Differentiation of small hyperechoic renal cell carcinoma from angiomyolipoma: computer-aided tissue echo quantification

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

BACKGROUND

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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

Ultrasound Image-Based Deep Features and Radiomics for the Discrimination of Small Fat-Poor Angiomyolipoma and Small Renal Cell Carcinoma

We evaluated the performance of ultrasound image-based deep features and radiomics for differentiating small fat-poor angiomyolipoma (sfp-AML) from small renal cell carcinoma (SRCC). This retrospective study included 194 patients with pathologically proven small renal masses (diameter ≤4 cm; 67 in the sfp-AML group and 127 in the SRCC group). We obtained 206 and 364 images from the sfp-AML and SRCC groups with experienced radiologist identification, respectively. We extracted 4024 deep features from the autoencoder neural network and 1497 radiomics features from the Pyradiomics toolbox; the latter included first-order, shape, high-order, Laplacian of Gaussian and Wavelet features. All subjects were allocated to the training and testing sets with a ratio of 3:1 using stratified sampling. The least absolute shrinkage and selection operator (LASSO) regression model was applied to select the most diagnostic features. Support vector machine (SVM) was adopted as the discriminative classifier. An optimal feature subset including 45 deep and 7 radiomics features was screened by the LASSO model. The SVM classifier achieved good performance in discriminating between sfp-AMLs and SRCCs, with areas under the curve (AUCs) of 0.96 and 0.85 in the training and testing sets, respectively. The classifier built using deep and radiomics features can accurately differentiate sfp-AMLs from SRCCs on ultrasound imaging.

Etiology of Small Echogenic Renal Masses

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Hyperechoic Renal Masses: Differentiation of Angiomyolipomas from Renal Cell Carcinomas using Tumor Size and Ultrasound Radiomics

A retrospective single-center study was performed to assess the performance of ultrasound image-based texture analysis in differentiating angiomyolipoma (AML) from renal cell carcinoma (RCC) on incidental hyperechoic renal lesions. Ultrasound reports of patients from 2012 to 2017 were queried, and those with a hyperechoic renal mass <5 cm in diameter with further imaging characterization and/or pathological correlation were included. Quantitative texture analysis was performed using a model including 18 texture features. Univariate logistic regression was used to identify texture variables differing significantly between AML and RCC, and the performance of the model was measured using the area under the receiver operating characteristic (ROC) curve. One hundred thirty hyperechoic renal masses in 127 patients characterized as RCCs (25 [19%]) and AMLs (105 [81%]) were included. Size (odds ratio [OR] = 0.12, 95% confidence interval [CI]: 0.04-0.43, p < 0.001) and 4 of 18 texture features, including entropy (OR = 0.09, 95% CI: 0.01-0.81, p = 0.03), gray-level non-uniformity (OR = 0.12, 95% CI: 0.02-0.72, p = 0.02), long-run emphasis (OR = 0.49, 95% CI: 0.27-0.91, p = 0.02) and run-length non-uniformity (OR = 2.18, 95% CI: 1.14-4.16, p = 0.02) were able to differentiate AMLs from RCCs. The area under the ROC curve for the performance of the model, including texture features and size, was 0.945 (p < 0.001). Ultrasound image-based textural analysis enables differentiation of hyperechoic RCCs from AMLs with high accuracy, which improves further when combined with tumor size.

Comparison of computed tomography (CT), magnetic resonance imaging (MRI) and contrast-enhanced ultrasound (CEUS) in the evaluation of unclear renal lesions

PURPOSE

 To compare the sensitivity and specificity of contrast-enhanced ultrasound (CEUS), computed tomography (CT) and magnetic resonance imaging (MRI) in the evaluation of unclear renal lesions to the histopathological outcome.

MATERIALS AND METHODS

 A total of 255 patients with a single unclear renal mass with initial imaging studies between 2005 and 2015 were included. Patient ages ranged from 18 to 86 with (mean age 62 years; SD ± 13). CEUS (255 patients), CT (88 out of 255 patients; 34.5 %) and MRI (36 out of 255 patients; 14.1 %) were used for determining malignancy or benignancy and initial findings were correlated with the histopathological outcome.

