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.

Patient and nonradiographic tumor characteristics predicting lipid-poor angiomyolipoma in small renal masses: Introducing the BEARS index

Purpose

To create a simple model using clinical variables for predicting lipid-poor angiomyolipoma (AML) in patients with small renal masses presumed to be renal cell carcinoma (RCC) from preoperative imaging.

Materials and Methods

A series of patients undergoing partial nephrectomy (PN) for renal masses ≤4 cm was identified using a prospectively maintained database. Patients were excluded if standard preoperative imaging was not consistent with RCC. Chi square and Mann-Whitney U analyses were used to evaluate differences in characteristics between patients with AML and other types of pathology. A logistic regression model was constructed for multivariable analysis of predictors of lipid-poor AML.

Results

A total of 730 patients were identified that underwent PN for renal masses ≤4 cm between 2007–2015, including 35 with lipid-poor AML and 620 with RCC. In multivariable analysis, the following features predicted AML: female sex (odds ratio, 6.89; 95% confidence interval, 2.35–20.92; p<0.001), age <56 years (2.84; 1.21–6.66; p=0.02), and tumor size <2 cm (5.87; 2.70–12.77; p<0.001). Sex, age, and tumor size were used to construct the BEnign Angiomyolipoma Renal Susceptibility (BEARS) index with the following point values for each particular risk factor: female sex (2 points), age <56 years (1 point), and tumor size <2 cm (2 points). Within the study population, the BEARS index distinguished AML from malignant lesions with an area under the curve of 0.84.

Conclusions

Young female patients with small tumors are at risk for having lipid-poor AML despite preoperative imaging consistent with RCC. Identification of these patients may reduce the incidence of unnecessary PN for benign renal lesions.

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.

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

PURPOSE

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).

MATERIALS AND METHODS

A total of 158 resected renal masses ≤4 cm [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.

RESULTS

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.

CONCLUSIONS

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.

Diffusion-weighted imaging of the abdomen: Impact of b-values on texture analysis features

The purpose of this work was to systematically assess the impact of the b-value on texture analysis in MR diffusion-weighted imaging (DWI) of the abdomen. In eight healthy male volunteers, echo-planar DWI sequences at 16 b-values ranging between 0 and 1000 s/mm² were acquired at 3 T. Three different apparent diffusion coefficient (ADC) maps were computed (0, 750/100, 390, 750 s/mm² /all b-values). Texture analysis of rectangular regions of interest in the liver, kidney, spleen, pancreas, paraspinal muscle and subcutaneous fat was performed on DW images and the ADC maps, applying 19 features computed from the histogram, grey-level co-occurrence matrix (GLCM) and grey-level run-length matrix (GLRLM). Correlations between b-values and texture features were tested with a linear and an exponential model; the best fit was determined by the smallest sum of squared residuals. Differences between the ADC maps were assessed with an analysis of variance. A Bonferroni-corrected p-value less than 0.008 (=0.05/6) was considered statistically significant. Most GLCM and GLRLM-derived texture features (12-18 per organ) showed significant correlations with the b-value. Four texture features correlated significantly with changing b-values in all organs (p < 0.008). Correlation coefficients varied between 0.7 and 1.0. The best fit varied across different structures, with fat exhibiting mostly exponential (17 features), muscle mostly linear (12 features) and the parenchymatous organs mixed feature alterations. Two GLCM features showed significant variability in the different ADC maps. Several texture features vary systematically in healthy tissues at different b-values, which needs to be taken into account if DWI data with different b-values are analyzed. Histogram and GLRLM-derived texture features are stable on ADC maps computed from different b-values.

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.

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.