Imaging spectrum of renal oncocytomas: a pictorial review with pathologic correlation

Predominantly multiple focal non-cystic renal lesions: an imaging approach

Multiple non-cystic renal lesions are occasionally discovered during imaging for various reasons and poses a diagnostic challenge to the practicing radiologist. These lesions may appear as a primary or dominant imaging finding or may be an additional abnormality in the setting of multiorgan involvement. Awareness of the imaging appearance of the various entities presenting as renal lesions integrated with associated extrarenal imaging findings along with clinical information is crucial for a proper diagnostic approach and patient work-up. This review summarizes the most relevant causes of infectious, inflammatory, vascular, and neoplastic disorders presenting as predominantly multiple focal non-cystic lesions.

Contrast-enhanced ultrasound of renal masses in the pre-transplant setting: literature review with case highlights

With the rising incidence of chronic kidney disease worldwide, an increasing number of patients are expected to require renal transplantation, which remains the definitive treatment of end stage renal disease. Medical imaging, primarily ultrasonography and contrast-enhanced CT and/or MRI, plays a large role in pre-transplantation assessment, especially in the characterization of lesions within the native kidneys. However, patients with CKD/ESRD often have relative contraindications to CT- and MR-contrast agents, limiting their utilization within this patient population. Contrast-enhanced ultrasound (CEUS), which combines the high temporal and spatial resolution of ultrasonography with intravascular microbubble contrast agents, provides a promising alternative. This review aims to familiarize the reader with the literature regarding the use of CEUS in the evaluation of cystic and solid renal lesions and provide case examples of its use at our institution in the pre-transplant setting.

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.

Recent Advances and Applications of Ambient Mass Spectrometry Imaging in Cancer Research: An Overview

Cancer metabolic variability has a significant impact on both diagnosis and treatment outcomes. The discovery of novel biological indicators and metabolic dysregulation, can significantly rely on comprehension of the modified metabolism in cancer, is a research focus. Tissue histology is a critical feature in the diagnostic testing of many ailments, such as cancer. To assess the surgical margin of the tumour on patients, frozen section histology is a tedious, laborious, and typically arbitrary method. Concurrent monitoring of ion images in tissues facilitated by the latest advancements in mass spectrometry imaging (MSI) is far more efficient than optical tissue image analysis utilized in conventional histopathology examination. This article focuses on the "desorption electrospray ionization (DESI)-MSI" technique's most recent advancements and uses in cancer research. DESI-MSI can provide wealthy information based on the variances in metabolites and lipids in normal and cancerous tissues by acquiring ion images of the lipid and metabolite variances on biopsy samples. As opposed to a systematic review, this article offers a synopsis of the most widely employed cutting-edge DESI-MSI techniques in cancer research.

Renal Mass Imaging with MRI Clear Cell Likelihood Score: A User's Guide

Small solid renal masses (SRMs) are frequently detected at imaging. Nearly 20% are benign, making careful evaluation with MRI an important consideration before deciding on management. Clear cell renal cell carcinoma (ccRCC) is the most common renal cell carcinoma subtype with potentially aggressive behavior. Thus, confident identification of ccRCC imaging features is a critical task for the radiologist. Imaging features distinguishing ccRCC from other benign and malignant renal masses are based on major features (T2 signal intensity, corticomedullary phase enhancement, and the presence of microscopic fat) and ancillary features (segmental enhancement inversion, arterial-to-delayed enhancement ratio, and diffusion restriction). The clear cell likelihood score (ccLS) system was recently devised to provide a standardized framework for categorizing SRMs, offering a Likert score of the likelihood of ccRCC ranging from 1 (very unlikely) to 5 (very likely). Alternative diagnoses based on imaging appearance are also suggested by the algorithm. Furthermore, the ccLS system aims to stratify which patients may or may not benefit from biopsy. The authors use case examples to guide the reader through the evaluation of major and ancillary MRI features of the ccLS algorithm for assigning a likelihood score to an SRM. The authors also discuss patient selection, imaging parameters, pitfalls, and areas for future development. The goal is for radiologists to be better equipped to guide management and improve shared decision making between the patient and treating physician. © RSNA, 2023 Quiz questions for this article are available in the supplemental material. See the invited commentary by Pedrosa in this issue.

