Differentiation Of Angiomyolipoma Research Articles

Utility of Ultrasound-Guided Attenuation Parameter (UGAP) in Renal Angiomyolipoma (AML): First Results.

Angiomyolipoma (AML) are the most common benign solid renal mass. Differentiation from malignant tumours is essential. Imaging features in ultrasound may overlap between malignant lesions, especially between renal cell carcinoma (RCC) and AML. So far, sectional imaging has been necessary for reliable differentiation. The aim of this study is to evaluate the use of the ultrasound-guided attenuation parameter (UGAP), a recently established tool for assessing hepatic steatosis, in the differentiation of AMLs from other renal masses. Therefore, 27 patients with unknown solid renal masses were examined by ultrasound including UGAP. The attenuation was assessed qualitatively by attenuation map and quantitatively in comparison to the surrounding renal tissue. UGAP was applicable in 26/27 patients. Findings were compared with CT/MRI as the current imaging standard. A total of 18 AML and 9 other renal tumours were found. The diagnostic performance of B-Mode (hyperechogenic lesion) ultrasound was 77.8% in identifying AML. The diagnostic performance of the attenuation map showed a diagnostic performance of 92.6%, whereby UGAP measurements were successful in 76.9% of cases. Quantitatively, we found a significant difference (p < 0.034) in mean measured attenuation between AML (0.764 ± 0.162 dB/cm/MHz) vs. other renal tumours (0.658 ± 0.155 dB/cm/MHz). The best performance was found by a combined parameter of a hyperechogenic lesion with a positive attenuation map with an accuracy of 95.0%. In conclusion, UGAP may represent a possibility for differentiating solid renal lesions more accurately by ultrasound, especially classic hyperechoic AMLs from other renal lesions. Further studies are needed to increase the diagnostic reliability further.

Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review.

Radiomics and artificial intelligence (AI) may increase the differentiation of benign from malignant kidney lesions, differentiation of angiomyolipoma (AML) from renal cell carcinoma (RCC), differentiation of oncocytoma from RCC, differentiation of different subtypes of RCC, to predict Fuhrman grade, to predict gene mutation through molecular biomarkers and to predict treatment response in metastatic RCC undergoing immunotherapy. Neural networks analyze imaging data. Statistical, geometrical, textural features derived are giving quantitative data of contour, internal heterogeneity and gray zone features of lesions. A comprehensive literature review was performed, until July 2022. Studies investigating the diagnostic value of radiomics in differentiation of renal lesions, grade prediction, gene alterations, molecular biomarkers and ongoing clinical trials have been analyzed. The application of AI and radiomics could lead to improved sensitivity, specificity, accuracy in detecting and differentiating between renal lesions. Standardization of scanner protocols will improve preoperative differentiation between benign, low-risk cancers and clinically significant renal cancers and holds the premises to enhance the diagnostic ability of imaging tools to characterize renal lesions.

Differentiation of Angiomyolipoma With Minimal Fat from Clear Cell Renal Cell Carcinoma Using Non-contrast Multiparametric Magnetic Resonance Imaging.

This study was conducted to ascertain the optimal combination of non-contrast magnetic resonance (MR) imaging sequences for the differential diagnosis between small angiomyolipoma (AML) with minimal fat and clear cell renal cell carcinoma (CCRCC). Thirty-nine patients with pathologically proven AML with minimal fat (n=6) or CCRCC (n=33) measuring 4 cm or less were included. All underwent MR imaging before partial nephrectomy or percutaneous biopsy. Four quantitative parameters of tumors were evaluated: signal intensity (SI) index of T1W- gradient-echo imaging, SI index of T2- fat suppression imaging (T2-SI index), apparent diffusion coefficient (ADC) value, and standard deviation (SD) of ADC. These quantitative parameters were compared using Wilcoxon rank-sum test and receiver operating characteristic (ROC) curve analyses. The optimal combination of quantitative parameters was sought using logistic regression analysis. Comparison of quantitative parameters showed that the T2-SI index (median, AML with minimal fat vs. CCRCC; 0.74 vs. 1.27, p<0.001), ADC value (1.12 vs. 1.75, p=0.005), and SD of ADC (104 vs. 233, p<0.001) were significantly lower in AML with minimal fat than CCRCC. From the ROC curve analysis, the highest area under the curve (1.000; 100% sensitivity; 100% specificity) was obtained using the logistic regression model with the SD of ADC and T2-SI index or ADC value as explanatory variables. SD of ADC combined with T2-SI index or ADC value exhibited the highest diagnostic performance for differentiating small AML with minimal fat from CCRCC.

