML) with minimal fat, clear cell renal cell cancer (ccRCC), and papillary renal cell cancer (pRCC) on computed tomography (CT) images and to determine the scanning phase, which contains the strongest discriminative power.

MATERIALS AND METHODS

Patients with pathologically proved AMLs (n = 18) lacking visible macroscopic fat at CT and patients with pathologically proved ccRCCs (n = 18) and pRCCs (n = 14) were included. All patients underwent CT scan with three phases (precontrast phase [PCP], corticomedullary phase [CMP], and nephrographic phase [NP]). The selected images were analyzed and classified with TA software (MaZda). Texture classification was performed for 1) minimal fat AML versus ccRCC, 2) minimal fat AML versus pRCC, and 3) ccRCC versus pRCC. The classification results were arbitrarily divided into several levels according to the misclassification rates: excellent (misclassification rates ≤10%), good (10%< misclassification rates ≤20%), moderate (20%< misclassification rates ≤30%), fair (30%< misclassification rates ≤40%), and poor (misclassification rates ≥40%).

RESULTS

Excellent classification results (error of 0.00%-9.30%) were obtained with nonlinear discriminant analysis for all the three groups, no matter which phase was used. On comparison of the three scanning phases, we observed a trend toward better lesion classification with PCP for minimal fat AML versus ccRCC, CMP, and NP images for ccRCC versus pRCC and found similar discriminative power for minimal fat AML versus pRCC.

CONCLUSIONS

TA might be a reliable quantitative method for the discrimination of minimal fat AML, ccRCC, and pRCC.

Predictive Value of Chemical-Shift MRI in Distinguishing Clear Cell Renal Cell Carcinoma From Non-Clear Cell Renal Cell Carcinoma and Minimal-Fat Angiomyolipoma

OBJECTIVE

The purpose of this study was to evaluate the diagnostic performance of chemical-shift MRI in the differentiation of clear cell renal cell carcinoma (RCC) from minimal-fat angiomyolipoma (AML) and non-clear cell RCC.

MATERIALS AND METHODS

In this retrospective study, 97 patients with solid renal tumors without macroscopic fat and with a pathologic diagnosis of clear cell RCC (n = 40), non-clear cell RCC (n = 31), or minimal-fat AML (n = 26) who had undergone renal chemical-shift MRI were included. Size, location, morphology, and signal intensity (SI) of the tumors and the contralateral normal kidneys on T2-weighted and in-phase and opposed-phase images were recorded by readers blinded to the pathology. Percentage tumor-to-renal parenchymal SI drop (percentage SI drop) was calculated and correlated to tumor histology. The statistical analysis was done using Kruskal-Wallis, one-way ANOVA, chi-square, and Fisher exact tests.

RESULTS

The percentage SI drop was significantly higher in clear cell RCC compared with non-clear cell RCC and minimal-fat AML (p < 0.001). Percentage SI drop of greater than 20% had 57.5% sensitivity, 96.5% specificity, and 92% positive predictive value (PPV); and percentage SI drop greater than 29% had 40% sensitivity and 100% specificity for diagnosis of clear cell RCC within the cohort of clear cell RCC, minimal-fat AML, and non-clear cell RCC. A significant proportion of minimal-fat AML (46.2%) displayed homogeneous low T2-weighted SI as opposed to clear cell RCC (5%) and non-clear cell RCC (29%) (p < 0.001).

CONCLUSION

The percentage SI drop on chemical-shift MRI had high specificity and moderate sensitivity in predicting clear cell RCC over non-clear cell RCC and minimal-fat AML. A percentage SI drop greater than 20% in a renal mass without macroscopically visible fat has high PPV for clear cell RCC over minimal-fat AML and non-clear cell RCC. Among morphologic features, homogeneous low T2 SI favors minimal-fat AML over RCC.

Fat-poor angiomyolipoma with cyst-like changes mimicking a cystic renal cell carcinoma: a case report

Angiomyolipoma is a common benign renal tumor. It is typically composed of adipose tissue and hence is easily diagnosed by using imaging methods such as ultrasonography, computed tomography, and magnetic resonance imaging. However, it is difficult to differentiate an atypical angiomyolipoma such as a fat-poor angiomyolipoma from a malignant tumor by using these imaging methods. We report a case of a fat-poor angiomyolipoma with cyst-like changes in a 35-year-old man. The angiomyolipoma was initially suspected to be a cystic renal cell carcinoma according to preoperative imaging studies. A 5-cm cystic tumor with an enhanced septal wall and exophytic formation was present in the middle section of the left kidney. The patient underwent partial nephrectomy. Pathological findings showed necrosis and hematoma in almost the entire lesion, with a small amount of adipose and muscle tissue. Finally, a fat-poor angiomyolipoma was diagnosed.

