Renal angiomyolipoma: preoperative identification of atypical fat-poor AML

A noninvasive comprehensive model based on medium sample size had good diagnostic performance in distinguishing renal fat-poor angiomyolipoma from homogeneous clear cell renal cell carcinoma

PURPOSE

To determine the diagnostic value of a comprehensive model based on unenhanced computed tomography (CT) images for distinguishing fat-poor angiomyolipoma (fp-AML) from homogeneous clear cell renal cell carcinoma (hm-ccRCC).

METHODS

We retrospectively reviewed 27 patients with fp-AML and 63 with hm-ccRCC. Demographic data and conventional CT features of the lesions were recorded (including sex, age, symptoms, lesion location, shape, boundary, unenhanced CT attenuation and so on). Whole tumor regions of interest were drawn on all slices to obtain histogram parameters (including minimum, maximum, mean, percentile, standard deviation, variance, coefficient of variation, skewness, kurtosis, and entropy) by two radiologists. Chi-square test, Mann-Whitney U test, or independent samples t-test were used to compare demographic data, CT features, and histogram parameters. Multivariate logistic regression analyses were used to screen for independent predictors distinguishing fp-AML from hm-ccRCC. Receiver operating characteristic curves were constructed to evaluate the diagnostic performances of the models.

RESULTS

Age, sex, tumor boundary, unenhanced CT attenuation, maximum tumor diameter, and tumor volume significantly differed between patients with fp-AML and those with hm-ccRCC (P < 0.05). The minimum, mean, first percentile (Perc.01), Perc.05, Perc.10, Perc.25, Perc.50, Perc.75, Perc.90, Perc.95, and Perc.99 of the Fp-AML group were higher than those of the hm-ccRCC group (P < 0.05). Coefficient of variance, skewness, and kurtosis were lower than those in the hm-ccRCC group (all P < 0.05). Age, maximum tumor diameter, unenhanced CT attenuation, and Perc.25 were independent predictors for distinguishing fp-AML from hm-ccRCC (all P < 0.05). The comprehensive model, incorporating age, maximum tumor diameter, unenhanced CT attenuation, and Perc.25, showed the best diagnostic performance (AUC = 0.979).

CONCLUSION

The comprehensive model based on unenhanced CT imaging can accurately distinguish fp-AML from hm-ccRCC and may assist clinicians in tailoring precise therapy, while also helping to improve the diagnosis and management of renal tumors, leading to the selection of effective treatment options.

High peripheral neutrophil and monocyte count distinguishes renal cell carcinoma from renal angiomyolipoma and predicts poor prognosis of renal cell carcinoma

Background

The presence of peripheral inflammatory cells has been linked to the prognosis of cancer. This study aims to investigate the distinct roles of absolute neutrophil count (ANC) and absolute monocyte count (AMC) in differentiating renal cell carcinoma (RCC) from renal angiomyolipoma (RAML), as well as their prognostic significance in RCC.

Methods

We conducted a comprehensive analysis of peripheral immune cell data, clinicopathological data, and tumor characteristics in patients diagnosed with RCC or RAML from January 2015 to December 2021. Receiver operating characteristic (ROC) curves, as well as univariate and multivariate analyses, were employed to assess the diagnostic utility of AMC and ANC in differentiating between RCC and RAML. Kaplan-Meier curve analysis was used to study the survival of RCC patients with different AMC and ANC. The prognostic value of AMC and ANC in RCC was investigated using COX univariate and multivariate analysis. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used for bioinformatic correlation analysis.

Results

A total of 1120 eligible patients were included in the study. The mean preoperative AMC and ANC in patients with RCC were found to be significantly higher compared to those in patients with RAML (P = 0.001 and P < 0.001, respectively). High preoperative AMC and ANC significantly correlated with smoking history, tumor length, gross hematuria, and high T Stage, N stage, and pathological grade. In multivariate analyses, an ANC> 3.205 *10^9/L was identified to be independently associated with the presence of RCC (HR = 1.618, P = 0.008). High AMC and ANC were significantly associated with reduced OS and PFS (P < 0.05), and ANC may be an independent prognostic factor. Public database analysis showed that signature genes of tumor-associated macrophages (TAMs) and tumor-associated neutrophils (TANs) were highly expressed in ccRCC.

Conclusions

Elevated preoperative ANC and AMC can distinguish RCC from RAML and predict poor prognosis in patients with RCC. Furthermore, the signature genes of TAMs and TANs exhibit high expression levels in clear cell RCC.