Hepatic Angiomyolipoma

To investigate whether quantitative contrast-enhanced ultrasound (CEUS) analysis and magnetic resonance imaging (MRI) can differentiate hepatic angiomyolipoma (AML) from hepatocellular carcinoma (HCC) and hepatic epithelial angiomyolipoma (EAML) from nonepithelial AML. Retrospective analysis of 26 CEUS lesions pathologically confirmed as AML and 60 patients with confirmed HCC. Clinical and ultrasound (US) features of AML versus HCC and EAML versus nonepithelial AML were analyzed. Quantitative parameters, including arrival time, ascending slope, time to peak, peak intensity (PI), fall time (FT), and mean transit time (MTT), were analyzed. Diagnostic performance of MRI and CEUS was compared. Angiomyolipoma was significantly different from HCC in US findings of solitary tumor (P < .001), well-defined border (P = .025), and hyperechogenicity (P < .001). Tumor types were significantly different in US findings of centripetal filling pattern (P < .001), less hypoenhancement in the CEUS delayed phase (P = .04), high PI (P = .007) and ascending slope (P = .006), and short FT (P = .001) and MTT (P < .05). The sensitivity and specificity of quantitative parameters were 61.5% to 96.2% and 55.0% to 71.7% (area under the curve, 0.642-0.773). An early draining vein and tumor vessels, intratumor fat, absence of pseudocapsules, mild signal intensity on diffusion-weighted imaging, and hypointensity in the MRI delayed phase were helpful for differentiation of AML from HCC (P < .05). The positive predictive values of CEUS and MRI were 46.3% to 100% and 42.9% to 75%, with no significant difference (P > .05). Epithelial AML was more likely to be hypoechoic with a centripetal filling pattern than nonepithelial AML (P < .05). The MTT and FT of EAML were shorter than those of nonepithelial AML (P < .05). The PI of EAML was significantly stronger than that of nonepithelial AML (P < .01). The sensitivity and specificity of quantitative parameters were 68.7% to 100.0% and 60.0% to 100.0% (area under the curve, 0.825-0.941). Less intratumor fat was helpful for differentiation of EAML from nonepithelial AML (P < .05). The positive predictive values of CEUS and MRI were 77.8% to 100% and 85.7%, with a significant difference (P < .05). Quantitative CEUS findings could be valuable in differentiating EAML from HCC.

The value of quantitative CT texture analysis in differentiation of angiomyolipoma without visible fat from clear cell renal cell carcinoma on four-phase contrast-enhanced CT images

To investigate the diagnostic performance and usefulness of texture analysis in differentiating angiomyolipoma (AML) without visible fat from clear cell renal cell carcinoma (ccRCC) on four-phase contrast-enhanced computed tomography (CECT). Seventeen patients with AML without visible fat and 50 patients with ccRCC of size ≤4.5 cm who had also undergone preoperative four-phase CECT were included in this study. The histogram, grey-level co-occurrence matrix (GLCM), and grey-level run length matrix (GLRLM) were evaluated. Sequential feature selection (SFS) and support vector machine (SVM) classifier with leave-one-out cross validation were used. Using the SFS and SVM classifiers, five texture features were selected; mean (unenhanced), standard deviation (unenhanced and excretory), cluster prominence (nephrographic), and long-run high grey-level emphasis (corticomedullary). Diagnostic performance of the five selected texture features for all CT phases was as follows: 82% sensitivity, 76% specificity, 85% accuracy, and 85 area under the receiver operating characteristic curve (AUC). In the subgroup analysis, the AUCs of each phase were significantly >0.5 (p<0.05). In the pairwise comparison of AUCs between four phases, there were no significant differences between the four phases except the unenhanced and corticomedullary phases (p=0.015), i.e., the unenhanced phase showed slightly higher AUC than the corticomedullary phase. Texture analysis of small renal masses (≤4.5 cm) on four-phase CECT can accurately differentiate AML without visible fat from ccRCC and showed good diagnostic performance for both the unenhanced and enhanced phases.

Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma.