MR texture analysis in the differentiation of renal oncocytoma with localized renal cell carcinoma subtypes

OBJECTIVES

We aimed to explore the diagnostic efficacy of MR texture analysis and imaging signs in the differentiation of renal oncocytoma from renal cell carcinoma (RCC).

METHODS

From January 2015 to March 2019, a total of 168 localized solid renal masses (37 oncocytomas, 131 RCCs) were retrospectively included. Two radiologists reviewed complete MR images and recorded imaging presentation. Texture parameters were extracted from 3D ROIs on axial FSE-T2WI. Univariate and multivariate logistic regressions were used for feature selection and nomogram construction. The diagnostic performances were assessed by receiver operating characteristic (ROC) curves.

RESULTS

Cystic change, hemorrhage, SEI and four texture parameters significantly correlated with oncocytoma in the training cohort. For differentiating oncocytoma from RCC, the nomogram yielded an AUC of 0.874 in the training cohort and 0.830 in the testing cohort. For differentiating oncocytoma from chRCC, the nomogram had an AUC of 0.889 in the training cohort and 0.861 in the testing cohort. For differentiating oncocytoma from pRCC, the nomogram had an AUC of 0.932 in the training cohort and 0.792 in the testing cohort. For differentiating oncocytoma from ccRCC, the nomogram had an AUC of 0.829 in the training cohort and 0.813 in the testing cohort.

CONCLUSION

The diagnostic nomogram combining MR texture parameters with imaging signs performed well in differentiating oncocytomas with localized RCC and its subtypes.

ADVANCES IN KNOWLEDGE

Few articles reported using the combination of MR texture analysis with imaging signs in differentiating RCC from oncocytoma. Our study established a useful nomogram in subtype characterization.

Magnetic Resonance Imaging Virtual Biopsy of Common Solid Renal Masses-A Pictorial Review

ABSTRACT

The expanded application of radiologic imaging resulted in an increased incidence of renal masses in the recent decade. Clinically, it is difficult to determine the malignant potential of the renal masses, thus resulting in complex management. Image-guided biopsies are the ongoing standard of care to identify molecular variance but are limited by tumor accessibility and heterogeneity. With the evolving importance of individualized cancer therapies, radiomics has displayed promising results in the identification of tumoral mutation status on routine imaging. This article discusses how magnetic resonance imaging features can guide a radiologist toward identifying renal mass characteristics.

CT differentiation of the oncocytoma and renal cell carcinoma based on peripheral tumor parenchyma and central hypodense area characterisation

BACKGROUND

Although the central scar is an essential imaging characteristic of renal oncocytoma (RO), its utility in distinguishing RO from renal cell carcinoma (RCC) has not been well explored. The study aimed to evaluate whether the combination of CT characteristics of the peripheral tumor parenchyma (PTP) and central hypodense area (CHA) can differentiate typical RO with CHA from RCC.

METHODS

A total of 132 tumors on the initial dataset were retrospectively evaluated using four-phase CT. The excretory phases were performed more than 20 min after the contrast injection. In corticomedullary phase (CMP) images, all tumors had CHAs. These tumors were categorized into RO (n = 23), clear cell RCC (ccRCC) (n = 85), and non-ccRCC (n = 24) groups. The differences in these qualitative and quantitative CT features of CHA and PTP between ROs and ccRCCs/non-ccRCCs were statistically examined. Logistic regression filters the main factors for separating ROs from ccRCCs/non-ccRCCs. The prediction models omitting and incorporating CHA features were constructed and evaluated, respectively. The effectiveness of the prediction models including CHA characteristics was then confirmed through a validation dataset (8 ROs, 35 ccRCCs, and 10 non-ccRCCs).

RESULTS

The findings indicate that for differentiating ROs from ccRCCs and non-ccRCCs, prediction models with CHA characteristics surpassed models without CHA, with the corresponding areas under the curve (AUC) being 0.962 and 0.914 versus 0.952 and 0.839 respectively. In the prediction models that included CHA parameters, the relative enhancement ratio (RER) in CMP and enhancement inversion, as well as RER in nephrographic phase and enhancement inversion were the primary drivers for differentiating ROs from ccRCCs and non-ccRCCs, respectively. The prediction models with CHA characteristics had the comparable diagnostic ability on the validation dataset, with respective AUC values of 0.936 and 0.938 for differentiating ROs from ccRCCs and non-ccRCCs.

