Angiomyolipoma: imaging findings in lesions with minimal fat

Stepwise algorithm using computed tomography and magnetic resonance imaging for differential diagnosis of fat-poor angiomyolipoma in small renal masses: A prospective validation study

OBJECTIVES

To validate the diagnostic accuracy of a stepwise algorithm to differentiate fat-poor angiomyolipoma (fp-AML) from renal cancer in small renal masses (SRMs).

METHODS

We prospectively enrolled 223 patients with solid renal masses <4 cm and no visible fat on unenhanced computed tomography (CT). Patients were assessed using an algorithm that utilized the dynamic CT and MRI findings in a stepwise manner. The diagnostic accuracy of the algorithm was evaluated in patients whose histology was confirmed through surgery or biopsy. The clinical course of the patients was further analyzed.

RESULTS

The algorithm classified 151 (68%)/42 (19%)/30 (13%) patients into low/intermediate/high AML probability groups, respectively. Pathological diagnosis was made for 183 patients, including 10 (5.5%) with fp-AML. Of these, 135 (74%)/36 (20%)/12 (6.6%) were classified into the low/intermediate/high AML probability groups, and each group included 1 (0.7%)/3 (8.3%)/6 (50%) fp-AMLs, respectively, leading to the area under the curve for predicting AML of 0.889. Surgery was commonly opted in the low and intermediate AML probability groups (84% and 64%, respectively) for initial management, while surveillance was selected in the high AML probability group (63%). During the 56-month follow-up, 36 (82%) of 44 patients initially surveyed, including 13 of 18 (72%), 6 of 7 (86%), and 17 of 19 (89%) in the low/intermediate/high AML probability groups, respectively, continued surveillance without any progression.

CONCLUSIONS

This study confirmed the high diagnostic accuracy for differentiating fp-AMLs. These findings may help in the management of patients with SRMs.

Qualitative and quantitative characteristics of CEUS for renal cell carcinoma and angiomyolipoma: a narrative review

Incidental findings of renal masses are increasing. However, a substantial portion of surgically treated renal masses turn out to be benign on histopathological examination. Thus, there is a clear need for improved pre-surgical assessment to minimize unnecessary invasive procedures. The challenge intensifies when distinguishing between renal cell carcinoma (RCC) and angiomyolipoma (AML) in renal lesions smaller than 4 cm with minimal adipose tissue. In such cases, contrast-enhanced ultrasound (CEUS) has emerged as a valuable diagnostic tool, by utilizing both qualitative and quantitative parameters. Quantitative measures offer objectivity, reliability, and reproducibility compared to qualitative parameters, enabling the characterization of RCC subtypes and differentiation from AML. Qualitative features as enhancement pattern, degree, and peak were less helpful in distinguishing triphasic minimal fat AML (TAML) from epithelioid AML (EAML), with the pseudocapsule sign potentially being the only distinguishing qualitative feature. The pseudocapsule sign was more frequently observed in ccRCCs (38.0%) than in AMLs (15.6%). Moreover, it was detected in 40.0% of EAMLs and 34.5% of ccRCCs but not in TAMLs due to similar growth patterns between EAMLs and low-grade ccRCCs. Quantitative measures such as the time-to-peak (TTP) ratio can further enhance diagnostic accuracy and also TOC ratio should be considered, as it was higher in clear cell RCCs (ccRCCs) and in EAMLs compared to TAMLs, indicating behavior similar to ccRCCs. However, CEUS remains an operator-dependent exam.

Scientific Status Quo of Small Renal Lesions: Diagnostic Assessment and Radiomics

Background: Small renal masses (SRMs) are defined as contrast-enhanced renal lesions less than or equal to 4 cm in maximal diameter, which can be compatible with stage T1a renal cell carcinomas (RCCs). Currently, 50-61% of all renal tumors are found incidentally. Methods: The characteristics of the lesion influence the choice of the type of management, which include several methods SRM of management, including nephrectomy, partial nephrectomy, ablation, observation, and also stereotactic body radiotherapy. Typical imaging methods available for differentiating benign from malignant renal lesions include ultrasound (US), contrast-enhanced ultrasound (CEUS), computed tomography (CT), and magnetic resonance imaging (MRI). Results: Although ultrasound is the first imaging technique used to detect small renal lesions, it has several limitations. CT is the main and most widely used imaging technique for SRM characterization. The main advantages of MRI compared to CT are the better contrast resolution and tissue characterization, the use of functional imaging sequences, the possibility of performing the examination in patients allergic to iodine-containing contrast medium, and the absence of exposure to ionizing radiation. For a correct evaluation during imaging follow-up, it is necessary to use a reliable method for the assessment of renal lesions, represented by the Bosniak classification system. This classification was initially developed based on contrast-enhanced CT imaging findings, and the 2019 revision proposed the inclusion of MRI features; however, the latest classification has not yet received widespread validation. Conclusions: The use of radiomics in the evaluation of renal masses is an emerging and increasingly central field with several applications such as characterizing renal masses, distinguishing RCC subtypes, monitoring response to targeted therapeutic agents, and prognosis in a metastatic context.

Renal angiomyolipoma: from imaging to intervention

A high volume of cross-sectional imaging has created a window of opportunity for radiologists to identify renal angiomyolipomas (AMLs). The purpose of this review is to help the reader recognise the spectrum of renal AML appearances using different imaging methods and to gain an understanding of the classic and atypical features for appropriate lesion characterisation. Risk factors for AML growth and rupture will be highlighted. An overview of the imaging features of acute AML rupture will be provided, principally relating to computed tomography (CT) assessment. A series of cases will be presented, including a case of peripartum renal AML rupture during Caesarean section leading to diagnostic dilemma. The indications for intervention and available treatment options will be considered: medical therapy, surgery, and interventional radiology (IR) techniques including their pros and cons. Emergency interventional radiology management with selective transarterial embolisation will be presented and analysed in relation to technique, angiographic appearances (pre and post embolisation) and associated complications.

Radiomics and Artificial Intelligence in Renal Lesion Assessment

Radiomics, the extraction and analysis of quantitative features from medical images, has emerged as a promising field in radiology with the potential to revolutionize the diagnosis and management of renal lesions. This comprehensive review explores the radiomics workflow, including image acquisition, feature extraction, selection, and classification, and highlights its application in differentiating between benign and malignant renal lesions. The integration of radiomics with artificial intelligence (AI) techniques, such as machine learning and deep learning, can help patients' management and allow the planning of the appropriate treatments. AI models have shown remarkable accuracy in predicting tumor aggressiveness, treatment response, and patient outcomes. This review provides insights into the current state of radiomics and AI in renal lesion assessment and outlines future directions for research in this rapidly evolving field.

Metanephric adenoma in children: A case report and literature review

Metanephric adenoma (MA) is a rare type of benign renal epithelial tumor that can develop at any age. Nonetheless, MA is extremely rare in children and only a few cases have been reported to date. The present study aimed to report the case of a 5-year-old female found to have a mass in the right kidney during a routine pre-enrollment physical examination. Computed tomography (CT) images revealed multiple high-density calcifications in the mass, and contrast-enhanced CT and magnetic resonance imaging demonstrated that the mass was significantly enhanced in the cortical phase and decreased in the medullary phase. Based on these findings, the mass was initially diagnosed as angiomyolipoma before surgery; however, postoperative pathology confirmed the mass to be a MA. MAs are typically a type of soft tissue mass with relatively uniform density or signal, showing delayed enhancement in contrast-enhanced scanning. However, the mass found in the present study presented diffused high-density calcification, which was obvious in the early phase of contrast-enhanced scanning but weakened in the delayed enhancement phase. In conclusion, the present case study demonstrated that MA should be considered as one of the imaging differential diagnoses of fat-poor angiomyolipoma, renal carcinoma and oncocytoma.