To evaluate the diagnostic performance of machine-learning based quantitative texture analysis of CT images to differentiate small (≤ 4 cm) angiomyolipoma without visible fat (AMLwvf) from renal cell carcinoma (RCC). This single-institutional retrospective study included 58 patients with pathologically proven small renal mass (17 in AMLwvf and 41 in RCC groups). Texture features were extracted from the largest possible tumorous regions of interest (ROIs) by manual segmentation in preoperative three-phase CT images. Interobserver reliability and the Mann-Whitney U test were applied to select features preliminarily. Then support vector machine with recursive feature elimination (SVM-RFE) and synthetic minority oversampling technique (SMOTE) were adopted to establish discriminative classifiers, and the performance of classifiers was assessed. Of the 42 extracted features, 16 candidate features showed significant intergroup differences (P < 0.05) and had good interobserver agreement. An optimal feature subset including 11 features was further selected by the SVM-RFE method. The SVM-RFE+SMOTE classifier achieved the best performance in discriminating between small AMLwvf and RCC, with the highest accuracy, sensitivity, specificity and AUC of 93.9 %, 87.8 %, 100 % and 0.955, respectively. Machine learning analysis of CT texture features can facilitate the accurate differentiation of small AMLwvf from RCC. • Although conventional CT is useful for diagnosis of SRMs, it has limitations. • Machine-learning based CT texture analysis facilitate differentiation of small AMLwvf from RCC. • The highest accuracy of SVM-RFE+SMOTE classifier reached 93.9 %. • Texture analysis combined with machine-learning methods might spare unnecessary surgery for AMLwvf.

Subjective and objective heterogeneity scores for differentiating small renal masses using contrast-enhanced CT.

The aim of this study was to assess the effect of denoising on objective heterogeneity scores and its diagnostic capability for the diagnosis of angiomyolipoma (AML) and renal cell carcinoma (RCC). A total of 158 resected renal masses ≤4cm [98 clear cell (cc) RCCs, 36 papillary (pap)-RCCs, and 24 AMLs] from 139 patients were evaluated. A representative contrast-enhanced computed tomography (CT) image for each mass was selected by a genitourinary radiologist. A largest possible region of interest was drawn on each mass by the radiologist, from which three objective heterogeneity indices were calculated: standard deviation (SD), entropy (Ent), and uniformity (Uni). Objective heterogeneity indices were also calculated after images were processed with a denoising algorithm (non-local means) at three strengths: weak, medium, and strong. Two genitourinary radiologists also subjectively scored each mass independently using a three-point scale (1-3; with 1 the least and 3 the most heterogeneous), which were added to represent the final subjective heterogeneity score of each mass. Heterogeneity scores were compared among mass types, and area under the ROC curve (AUC) was calculated. For all heterogeneity indices, cc-RCC was significantly more heterogeneous than pap-RCC and AML (p<0.001), but no significant difference was found between pap-RCC and AML (p>0.01). For cc-RCC and pap-RCC differentiation, AUCs were 0.91, 0.81, 0.78, and 0.78 for the subjective score, SD, Ent, and Uni, respectively, using original images. The corresponding AUC values were 0.84, 0.74, 0.79, and 0.80 for differentiation of AML and cc-RCC. Noise reduction at weak setting improves AUC values by 0.03, 0.05, and 0.05 for SD, entropy, and uniformity for differentiation of cc-RCC from pap-RCC. Further increase of filtering strength did not improve AUC values. For differentiation of AML vs. cc-RCC, the AUC values stayed relatively flat using the noise reduction technique at different strengths for all three indices. Both subjective and objective heterogeneity indices can differentiate cc-RCC from pap-RCC and AML. Noise reduction improved differentiation of cc-RCC from pap-RCC, but not differentiation of AML from cc-RCC.

Small (&lt;4 cm) Renal Mass: Differentiation of Angiomyolipoma without Visible Fat from Renal Cell Carcinoma Utilizing MR Imaging