CONCLUSION

The prediction models with CHA characteristics can help better differentiate typical ROs from RCCs. When a mass with CHA is discovered, particularly if RO is suspected, EP images with longer delay scanning periods should be acquired to evaluate the enhancement inversion characteristics of CHA.

Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review

Radiomics and artificial intelligence (AI) may increase the differentiation of benign from malignant kidney lesions, differentiation of angiomyolipoma (AML) from renal cell carcinoma (RCC), differentiation of oncocytoma from RCC, differentiation of different subtypes of RCC, to predict Fuhrman grade, to predict gene mutation through molecular biomarkers and to predict treatment response in metastatic RCC undergoing immunotherapy. Neural networks analyze imaging data. Statistical, geometrical, textural features derived are giving quantitative data of contour, internal heterogeneity and gray zone features of lesions. A comprehensive literature review was performed, until July 2022. Studies investigating the diagnostic value of radiomics in differentiation of renal lesions, grade prediction, gene alterations, molecular biomarkers and ongoing clinical trials have been analyzed. The application of AI and radiomics could lead to improved sensitivity, specificity, accuracy in detecting and differentiating between renal lesions. Standardization of scanner protocols will improve preoperative differentiation between benign, low-risk cancers and clinically significant renal cancers and holds the premises to enhance the diagnostic ability of imaging tools to characterize renal lesions.

The value of CT features and demographic data in the differential diagnosis of type 2 papillary renal cell carcinoma from fat-poor angiomyolipoma and oncocytoma

PURPOSES

To determine the CT features and demographic data predictive of type 2 papillary renal cell carcinoma (PRCC) that can help distinguish this neoplasm from fat-poor angiomyolipoma (fpAML) and oncocytoma.

METHODS

Fifty-four patients with type 2 PRCC, 48 with fpAML, and 47 with oncocytoma in the kidney from multiple centers were retrospectively reviewed. The demographic data and CT features of type 2 PRCC were analyzed and compared with those of fpAML and oncocytoma by univariate analysis and multiple logistic regression analysis to determine the predictive factors for differential diagnosis. Then, receiver operating characteristic (ROC) curve analysis was performed to further assess the logistic regression model and set the threshold level values of the numerical parameters.

RESULTS

Older age (≥ 46.5 years), unenhanced lesion-to-renal cortex attenuation (RLRCA) < 1.21, corticomedullary ratio of lesion to renal cortex net enhancement (RLRCNE) < 0.32, and size ≥ 30.1 mm were independent predictors for distinguishing type 2 PRCC from fpAML (OR 14.155, 8.332, and 57.745, respectively, P < 0.05 for all). The area under the curve (AUC) of the multiple logistic regression model in the ROC curve analysis was 0.970. In the combined evaluation, the four independent predictors had a sensitivity and specificity of 0.896 and 0.889, respectively. A corticomedullary RLRCNE < 0.61, irregular shape, and male sex were independent predictors for the differential diagnosis of type 2 PRCC from oncocytoma (OR 15.714, 12.158, and 6.175, respectively, P < 0.05 for all). In the combined evaluation, the three independent predictors had a sensitivity and specificity of 0.889 and 0.979, respectively. The AUC of the multiple logistic regression model in the ROC curve analysis was 0.964.

CONCLUSION

The combined application of CT features and demographic data had good ability in distinguishing type 2 PRCC from fpAML and oncocytoma, respectively.

Radiomics improves the utility of ADC for differentiation between renal oncocytoma and chromophobe renal cell carcinoma: Preliminary findings

OBJECTIVE

Differentiation between renal oncocytoma (RON) and chromophobe renal cell carcinoma (chRCC) remains challenging. We aimed to assess the accurate apparent diffusion coefficient (ADC) radiomics features in differentiating these tumors.

MATERIALS AND METHODS

This single-center retrospective study included 14 patients with histopathologically proven RON (n = 6) and chRCC (n = 8) who underwent magnetic resonance imaging. Features were extracted from ADC maps. Features with an intraclass correlation coefficient >0.90, an intergroup p < 0.01 and interrater differences with normal distribution underwent agreement and receiver operating characteristic curve analyses.

RESULTS

Overall, 6 features qualified for further analysis and Bland-Altman plots revealed acceptable agreement for all. Only 1 first order feature and 5 high order texture features successfully predicted RON with more than 90% sensitivities and specificities more than 80%.