Clinical Practice Guidelines for tuberous sclerosis complex-associated renal angiomyolipoma by the Japanese Urological Association: Summary of the update

New clinical issues have been raised through an interval of 7 years from the previous version (2016). In this study, we update the "Clinical Practice Guidelines for tuberous sclerosis complex-associated renal angiomyolipoma" as a 2023 version under guidance by the Japanese Urological Association. The present guidelines were cooperatively prepared by the Japanese Urological Association and Japanese Society of Tuberous Sclerosis Complex; committee members belonging to one of the two societies or specializing in the treatment of this disease were selected to prepare the guidelines in accordance with the "Guidance for preparing treatment guidelines" published by Minds (2020 version). The "Introduction" consisted of four sections, "Background Questions (BQ)" consisted of four sections, "Clinical Questions (CQ)" consisted of three sections, and "Future Questions (FQ)" consisted of three sections (total: 14 sections). Concerning CQ, an agreement was confirmed through voting by the committee members based on the direction and strength of recommendation, accuracy of evidence, and recommendation comments. The present guidelines were updated based on the current evidence. We hope that the guidelines will provide guiding principles for the treatment of tuberous sclerosis complex-associated renal angiomyolipoma to many urologists, becoming a foundation for subsequent updating.

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.

The Role of CT Imaging in Characterization of Small Renal Masses

Small renal masses (SRM) are increasingly detected incidentally during imaging. They vary widely in histology and aggressiveness, and include benign renal tumors and renal cell carcinomas that can be either indolent or aggressive. Imaging plays a key role in the characterization of these small renal masses. While a confident diagnosis can be made in many cases, some renal masses are indeterminate at imaging and can present as diagnostic dilemmas for both the radiologists and the referring clinicians. This review focuses on CT characterization of small renal masses, perhaps helping us understand small renal masses. The following aspects were considered for the review: (a) assessing the presence of fat, (b) assessing the enhancement, (c) differentiating renal tumor subtype, and (d) identifying valuable CT signs.

Fat-poor renal angiomyolipoma with prominent cystic degeneration: A case report and review of the literature

BACKGROUND

Angiomyolipoma (AML), the most common benign tumor of the kidney, is usually composed of dysmorphic blood vessels, smooth muscle, and mature adipose tissue. To our knowledge, AML with cystic degeneration has rarely been documented. Cystic degeneration, hemorrhage, and a lack of fat bring great challenges to the diagnosis.

CASE SUMMARY

A 60-year-old man with hypertension presented with a 5-year history of cystic mass in his left kidney. He fell 2 mo ago. A preoperative computed tomography (CT) scan showed a mixed-density cystic lesion without macroscopic fat density, the size of which had increased compared with before, probably due to hemorrhage caused by a trauma. Radical nephrectomy was performed. Histopathological studies revealed that the lesion mainly consisted of tortuous, ectatic, and thick-walled blood vessels, mature adipose tissue, and smooth muscle-like spindle cells arranged around the abnormal blood vessels. The tumor cells exhibited positivity for human melanoma black-45, Melan-A, smooth muscle actin, calponin, S-100, and neuron-specific enolase, rather than estrogen receptor, progesterone receptor, CD68, and cytokeratin. The Ki-67 labeling index was less than 5%. The final diagnosis was a fat-poor renal AML (RAML) with prominent cystic degeneration.

CONCLUSION

When confronting a large renal cystic mass, RAML should be included in the differential diagnosis.

♣Evaluation of clinicopathological profiles and development of a risk model in renal epithelioid angiomyolipoma patients: a large-scale retrospective cohort study

BACKGROUND

To identify the malignant potential and prognostic indicators of renal epithelioid angiomyolipoma (eAML), clinicopathological and molecular features as well as the drug efficacy of 67 eAML cases were analyzed.

MATERIALS AND METHODS

Sixty-seven renal eAML patients were enrolled and the immunohistochemical features of these patients were examined. FFPE slides of all patients were re-examined. 21 patients with metastasis received Everolimus 10 mg orally once daily. Responses were evaluated with RECIST criteria by three authors. A risk stratification model was constructed using the following factors: pT3 and pT4, presence of necrosis, mitotic count ≥ 2; the presence of atypical mitoses; severe nuclear atypia, SMA negative, Ki-67 ≥ 10%.

RESULTS

The average percentage of the epithelioid component was 85.6% (range 80-95%). Immunohistochemically, Ki-67 ≥ 10% and negative SMA staining were significantly correlated with malignant characteristics (Ki-67: p < 0.001; SMA: p = 0.001). Survival analysis suggested that pT3-pT4 stage, presence of necrosis, severe nuclear atypia, presence of atypical mitoses, mitotic count ≥ 2, Ki-67 ≥ 10% and negative SMA expression were significantly associated with poorer PFS and OS (p < 0.05). The risk model sufficiently discriminated recurrence/metastasis (AUC = 0.897) and cancer-specific mortality (AUC = 0.932) of renal eAML patients in different risk groups. 21 patients had received Everolimus targeted therapy after recurrence/metastasis. The best response for Everolimus treatment was 8/21 (38.1%) partial responses (PR), 9/21 (42.9%) stable disease (SD) and 4/21 (19.0%) progressive disease (PD).

CONCLUSION

The risk stratification model could well distinguish eAML patients at high risk of recurrence/metastasis. Everolimus targeted treatment showed good efficacy in patients with recurrence/metastasis.

Circulating exosomal mRNA signatures for the early diagnosis of clear cell renal cell carcinoma

BACKGROUND

There are no proven tumor biomarkers for the early diagnosis of clear cell renal cell carcinoma (ccRCC) thus far. This study aimed to identify novel biomarkers of ccRCC based on exosomal mRNA (emRNA) profiling and develop emRNA-based signatures for the early detection of ccRCC.

METHODS

Four hundred eighty-eight participants, including 226 localized ccRCCs, 73 patients with benign renal masses, and 189 healthy controls, were recruited. Circulating emRNA sequencing was performed in 12 ccRCCs and 22 healthy controls in the discovery phase. The candidate emRNAs were evaluated with 108 ccRCCs and 70 healthy controls in the test and training phases. The emRNA-based signatures were developed by logistic regression analysis and validated with additional cohorts of 106 ccRCCs, 97 healthy controls, and 73 benign individuals.

RESULTS

Five emRNAs, CUL9, KMT2D, PBRM1, PREX2, and SETD2, were identified as novel potential biomarkers of ccRCC. We further developed an early diagnostic signature that comprised KMT2D and PREX2 and a differential diagnostic signature that comprised CUL9, KMT2D, and PREX2 for RCC detection. The early diagnostic signature displayed high accuracy in distinguishing ccRCCs from healthy controls, with areas under the receiver operating characteristic curve (AUCs) of 0.836 and 0.830 in the training and validation cohorts, respectively. The differential diagnostic signature also showed great performance in distinguishing ccRCCs from benign renal masses (AUC = 0.816), including solid masses (AUC = 0.810) and cystic masses (AUC = 0.832).

CONCLUSIONS

We established and validated novel emRNA-based signatures for the early detection of ccRCC and differential diagnosis of uncertain renal masses. These signatures could be promising and noninvasive biomarkers for ccRCC detection and thus improve the prognosis of ccRCC patients.