Renal angiomyolipoma: a radiological classification and update on recent developments in diagnosis and management

Angiomyolipoma is the most common benign solid renal neoplasm observed in clinical practice. Once thought to be a hamartoma and almost always diagnosed by the imaged-based detection of fat, angiomyolipomas are now known to consist of a heterogeneous group of neoplasms. Although all are considered perivascular epithelioid cell tumors, many display different pathology, imaging features, and clinical behavior. The importance of understanding this group of neoplasms is emphasized by the fact that many types of angiomyolipoma contain little to no fat, and despite being benign, sometimes escape a pre-operative diagnosis. These types of angiomyolipomas can all be considered when encountering a renal mass that is both hyperattenuating relative to renal parenchyma on unenhanced CT and T2-hypointense, features that reflect their predominant smooth muscle component. We review recent developments and provide a radiological classification of angiomyolipomas that helps physicians understand the various types and learn how to both diagnose and manage them.

Imaging findings of common benign renal tumors in the era of small renal masses: differential diagnosis from small renal cell carcinoma: current status and future perspectives

The prevalence of small renal masses (SRM) has risen, paralleling the increased usage of cross-sectional imaging. A large proportion of these SRMs are not malignant, and do not require invasive treatment such as nephrectomy. Therefore, differentation between early renal cell carcinoma (RCC) and benign SRM is critical to achieve proper management. This article reviews the radiological features of benign SRMs, with focus on two of the most common benign entities, angiomyolipoma and oncocytoma, in terms of their common imaging findings and differential features from RCC. Furthermore, the role of percutaneous biopsy is discussed as imaging is yet imperfect, therefore necessitating biopsy in certain circumstances to confirm the benignity of SRMs.

Ten uncommon and unusual variants of renal angiomyolipoma (AML): radiologic-pathologic correlation

Classic (triphasic) renal angiomyolipoma (AML) is currently classified as a neoplasm of perivascular epithelioid cells. For diagnosis of AML, the use of thin-section non-contrast enhanced CT (NECT) improves diagnostic accuracy; however, identifying gross fat within a very small AML is challenging and often better performed with chemical-shift MRI. Although the presence of gross intra-tumoural fat is essentially diagnostic of AML; co-existing intra-tumoural fat and calcification may represent renal cell carcinoma (RCC). Differentiating AML from retroperitoneal sarcoma can be difficult when AML is large; the feeding vessel and claw signs are suggestive imaging findings. AML can haemorrhage, with intra-tumoural aneurysm size >5 mm a more specific predictor of future haemorrhage than tumor size >4 cm. Diagnosis of AML in the setting of acute haemorrhage is complex; comparison studies or follow-up imaging may be required. Not all AML contain gross fat and imaging features of AML without visible fat overlap with RCC; however, homogeneity, hyperdensity at NECT, low T2-weighted signal intensity and, microscopic fat are suggestive features. Patients with tuberous sclerosis often demonstrate a combination of classic and minimal fat AML, but are also at a slightly increased risk for RCC and should be imaged cautiously. Several rare pathological variants of AML exist including AML with epithelial cysts and epithelioid AML, which have distinct imaging characteristics. Classic AML, although benign, can be locally invasive and the rare epithelioid AML can be frankly malignant. The purpose of this review is to highlight the imaging manifestations of 10 uncommon and unusual variants of AML using pathological correlation.

Solid renal masses: what the numbers tell us

OBJECTIVE

Solid renal masses are most often incidentally detected at imaging as small (≤ 4 cm) localized lesions. These lesions comprise a wide spectrum of benign and malignant histologic subtypes, but are largely treated with surgical resection given the limited ability of imaging to differentiate among them with consistency and high accuracy. Numerous studies have thus examined the ability of CT and MRI techniques to separate benign lesions from malignancies and to predict renal cancer histologic grade and subtype. This article synthesizes the evidence regarding renal mass characterization at CT and MRI, provides diagnostic algorithms for evidence-based practice, and highlights areas of further research needed to drive imaging-based management of renal masses.

CONCLUSION

Despite extensive study of morphologic and quantitative criteria at conventional imaging, no CT or MRI techniques can reliably distinguish solid benign tumors, such as oncocytoma and lipid-poor angiomyolipoma, from malignant renal tumors. Larger studies are required to validate recently developed techniques, such as diffusion-weighted imaging. Evidence-based practice includes MRI to assess renal lesions in situations where CT is limited and to help guide management in patients who are considered borderline surgical candidates.