RESULTS

 CEUS showed a sensitivity of 99.1 % (95 % confidence interval (CI): 96.7 %, 99.9 %), a specificity of 80.5 % (95 % CI: 65.1 %, 91.2 %), a positive predictive value (PPV) of 96.4 % (95 % CI: 93.0 %, 98.4 %) and a negative predictive value (NPV) of 94.3 % (95 % CI: 80.8 %, 99.3 %). CT showed a sensitivity of 97.1 % (95 % CI: 89.9 %, 99.6 %), a specificity of 47.4 % (95 % CI: 24.4 %, 71.1 %), a PPV of 87.0 % (95 % CI: 77.4 %, 93.6 %) and a NPV of 81.8 % (95 % CI: 48.2 %, 97.7 %). MRI showed a sensitivity of 96.4 % (95 % CI: 81.7 %, 99.9 %), a specificity of 75.0 % (95 % CI: 34.9 %, 96.8 %), a PPV of 93.1 % (95 % CI: 77.2 %, 99.2 %) and a NPV of 85.7 % (95 % CI: 42.1 %, 99.6 %). Out of the 212 malignant lesions a total of 130 clear cell renal carcinomas, 59 papillary renal cell carcinomas, 7 chromophobe renal cell carcinomas, 4 combined clear cell and papillary renal cell carcinomas and 12 other malignant lesions, e. g. metastases, were diagnosed. Out of the 43 benign lesions a total 10 angiomyolipomas, 3 oncocytomas, 8 benign renal cysts and 22 other benign lesions, e. g. renal adenomas were diagnosed. Using CEUS, 10 lesions were falsely identified as malignant or benign, whereas 8 lesions were false positive and 2 lesions false negative.

CONCLUSION

 CEUS is an useful method which can be additionally used to clinically differentiate between malignant and benign renal lesions. CEUS shows a comparable sensitivity, specificity, PPV and NPV to CT and MRI. In daily clinical routine, patients with contraindications for other imaging modalities can particularly benefit using this method.

KEY POINTS

  · Wide availability. · Safe applicability in patients with known renal insufficiency or allergies to iodine or gadolinium. · Comparable sensitivity, specificity, PPV and NPV to CT and MRT. · May lead to a reduction in interventional radiological or surgical interventions.

CITATION FORMAT

· Marschner CA, Ruebenthaler J, Schwarze V et al. Comparison of computed tomography (CT), magnetic resonance imaging (MRI) and contrast-enhanced ultrasound (CEUS) in the evaluation of unclear renal lesions. Fortschr Röntgenstr 2020; 192: 1053 - 1058.

Evaluation of renal lesions using contrast-enhanced ultrasound (CEUS); a 10-year retrospective European single-centre analysis

OBJECTIVE

To investigate the usefulness of contrast-enhanced ultrasound (CEUS) in the evaluation of renal masses.

METHODS

This study included 255 patients with renal masses. Ages ranged from 18-86 years. CEUS was used for determining malignancy or benignancy and findings were correlated with the histopathological outcome. Out of 255 lesions, 212 lesions were malignant (83.1%) and 43 were benign (16.9%). Diagnostic accuracy was tested using the histopathological diagnosis as the gold standard.

RESULTS

CEUS showed a sensitivity of 99.1% [95% confidence interval (CI): 96.7%, 99.9%], a specificity of 80.5% (CI: 65.1%, 91.2%), a positive predictive value of 96.4% (CI: 93.0%, 98.4%) and a negative predictive value of 94.3% (CI: 80.8%, 99.3%). Kappa for diagnostic accuracy was κ = 0.85 (CI: 0.75, 0.94). Of 212 malignant lesions, 200 renal cell carcinomas and 12 other malignant lesions were diagnosed. Out of 43 benign lesions, 10 angiomyolipomas, 3 oncocytomas, 8 renal cysts and 22 other benign lesions were diagnosed.

CONCLUSION

CEUS is an useful method to differentiate between malignant and benignant renal lesions. To date, to our knowledge, this is the largest study in Europe for the evaluation of renal lesions using CEUS with a histopathological validation.