Utility of radiomics features of diffusion-weighted magnetic resonance imaging for differentiation of fat-poor angiomyolipoma from clear cell renal cell carcinoma: model development and external validation

PURPOSE

To investigate the utility of radiomics features of diffusion-weighted magnetic resonance imaging (DW-MRI) to differentiate fat-poor angiomyolipoma (fpAML) from clear cell renal cell carcinoma (ccRCC).

MATERIALS AND METHODS

This multi-institutional study included two cohorts with pathologically confirmed renal tumors: 65 patients with ccRCC and 18 with fpAML in the model development cohort, and 17 with ccRCC and 13 with fpAML in the external validation cohort. All patients underwent magnetic resonance imaging (MRI) including DW-MRI. Radiomics analysis was used to extract 39 imaging features from the apparent diffusion coefficient (ADC) map. The radiomics features were analyzed with unsupervised hierarchical cluster analysis. A random forest (RF) model was used to identify radiomics features important for differentiating fpAML from ccRCC in the development cohort. The diagnostic performance of the RF model was evaluated in the development and validation cohorts.

RESULTS

The cases in the developmental cohort were classified into three groups with different frequencies of fpAML by cluster analysis of radiomics features. RF analysis of the development cohort showed that the mean ADC value was important for differentiating fpAML from ccRCC, as well as higher-texture features including gray-level run length matrix (GLRLM)_long-run low gray-level enhancement (LRLGE), and GLRLM_low gray-level run emphasis (LGRE). The area under the curve values of the development [0.90, 95% confidence interval (CI) 0.80-1.00] and validation cohorts (0.87, 95% CI 0.74-1.00) were similar (P = 0.91).

CONCLUSION

The radiomics features of ADC maps are useful for differentiating fpAML from ccRCC.

Deep Learning Algorithm for Fully Automated Detection of Small (≤4 cm) Renal Cell Carcinoma in Contrast-Enhanced Computed Tomography Using a Multicenter Database

OBJECTIVES

Renal cell carcinoma (RCC) is often found incidentally in asymptomatic individuals undergoing abdominal computed tomography (CT) examinations. The purpose of our study is to develop a deep learning-based algorithm for fully automated detection of small (≤4 cm) RCCs in contrast-enhanced CT images using a multicenter database and to evaluate its performance.

MATERIALS AND METHODS

For the algorithmic detection of RCC, we retrospectively selected contrast-enhanced CT images of patients with histologically confirmed single RCC with a tumor diameter of 4 cm or less between January 2005 and May 2020 from 7 centers in the Japan Medical Image Database. A total of 453 patients from 6 centers were selected as dataset A, and 132 patients from 1 center were selected as dataset B. Dataset A was used for training and internal validation. Dataset B was used only for external validation. Nephrogenic phase images of multiphase CT or single-phase postcontrast CT images were used. Our algorithm consisted of 2-step segmentation models, kidney segmentation and tumor segmentation. For internal validation with dataset A, 10-fold cross-validation was applied. For external validation, the models trained with dataset A were tested on dataset B. The detection performance of the models was evaluated using accuracy, sensitivity, specificity, and the area under the curve (AUC).

RESULTS

The mean ± SD diameters of RCCs in dataset A and dataset B were 2.67 ± 0.77 cm and 2.64 ± 0.78 cm, respectively. Our algorithm yielded an accuracy, sensitivity, and specificity of 88.3%, 84.3%, and 92.3%, respectively, with dataset A and 87.5%, 84.8%, and 90.2%, respectively, with dataset B. The AUC of the algorithm with dataset A and dataset B was 0.930 and 0.933, respectively.

CONCLUSIONS

The proposed deep learning-based algorithm achieved high accuracy, sensitivity, specificity, and AUC for the detection of small RCCs with both internal and external validations, suggesting that this algorithm could contribute to the early detection of small RCCs.

Ultrasonography findings for the diagnosis of renal oncocytoma

Renal oncocytomas are rare benign epithelial tumors of the kidney. However, they are easily misdiagnosed as renal cancers, resulting in unnecessary radical nephrectomy. This review summarizes the use of ultrasound for the diagnosis of renal oncocytomas. On two-dimensional grayscale ultrasound, renal oncocytomas appear as solid, well-defined, round or oval, and relatively isoechoic or slightly hyperechoic masses. On color Doppler flow imaging, the "spoke-wheel" sign is evident. On power Doppler flow imaging, renal oncocytomas show mixed penetrating and peripheral patterns. Renal oncocytomas usually appear as highly enhanced on contrast-enhanced ultrasound images, and irregular nonenhanced areas in larger tumors. This review will help sonographers recognize renal oncocytomas.

The Potential of Visceral Adipose Tissue in Distinguishing Clear Cell Renal Cell Carcinoma from Renal Angiomyolipoma with Minimal Fat

Purpose

To overcome the challenge of preoperative differentiation between clear cell renal cell carcinoma (ccRCC) and renal angiomyolipoma with minimal fat (RMFAML), we evaluated the potential of visceral adipose tissue (VAT) in distinguishing RMFAML from ccRCC.

Patients and Methods

Patients (191) were divided into ccRCC and RMFAML groups according to postoperative pathology. Umbilical horizontal computed tomography (CT) images were used for visceral fat area (VFA), subcutaneous fat area (SFA) and total fat area (TFA) measurements. Logistic regression was used to identify risk factors for ccRCC. Areas under the receiver operating characteristic (ROC) curve (AUCs) were compared to identify the most valuable indicator for identifying ccRCC and RMFAML.

Results

In total, 166 patients had ccRCC, and 25 had RMFAML. ccRCC and RMFAML patients showed significant differences in age (P<0.001), sex (P<0.001), hypertension (P=0.027), BMI (P<0.001), SFA (P=0.046), VFA (P<0.001) and TFA (P<0.001). According to multiple logistic regression analysis, male sex [4.311 (1.469~12.653), p=0.008]; older age [1.047 (1.008~1.088), p=0.017]; and higher BMI [1.305 (1.088~1.566), p=0.004], SFA [1.013 (1.003~1.023), p=0.008], VFA [1.026 (1.012~1.041), p<0.001] and TFA [1.011 (1.005~1.017), p=0.001] were associated with ccRCC. The AUCs of sex (male), age, BMI, TFA, VFA, and SFA were 0.726, 0.687, 0.783, 0.769, 0.840, and 0.645, respectively. The VFA cut-off value was 69.99 cm². The sensitivity and specificity of higher VFA (≥69.99 cm²) for ccRCC diagnosis were 79.52% and 80.00%, respectively.

Conclusion

In differentiating ccRCC from RMFAML, male sex, older age, and higher BMI, TFA, SFA, and VFA are risk factors for ccRCC. VFA is the most effective indicator for identifying ccRCC.

Role of multidetector ct in quantitative enhancement- washout analysis of solid renal masses

Background

Enhancement washout technique in solid renal masses using multidetector computed tomography (MDCT) can differentiate different type of lesions. 99 Patients who are presenting with suspected renal masses or renal tumour for staging are included in this study. CT examination are carried out at urology and nephrology centre using MDCT. The attenuation values (Hounsfield Unit) will be assesed for each lesion on the pre enhanced, corticomedullary, nephrographic and delayed phases. Washout ratio will be calculated for each phase of enhancement in comparison to the unenhanced attenuation value. The characteristics of enhancement-washout will be correlated with the final histopathological diagnosis.