Renal angiomyolipoma: a radiological classification and update on recent developments in diagnosis and management

Angiomyolipoma is the most common benign solid renal neoplasm observed in clinical practice. Once thought to be a hamartoma and almost always diagnosed by the imaged-based detection of fat, angiomyolipomas are now known to consist of a heterogeneous group of neoplasms. Although all are considered perivascular epithelioid cell tumors, many display different pathology, imaging features, and clinical behavior. The importance of understanding this group of neoplasms is emphasized by the fact that many types of angiomyolipoma contain little to no fat, and despite being benign, sometimes escape a pre-operative diagnosis. These types of angiomyolipomas can all be considered when encountering a renal mass that is both hyperattenuating relative to renal parenchyma on unenhanced CT and T2-hypointense, features that reflect their predominant smooth muscle component. We review recent developments and provide a radiological classification of angiomyolipomas that helps physicians understand the various types and learn how to both diagnose and manage them.

Comparison of T2-weighted MRI with and without fat suppression for differentiating renal angiomyolipomas without visible fat from other renal tumors

OBJECTIVE

The purpose of this study was to retrospectively compare the usefulness of T2-weighted imaging with and without fat suppression for differentiating angiomyolipomas (AMLs) without visible fat from other renal tumors.

MATERIALS AND METHODS

MRI was performed in 111 patients (66 men and 46 women; age range, 17-78 years) who had pathologically diagnosed (14 AMLs, 86 renal cell carcinomas [RCCs], and three other tumors) and clinically diagnosed (eight AMLs) renal masses without visible fat or a cystic portion on unenhanced CT. The signal intensity (SI), tumor-to-kidney SI ratio, tumor-to-spleen SI ratio on T2-weighted imaging and fat-suppressed T2-weighted imaging, and tumor-fat subtraction index were measured for each tumor. Receiver operating characteristic (ROC) analysis was used to evaluate diagnostic accuracy of SI ratios.

RESULTS

The highest area under the ROC curve was 0.886 for tumor-to-kidney SI ratio on fat-suppressed T2-weighted imaging. With a tumor-to-kidney SI ratio of 0.9 on fat-suppressed T2-weighted imaging, the sensitivity, specificity, positive predictive value, and negative predictive value were 90.9%, 71.1%, 43.5%, and 97%, respectively. The highest tumor-to-kidney SI ratio of AMLs without visible fat was 1.09. Ninety-eight percent of renal tumors with a tumor-to-kidney SI ratio greater than 1.09 were RCCs (51/52), especially clear cell RCCs (82.7%, 43/52).

CONCLUSION

Fat-suppressed T2-weighted imaging is more useful than T2-weighted imaging for differentiating AMLs without visible fat from non-AMLs. The high SI of solid renal masses on fat-suppressed T2-weighted imaging can be indicative of non-AMLs, especially RCCs.

Are there useful CT features to differentiate renal cell carcinoma from lipid-poor renal angiomyolipoma?

OBJECTIVE

This study was an attempt to identify key CT features that can potentially be used to differentiate between lipid-poor renal angiomyolipoma and renal cell carcinoma (RCC).

MATERIALS AND METHODS

We conducted an analysis of patients who received nephrectomy or renal biopsy from 2002 to 2011 with suspected RCC. We included tumors smaller than 7 cm with a completed three-phase CT examination. A radiologist and a urology fellow, blinded to histopathologic diagnosis, recorded the imaging findings by consensus and compared the values for each parameter between lipid-poor angiomyolipoma, RCC subtypes, and RCC as a group. Multivariate logistic regression analysis was performed for each univariate significant feature.

RESULTS

The sample in our study consisted of 132 patients with 135 renal tumors, including 51 men (age range, 26-84 years; mean age, 57 years) and 81 women (age range, 29-91 years; mean age, 57 years). These tumors included 33 lipid-poor angiomyolipomas, 54 clear-cell RCC, 31 chromophobe RCC, and 17 papillary RCC. Multivariate analysis revealed four significant parameters for differentiating RCC as a group from lipid-poor angiomyolipoma (angular interface, p = 0.023; hypodense rim, p = 0.045; homogeneity, p = 0.005; unenhanced attenuation > 38.5 HU, p < 0.001), five for clear-cell RCC, two for chromophobe RCC, and one for papillary RCC. Lipid-poor angiomyolipoma and clear-cell RCC showed early strong enhancement and a washout pattern, whereas chromophobe RCC and papillary RCC showed gradual enhancement over time.