CONCLUSION

Squared mean ADC and certain gray level run length matrix features extracted by radiomics of ADC mapping provide quite high diagnostic precision in terms of distinguishing between RON and chRCC.

Multimodality imaging review of focal renal lesions

Background

Focal lesions of the kidney comprise a spectrum of entities that can be broadly classified as malignant tumors, benign tumors, and non-neoplastic lesions. Malignant tumors include renal cell carcinoma subtypes, urothelial carcinoma, lymphoma, post-transplant lymphoproliferative disease, metastases to the kidney, and rare malignant lesions. Benign tumors include angiomyolipoma (fat-rich and fat-poor) and oncocytoma. Non-neoplastic lesions include infective, inflammatory, and vascular entities. Anatomical variants can also mimic focal masses.

Main body of the abstract

A range of imaging modalities are available to facilitate characterization; ultrasound (US), contrast-enhanced ultrasound (CEUS), computed tomography (CT), magnetic resonance (MR) imaging, and positron emission tomography (PET), each with their own strengths and limitations. Renal lesions are being detected with increasing frequency due to escalating imaging volumes. Accurate diagnosis is central to guiding clinical management and determining prognosis. Certain lesions require intervention, whereas others may be managed conservatively or deemed clinically insignificant. Challenging cases often benefit from a multimodality imaging approach combining the morphology, enhancement and metabolic features.

Short conclusion

Knowledge of the relevant clinical details and key imaging features is crucial for accurate characterization and differentiation of renal lesions.

Quantitative image features from radiomic biopsy differentiate oncocytoma from chromophobe renal cell carcinoma

Purpose: To differentiate oncocytoma and chromophobe renal cell carcinoma (RCC) using radiomics features computed from spherical samples of image regions of interest, "radiomic biopsies" (RBs). Approach: In a retrospective cohort study of 102 CT cases [68 males (67%), 34 females (33%); mean age ± SD, 63 ± 12    years ], we pathology-confirmed 42 oncocytomas (41%) and 60 chromophobes (59%). A board-certified radiologist performed two RB rounds. From each RB round, we computed radiomics features and compared the performance of a random forest and AdaBoost binary classifier trained from the features. To control for overfitting, we performed 10 rounds of 70% to 30% train-test splits with feature-selection, cross-validation, and hyperparameter-optimization on each split. We evaluated the performance with test ROC AUC. We tested models on data from the other RB round and compared with the same round testing with the DeLong test. We clustered important features for each round and measured a bootstrapped adjusted Rand index agreement. Results: Our best classifiers achieved an average AUC of 0.71 ± 0.024 . We found no evidence of an effect for RB round ( p = 1 ). We also found no evidence for a decrease in model performance when tested on the other RB round ( p = 0.85 ). Feature clustering produced seven clusters in each RB round with high agreement ( Rand index = 0.981 ± 0.002 , p < 0.00001 ). Conclusions: A consistent radiomic signature can be derived from RBs and could help distinguish oncocytoma and chromophobe RCC.

Cystic renal oncocytoma: A rare case report

Cystic renal lesions are extremely common.

The major clinical concern is differentiating simple renal cysts from complex cysts to assess the risk of malignancy.

The Bosniak classification of renal cystic tumors is employed to distinguish benign cysts from potential malignant cysts.

Benign renal tumors can be rarely encountered in Bosniak type 4 cysts.

Herein, we report a case of 56-year-old female with a single right mediorenal solid-cystic mass classified bosniak 4. An open surgery was planned: There was a 2-cm-sized cystic tumor, mediorenal, in contact with the hilum. A lumpectomy was performed.

Anatomopathological examination revealed a cystic oncocytoma.

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.

Kidney cancer: from genes to therapy

Renal cell carcinoma incidence is rising worldwide with increasing subtype stratification by the World Health Organization. Each subtype has unique genetic alterations, cell biology changes and clinical findings. Such genetic alterations offer the potential for individualized therapeutic approaches that are rapidly progressing. This review highlights the most common subtypes of renal cell carcinoma, including both hereditary and sporadic forms, with a focus on genetic changes, clinical findings and ongoing clinical trials.