Results

Early enhancement and washout pattern was noted in 54 renal lesions (54.5%) representing 4 types of renal lesions; Oncocytoma (n = 13), clear cell renal cell carcinoma (n = 16), Chromophobe renal cell carcinoma (n = 15) and unclassified renal cell carcinoma (n = 10).Prolonged enhancement pattern was noted 45 lesions (45.4%); PRCC (n = 14), 10 case of lipid poor AML (n = 10), metanephric adenoma (n = 10) and Xp11 RCC (n = 11). High pre-contrast attenuation was noted in Xp 11RCC showing attenuation value 41.7 ± 6.823HU. The highest CMP values were noted in CCRCC (151.9 ± 20.4) followed by oncocytomas (137.6 ± 19.15HU) and then CHRCC (123.6 ± 16.6 HU)while the lowest values were noted in Metanephric adenoma)57.1 ± 17.4HU)and followed by PRCC (59.9 ± 4.8)and followed by lipid poor AML (79.17 ± 13.666) and RCC unclassified (89.06 ± 18.1).

Conclusions

Four-phase MDCT (the unenhanced, corticomedullary, nephrographic, and excretory phases) evaluate role of MDCT in differentiation of solid renal masses.

Solid renal masses in adults

With the ever increasing trend of using cross-section imaging in today's era, incidental detection of small solid renal masses has dramatically multiplied. Coincidentally, the number of asymptomatic benign lesions being detected has also increased. The role of radiologists is not only to identify these lesions, but also go a one step further and accurately characterize various renal masses. Earlier detection of small renal cell carcinomas means identifying at the initial stage which has an impact on prognosis, patient management and healthcare costs. In this review article we share our experience with the typical and atypical solid renal masses encountered in adults in routine daily practice.

The Incidental Renal Mass- Update on Characterization and Management

Renal masses are commonly encountered on cross-sectional imaging examinations performed for nonrenal indications. Although most can be dismissed as benign cysts, a subset will be either indeterminate or suspicious; in many cases, imaging cannot be used to reliably differentiate between benign and malignant masses. On-going research in defining characteristics of common renal masses on advanced imaging shows promise in offering solutions to this issue. A recent update of the Bosniak classification (used to categorize cystic renal masses) was proposed with the goals of decreasing imaging follow-up in likely benign cystic masses, and therefore avoiding unnecessary surgical resection of such masses.

A Non-Invasive Scoring System to Differential Diagnosis of Clear Cell Renal Cell Carcinoma (ccRCC) From Renal Angiomyolipoma Without Visible Fat (RAML-wvf) Based on CT Features

Background

Renal angiomyolipoma without visible fat (RAML-wvf) and clear cell renal cell carcinoma (ccRCC) have many overlapping features on imaging, which poses a challenge to radiologists. This study aimed to create a scoring system to distinguish ccRCC from RAML-wvf using computed tomography imaging.

Methods

A total of 202 patients from 2011 to 2019 that were confirmed by pathology with ccRCC (n=123) or RAML (n=79) were retrospectively analyzed by dividing them randomly into a training cohort (n=142) and a validation cohort (n=60). A model was established using logistic regression and weighted to be a scoring system. ROC, AUC, cut-off point, and calibration analyses were performed. The scoring system was divided into three ranges for convenience in clinical evaluations, and the diagnostic probability of ccRCC was calculated.

Results

Four independent risk factors are included in the system: 1) presence of a pseudocapsule, 2) a heterogeneous tumor parenchyma in pre-enhancement scanning, 3) a non-high CT attenuation in pre-enhancement scanning, and 4) a heterogeneous enhancement in CMP. The prediction accuracy had an ROC of 0.978 (95% CI, 0.956–0.999; P=0.011), similar to the primary model (ROC, 0.977; 95% CI, 0.954–1.000; P=0.012). A sensitivity of 91.4% and a specificity of 93.9% were achieved using 4.5 points as the cutoff value. Validation showed a good result (ROC, 0.922; 95% CI, 0.854–0.991, P=0.035). The number of patients with ccRCC in the three ranges (0 to <2 points; 2–4 points; >4 to ≤11 points) significantly increased with increasing scores.

Conclusion

This scoring system is convenient for distinguishing between ccRCC and RAML-wvf using four computed tomography features.

The role of diffusion-weighted MRI and contrast-enhanced MRI for differentiation between solid renal masses and renal cell carcinoma subtypes

PURPOSE

To assess the value of diffusion-weighted magnetic resonance imaging (DW-MRI) and contrast-enhanced MRI (CE-MRI) for differentiation between benign and malignant solid renal masses, renal cell carcinoma (RCC) subtypes, oncocytomas, and lipid-poor angiomyolipomas (LP-AML).

METHODS

Minimum or lowest 'apparent diffusion coefficient' (ADC₁) and representative ADC values (ADC₂) of 112 renal masses (n: 46 benign renal mass, n: 66 malignant renal mass) were measured on DW-MRI images (b 50, 400, 800 s/mm²). Signal intensity (SI) measurements were performed in normal renal parenchyma and most avid enhanced area of the renal masses at precontrast, corticomedullary, and nephrographic phases on CE-MRI. Contrast enhancement rate (CER) and contrast enhancement index (CEI) values of renal masses were compared between benign-malignant renal masses and RCC subtypes, oncocytomas, and LP-AMLs.

RESULTS

There was no significant difference between ADC₁, ADC₂ values, and SI of benign and malignant renal masses (p = 0.721, p = 0.255, p = 0.872). Mean ADC₁ and ADC₂ values of clear cell RCCs were significantly higher than nonclear cell RCCs (p = 0.005 p = 0.002). Mean CER value of clear cell RCCs was significantly higher than nonclear cell RCCs in nephrographic phase (p = 0.003). Mean CEI values of clear cell RCCs were significantly higher than nonclear cell RCCs in the corticomedullary and nephrographic phase (p = 0.027 vs. 0.008). LP-AMLs were differentiated from other renal masses with wash-out phenomenon.

CONCLUSION

Combined usage of ADC, SI, CER, and CEI values may be useful for discrimination between RCC subtypes, oncocytomas, and lipid-poor AMLs.

Circularity Index on Contrast-Enhanced Computed Tomography Helps Distinguish Fat-Poor Angiomyolipoma from Renal Cell Carcinoma: Retrospective Analyses of Histologically Proven 257 Small Renal Tumors Less Than 4 cm

OBJECTIVE

To evaluate circularity as a quantitative shape factor of small renal tumor on computed tomography (CT) in differentiating fat-poor angiomyolipoma (AML) from renal cell carcinoma (RCC).

MATERIALS AND METHODS

In 257 consecutive patients, 257 pathologically confirmed renal tumors (either AML or RCC less than 4 cm), which did not include visible fat on unenhanced CT, were retrospectively evaluated. A radiologist drew the tumor margin to measure the perimeter and area in all the contrast-enhanced axial CT images. In each image, a quantitative shape factor, circularity, was calculated using the following equation: 4 × π × (area ÷ perimeter²). The median circularity (circularity index) was adopted as a representative value in each tumor. The circularity index was compared between fat-poor AML and RCC, and the receiver operating characteristic (ROC) curve analysis was performed. Univariable and multivariable binary logistic regression analysis was performed to determine the independent predictor of fat-poor AML.