HomeRadiologyVol. 280, No. 2 PreviousNext CommunicationsFree AccessErrataSmall (<4 cm) Renal Mass: Differentiation of Angiomyolipoma without Visible Fat from Renal Cell Carcinoma Utilizing MR ImagingKewalee Sasiwimonphan, Naoki Takahashi, Bradley C. Leibovich, Rickey E. Carter, Thomas D. Atwell, Akira KawashimaKewalee Sasiwimonphan, Naoki Takahashi, Bradley C. Leibovich, Rickey E. Carter, Thomas D. Atwell, Akira KawashimaKewalee SasiwimonphanNaoki TakahashiBradley C. LeibovichRickey E. CarterThomas D. AtwellAkira KawashimaPublished Online:Jul 18 2016https://doi.org/10.1148/radiol.2016164024MoreSectionsPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In Originally published in:Radiology 2012;263(1):160–168 DOI: 10.1148/radiol.12111205Erratum in:Radiology 2016;280(2):653 DOI:10.1148/radiol.2016164024A symbol coding issue caused the minus sign to appear as a number 2 or as a multiplication sign in some online versions of the article. Correct equations are as follows: SI index (SII) = (SIin − SIopp)/(SIin) × 100; arterial-to-delayed enhancement ratio = (SIart − SIpre)/(SIdel − SIpre); start of arterial phase imaging was determined by using the following formula: CMarr + CMinjd/2 − Idur/2 sec + 3 sec.Article HistoryPublished online: July 18 2016Published in print: Aug 2016 FiguresReferencesRelatedDetailsCited ByDiagnostic algorithm of solid kidney lesions with contrast-enhanced ultrasoundR. A.Kadyrleev, E. A.Busko, E. V.Kostromina, L. N.Shevkunov, K. V.Kozubova, S. S.Bagnenko2021 | Diagnostic radiology and radiotherapy, Vol. 12, No. 1Imaging of Renal CancerSatheeshKrishna, AshleyLeckie, AniaKielar, RobertHartman, AshishKhandelwal2020 | Seminars in Ultrasound, CT and MRI, Vol. 41, No. 2Comparison of Monoexponential, Biexponential, Stretched-Exponential, and Kurtosis Models of Diffusion-Weighted Imaging in Differentiation of Renal Solid MassesJianjianZhang, ShitengSuo, GuiqinLiu, ShanZhang, ZizhouZhao, JianrongXu, GuangyuWu2019 | Korean Journal of Radiology, Vol. 20, No. 5Imaging of Solid Renal MassesFernando U.Kay, IvanPedrosa2018 | Urologic Clinics of North America, Vol. 45, No. 3Percutaneous image-guided biopsies of small renal tumors: Current practice and perspectivesC.Marcelin, D.Ambrosetti, J.C.Bernhard, C.Roy, N.Grenier, F.H.Cornelis2017 | Diagnostic and Interventional Imaging, Vol. 98, No. 9Accompanying This ArticleSmall (<4 cm) Renal Mass: Differentiation of Angiomyolipoma without Visible Fat from Renal Cell Carcinoma Utilizing MR ImagingApr 1 2012RadiologyRecommended Articles Triple Arterial Phase CT of the Liver with Radiation Dose Equivalent to That of Single Arterial Phase CT: Initial ExperienceRadiology2018Volume: 289Issue: 1pp. 111-118Intravenous Gadoxetate Disodium Administration Reduces Breath-holding Capacity in the Hepatic Arterial Phase: A Multi-Center Randomized Placebo-controlled TrialRadiology2016Volume: 282Issue: 2pp. 361-368Interobserver Variability and Diagnostic Performance of Gadoxetic Acid–enhanced MRI for Predicting Microvascular Invasion in Hepatocellular CarcinomaRadiology2020Volume: 297Issue: 3pp. 573-581Gadoxetate Disodium versus Gadoterate Meglumine: Quantitative Respiratory and Hemodynamic Metrics by Using Compressed-Sensing MRIRadiology2019Volume: 293Issue: 2pp. 317-326Parathyroid 4D CT: What the Surgeon Wants to KnowRadioGraphics2020Volume: 40Issue: 5pp. 1383-1394See More RSNA Education Exhibits “Point of Care” in Abdominal Trauma: What Not to MissDigital Posters2019Focal Liver Reaction Following Localized Radiation Therapy of Hepatocellular CarcinomaDigital Posters2020All Manner of Things: Hepatocellular Carcinoma Imaging and TherapyDigital Posters2018 RSNA Case Collection Arterioportal fistulaRSNA Case Collection2021Oxaliplatin-induced focal nodular hyperplasiaRSNA Case Collection2021LI-RADS 5RSNA Case Collection2022 Vol. 280, No. 2 Metrics Altmetric Score PDF download

Small (< 4 cm) Renal Masses: Differentiation of Angiomyolipoma Without Visible Fat From Renal Cell Carcinoma Using Unenhanced and Contrast-Enhanced CT.