CONCLUSION

Specific CT features can potentially be used to differentiate lipid-poor renal angiomyolipoma from renal cell carcinoma.

Renal epithelioid angiomyolipoma: imaging characteristics in nine cases with radiologic-pathologic correlation and review of the literature

OBJECTIVE

The purpose of this study was to describe the imaging features of renal epithelioid angiomyolipoma (EAML), a rare subtype of angiomyolipoma, with clinical and pathologic correlation.

MATERIALS AND METHODS

This study was a retrospective review of nine cases from a single institution in which total resection and preoperative imaging were performed and the diagnosis of EAML was made. Imaging included CT (nine cases), MRI (five cases), and ultrasound (one case), and the images were reviewed in consensus by two radiologists. Patient demographics, disease associations, presentation, and outcomes were determined by chart review.

RESULTS

The patients were nine women and one man (mean age, 42 years). Two patients had tuberous sclerosis complex. The size of the nine EAMLs ranged from 1.4 to 22 cm (mean, 7.8 cm). Six lesions had minor components of fat identifiable at imaging. The contrast enhancement pattern was heterogeneous in eight lesions, five of which contained cysts, necrosis, and hematoma. Four presentations were acute hemorrhage, with ruptured EAML in three of the four. Five tumors extended into the renal sinus. Two tumors were locally invasive. One patient had metastatic disease at presentation with epithelioid tumor identified in a single lymph node. The follow-up periods ranged from 0 to 89 months, and there was one case of suspected but not yet proved recurrence.

CONCLUSION

Renal EAML can have a range of imaging appearances and can be indistinguishable from renal cell carcinoma and angiomyolipoma with minimal fat. EAML can be considered when a mass is found that has small foci of macroscopic fat without calcification or when acute hemorrhage of a renal mass occurs.

Fat poor angiomyolipoma with lymphadenopathy: Diagnostic dilemma

A 24-year-old lady presented with left flank pain of 3 months duration. She had stigmata of tuberous sclerosis complex in the form of angiofibromas on face, ash-leaf macules on back and right upper limb and shagreen patches over back. Computed tomography scan of the abdomen showed 6.5 cm × 5.0 cm × 4.4 cm lobulated intensely enhancing exophytic mass lesion in mid pole of left kidney with significant para-aortic lymphadenopathy with no evidence of fat in the mass. She underwent radical left nephrectomy with a provisional diagnosis of renal cell carcinoma. Histopathological examination showed multicenteric angiomyolipoma involving kidney and para-aortic lymph nodes. This case report underscores the need for further research to differentiate fat-poor angiomyolipoma and lymphadenopathy from renal cell carcinoma.

Angiomyolipoma with minimal fat: can it be differentiated from clear cell renal cell carcinoma by using standard MR techniques?

PURPOSE

To retrospectively assess whether magnetic resonance (MR) imaging with opposed-phase and in-phase gradient-echo (GRE) sequences and MR feature analysis can differentiate angiomyolipomas (AMLs) that contain minimal fat from clear cell renal cell carcinomas (RCCs), with particular emphasis on small (<3-cm) masses.

MATERIALS AND METHODS

Institutional review board approval and a waiver of informed consent were obtained for this HIPAA-compliant study. MR images from 108 pathologically proved renal masses (88 clear cell RCCs and 20 minimal fat AMLs from 64 men and 44 women) at two academic institutions were evaluated. The signal intensity (SI) of each renal mass and spleen on opposed-phase and in-phase GRE images was used to calculate an SI index and tumor-to-spleen SI ratio. Two radiologists who were blinded to the pathologic results independently assessed the subjective presence of intravoxel fat (ie, decreased SI on opposed-phase images compared with that on in-phase images), SI on T1-weighted and T2-weighted images, cystic degeneration, necrosis, hemorrhage, retroperitoneal collaterals, and renal vein thrombosis. Results were analyzed by using the Wilcoxon rank sum test, two-tailed Fisher exact test, and multivariate logistic regression analysis for all renal masses and for small masses. A P value of less than .05 was considered to indicate a statistically significant difference.

RESULTS

There were no differences between minimal fat AMLs and clear cell RCCs for the SI index (8.05%±14.46 vs 14.99%±19.9; P=.146) or tumor-to-spleen ratio (-8.96%±16.6 and -15.8%±22.4; P=.227) when all masses or small masses were analyzed. Diagnostic accuracy (area under receiver operating characteristic curve) for the SI index and tumor-to-spleen ratio was 0.59. Intratumoral necrosis and larger size were predictive of clear cell RCC (P<.001) for all lesions, whereas low SI (relative to renal parenchyma SI) on T2-weighted images, smaller size, and female sex correlated with minimal fat AML (P<.001) for all lesions.