RESULTS

Of the 257 tumors, 26 were AMLs and 231 were RCCs (184 clear cell RCCs, 25 papillary RCCs, and 22 chromophobe RCCs). The mean circularity index of AML was significantly lower than that of RCC (0.86 ± 0.04 vs. 0.93 ± 0.02, p < 0.001). The mean circularity index was not different between the subtypes of RCCs (0.93 ± 0.02, 0.92 ± 0.02, and 0.92 ± 0.02 for clear cell, papillary, and chromophobe RCCs, respectively, p = 0.210). The area under the ROC curve of circularity index was 0.924 for differentiating fat-poor AML from RCC. The sensitivity and specificity were 88.5% and 90.9%, respectively (cut-off, 0.90). Lower circularity index (≤ 0.9) was an independent predictor (odds ratio, 41.0; p < 0.001) for predicting fat-poor AML on multivariable logistic regression analysis.

CONCLUSION

Circularity is a useful quantitative shape factor of small renal tumor for differentiating fat-poor AML from RCC.

Radiologic features of mixed epithelial and stromal tumors of the kidney: Hyperattenuating on unenhanced computed tomography and T2-hypointensity on magnetic resonance imaging

In the 2016 World Health Organization renal tumor classification, the mixed epithelial and stromal tumor family was introduced as a new entity. This family encompasses a spectrum of tumors, ranging from predominantly cystic tumors (adult cystic nephromas) to tumors that are variably solid (mixed epithelial and stromal tumors). The majority of previous studies incorporating “mixed epithelial and stromal tumor” in the titles were actually reports of imaging findings of adult cystic nephroma. Thus, the solid component of mixed epithelial and stromal tumors has not been well evaluated. In this study, we present 2 cases of mixed epithelial and stromal tumors, as defined by the 2016 World Health Organization classification, showing a predominantly solid component. The characteristic findings of the solid component of these tumors were T2-hypointensity on magnetic resonance imaging and hyperattenuation on unenhanced computed tomography. Angiomyolipoma with epithelial cysts and epithelioid angiomyolipoma should be considered in the differential diagnosis of mixed epithelial and stromal tumors.

Review of Value of CT Texture Analysis and Machine Learning in Differentiating Fat-Poor Renal Angiomyolipoma from Renal Cell Carcinoma

The diagnosis of patients with suspected angiomyolipoma relies on the detection of abundant macroscopic intralesional fat, which is always of no use to differentiate fat-poor angiomyolipoma (fp-AML) from renal cell carcinoma and diagnosis of fp-AML excessively depends on individual experience. Texture analysis was proven to be a potentially useful biomarker for distinguishing between benign and malignant tumors because of its capability of providing objective and quantitative assessment of lesions by analyzing features that are not visible to the human eye. This review aimed to summarize the literature on the use of texture analysis to diagnose patients with fat-poor angiomyolipoma vs those with renal cell carcinoma and to evaluate its current application, limitations, and future challenges in order to avoid unnecessary surgical resection.

Diagnostic Performance of Contrast-Enhanced Ultrasound (CEUS) in the Evaluation of Solid Renal Masses

BACKGROUND

The present study aims to evaluate the diagnostic performance of contrast-enhanced ultrasound (CEUS) for discriminating between benign and malignant solid renal masses.

METHODS

18 patients with histopathologically confirmed benign solid renal masses (11 oncocytomas, seven angiomyolipomas) as well as 96 patients with confirmed renal cell carcinoma (RCC) who underwent CEUS followed by radical or partial nephrectomy were included in this single-center study. CEUS examinations were performed by an experienced radiologist (EFSUMB Level 3) and included the application of a second-generation contrast agent.

RESULTS

Renal angiomyolipomas, oncocytomas, and renal cell carcinomas showed varying sonomorphological characteristics in CEUS. Angiomyolipomas showed heterogeneous echogenicity (57% hypo-, 43% hyperechoic), while all lesions showed rapid contrast-enhancement with two lesions also showing venous wash-out (29%). Notably, 9/11 oncocytomas could be detected in conventional ultrasound (64% hypo-, 9% hyper-, 9% isoechoic) and 2/11 only demarcated upon intravenous application of contrast agent (18%). All oncocytomas showed hyperenhancement in CEUS, venous wash-out was registered in 7/11 lesions (64%).

CONCLUSIONS

In line with the current state of knowledge, no specific sonomorphological characteristics allowing for accurate distinction between benign and malignant solid renal masses in CEUS could be detected in our study.

[Prevalence of the tuberous sclerosis complex at patients taken care for a renal angiomyolipoma]

INTRODUCTION

Bourneville's tuberous sclerosis or Tuberous Sclerosis Complex (TSC) is an autosomal dominant hereditary phakomatosis associated with angiomyolipomas (AML) of the kidney. The aim of this study was to identify the prevalence of TSC in patients diagnosed and cared for AML in our department of urology.

MATERIALS AND METHODS

All the patients with AML were included between March 2009 and June 2016 in a French university hospital. Each patient was reviewed in consultation with a clinical examination and imaging. Specific clinical criteria were used to refer patients to genetic analysis. Patients with a high TSC probability had a genetic analysis to search TSC1 and TSC2 genes mutations.

RESULTS

In all, 28 patients were included and 3 (11%) were diagnosed TSC. The median age of the patients was 62 years (36-82 years). The most frequent clinical criteria were facial angiofibromas in 7 patients (25%). Among the 8 patients (29%) with evocative clinical criteria, a mutation of the TSC1 and TSC2 genes was identified in 3 patients (11%) with a diagnosis of TSC made before the AML diagnosis.

CONCLUSION

In this study, 8 patients (29%) presented clinical criteria suggestive of TSC, preferentially dermatological. The diagnosis was confirmed by screening TSC1 and TSC2 genes mutations in 3 patients (11%), nevertheless prevalence of TSC is most probably underestimated by the genetic mosaïcisme of this pathology.

Radiomics and Artificial Intelligence for Renal Mass Characterization

Radiomics allows for high throughput extraction of quantitative data from images. This is an area of active research as groups try to capture and quantify imaging parameters and convert these into descriptive phenotypes of organs or tumors. Texture analysis is one radiomics tool that extracts information about heterogeneity within a given region of interest. This is used with or without associated machine learning classifiers or a deep learning approach is applied to similar types of data. These tools have shown utility in characterizing renal masses, renal cell carcinoma, and assessing response to targeted therapeutic agents in metastatic renal cell carcinoma.

An Incidental Diagnosis of Microscopic Renal Angiomyolipoma Completely Excised on Renal Biopsy: A Case Report

Patient: Female, 44-year-old

Final Diagnosis: Angiomyolipoma

Symptoms: Asymptomatic proteinuria

Medication:—

Clinical Procedure: —

Specialty: Pathology

Differentiation between fat-poor angiomyolipoma and clear cell renal cell carcinoma: qualitative and quantitative analysis using arterial spin labeling MR imaging

PURPOSE

To assess the diagnostic effectiveness of arterial spin labeling (ASL) MR imaging in differentiating fat-poor AML from clear cell renal cell carcinoma (ccRCC).

METHODS

In this prospective study, 29 ccRCC patients and 9 fat-poor AML patients underwent routine anatomical MRI and ASL at 3T before surgery after signing written informed consent form. For each tumor, tumor blood flow (TBF) was measured in a region of interest (ROI) which was positioned to outline the edge of the target lesions on ASL perfusion maps. Additionally, the mean TBF values were obtained by standardizing the TBF using a blood flow measurement in the reference ROI. Moreover, a cluster containing more than 10 voxels was chosen from the renal cortex and medulla area in normal contralateral kidney as a reference ROI to calculate tumor-to-cortex ratio and tumor-to-medulla ratio. Independent sample t test was used to examine the alteration among the groups of fat-poor AML and ccRCC. ASL images were together analyzed by two radiologists to assess the following characteristics of the renal mass: predominant SI in the tumor on ASL images was lower than, as same as, or higher than SI of the cortex. For qualitative variables, Fisher's exact test was employed to compare the proportions of these two groups. The sensitivity, specificity ,and accuracy required for discrimination of fat-poor AML from ccRCC were quantified using receiver operating characteristic (ROC) curve. The corresponding optimal cutoff value was obtained for each parameter as well.