The purpose of this study was to evaluate if small (< 4 cm) angiomyolipoma without visible fat can be differentiated from renal cell carcinoma (RCC) using contrast-enhanced CT alone and using unenhanced and contrast-enhanced CT in combination. Twenty-three patients with 24 angiomyolipomas without visible fat and 130 patients with 148 RCCs underwent unenhanced and contrast-enhanced CT. Demographic data and size, shape, CT attenuation, and heterogeneity (entropy and subjective score) of the renal mass on unenhanced CT and contrast-enhanced CT were recorded. Multivariate logistic regression models were constructed for parameters obtained by contrast-enhanced CT alone and by both unenhanced and contrast-enhanced CT. Demographic data and size and shape of renal mass were used in each model. Sensitivity and specificity were calculated. Logistic regression model from contrast-enhanced CT data included sex, percentage of exophytic growth, entropy, and CT attenuation on contrast-enhanced CT. Model from both unenhanced and contrast-enhanced CT data included age, sex, short-axis diameter, percentage of exophytic growth, lesion-to-kidney CT attenuation difference on unenhanced CT, and CT attenuation on contrast-enhanced CT. The contrast-enhanced CT-based model and combined unenhanced and contrast-enhanced CT-based model differentiated angiomyolipoma from RCC with sensitivity and specificity of 42% and 98% versus 50% and 98%, respectively. Combinations of various CT and demographic findings allowed differentiation of angiomyolipoma from RCC.

The diagnostic value of contrast-enhanced ultrasound in differentiating small renal carcinoma and angiomyolipoma.

The aim of this study was to explore the value of contrast-enhanced ultrasound (CEUS) in differentiating small renal masses. A total of 102 small renal masses (≤ 3 cm) in 99 patients were examined using conventional ultrasound (CUS) and CEUS, and the findings were reviewed and evaluated in comparison to pathology. Significant differences between renal cell carcinomas (RCCs) and angiomyolipomas (AMLs) were noted in terms of the orientation and echogenicity on CUS (p < 0.05 for both), but the location, shape, margins, homogeneity, and blood flow signals of RCCs on color Doppler flow imaging (CDFI) were similar to those of AMLs (p > 0.05 for all). On CEUS, however, the enhancement intensity, washout in the late phase, and perilesional rim-like enhancement differed significantly for RCCs and AMLs (p = 0.000 for all). Significant differences between CEUS and CUS in terms of sensitivity (88.9% vs. 55.6%), the negative predictive value (68.0% vs. 29.5%), the false negative rate (9.9% vs. 44.5%), and accuracy (88.3% vs. 58.9%) were noted (p < 0.05 for all). CEUS, with its unique features, has value in diagnosing small RCCs and AMLs and outperforms CUS in differentiation of small RCCs and AMLs.

Angiomyolipoma with Minimal Fat: Can It Be Differentiated from Clear Cell Renal Cell Carcinoma by Using Standard MR Techniques?

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. 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. 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. 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 (&lt;4 cm) Renal Mass: Differentiation of Angiomyolipoma without Visible Fat from Renal Cell Carcinoma Utilizing MR Imaging

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

Diffusion‐weighted magnetic resonance imaging in the differentiation of angiomyolipoma with minimal fat from clear cell renal cell carcinoma

The aim of the present study was to evaluate diffusion-weighted (DW) magnetic resonance imaging (MRI) in differentiating between minimal fat angiomyolipoma (MFAML) and clear cell renal cell carcinoma (CCRCC). Forty-one solid renal tumors without visible macroscopic fat on unenhanced computed tomography images were evaluated by MRI, including DW-MRI, and were diagnosed pathologically as CCRCC (n = 36) or MFAML (n = 5). To evaluate the heterogeneity of diffusion in each tumor, the signals of the tumors on DW-MRI were analyzed subjectively and the apparent diffusion coefficient (ADC) values and histograms assessed objectively. Thirty-three of 36 CCRCC (92%) exhibited a heterogeneous signal on DW-MRI and several peaks in the ADC value histogram, whereas four of five MFAML exhibited a homogeneous signal on DW-MRI and a single prominent peak in the histogram. The standard deviations of the ADC values were significantly smaller for MFAML than for CCRCC (P = 0.0015). In conclusion, DW-MRI can be considered a useful and noninvasive addition to the preoperative differentiation of CCRCC and MFAML.