Cross-sectional imaging assessment of renal masses with emphasis on MRI

Magnetic resonance imaging (MRI) is a useful complementary imaging tool for the diagnosis and characterization of renal masses, as it provides both morphologic and functional information. A core MRI protocol for renal imaging should include a T1-weighted sequence with in- and opposed-phase images (or, alternatively with DIXON technique), T2-weighted and diffusion-weighted images as well as a dynamic contrast-enhanced sequence with subtraction images, followed by a delayed post-contrast T1-weighted sequence. The main advantages of MRI over computed tomography include increased sensitivity for contrast enhancement, less sensitivity for detection of calcifications, absence of pseudoenhancement, and lack of radiation exposure. MRI may be applied for renal cystic lesion characterization, differentiation of renal cell carcinoma (RCC) from benign solid renal tumors, RCC histologic grading, staging, post-treatment follow-up, and active surveillance of patients with treated or untreated RCC.

Deep learning with a convolutional neural network model to differentiate renal parenchymal tumors: a preliminary study

PURPOSE

With advancements in medical imaging, more renal tumors are detected early, but it remains a challenge for radiologists to accurately distinguish subtypes of renal parenchymal tumors. We aimed to establish a novel deep convolutional neural network (CNN) model and investigate its effect on identifying subtypes of renal parenchymal tumors in T2-weighted fat saturation sequence magnetic resonance (MR) images.

METHODS

This retrospective study included 199 patients with pathologically confirmed renal parenchymal tumors, including 77, 46, 34, and 42 patients with clear cell renal cell carcinoma (ccRCC), chromophobe renal cell carcinoma (chRCC), angiomyolipoma (AML), and papillary renal cell carcinoma (pRCC), respectively. All enrolled patients underwent kidney MR scans with the field strength of 1.5 Tesla (T) or 3.0 T before surgery. We selected T2-weighted fat saturation sequence images of all patients and built a deep learning model to determine the type of renal tumors. Receiver operating characteristic (ROC) curve was depicted to estimate the performance of the CNN model; the accuracy, precision, sensitivity, specificity, F₁-score, and area under the curve (AUC) were calculated. One-way analysis of variance and χ² tests of independent samples were used to analyze the variables.

RESULTS

The experimental results demonstrated that the model had a 60.4% overall accuracy, a 61.7% average accuracy, and a macro-average AUC of 0.82. The AUCs for ccRCC, chRCC, AML, and pRCC were 0.94, 0.78, 0.80, and 0.76, respectively.

CONCLUSION

Deep CNN model based on T2-weighted fat saturation sequence MR images was useful to classify the subtypes of renal parenchymal tumors with a relatively high diagnostic accuracy.

Successful Utilization of a Live Donor Kidney with Angiomyolipoma

The gap between the kidney transplant recipient list and the number of organs available for transplantation continues to grow. Kidneys from living donors are a major source of high-quality organs. However, they commonly have benign conditions such as cysts and benign tumors that present as operative challenges. This case presents a donor kidney that had a benign angiomyolipoma. The kidney was donated in a standard, minimally invasive fashion. The tumor was then removed on the back table and transplanted without an issue. Both donor and recipient enjoyed a speedy recovery with no significant complications.

Diagnostic accuracy of signal loss in in-phase gradient-echo images for differentiation between small renal cell carcinoma and lipid-poor angiomyolipomas

OBJECTIVES

To assess the diagnostic accuracy of signal loss on in-phase (IP) gradient-echo (GRE) images for differentiation between renal cell carcinomas (RCCs) and lipid-poor angiomyolipomas (lpAMLs).

METHODS

We retrospectively searched our institutional database for histologically proven small RCCs (<5.0 cm) and AMLs without visible macroscopic fat (lpAMLs). Two experienced radiologists assessed MRIs qualitatively, to depict signal loss foci on IP GRE images. A third radiologist drew regions of interest (ROIs) on the same lesions, on IP and out-of-phase (OP) images to calculate the ratio of signal loss. Diagnostic accuracy parameters were calculated for both techniques and the inter-reader agreement for the qualitative analysis was evaluated using the κ test.

RESULTS

15 (38.4%) RCCs lost their signal on IP images, with a sensitivity of 38.5% (95% CI = 23.4-55.4), a specificity of 100% (71.1-100), a positive predictive value (PPV) of 100% (73.4-100), a negative predictive value (NPV) of 31.4% (26.3-37.0), and an overall accuracy of 52% (37.4-66.3%). In terms of the quantitative analysis, the signal intensity index (SII= [(SI_IP - SI_OP) / SI_OP] x 100) for RCCs was -0.132 ± 0.05, while for AMLs it was -0.031 ± 0.02, p = 0.26. The AUC was 0.414 ± -0.09 (0.237-0.592). Using 19% of signal loss as the threshold, sensitivity was 16% and specificity was 100%. The κappa value for subjective analysis was 0.63.