The value of 99mTc-sestamibi single-photon emission computed tomography-computed tomography in the evaluation and risk stratification of renal masses

INTRODUCTION

Differentiation of renal cell carcinoma (RCC) from oncocytoma is a common diagnostic dilemma. A few studies have shown that ^99mTc-sestamibi (MIBI) imaging has the potential to characterize indeterminate renal masses. This comparative study evaluated the utility of MIBI single-photon emission computed tomography-computed tomography (SPECT-CT) in the assessment and risk stratification of renal masses.

METHODS

A total of 29 patients with 31 renal masses who had cross-sectional imaging and MIBI SPECT-CT were included. Lesions were categorized as either MIBI-positive or -negative on SPECT-CT. Individual lesion density ranged from 22-56 Hounsfield units (HU) on the non-contrast CT part of SPECT-CT. Quantitative relative MIBI uptake was calculated by measuring tumor to ipsilateral renal parenchymal uptake. The imaging results were correlated with histopathology.

RESULTS

All oncocytic lesions, including seven oncocytomas and one hybrid oncocytic chromophobe tumor (100%), were positive on MIBI. One chromophobe RCC showed low-grade MIBI uptake. The remaining RCC subtypes, including 15 clear-cell, four papillary, two mixed clear-cell and papillary, and one chromophobe, were MIBI-negative. The quantitative relative tumor uptake showed statistically significant higher uptake in the low-risk/oncocytic lesions compared to RCCs.

CONCLUSIONS

This study demonstrates that MIBI SPECT-CT is valuable in the characterization of indeterminate renal masses. The combination of MIBI uptake on SPECT and lesion density on non-contrast CT can be used for risk stratification of renal masses. This technique may reduce the need for further imaging (multiphasic CT or magnetic resonance imaging), renal mass biopsy, or surgical resection of low-risk renal masses. Subsequently, more patients could be followed with active surveillance.

MR texture analysis in differentiating renal cell carcinoma from lipid-poor angiomyolipoma and oncocytoma

OBJECTIVES

To assess the utility of magnetic resonance texture analysis (MRTA) in differentiating renal cell carcinoma (RCC) from lipid-poor angiomyolipoma (lpAML) and oncocytoma.

METHODS

After ethical approval, 42 patients with 54 masses (34 RCC, 14 lpAML and six oncocytomas) who underwent MRI on a 1.5 T scanner (Avanto, Siemens, Erlangen, Germany) between January 2011 and December 2012 were retrospectively included in the study. MRTA was performed on the TexRAD research software (Feedback Plc., Cambridge, UK) using free-hand polygonal region of interest (ROI) drawn on the maximum cross-sectional area of the tumor to generate six first-order statistical parameters. The Mann-Whitney U test was used to look for any statically significant difference. The receiver operating characteristic (ROC) curve analysis was done to select the parameter with the highest class separation capacity [area under the curve (AUC)] for each MRI sequence.

RESULTS

Several texture parameters on MRI showed high-class separation capacity (AUC > 0.8) in differentiating RCC from lpAML and oncocytoma. The best performing parameter in differentiating RCC from lpAML was mean of positive pixels (MPP) at SSF 2 (AUC: 0.891) on DWI b500. In differentiating RCC from oncocytoma, the best parameter was mean at SSF 0 (AUC: 0.935) on DWI b1000.

CONCLUSIONS

MRTA could potentially serve as a useful non-invasive tool for differentiating RCC from lpAML and oncocytoma.

ADVANCES IN KNOWLEDGE

There is limited literature addressing the role of MRTA in differentiating RCC from lpAML and oncocytoma. Our study demonstrated several texture parameters which were useful in this regard.

Use of Contrast Ultrasound for Renal Mass Evaluation

An introduction to the expanding modality of contrast-enhanced ultrasound is provided, along with basics on contrast agents and technique. The contrast ultrasound findings of multiple renal tumors are reviewed with examples, including clear cell renal cell carcinoma, papillary renal cell carcinoma, chromophobe renal cell carcinoma, other rare renal cell carcinoma subtypes, oncocytoma, upper tract urothelial carcinoma, lymphoma, and angiomyolipoma, followed also by brief discussions of renal infections and pseudolesions.

Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature

Radiomics texture analysis offers objective image information that could otherwise not be obtained by radiologists′ subjective radiological interpretation. We investigated radiomics applications in renal tumor assessment and provide a comprehensive review. A detailed search of original articles was performed using the PubMed-MEDLINE database until 20 March 2020 to identify English literature relevant to radiomics applications in renal tumor assessment. In total, 42 articles were included in the analysis and divided into four main categories: renal mass differentiation, nuclear grade prediction, gene expression-based molecular signatures, and patient outcome prediction. The main area of research involves accurately differentiating benign and malignant renal masses, specifically between renal cell carcinoma (RCC) subtypes and from angiomyolipoma without visible fat and oncocytoma. Nuclear grade prediction may enhance proper patient selection for risk-stratified treatment. Radiomics-predicted gene mutations may serve as surrogate biomarkers for high-risk disease, while predicting patients’ responses to targeted therapies and their outcomes will help develop personalized treatment algorithms. Studies generally reported the superiority of radiomics over expert radiological interpretation. Radiomics provides an alternative to subjective image interpretation for improving renal tumor diagnostic accuracy. Further incorporation of clinical and imaging data into radiomics algorithms will augment tumor prediction accuracy and enhance individualized medicine.

Mass Spectrometry Imaging Enables Discrimination of Renal Oncocytoma from Renal Cell Cancer Subtypes and Normal Kidney Tissues

Precise diagnosis and subtyping of kidney tumors are imperative to optimize and personalize treatment decision for patients. Patients with the most common benign renal tumor, renal oncocytomas, may be overtreated with surgical resection because of limited preoperative diagnostic methods that can accurately identify the benign condition with certainty. In this study, desorption electrospray ionization (DESI)-mass spectrometry (MS) imaging was applied to study the metabolic and lipid profiles of various types of renal tissues, including normal kidney, renal oncocytoma, and renal cell carcinomas (RCC). A total of 73,992 mass spectra from 71 patient samples were obtained and used to build predictive models using the least absolute shrinkage and selection operator (Lasso). Overall accuracies of 99.47% per pixel and 100% per patient for prediction of the three tissue types were achieved. In particular, renal oncocytoma and chromophobe RCC, which present the most significant morphologic overlap and are sometimes indistinguishable using histology alone, were also investigated and the predictive models built yielded 100% accuracy in discriminating these tumor types. Discrimination of three subtypes of RCC was also achieved on the basis of DESI-MS imaging data. Importantly, several small metabolites and lipids species were identified as characteristic of individual tissue types and chemically characterized using tandem MS and high mass accuracy measurements. Collectively, our study shows that the metabolic data acquired by DESI-MS imaging in conjunction with statistical modeling allows discrimination of renal tumors and thus has the potential to be used in the clinical setting to improve treatment of patients with kidney tumor. SIGNIFICANCE: Metabolic data acquired by mass spectrometry imaging in conjunction with statistical modeling allows discrimination of renal tumors and has the potential to be used in the clinic to improve treatment of patients.

Characterization of clear cell renal cell carcinoma and other renal tumors: evaluation of dual-energy CT using material-specific iodine and fat imaging

OBJECTIVE

This study aimed to assess material-specific iodine and fat images for diagnosis of clear cell renal cell carcinoma (cc-RCC) compared to papillary RCC (p-RCC) and other renal masses.

MATERIALS AND METHODS

With IRB approval, we identified histologically confirmed solid renal masses that underwent rapid-kVp-switch DECT between 2016 and 2018: 25 cc-RCC (7 low grade versus 18 high grade), 11 p-RCC, and 6 other tumors (2 clear cell papillary RCC, 2 chromophobe RCC, 1 oncocytoma, 1 renal angiomyomatous tumor). A blinded radiologist measured iodine and fat concentration on material-specific iodine-water and fat-water basis pair images. Comparisons were performed between groups using univariate analysis and diagnostic accuracy calculated by ROC.