2300 DIFFUSION-WEIGHTED MAGNETIC RESONANCE IMAGING IN DIFFERENTIATION OF ANGIOMYOLIPOMA WITH MINIMAL FAT FROM CLEAR CELL RENAL CELL CARCINOMA

You have accessJournal of UrologyImaging/Radiology: Uroradiology1 Apr 20112300 DIFFUSION-WEIGHTED MAGNETIC RESONANCE IMAGING IN DIFFERENTIATION OF ANGIOMYOLIPOMA WITH MINIMAL FAT FROM CLEAR CELL RENAL CELL CARCINOMA Hajime Tanaka, Soichiro Yoshida, Yasuhisa Fujii, Chikako Ishii, Fumitaka Koga, Kazutaka Saito, Hitoshi Masuda, Satoru Kawakami, and Kazunori Kihara Hajime TanakaHajime Tanaka Tokyo, Japan More articles by this author , Soichiro YoshidaSoichiro Yoshida Tokyo, Japan More articles by this author , Yasuhisa FujiiYasuhisa Fujii Tokyo, Japan More articles by this author , Chikako IshiiChikako Ishii Tokyo, Japan More articles by this author , Fumitaka KogaFumitaka Koga Tokyo, Japan More articles by this author , Kazutaka SaitoKazutaka Saito Tokyo, Japan More articles by this author , Hitoshi MasudaHitoshi Masuda Tokyo, Japan More articles by this author , Satoru KawakamiSatoru Kawakami Tokyo, Japan More articles by this author , and Kazunori KiharaKazunori Kihara Tokyo, Japan More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2011.02.2546AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES The widespread use of modern imaging has resulted in the increased detection of small, asymptomatic renal tumors. Recent studies have revealed that approximately 20% of these small renal masses are pathologically benign at nephrectomy despite expert preoperative radiologic evaluation. The high level of noncancerous lesions is, to some extent, due to that no imaging feature can accurately distinguish oncocytoma and angiomyolipoma (AML) with minimal fat from renal cell carcinoma. We evaluated the value of diffusion-weighted magnetic resonance imaging (DW-MRI) in differentiating between AML with minimal fat and clear cell renal cell carcinoma (CCRCC). METHODS Between April 2006 and March 2009, 41 renal tumors without visible macroscopic fat on unenhanced CT images were evaluated by MRI, including DW-MRI, and were pathologically diagnosed as CCRCC (n = 36) or AML with minimal fat (n = 5). To evaluate the heterogeneity of diffusion in each tumor, we analyzed the signals of the tumors on DW-MRI subjectively and the apparent diffusion coefficient (ADC) values and histograms objectively. RESULTS Thirty-three (92%) of the 36 CCRCCs exhibited a heterogeneous signal on DW-MRI and several peaks in the ADC value histogram whereas 4 (80%) of the 5 AMLs with minimal fat exhibited a homogeneous signal on DW-MRI and a single prominent peak in the histogram. Although there was no significant difference in the minimum ADC value between them, the standard deviations (SDs) of the ADC value of AMLs were significantly smaller than those of CCRCCs (P = 0.0015). These differences were also found in the size-matched cohort. CONCLUSIONS DW-MRI may be a useful and noninvasive addition to the preoperative differentiation of CCRCC and AML with minimal fat. Most AMLs with minimal fat exhibited homogeneity of diffusion on DW-MRI whereas many CCRCCs exhibited heterogeneity. These findings were confirmed objectively by the significant difference in the SDs of ADC values and the patterns of the ADC value histogram. © 2011 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 185Issue 4SApril 2011Page: e922 Advertisement Copyright & Permissions© 2011 by American Urological Association Education and Research, Inc.MetricsAuthor Information Hajime Tanaka Tokyo, Japan More articles by this author Soichiro Yoshida Tokyo, Japan More articles by this author Yasuhisa Fujii Tokyo, Japan More articles by this author Chikako Ishii Tokyo, Japan More articles by this author Fumitaka Koga Tokyo, Japan More articles by this author Kazutaka Saito Tokyo, Japan More articles by this author Hitoshi Masuda Tokyo, Japan More articles by this author Satoru Kawakami Tokyo, Japan More articles by this author Kazunori Kihara Tokyo, Japan More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