CONCLUSION

Signal loss in "IP" images, assessed subjectively, was highly specific for distinction between RCCs and lpAMLs, although with low sensitivity. The findings can be used to improve the preoperative diagnostic accuracy of MRI for renal masses.

ADVANCES IN KNOWLEDGE

Signal loss on "IP" GRE images is a reliable sign for differentiation between RCC and lpAMLs.

Quantitative Analysis of Multiphase Contrast-Enhanced CT Images: A Pilot Study of Preoperative Prediction of Fat-Poor Angiomyolipoma and Renal Cell Carcinoma

OBJECTIVE. The objective of our study was to preoperatively predict fat-poor angiomyolipoma (fp-AML) and renal cell carcinoma (RCC) by conducting quantitative analysis of contrast-enhanced CT images. MATERIALS AND METHODS. One hundred fifteen patients with a pathologic diagnosis of fp-AML or RCC from a single institution were randomly allocated into a train set (tumor size: mean ± SD, 4.50 ± 2.62 cm) and test set (tumor size: 4.32 ± 2.73 cm) after data augmentation. High-dimensional histogram-based features, texture-based features, and Laws features were first extracted from CT images and were then combined as different combinations sets to construct a logistic prediction model based on the least absolute shrinkage and selection operator procedure for the prediction of fp-AML and RCC. Prediction performances were assessed by classification accuracy, area under the ROC curve (AUC), positive predictive value, negative predictive value, true-positive rate, and false-positive rate (FPR). In addition, we also investigated the effects of different gray-scales of quantitative features on prediction performances. RESULTS. The following combination sets of features achieved satisfying performances in the test set: histogram-based features (mean AUC = 0.8492, mean classification accuracy = 91.01%); histogram-based features and texture-based features (mean AUC = 0.9244, mean classification accuracy = 91.81%); histogram-based features and Laws features (mean AUC = 0.8546, mean classification accuracy = 88.76%); and histogram-based features, texture-based features, and Laws features (mean AUC = 0.8925, mean classification accuracy = 90.36%). The different quantitative gray-scales did not have an obvious effect on prediction performances. CONCLUSION. The integration of histogram-based features with texture-based features and Laws features provided a potential biomarker for the preoperative diagnosis of fp-AML and RCC. The accurate diagnosis of benign or malignant renal masses would help to make the clinical decision for radical surgery or close follow-up.

Impact of a fast free-breathing 3-T abdominal MRI protocol on improving scan time and image quality for pediatric patients with tuberous sclerosis complex

BACKGROUND

Magnetic resonance imaging (MRI) of the abdomen can be especially challenging in pediatric patients because of image quality degradation from respiratory motion. Abdominal MR protocols tailored for free-breathing children can potentially improve diagnostic image quality and reduce scan time.

OBJECTIVE

To evaluate the performance of a free-breathing 3-T MRI protocol for renal evaluation in pediatric patients with tuberous sclerosis complex (TSC).

MATERIALS AND METHODS

A single institution, Institutional Review Board-approved, retrospective database query identified pediatric TSC patients who underwent a free-breathing 3-T MR abdominal protocol including radial and respiratory-triggered pulse sequences and who also had a prior abdominal MRI on the same scanner using a traditional MR protocol utilizing signal averaging and Cartesian k-space sampling. Scan times and use of sedation were recorded. MR image quality was compared between the two protocols using a semiquantitative score for overall image quality and sharpness.

RESULTS

Forty abdominal MRI studies in 20 patients were evaluated. The mean scan time of the fast free-breathing protocol was significantly lower (mean: 42.5±9.8 min) compared with the traditional protocol (58.7±11.7 min; P=<0.001). Image sharpness was significantly improved for radial T2-weighted and T1-weighted triggered Dixon and radial T1-weighted fat-suppressed post-contrast images in the free-breathing protocol, while image quality was significantly higher on radial and Dixon T1-weighted sequences.

CONCLUSION

A free-breathing abdominal MR protocol in pediatric TSC patients decreases scan time and improves image quality and should be considered more widely for abdominal MRI in children.

A CT-based radiomics nomogram for differentiation of renal angiomyolipoma without visible fat from homogeneous clear cell renal cell carcinoma

OBJECTIVES

To develop and validate a radiomics nomogram for preoperative differentiating renal angiomyolipoma without visible fat (AML.wovf) from homogeneous clear cell renal cell carcinoma (hm-ccRCC).