RESULTS

The TBF value was significantly higher in ccRCC group than that in fat-poor AML (270.49 ± 78.88 ml/100 g/min vs. 146.68 ± 47.21 ml/100 g/min; P < 0.01). Both tumor-to-cortex and tumor-to-medulla ratios were notably higher in ccRCC group compared with those in fat-poor AML group (1.22 ± 0.26 vs. 0.74 ± 0.14, 3.13 ± 0.94 vs. 1.77 ± 0.55; P < 0.05). The values of area under the ROC curve (AUC) for TBF, tumor-to-cortex ratio, and tumor-to-medulla ratio were 0.931, 0.964, and 0.900, respectively. No significant difference in AUC values among these three measurements was observed. For qualitative variables, the SI of fat-poor AML was equal to or slightly lower than that of renal medulla and the SI of ccRCC was found to be higher than renal cortex in ASL.

CONCLUSION

ASL MRI performs well in differentiating fat-poor AML from ccRCC in both qualitative and quantitative analyses.

Renal angiomyolipoma with tuberous sclerosis complex: How it differs from sporadic angiomyolipoma in both management and care

Renal angiomyolipoma (AML) is the most common benign tumor of the kidney. It consists of blood vessels, smooth muscle and fat components in varying proportions. AML is divided into the sporadic type and tuberous sclerosis complex (TSC)-associated type. TSC-associated AML develops at a younger age and tends to exhibit a much faster growth rate over time than sporadic AML. AMLs are classified as classic AML, fat-poor AML and epithelioid AML. Epithelioid AML, though rare, shows aggressive behavior leading to distant metastasis and mortality. TSC-associated AML is more likely to have an epithelioid component than sporadic AML. Active surveillance is the suggested management for small AML. Clinical intervention is mainly indicated when there is a substantial risk of rupture. Minimally invasive therapies, including partial nephrectomy, transcatheter arterial embolization, and mammalian target of rapamycin (mTOR) inhibitor treatment are employed for patients who require treatment. An updated algorithm for the management of AML is herein described. According to this algorithm, treatment intervention is recommended for TSC-associated AML >3 cm, even in asymptomatic cases. In cases with asymptomatic sporadic AML >4 cm in size or with an intra-tumoral aneurysm of >5 mm, treatment, including transcatheter arterial embolization or partial nephrectomy, is advised. The major complication of AML is intra-tumoral or retroperitoneal hemorrhage due to rupture that may be serious and life threatening. Thus, correct diagnosis, proper observation, and appropriate treatment are very important in the management of renal AML.

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.

Renal Masses: Evaluation with Contrast-Enhanced Ultrasound, with a Special Focus on the Pseudocapsule Sign

The aim of this study was to analyze the diagnostic performance of contrast-enhanced ultrasound (CEUS) in differentiating between benign and malignant renal masses, with a special emphasis on the value of the pseudocapsule sign. A total of 163 consecutive patients with 163 renal masses were involved. The conventional ultrasonography and CEUS features were assessed. Sensitivity, specificity and the area under the receiver operating characteristic curve (Az) were calculated for qualitative CEUS, and a multivariate analysis was performed to analyze the correlation between the sonographic features and malignancy. Time to peak (TTP) and peak intensity (PI) were compared between benign and malignant renal masses for quantitative CEUS analysis in 72 of 163 patients. Intraclass correlations were calculated for variability in intensity and time parameters between qualitative and quantitative evaluation. Among all qualitative CEUS features, the pseudocapsule sign showed the highest Az (0.777; 95% confidence interval: 0.701-0.853) and yielded the highest sensitivity (67.4%) and specificity (88.0%); multivariate logistic regression analysis showed that the pseudocapsule sign and color Doppler flow imaging patterns were the two strongest independent predictors for malignancy. For quantitative CEUS analysis, higher PI and shorter TTP were found in malignant renal masses than those in benign ones. The Intraclass correlation coefficient values among qualitative and the quantitative assessments were 0.00 for time and 0.03 for intensity. The pseudocapsule sign offered the most efficient performance among all the qualitative and quantitative CEUS features.

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

AIM

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).

MATERIALS AND METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Renal Angiomyolipoma Based on New Classification: How to Differentiate It From Renal Cell Carcinoma

OBJECTIVE

The purpose of this article is to describe useful imaging features for differentiating angiomyolipoma (AML) subtypes from renal cell carcinoma subtypes.

CONCLUSION

A newer radiologic classification of renal AML consists of fat-rich AML (≤ -10 HU), fat-poor AML (> -10 HU; tumor-to-spleen ratio < 0.71; signal intensity index, > 16.5%), and fat-invisible AML (> -10 HU; tumor-to-spleen ratio, > 0.71; signal intensity index, < 16.5%). Each subtype must be differentiated from the renal cell carcinoma subtype because of overlapping imaging features.

Diagnostic Imaging for Solid Renal Tumors: A Pictorial Review

The prognosis of renal tumors depends on histologic subtype. The increased use of abdominal imaging has resulted in an increase in the number of small renal incidentaloma in recent decades. Of these incidentally discovered tumors, 20% are benign lesions warranting conservative management, but most are renal cell carcinomas that warrant a more aggressive therapeutic approach due to their malignant potential. Dedicated diagnostic renal imaging is important for characterization of renal tumors to facilitate treatment planning. This review discusses the ability to detect and differentiate renal cell carcinoma subtypes, angiomyolipoma and oncocytoma based on ultrasound imaging, computed tomography, multiparametric magnetic resonance, and nuclear imaging.

Sonographic Features of Small (< 4 cm) Renal Tumors With Low Signal Intensity on T2-Weighted MR Images: Differentiating Minimal-Fat Angiomyolipoma From Renal Cell Carcinoma

OBJECTIVE

The purpose of this study is to characterize and assess the diagnostic utility of sonographic features of minimal-fat angiomyolipoma (AML) and renal cell carcinoma (RCC) with regard to small (< 4 cm) renal masses with a predominantly low signal intensity (SI) on T2-weighted MR images.

MATERIALS AND METHODS

Fifty small renal masses with a predominantly low SI on T2-weighted MR images and no macroscopic fat, all of which had US images available, were assessed. MRI variables (T2 ratio, signal intensity index [SII], and tumor-to-spleen ratio on chemical-shift images), CT features (enhancement patterns and attenuations values on unenhanced images and images obtained in the corticomedullary and nephrographic phases), and sonographic features (echogenicity, heterogeneity, and the presence of acoustic shadowing, a hypoechoic rim, or an intratumoral cyst) were recorded in a blinded manner. Echo-genicity was classified as hypo-, iso-, or hyperechoic compared with the renal parenchyma or markedly hyperchoic when equivalent to that of the renal sinus fat.