MRI Findings of Angiomyolipoma of the Renal Sinus in 5 Cases

The magnetic resonance imaging (MRI) appearance of 5 cases of angiomyolipoma (AML) centered in the renal sinus is presented. All cases exhibited similar imaging findings, including well-circumscribed margins, minimal perceived enhancement without soft tissue components, insinuation around the renal collecting system, and localized hydrocalicosis with associated localized renal parenchymal atrophy but without generalized hydronephrosis. Findings in some cases included extension out of the renal sinus and presence of aneurysmal vessels. Although identification of a renal parenchymal defect has been reported to be useful for the differentiation of a large exophytic AML from a perirenal liposarcoma, none of the cases in our series demonstrated this finding on MRI. To our knowledge, there have been only 2 previous reported cases showing the cross-sectional imaging appearance of an AML of the renal sinus without a renal parenchymal defect. In view of the shared and characteristic imaging features of the 5 cases in our series, we believe that the diagnosis of this condition can be strongly suggested by MRI despite the absence of a visible parenchymal defect. While histology remains the only way to definitively establish the diagnosis, the typical imaging appearance of this entity should enable conservative management with follow-up imaging in an asymptomatic patient. In some cases, embolization and needle biopsy could be used to avoid a surgical approach.

Clinical Correlates of Renal Angiomyolipoma Subtypes in 209 Patients: Classic, Fat Poor, Tuberous Sclerosis Associated and Epithelioid

Angiomyolipomas classically present radiographically as fat containing lesions but some fail to demonstrate fat content. Histologically confirmed angiomyolipomas uniformly follow a benign course but rare epithelioid variants of angiomyolipoma can recur and metastasize. We investigated the clinical, radiographic and histological characteristics of each angiomyolipoma subtype. Pertinent data were recorded for 209 patients surgically treated for angiomyolipoma in 219 kidneys from 1981 to 2007. Classic and fat poor angiomyolipomas were classified radiographically based on the presence or absence of fat and classified histologically based on the presence of triphasic, monophasic or epithelioid histology. Median radiographic size was 3.2, 4.9 and 10 cm in patients with a single angiomyolipoma (59% of patients), multiple angiomyolipomas and tuberous sclerosis (probable or definite), respectively. In these 3 groups 65%, 47% and 33% of lesions were not suspected radiographically (fat poor angiomyolipoma). Fat poor angiomyolipomas were more commonly single, smaller and in older patients. Triphasic histology was evident in 76% of angiomyolipomas with 16% demonstrating a predominance of 1 component and 8% containing epithelioid features. Despite potentially aggressive findings in 18% (eg presence within the perinephric fat, lymph node involvement) no angiomyolipoma recurred during a mean followup of 3.4 years (range 0 to 24). A total of 28 (13%) patients with angiomyolipoma had concomitant renal cell carcinoma. A surprisingly high number of resected angiomyolipomas was not suspected radiographically indicating the importance of precise radiographic characterization to minimize nephrectomy for fat poor angiomyolipoma, which should remain a research priority. In this sizeable single institution series no triphasic, monophasic or epithelioid angiomyolipoma recurred despite potentially aggressive findings in a substantial proportion of cases.

CT Histogram Analysis: Differentiation of Angiomyolipoma without Visible Fat from Renal Cell Carcinoma at CT Imaging

To retrospectively evaluate the diagnostic performance of computed tomographic (CT) histogram analysis for differentiating angiomyolipoma (AML) without visible fat from renal cell carcinoma (RCC) at CT, by using pathologic analysis and clinical diagnosis as the reference standard. This retrospective study was approved by the institutional review board; informed consent was waived. The authors reviewed the medical records of 144 patients with pathologic confirmation of RCC or AML (105 men, 39 women; mean age, 52 years). Analysis of unenhanced CT histograms was performed on 34 AMLs without visible fat at CT and 110 size-matched RCCs. The percentages of voxels and pixels were compared in the two groups according to the CT number categories. The diagnostic performance of CT histogram analysis in differentiating AML from RCC was determined by using receiver operating characteristic (ROC) analysis. The percentages of voxels and pixels with a CT number less than -30 HU (2.7% and 3.4% vs 0.1% and 0.0%), less than -20 HU (4.3% and 5.1% vs 0.2% and 0.1%), less than -10 HU (7.0% and 8.1% vs 0.6% and 0.4%), and less than 0 HU (12.0% and 13.9% vs 2.0% and 2.0%) were significantly greater in the AML group than in the RCC group (P < .01), respectively. The area under the ROC curve was as high as 0.706 when a pixel percentage with a CT number less than -10 HU was used as a differentiating parameter. Corresponding to the specificity of 100% for differentiating AML from RCC, the sensitivity was as high as 20% when a pixel percentage of 6% with a CT number less than -10 HU was used as a criterion. CT histogram analysis may be useful for differentiating AML without visible fat from RCC at CT.

