Diagnosis of angiomyolipoma using computed tomography—region of interest ≤−10 HU or 4 adjacent pixels ≤−10 HU are recommended as the diagnostic thresholds

doi:10.1016/j.crad.2005.12.013

Clinical Radiology

Volume 61, Issue 5, May 2006, Pages 410-416

https://doi.org/10.1016/j.crad.2005.12.013 Get rights and content

AIM

To study and compare the diagnostic accuracy of region of interest (ROI) density measurement and pixel mapping [computed tomography (CT) density of individual pixels] for the diagnosis of renal angiomyolipoma (AML) using CT.

MATERIALS AND METHODS

A study group of histologically proven AMLs was compared with a control group of histologically proven renal cell cancers, normal renal parenchyma, and simple renal cysts. The mean tissue density (ROI circle) and a pixel density map were recorded. The diagnostic accuracy of various thresholds of ROI and pixel mapping values were compared using receiver operating characteristic curves.

RESULTS

Twenty-two AMLs, 16 renal cell carcinomas (RCCs), 30 simple cysts, and 30 sites of renal parenchyma were evaluated. The mean (±1 SD) density of the AMLs was significantly lower [−15.2(20.8) units] than the three control groups [+36.0(8.1) units, +5.4(3.4) units and +22.2(46.5) units for RCC, renal cyst and parenchyma respectively; p<0.001 (analysis of variance)]. The sensitivities and specificities of the ROI diagnostic thresholds of ≤0 units, ≤−10 units and ≤−20 units were 77 and 97%, 73 and 100% and 50 and 100%, respectively. Using pixel mapping [diagnostic thresholds of either a line of 4 pixels ≤−10 units or a square of 4 pixels ≤−10 units] the sensitivity improves to 86% with a specificity of 97%.

CONCLUSION

Although a ROI threshold value of ≤−10 units has a very high specificity (100% in the present study) the sensitivity is modest at only 73%. Pixel mapping is more sensitive for recognizing small clusters of fat. In practice, both methods can be recommended for the analysis of suspected AMLs. ROI density measurement is convenient when analysing large areas of suspected fat and ≤−10 units should be used as the diagnostic threshold. When faced with small lucent areas or indeterminate values after ROI analysis, pixel mapping is recommended using a line of 4 pixels ≤−10 units or a square of 4 pixels ≤−10 units as the discriminating thresholds.

Introduction

The imaging diagnosis of angiomyolipoma (AML) rests on the confirmation of intra-tumoural fat. Using computed tomography (CT) fat is identified with a region of interest circle (ROI), and in routine practice its presence is sufficient to confirm an AML. However, there is some disagreement over the optimal threshold ROI value. Most consider a mean ROI value ≤−10 units as indicating fat1, 2 but others have advocated ≤−15,³ ≤−20,⁴ ≤−30,³ or even ≤−40 units.⁵ Furthermore, some AMLs may contain no or barely any fat,1, 2, 6, 7 so-called atypical or minimal fat AMLs. Such atypical AMLs are visually indistinguishable from renal cell carcinoma (RCC)⁸ and present particular diagnostic difficulties.

A more precise method for the analysis of small clusters of fat would improve management of this sub-group. Pixel mapping (analysis of the CT number or Hounsfield units of individual pixels, or more accurately voxels) has been advocated9, 10 but, as with ROI measurements, the diagnostic thresholds have not been defined. Kurosaki et al.⁹ considered a single pixel of ≤−30 units as signifying AML, while Takahashi and co-workers¹⁰ recommended 3–6 contiguous pixels ≤0 units but both these studies were small and uncontrolled. The comparative accuracy of ROI mapping versus pixel mapping also remains undefined. The aim of this study was to define the sensitivity and specificity of the various thresholds of ROI and pixel values for the CT diagnosis of AML.

Section snippets

Patients

A retrospective, cross-sectional study was conducted in a teaching hospital with a tertiary referral service for urology and clinical genetics. The study group was identified by review of the pathology database. All patients with histologically proven AMLs who had also undergone a dedicated renal CT examination in our hospital over the study period (March 2000–June 2004) were selected.

The control group, also identified from the pathology department database, were patients who had undergone

Results

Twenty-two AMLs [three patients had sporadic AML and the rest were in patients with tuberose sclerosis complex (TSC)] were identified from the database and all are included in this study. Six cases were indeterminate renal masses, three were large AMLs and the rest were small AMLs (<4 cm) that were contained in the kidneys with the indeterminate masses or the large AMLs. The diameter of the AMLs was 2–16 cm. The age range was 24–63 years, 55% were female. The control group comprised 16 renal

Discussion

Imaging plays a central role in renal mass categorization⁴ and suspicious renal masses are best evaluated using CT or magnetic resonance imaging (MRI).¹² CT is usually preferred as it is more readily available, a standardized approach has been clearly defined, and the precise numerical assessment of pre- and post-contrast densities (using ROI circles) and the enhancement gradient provide reliable diagnostic information.¹² Given this information, and as long as the CT examination has been taken

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The weighted kappa value was 0.956 (95% confidence interval, 92.0–99.1%). When a region of interest (ROI) was placed within the most hypodense area on unenhanced CT or within the most signal-dropped area on chemical shift image, the mean attenuation values, TSRs, and SIIs of fat-poor versus fat-invisible AMLs were 19.5 ± 8.1 HU versus 38.1 ± 9.9 HU, 0.59 ± 0.19 versus 0.96 ± 0.01, and 43.7 ± 16.9% versus −5.4 ± 21.1%, respectively (p < 0.0001). When a ROI was placed within the other area on CT or chemical shift images, 90.1% (48/53) of fat-rich AMLs were mis-classified as fat-poor or fat-invisible AML and 50% (11/22) of fat-poor AMLs as fat-invisible AML.
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Many different thresholds of attenuation have been used till date for the identification of small amounts fat. Most studies support a threshold of −10 HU as being reasonably sensitive and specific.33,35,36 Davenport et al36 suggested that an ROI of atleast 19-24 mm2 be used to avoid sampling stochastic noise (quantum mottle) and erroneously diagnosing fat.
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.

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Diagnosis of angiomyolipoma using computed tomography—region of interest ≤−10 HU or 4 adjacent pixels ≤−10 HU are recommended as the diagnostic thresholds

AIM

MATERIALS AND METHODS

RESULTS

CONCLUSION

Introduction

Section snippets

Patients

Results

Discussion

Semin US, CT and MRI

J Urol

Clin Radiol

J Urol

Small (<3 cm) hyperechoic renal masses: comparison of helical and conventional CT for diagnosing angiomyolipoma

AJR Am J Roentgenol

CT diagnosis of renal angiomyolipoma: the importance of detecting small amounts of fat

AJR Am J Roentgenol

Imaging of small renal masses: a medical success story

AJR Am J Roentgenol

Management of small renal neoplasms and angiomyolipomas: a growing problem

Radiology

Angiomyolipomas: imaging findings in lesions with minimal fat

Radiology