METHODS

Ninety-nine patients with AML.wovf (n = 36) and hm-ccRCC (n = 63) were divided into a training set (n = 80) and a validation set (n = 19). Radiomics features were extracted from corticomedullary phase and nephrographic phase CT images. A radiomics signature was constructed and a radiomics score (Rad-score) was calculated. Demographics and CT findings were assessed to build a clinical factors model. Combined with the Rad-score and independent clinical factors, a radiomics nomogram was constructed. Nomogram performance was assessed with respect to calibration, discrimination, and clinical usefulness.

RESULTS

Fourteen features were used to build the radiomics signature. The radiomics signature showed good discrimination in the training set (AUC [area under the curve], 0.879; 95%; confidence interval [CI], 0.793-0.966) and the validation set (AUC, 0.846; 95% CI, 0.643-1.000). The radiomics nomogram showed good calibration and discrimination in the training set (AUC, 0.896; 95% CI, 0.810-0.983) and the validation set (AUC, 0.949; 95% CI, 0.856-1.000) and showed better discrimination capability (p < 0.05) compared with the clinical factor model (AUC, 0.788; 95% CI, 0.683-0.893) in the training set. Decision curve analysis demonstrated the nomogram outperformed the clinical factors model and radiomics signature in terms of clinical usefulness.

CONCLUSIONS

The CT-based radiomics nomogram, a noninvasive preoperative prediction tool that incorporates the Rad-score and clinical factors, shows favorable predictive efficacy for differentiating AML.wovf from hm-ccRCC, which might assist clinicians in tailoring precise therapy.

KEY POINTS

• Differential diagnosis between AML.wovf and hm-ccRCC is rather difficult by conventional imaging modalities. • A radiomics nomogram integrated with the radiomics signature, demographics, and CT findings facilitates differentiation of AML.wovf from hm-ccRCC with improved diagnostic efficacy. • The CT-based radiomics nomogram might spare unnecessary surgery for AML.wovf.

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.

Contrast-enhanced ultrasound for differentiating benign from malignant solid small renal masses: comparison with contrast-enhanced CT

PURPOSE

The study aimed to compare the diagnostic efficiency of contrast-enhanced ultrasound (CEUS) with that of contrast-enhanced computed tomography (CECT) in the evaluation of benign and malignant small renal masses (SRMs) (<4 cm) confirmed by pathology.

METHODS

A total of 118 patients with 118 renal masses smaller than 4 cm diagnosed by both CEUS and CECT were enrolled in this study, including 25 benign lesions and 93 malignant lesions. All lesions were confirmed by histopathologic diagnosis after surgical resection. The diagnostic imaging studies of the patients were retrospectively reviewed by two independent ultrasonologists and two independent radiologists blinded to the CT or ultrasound findings and final histological results. All lesions on both CEUS and CECT were independently scored on a 3-point scale (1: benign, 2: equivocal, and 3: malignant). The concordance between interobserver agreement was interpreted using a weighted kappa statistic. The diagnostic efficiency of the evaluation of benign and malignant lesions was compared between CEUS and CECT.

RESULTS

All the 118 included lesions were detected by both CEUS and CECT. In CEUS and CECT imaging evaluation of the 118 lesions, the weighted kappa value interpreting the concordance between interobserver agreement was 0.89 (95% CI 0.79-0.98) and 0.93 (95% CI 0.87-0.99), respectively. Both CEUS and CECT demonstrated good diagnostic performance in differential diagnosis of benign and malignant SRMs with sensitivity of 93.5% and 89.2%, specificity of 68% and 76%, PPV of 91.6% and 93.3%, NPV of 73.9% and 65.5%, and AUC of 0.808 and 0.826, respectively. There was no statistically significant difference in any of the diagnostic performance indices between these two methods (P > 0.05). However, the qualitative diagnosis of small papillary renal cell carcinoma (RCC) by CEUS was significantly better than that by CECT (P < 0.05), while there was no significant difference in qualitative diagnostic accuracy on other histotypes of SRMs between CEUS and CECT (P > 0.05).

CONCLUSIONS

Both CEUS and CECT imaging modalities are effective for the differential diagnosis of benign and malignant SRMs. Furthermore, CEUS may be more effective than CECT for the qualitative diagnosis of small papillary RCC.