KEY POINTS

• CEUS helps clinicians detect and characterise unclear solid and cystic renal lesions • CEUS shows a high diagnostic accuracy in the characterization of these lesions • Proper surgical treatment or follow-up can be given with better diagnostic confidence.

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.

Can "Tumor-to-Cortex Echogenicity Ratio" Differentiate Angiomyolipomas from Other Hyper-Echoic Renal Masses

A retrospective study was performed to evaluate the diagnostic value of tumor-to-cortex echogenicity ratio (TCER) in the characterization of hyper-echoic renal masses. The radiology database was queried between 2012 and 2014 for hyper-echoic renal masses on the basis of defined exclusion and inclusion criteria. Each included mass was characterized as either an angiomyolipoma (AML) or a non-AML based on pre-defined criteria. The ratio of renal mass echogenicity to that of adjacent renal cortex (TCER) was calculated for each mass using commercially available software. A total of 70 masses in 65 patients were identified, including 49 AMLs. TCER values >2.26 were associated with a sensitivity and specificity of 81.6% and 71.4%, respectively, for diagnosis of AML. Moreover, TCER values >3.98 resulted in 100% specificity for AML diagnosis with a sensitivity of 28.6%. These findings suggest that the TCER may be a valuable tool for the characterization of hyper-echoic renal masses.

Comparison of magnetic resonance imaging (MRI) and contrast-enhanced ultrasound (CEUS) in the evaluation of unclear solid renal lesions

PURPOSE

To compare the sensitivity and specificity of contrast-enhanced ultrasound (CEUS) and magnetic resonance imaging (MRI) in the evaluation of unclear renal lesions to the histopathological outcome.

MATERIALS AND METHODS

A total of 36 patients with a single unclear solid renal lesion with initial imaging studies between 2005 and 2015 were included. CEUS and MRI were used for determining malignancy or benignancy and initial findings were correlated with the histopathological outcome. Out of the 36 renal masses a total of 28 lesions were malignant (77.8%) and 8 were found to be benign (22.2%). Diagnostic accuracy was testes by using the histopathological diagnosis as the gold standard.

RESULTS

CEUS showed a sensitivity of 96.4%, a specificity of 100.0%, a positive predictive value (PPV) of 100.0% and a negative predictive value (NPV) of 88,9%. MRI showed a sensitivity of 96.4%, a specificity of 75.0%, a PPV of 93.1% and a NPV of 85.7%. Out of the 28 malignant lesions a total of 18 clear cell renal carcinomas, 6 papillary renal cell carcinomas and 4 other malignant lesions, e.g. metastases, were diagnosed. Out of the 8 benign lesions a total 3 angiomyolipomas, 2 oncocytomas, 1 benign renal cyst and 2 other benign lesions, e.g. renal adenomas were diagnosed. Using CEUS, 1 lesion was falsely identified as benign. Using MRI, 2 lesions were falsely identified as benign and 1 lesion was falsely identified as malignant.

CONCLUSION

CEUS is an useful method which can be additionally used to clinically differentiate between malignant and benign renal lesions. CEUS shows a comparable sensitivity, specificity, PPV and NPV to MRI. In daily clinical routine, patients with contraindications for other imaging modalities can particularly benefit using this method.

Evaluation of indeterminate renal masses with contrast-enhanced US: a diagnostic performance study

PURPOSE

To determine the utility of contrast material-enhanced ultrasonography (US) in the characterization of indeterminate renal masses.

MATERIALS AND METHODS

This retrospective performance study was approved by the institutional review board and was HIPAA compliant, with waiver of informed consent. Patients included 721 individuals referred for contrast-enhanced US with 1018 indeterminate renal masses from 1999 to 2010, identified initially with an imaging study. Three hundred twenty patients (44.4%) were female, and 401 (55.6%) were male. Patient ages ranged from 17 to 95 years (mean ± standard deviation, 70 years ± 15). Lesion size varied from 2 to 161 mm (mean, 26.6 mm ± 19.5). Contrast-enhanced US enhancement patterns were used to characterize masses as benign or malignant. For lesions with a definitive diagnosis, 306 of 1018 (30.0%) were correlated with contrast-enhanced US findings: 167 (54.6%) were benign, and 139 (45.4%) were malignant. For lesions without a pathologic diagnosis, 712 (70.0%) were followed for as long as 10 years. Diagnostic accuracy measures were calculated by using pathologic diagnosis as the reference standard, as well as lesion stability at 3 and 5 years.