Renal Angiomyolipoma with Minimal Fat: Differentiation from Other Neoplasms at Double-Echo Chemical Shift FLASH MR Imaging

To prospectively evaluate the diagnostic performance of double-echo gradient-echo (GRE) chemical shift magnetic resonance (MR) imaging in the differentiation of angiomyolipoma (AML) with minimal fat from other renal neoplasms, with pathologic examination or follow-up data serving as the reference standard. Institutional review board approval and informed consent were obtained. Double-echo GRE chemical shift MR imaging was performed in 55 patients (29 men and 26 women; mean age, 49 years +/- 14 [standard deviation]) with 55 renal tumors, including 37 (67%) pathologically proved tumors (23 renal cell carcinomas, nine AMLs, two oncocytomas, two lymphomas, and one reninoma) and 18 (33%) clinically diagnosed tumors (17 AMLs and one indeterminate malignancy). All tumors showed no intratumoral fat and had homogeneous enhancement and a prolonged or gradual enhancement pattern on biphasic helical computed tomographic scans. Signal intensity was measured in the renal tumor and spleen on in-phase and opposed-phase images. The signal intensity index and tumor-to-spleen ratio in AMLs and non-AMLs were calculated and compared with the Student t test. Receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic accuracy of the signal intensity index and tumor-to-spleen ratio and to extract the optimal cut-off values in the differentiation of AMLs and non-AMLs. The signal intensity index and tumor-to-spleen ratio were different between AMLs (42% +/- 11 and -43% +/- 17, respectively) and non-AMLs (5% +/- 14 and -4% +/- 16, respectively) (P < .001). The area under the ROC curve was 0.975 for the signal intensity index and 0.952 for the tumor-to-spleen ratio. For differentiation of AMLs from non-AMLs, sensitivity and specificity were (a) 96% and 93%, respectively, with a signal intensity index of 25% and (b) 88% and 97%, respectively, with a tumor-to-spleen ratio of -32%. Double-echo GRE chemical shift MR imaging can be used to differentiate AML with minimal fat from other renal neoplasms.

Role of Percutaneous Image-Guided Biopsy in the Evaluation of Renal Masses

Objective: The objective of the present study was to evaluate the indications, accuracy, complications and impact of image-guided percutaneous biopsy of renal masses. Materials and Methods: Between 1994 and 1999, percutaneous biopsies under ultrasonography or computerized tomography guidance were performed in 33 patients with renal mass (22 men and 11 women, mean age 57.5 years, range 21–88). We retrospectively analyzed the relationship between clinical and histopathological findings, and discuss the appropriateness of the indications for image-guided percutaneous biopsy in the diagnosis of renal masses. Results: The indications used in our institution were as follows: (1) clinical and radiological findings to suggest a diagnosis other than primary renal cell carcinoma (RCC) (n = 15); (2) suspicious lesions of RCC in multiple cystic renal masses (n = 7); (3) differentiation of transitional cell carcinoma of the renal pelvis from RCC (n = 7); (4) differentiation of angiomyolipoma from RCC (n = 4). Sufficient amounts of tissues were obtained from all patients for pathological diagnosis. Among 33 patients, 21 (63.6%) were diagnosed positive for malignancy, and 15 underwent surgical intervention. The histopathological findings between percutaneous biopsy and surgically resected tissue were identical in 13 cases (86.7%). No patient developed major complications requiring surgical treatment. Conclusion: If performed under appropriate selection of patients, percutaneous image-guided biopsy is a safe, reliable and accurate method of managing suspicious and/or indeterminate renal mass, and may contribute to the selection of appropriate clinical management by avoiding unnecessary procedures.

NUO 2001 Ultrasound differentiation of angiomyolipoma (AML) from renal cell carcinoma (RCC)

NUO 2001 Ultrasound differentiation of angiomyolipoma (AML) from renal cell carcinoma (RCC)