Angiomyolipoma of the scrotum: report of a rarely seen case and review of the literature

Angiomyolipoma (AML) is a benign, histologically complex mesenchymal tumor arising mainly from the kidney and liver. The majority (80%) of these tumors arise as sporadic tumors, while 20% of them are associated with tuberous sclerosis. Extra-renal sites of AML, though rare, have been reported in literature. In this report, we describe a case of AML arising from the scrotal skin, and presenting as a scrotal mass. Although skin is the most commonly reported site after kidney and liver, scrotal skin AML presents as an intriguing mass in a region known for germ cell tumors which has been reported only once before. A 35-year-old male presented with scrotal swelling. His physical examination, laboratory investigations and imaging studies were non-specific. Excision of the lesion with subsequent histopathological examination revealed the true nature of the lesion. This lesion should be included in the differential diagnosis of scrotal masses.

Imaging and Management of Incidental Renal Lesions

The increased use of imaging modalities in the last years has led to a greater incidence in depicting abdominal incidental lesions. In particular, "incidentalomas" of the kidney are discovered in asymptomatic patients or patients who suffer from diseases not directly related to the kidneys. The aim of this paper is to provide the radiologist with a useful guide to recognize and classify the main incidental renal findings with the purpose of establishing the correct management. First we describe the so-called "pseudotumors" which are important to recognize in order to avoid a misdiagnosis. Afterwards we categorize true renal lesions into cystic and solid types, reporting radiological signs helpful in differentiating between benign and malignant nature.

Review of renal cell carcinoma and its common subtypes in radiology

Representing 2%-3% of adult cancers, renal cell carcinoma (RCC) accounts for 90% of renal malignancies and is the most lethal neoplasm of the urologic system. Over the last 65 years, the incidence of RCC has increased at a rate of 2% per year. The increased incidence is at least partly due to improved tumor detection secondary to greater availability of high-resolution cross-sectional imaging modalities over the last few decades. Most RCCs are asymptomatic at discovery and are detected as unexpected findings on imaging performed for unrelated clinical indications. The 2004 World Health Organization Classification of adult renal tumors stratifies RCC into several distinct histologic subtypes of which clear cell, papillary and chromophobe tumors account for 70%, 10%-15%, and 5%, respectively. Knowledge of the RCC subtype is important because the various subtypes are associated with different biologic behavior, prognosis and treatment options. Furthermore, the common RCC subtypes can often be discriminated non-invasively based on gross morphologic imaging appearances, signal intensity on T2-weighted magnetic resonance images, and the degree of tumor enhancement on dynamic contrast-enhanced computed tomography or magnetic resonance imaging examinations. In this article, we review the incidence and survival data, risk factors, clinical and biochemical findings, imaging findings, staging, differential diagnosis, management options and post-treatment follow-up of RCC, with attention focused on the common subtypes.

Angiomyolipoma being surgically excised for presumed kidney carcinoma

OBJECTIVE

To explore the important factors involved in angiomyolipoma (AML) being preoperatively misclassified and surgically removed for presumed kidney carcinoma.

MATERIALS AND METHODS

From 2008 to 2014, AML was pathologically confirmed in 38 patients who underwent radical or partial nephrectomy for presumed malignant renal tumor. Control group 1 were patients with renal cell carcinoma (RCC) matched for age and tumor size; control group 2 were patients with typical AML matched for age and sex. Pertinent data of the studied group and its matched control groups were recorded and analyzed.

RESULTS

The mean age of the patients in study group was 48.11 ± 12.92 years, and the mean tumor size was 3.12 ± 1.68 cm (range 0.9-9.4). More than 84 % of the misclassified AMLs measured ≤4 cm, and over 21 % patients underwent radical nephrectomy. The only statistically significant feature between the misdiagnosed AML group and the matched RCC group is mean age (48.11 ± 12.92 vs. 56.92 ± 10.28, P = 0.002). Compared with the matched typical AML group, the misdiagnosed AML group has smaller mean tumor size (3.12 ± 1.68 vs. 5.85 ± 3.33, P < 0.001), but more patients undergoing radical nephrectomy (21.05 vs. 0 %, P = 0.003). Two main imaging features, which are hypoechoic on ultrasonography and fat density on computed tomography (CT), were statistically different between the two groups. The misdiagnosis of AML was significantly associated with no fat density on CT (OR 5.528, P = 0.004) and hypoechoic on ultrasonography (OR 3.845, P = 0.017).

CONCLUSIONS

A number of AMLs were misdiagnosed as RCCs, causing a large number of unnecessary surgeries. No fat density on CT and no hyperechoic on ultrasonography resulting from small tumor size were the two most important factors causing AML being excised for presumed kidney carcinoma. Ultrasonography and CT cannot differentiate atypical AML from kidney carcinoma effectively, so improved renal biopsy and noninvasive biomarkers are urgently warranted to prevent us from excising benign renal tumor aggressively.