CONCLUSION

The diagnostic accuracy of opposed-phase and in-phase GRE MR imaging for the differentiation of minimal fat AML and clear cell RCC is poor. In this cohort, low SI on T2-weighted images relative to renal parenchyma and small size suggested minimal fat AML, whereas intratumoral necrosis and large size argued against this diagnosis.

Small (<4 cm) renal mass: differentiation of angiomyolipoma without visible fat from renal cell carcinoma utilizing MR imaging

PURPOSE

To determine whether a combination of magnetic resonance (MR) parameters can help differentiate small angiomyolipomas (AMLs) without visible fat from renal cell carcinomas (RCCs).

MATERIALS AND METHODS

This HIPAA-compliant retrospective study received institutional review board approval; 69 men and 42 women (mean age, 59.7 years) with 15 AMLs without visible fat and 104 RCCs underwent MR. The development set consisted of 10 AMLs and 71 RCCs; the validation set consisted of five AMLs and 33 RCCs. T1-weighted fast spin-echo (SE), fat-suppressed T2-weighted fast SE, in- and opposed-phase gradient-echo (GRE), and fat-suppressed three-dimensional T1-weighted spoiled GRE sequences were performed before and after contrast material administration. Tumor signal intensity (SI) was measured. T1 and T2 SI ratio (ratio of tumor to renal cortex SI on T1- and T2-weighted images, respectively), SI index (SII) ([SI(in) 2 SI(opp)]/[SI(in)] × 100; SI(in) and SI(opp) are tumor SI on in- and opposed-phase images, respectively), and arterial-to-delayed enhancement ratio ([SI(art) 2 SI(pre)]/[SI(del) 2 SI(pre)]; SI(pre), SI(art), and SI(del) are tumor SI on unenhanced, arterial phase, and delayed phase three-dimensional T1-weighted spoiled GRE images, respectively) were compared. Combinations of MR parameter threshold levels were constructed from development set and validated with validation set. Sensitivity, specificity, and accuracy for differentiating between AML and RCC were calculated for combinations of MR parameter threshold levels.

RESULTS

AML had significantly higher T1 SI ratio (P = .04), lower T2 SI ratio (P = .001), higher SII (P = .02), and higher arterial-to-delayed enhancement ratio (P < .001) than RCC. Sensitivity, specificity, and accuracy for combination of T2 SI ratio less than 0.9 and ([SII greater than 20% and T1 SI ratio greater than 1.2] or arterial-to-delayed enhancement ratio greater than 1.5) were 73% (11 of 15), 99% (103 of 104), and 96% (114 of 119), respectively, for differentiating AML from RCC.

CONCLUSION

A combination of T2 SI ratio less than 0.9 and ([SII greater than 20% and T1 SI ratio greater than 1.2] or arterial-to-delayed enhancement ratio greater than 1.5) was accurate in differentiating AML from RCC.

Subtype differentiation of small renal cell carcinomas on three-phase MDCT: usefulness of the measurement of degree and heterogeneity of enhancement

BACKGROUND

Subtype differentiation of small renal cell carcinomas (RCCs) can provide more information to surgeons and patients and get more useful information about imaging features of small renal tumors.

PURPOSE

To evaluate the usefulness of the measurement of degree and heterogeneity of enhancement in subtype differentiation of small renal cell carcinomas (RCCs) by three-phase multidetector-row CT (MDCT).

MATERIAL AND METHODS

We reviewed 149 pathologically confirmed small (<4cm) RCCs in 143 patients: 114 (clear cell), 17 (chromophobe), and 18 papillary (8 papillary type 1 and 10 papillary type 2). Scans in pre-contrast, corticomedullary, and nephrographic phases were obtained. We assessed the mean and standard deviation of the Hounsfield units (HU) in a region of interest (ROI) for the degree of enhancement and the heterogeneity of enhancement, respectively. We compared the attenuation values, and the degree and heterogeneity of enhancement among the subtypes.

RESULTS

The clear cell type showed the highest enhancement and heterogeneity of enhancement followed by chromophobe and papillary types. There was a significant difference in enhancement between the clear cell and papillary types in the corticomedullary phase (P < 0.01), and between clear and non-clear cell types in the nephrographic phase (P < 0.05). Heterogeneity of enhancement showed a significant difference between clear cell and non-clear cell types in the corticomedullary phase (P < 0.05).

CONCLUSION

The measurement of degree and heterogeneity of enhancement on contrast-enhanced MDCT may be a simple and useful method to differentiate between the different types of small RCCs.