RESULTS

Contrast-enhanced US had a sensitivity of 100% (126 of 126; 95% confidence interval [CI]: 97.1%, 100%), specificity of 95.0% (132 of 139; 95% CI: 89.9%, 98.0%), positive predictive value (PPV) of 94.7% (126 of 133), and negative predictive value (NPV) of 100% (132 of 132). The five false-positive masses included three oncocytomas and two Bosniak category 3 cystic lesions. Of the 290 lesions that had follow-up of at least 36 months, none of the lesions demonstrated changes that necessitated lesion reclassification. If these lesions were included, assuming lesions classified as malignant were malignant, then of the 596 lesions, sensitivity was 100% (161 of 161), specificity was 96.6% (420 of 435), PPV was 91.5% (161 of 176), and NPV was 100% (420 of 420).

CONCLUSION

Contrast-enhanced US evaluation is a highly sensitive and specific method for characterization of indeterminate renal masses. Online supplemental material is available for this article.

Characterization and diagnostic confidence of contrast-enhanced ultrasound for solid renal tumors

The objective was to determine whether contrast-enhanced ultrasound (CEUS) could improve the diagnostic confidence of solid renal masses. CEUS examinations were performed on 51 patients with renal tumors. Histologic findings from surgical specimens (n = 24) or magnetic resonance imaging follow-up (n = 27) were used as reference procedures for definitive diagnosis. Diffuse heterogeneous/homogeneous enhancement and quick peripheralnodularenhancement were found to be characteristic patterns in renal cell carcinoma (RCC). Dotlike or diffuse heterogeneous/homogeneous enhancement and slow peripheral nodular enhancement were observed as typical enhancement patterns in angiomyolipoma. The results show that CEUS combined with conventional ultrasound significantly improves diagnostic confidence. The sensitivity for RCC diagnosis with this imaging approach was 86% and the specificity was 93%. Both positive and negative predictive values of detection were 90% and the overall accuracy was 90%.

How echogenic is echogenic? Quantitative acoustics of the renal cortex

The echogenicity of the cortex is an important parameter in interpreting renal sonograms that suggest changes in cortical structure. Echogenicity is currently measured qualitatively, and no attempts have been made at quantification. We developed a method to quantify renal cortical echogenicity in reference to the liver and evaluated its reproducibility, dependence on scanning variables, and potential utility. Sonograms of the right kidney were digitized, and the mean pixel density of regions of the renal cortex and liver was measured and normalized to the gray scale. Echogenicity was expressed as the ratio of the brightness (inverse of mean pixel density) of the cortex to that of the liver. The mean coefficient of variation among measurements performed on multiple sonograms from the same study was 2.8%, and the coefficient of variation among multiple measurements performed on the same kidney over 1 year was 1.8%. The correlation between measurements obtained by two different individuals on identical images was 0.92, with a mean variation of 3.0%. Echogenicity was not significantly affected by type of scanner or probe frequency, but varied inversely with gain. However, the effect of gain was very small within the useful range. Water loading after an overnight fast increased echogenicity in all cases, with a mean increase of 6.4%. Echogenicity of normal kidneys was significantly less than that of the liver (range, 0.810 to 0.987), and in clinical sonograms analyzed retrospectively but blindly, echogenicity correlated with the qualitative gradations of echogenicity originally assigned. The most echogenic kidneys were 62% brighter than normal kidneys, many times greater than the variability of the measurement. We conclude that quantification of renal cortical echogenicity is feasible and reproducible and may be useful in detecting and following renal disease. Echogenicity of the renal cortex is less than that of the liver in healthy subjects and is influenced by the state of diuresis.