RESULTS

Minimal-fat AML and RCC were confirmed in 22 and 28 patients, respectively. T2 ratios were significantly lower for minimal-fat AML versus RCCs (p = 0.044). Minimal-fat AMLs exhibited echogenicities that were considered hypoechoic (31.8%), isoechoic (4.5%), hyperechoic (18.2%), or markedly hyperechoic (45.5%). No RCC showed marked hyperechogenicity. CT attenuation values were significantly higher for the minimal-fat AMLs seen in all imaging phases. When the combination of the T2 ratio, nephrographic phase attenuation, and echogenicity was assessed, the AUC value was 0.93 (95% CI, 0.81-0.98), which was a significant increase over the AUC value of 0.83 (95% CI, 0.69-0.92) for noted the combination of the T2 ratio and nephrographic phase attenuation.

CONCLUSION

Additional reviews of the echogenicity of small renal masses with low SI on T2-weighted MR images may aid the diagnosis of minimal-fat AML.

Differentiating solid, non-macroscopic fat containing, enhancing renal masses using fast Fourier transform analysis of multiphase CT

OBJECTIVE

To test the feasibility of two-dimensional fast Fourier transforms (FFT)-based imaging metrics in differentiating solid, non-macroscopic fat containing, enhancing renal masses using contrast-enhanced CT images. We quantify image-based intratumoral textural variations (indicator of tumor heterogeneity) using frequency-based (FFT) imaging metrics.

METHODS

In this Institutional Review Board approved, Health Insurance Portability and Accountability Act -compliant, retrospective case-control study, we evaluated 156 patients with predominantly solid, non-macroscopic fat containing, enhancing renal masses identified between June 2009 and June 2016. 110 cases (70%) were malignant RCC, including clear cell, papillary and chromophobe subtypes and, 46 cases (30%) were benign renal masses: oncocytoma and lipid-poor angiomyolipoma. Whole lesions were manually segmented using Synapse 3D (Fujifilm, CT) and co-registered from the multiphase CT acquisitions for each tumor. Pathological diagnosis of all tumors was obtained following surgical resection. Matlab function, FFT2 was used to perform the image to frequency transformation.

RESULTS

A Wilcoxon rank sum test showed that FFT-based metrics were significantly (p < 0.005) different between 1. benign vs malignant renal masses, 2. oncocytoma vs clear cell renal cell carcinoma and 3. oncocytoma vs lipid-poor angiomyolipoma. Receiver operator characteristics analysis revealed reasonable discrimination (area under the curve >0.7, p < 0.05) within these three groups of comparisons.

CONCLUSION

In combination with other metrics, FFT-metrics may improve patient management and potentially help differentiate other renal tumors. Advances in knowledge: We report for the first time that FFT-based metrics can differentiate between some solid, non-macroscopic fat containing, enhancing renal masses using their contrast-enhanced CT data.

Imaging of Solid Renal Masses

The increase in serendipitous detection of solid renal masses on imaging has not resulted in a reduction in mortality from renal cell carcinoma. Consequently, efforts for improved lesion characterization have been pursued and incorporated into management algorithms for distinguishing clinically significant tumors from those with favorable histology or benign conditions. Although diagnostic imaging strategies have evolved for optimized lesion detection, distinction between benign tumors and both indolent and aggressive malignant neoplasms remain an important diagnostic challenge. Recent advances in cross-sectional imaging have expanded the role of these tests in the noninvasive characterization of solid renal tumors.

CT and MRI of small renal masses

Small renal masses are increasingly detected incidentally at imaging. They vary widely in histology and aggressiveness, and include benign renal tumors and renal cell carcinomas that can be either indolent or aggressive. Imaging plays a key role in the characterization of these small renal masses. While a confident diagnosis can be made in many cases, some renal masses are indeterminate at imaging and can present as diagnostic dilemmas for both the radiologists and the referring clinicians. This article will summarize the current evidence of imaging features that correlate with the biology of small solid renal masses, and discuss key approaches in imaging characterization of these masses using CT and MRI.

Fat poor angiomyolipoma differentiation from renal cell carcinoma at 320-slice dynamic volume CT perfusion

PURPOSE

To compare various CT perfusion features of fat poor angiomyolipoma (AML) with those of size-matched renal cell carcinoma (RCC).

METHODS

One hundred and seventy-four patients [16 with fat poor AML (mean diameter, 3.1 cm; range, 1.5-5.5 cm) and 158 with RCC (mean diameter, 3.2 cm; range, 2.4-5.4 cm)] who had undergone 320-slice dynamic volume CT perfusion were evaluated. Equivalent blood volume (BV _Equiv), permeability surface-area product (PS), and blood flow (BF) of tumor were measured and analyzed. Fat poor AML was compared with each subtype of RCC (132 clear cell, 9 papillary, and 17 chromophobe). Receiver operating characteristic (ROC) curve analysis was performed for the comparison of fat poor AML and RCC. ROC curve analysis was not performed for the papillary RCC subtype because of the small number of masses of this subtype.

RESULTS

BV _Equiv and BF were significantly lower in fat poor AML than in clear cell RCC (P < 0.05 for both). Fat poor AML had higher BV _Equiv, PS, and BF than papillary RCC (P < 0.05 for all). PS and BF in fat poor AML significantly exceeded those in chromophobe RCC (P < 0.05 for both). For differentiating fat poor AML from clear cell RCC, area under the ROC curve (AUC) of BV _Equiv and BF were 0.82 and 0.69. Using the optimal threshold value, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 0.82, 0.81, 0.35, 0.97 for BV _Equiv and 0.71, 0.75, 0.24, 0.96 for BF, respectively. For differentiating fat poor AML from chromophobe RCC, AUC of PS and BF were 0.77 and 0.79, respectively. The optimal sensitivity, specificity, PPV, and NPV were 0.77, 0.75, 0.75, 0.76 for PS and 0.71, 0.81, 0.72, 0.80 for BF, respectively.

CONCLUSIONS

Fat poor AML and subtypes of RCCs demonstrate different perfusion features at 320-slice dynamic volume CT, allowing their differentiations with BV _Equiv, PS, and BF being valuable perfusion parameters.

Applications of contrast-enhanced ultrasound in the kidney

Incidental discovery of renal lesions on cross-sectional imaging studies performed for other indications is not uncommon. With the increased reliance on medical imaging, the number of incidentally detected renal lesions has also grown over time. While simple cysts account for the majority of these lesions, the presence of complex features within a cystic lesion, such as septations and solid components, can present a confusing picture. Solid lesions, too, can be indeterminate, and distinguishing between benign solid masses (like lipid-poor angiomyolipomas and oncocytomas) and renal cell carcinoma affects patient management and can prevent unnecessary interventions. Indeterminate renal lesions are traditionally further characterized by multiphase imaging, such as contrast-enhanced computed tomography and magnetic resonance imaging. Contrast-enhanced ultrasound (CEUS) is a new, relatively inexpensive technique that has become increasingly employed in the diagnostic workup of indeterminate renal lesions. With its lack of nephrotoxicity, the absence of ionizing radiation, and the ability to evaluate the enhancement pattern of renal lesions quickly and in real-time, CEUS has unique advantages over traditional imaging modalities. This article provides an overview of the current clinical applications of CEUS in characterizing renal lesions, both cystic and solid. Additional applications of CEUS in the kidney, including its roles in renal transplant evaluation and guidance for percutaneous biopsy, will also be briefly discussed.

Angiomyolipoma of the Kidneys: Current Perspectives and Challenges in Diagnostic Imaging and Image-Guided Therapy

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.

Are growth patterns on MRI in small (< 4 cm) solid renal masses useful for predicting benign histology?

PURPOSE

To evaluate previously described growth patterns in < 4 cm solid renal masses.

MATERIALS AND METHODS

With IRB approval, 63 renal cell carcinomas (RCC; clear cell n = 22, papillary n = 28, chromophobe n = 13) and 36 benign masses [minimal-fat (mf) angiomyolipoma (AML) n = 13, oncocytoma n = 23) from a single institution were independently evaluated by two blinded radiologists (R1/R2) using T2-weighted MRI for (1) the angular interface sign (AIS), (2) bubble-over sign (BOS), (3) percentage (%) exophytic growth and (4) long-to-short axis ratio. Comparisons were performed using ANOVA, chi-square and multi-variate regression.

RESULTS

AIS was present in 11.1% (7/63) -9.5% (6/63) R1/R2 RCC compared to 13.9% (5/36) -19.4% (7/36) R1/R2 benign masses (p = 0.68 and 0.16). BOS was present in 11.1% (7/63) -3.2% (2/63) R1/R2 RCC compared to 16.7% (6/36) -8.3% (3/36) R1/R2 benign masses (p = 0.432 and 0.261). Agreement was moderate (K = 0.50 and 0.55). mf-AML [66 ± 32% (range 0-100%)] and oncocytoma [53 ± 26% (0-90%)] had larger % exophytic growth compared to RCC [32 ± 23% (0-80%)] (p < 0.001). No RCC had 90-100% exophytic growth, present in 38.5% (5/13) mf-AMLs and 17.4% (4/23) oncocytomas. The long-to-short axis did not differ between groups (p = 0.053).

CONCLUSIONS

Benign masses show greater % exophytic growth whereas other growth patterns are not useful. Future studies evaluating % exophytic growth using multi-variate MR analysis in renal masses are required.

KEY POINTS

• Greater exophytic growth is associated with benignity among solid renal masses. • Only minimal fat AMLs and oncocytomas had 90-100% exophytic growth. • The angular interface sign was not useful to differentiate benign masses from RCC. • The bubble-over sign was not useful to differentiate benign masses from RCC. • Subjective analysis of growth patterns had fair-to-moderate agreement.

Solid Small Renal Mass Without Gross Fat: CT Criteria for Achieving Excellent Positive Predictive Value for Renal Cell Carcinoma

OBJECTIVE

The purpose of this study was to evaluate CT criteria for achieving high positive predictive value (PPV) for renal cell carcinoma (RCC) in patients with solid small renal masses (SRMs) less than 4 cm without macroscopic fat.

MATERIALS AND METHODS

One hundred fifty consecutive patients with a solid SRM without macroscopic fat (mean size ± SD, 2.5 ± 0.8 cm) who underwent CT including unenhanced, corticomedullary (CMP), and nephrographic phases (NP) were evaluated. Pathologically proven solid SRMs without macroscopic fat were classified into RCC (n = 131) and not RCC (n = 19). A "persistent low" sign was defined as a focal area or areas of low attenuation seen at the same location within the lesion on both CMP and NP imaging. Calcification, shape, and lesion attenuation on unenhanced CT were analyzed by two independent readers.

RESULTS

PPV of CT criteria (calcification [criterion 1] or spherical shape, lower or equal attenuation, and persistent low sign [criterion 2]) for RCC was 98.3% (58/59) for reader 1 and 100% (53/53) for reader 2. Weighted kappa of interreader agreement was 1.000 for calcification, 0.966 of lower or equal attenuation, 0.834 for spherical shape, 0.823 for persistent low sign, and 0.829 for CT criteria.

CONCLUSION

Interpretation of CT allowed reproducible and excellent PPV for RCC. Current CT criteria may effectively shorten the management process for solid SRMs without macroscopic fat by reducing unnecessary biopsy for a substantial number of RCCs showing typical CT findings.

Treatment effect of mTOR-inhibition on tissue composition of renal angiomyolipomas in tuberous sclerosis complex (TSC)

Purpose

Tuberous sclerosis complex (TSC)-associated renal angiomyolipoma (AML) have a high lifetime risk of acute bleeding. MTOR-inhibitors are a promising novel treatment for TSC-AML, however adequate response to therapy can be difficult to assess. Early changes in MRI signal may serve as a novel early indicator for a satisfactory response to mTOR-inhibitor therapy of AML.

Materials and methods

Thirty-eight patients with the definite diagnosis of tuberous sclerosis receiving everolimus therapy and n = 19 patients without specific therapy were included. 1.5 Tesla MRI was performed including sequences with a selective fat suppression. Patients were investigated prior to the initiation of therapy (baseline) and after <3 months (n = 21 patients), 3 to 6 months (n = 32) and 18 to 24 months (n = 28). Signal and size changes of renal AMLs were assessed at all different timepoints. Signal-to-noise-ratio (SNR), contrast-to-noise-ratio (CNR) and size of angiomyolipomas were evaluated.

Results

Signal changes in 273 AMLs were evaluated. A significant and strong decrease of the CNR of AMLs following the initiation of therapy was measured in the fat-suppressed MR sequence at all time points, compared to the baseline: From 7.41±6.98 to 3.84±6.25 (p ≤ 0.05p = 0.002), 3.36±6.93 (p<0.0001), and 2.50±6.68 (p<0.0001) after less than 3 months, 3–6 months or 18–24 months of everolimus treatment, respectively. Also, a significant, however less pronounced, reduction of angiomyolipoma size in the different groups was measured (from baseline 2022.2±2657.7 mm2 to 1854.4±1670.9 mm2 (p = 0.009), 1875.5±3190.1 mm2 (p<0.001), and 1365.8 ± 1628.8 mm2 (p<0.0001) after less than 3 months, 3–6 months or 18–24 months of everolimus treatment, respectively). No significant changes in CNR (p>0.05) and size (p>0.05) were measured in the control group.

Conclusion

mTOR inhibitor therapy in TSC patients results in an early and pronounced fatty transformation of AMLs on MRI. Fatty transformation could represent a novel early indicator of response to therapy in this patient collective.

Chemical shift magnetic resonance imaging for distinguishing minimal-fat renal angiomyolipoma from renal cell carcinoma: a meta-analysis

OBJECTIVES

To determine the performance of chemical shift signal intensity index (CS-SII) values for distinguishing minimal-fat renal angiomyolipoma (mfAML) from renal cell carcinoma (RCC) and to assess RCC subtype characterisation.

METHODS

We identified eligible studies on CS magnetic resonance imaging (CS-MRI) of focal renal lesions via PubMed, Embase, and the Cochrane Library. CS-SII values were extracted by lesion type and evaluated using linear mixed model-based meta-regression. RCC subtypes were analysed. Two-sided p value <0.05 indicated statistical significance. Methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool.

RESULTS

Eleven articles involving 850 patients were included. Minimal-fat AML had significantly higher CS-SII value than RCC (p < 0.05); there were no significant differences between mfAML and clear cell RCC (cc-RCC) (p = 0.112). Clear cell RCC had a significantly higher CS-SII value than papillary RCC (p-RCC) (p < 0.001) and chromophobe RCC (ch-RCC) (p = 0.045). The methodological quality was relatively high, and Begg's test data points indicated no obvious publication bias.

CONCLUSIONS

The CS-SII value for differentiating mfAML from cc-RCC remains unproven, but is a promising method for differentiating cc-RCC from p-RCC and ch-RCC.

KEY POINTS

• RCC CS-SII values are significantly lower than those of mfAML overall. • CS-SII values cannot aid differentiation between mfAML and cc-RCC. • CS-SII values might help characterise RCC subtypes.