RESULTS

Iodine concentration was higher in cc-RCC (6.14 ± 1.79 mg/mL) compared to p-RCC (1.40 ± 0.54 mg/mL, p < 0.001), but not compared to other tumors (5.0 ± 2.2 mg/mL, p = 0.370). Intratumoral fat was seen in 36.0% (9/25) cc-RCC (309.6 ± 234.3 mg/mL [71.1-762.3 ng/mL]), 9.1% (1/11) papillary RCC (97.11 mg/mL), and no other tumors (p = 0.036). Iodine concentration ≥ 3.99 mg/mL achieved AUC and sensitivity/specificity of 0.88 (CI 0.76-1.00) and 92.31%/82.40% to diagnose cc-RCC. To diagnose p-RCC, iodine concentration ≤ 2.5 mg/mL achieved AUC and sensitivity/specificity of 0.99 (0.98-1.00) and 100%/100%. The presence of intratumoral fat had AUC 0.64 (CI 0.53-0.75) and sensitivity/specificity of 34.6%/93.8% to diagnose cc-RCC. A logistic regression model combining iodine concentration and presence of fat increased AUC to 0.91 (CI 0.81-1.0) with sensitivity/specificity of 80.8%/93.8% to diagnose cc-RCC.

CONCLUSION

Iodine concentration values are highly accurate to differentiate clear cell RCC from papillary RCC; however, they overlap with other tumors. Fat-specific images may improve differentiation of clear cell RCC from other avidly enhancing tumors.

KEY POINTS

• Clear cell renal cell carcinoma (RCC) has significantly higher iodine concentration than papillary RCC, but there is an overlap in values comparing clear cell RCC to other renal tumors. • Iodine concentration ≤ 2.5 mg/mL is highly accurate to differentiate papillary RCC from clear cell RCC and other renal tumors. • The presence of microscopic fat on material-specific fat images was specific for clear cell RCC, helping to differentiate clear cell RCC from other avidly enhancing renal tumors.

Deep learning and radiomics: the utility of Google TensorFlow™ Inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT

PURPOSE

Currently, all solid enhancing renal masses without microscopic fat are considered malignant until proven otherwise and there is substantial overlap in the imaging findings of benign and malignant renal masses, particularly between clear cell RCC (ccRCC) and benign oncocytoma (ONC). Radiomics has attracted increased attention for its utility in pre-operative work-up on routine clinical images. Radiomics based approaches have converted medical images into mineable data and identified prognostic imaging signatures that machine learning algorithms can use to construct predictive models by learning the decision boundaries of the underlying data distribution. The TensorFlow™ framework from Google is a state-of-the-art open-source software library that can be used for training deep learning neural networks for performing machine learning tasks. The purpose of this study was to investigate the diagnostic value and feasibility of a deep learning-based renal lesion classifier using open-source Google TensorFlow™ Inception in differentiating ccRCC from ONC on routine four-phase MDCT in patients with pathologically confirmed renal masses.

METHODS

With institutional review board approval for this 1996 Health Insurance Portability and Accountability Act compliant retrospective study and a waiver of informed consent, we queried our institution's pathology, clinical, and radiology databases for histologically proven cases of ccRCC and ONC obtained between January 2000 and January 2016 scanned with a an intravenous contrast-enhanced four-phase renal mass protocol (unenhanced (UN), corticomedullary (CM), nephrographic (NP), and excretory (EX) phases). To extract features to be used for the machine learning model, the entire renal mass was contoured in the axial plane in each of the four phases, resulting in a 3D volume of interest (VOI) representative of the entire renal mass. We investigated thirteen different approaches to convert the acquired VOI data into a set of images that adequately represented each tumor which was used to train the final layer of the neural network model. Training was performed over 4000 iterations. In each iteration, 90% of the data were designated as training data and the remaining 10% served as validation data and a leave-one-out cross-validation scheme was implemented. Accuracy, sensitivity, specificity, positive (PPV) and negative predictive (NPV) values, and CIs were calculated for the classification of the thirteen processing modes.

RESULTS

We analyzed 179 consecutive patients with 179 lesions (128 ccRCC and 51 ONC). The ccRCC cohort had a mean size of 3.8 cm (range 0.8-14.6 cm) and the ONC cohort had a mean lesion size of 3.9 cm (range 1.0-13.1 cm). The highest specificity and PPV (52.9% and 80.3%, respectively) were achieved in the EX phase when we analyzed the single mid-slice of the tumor in the axial, coronal and sagittal plane, and when we increased the number of mid-slices of the tumor to three, with an accuracy of 75.4%, which also increased the sensitivity to 88.3% and the PPV to 79.6%. Using the entire tumor volume also showed that classification performance was best in the EX phase with an accuracy of 74.4%, a sensitivity of 85.8% and a PPV of 80.1%. When the entire tumor volume, plus mid-slices from all phases and all planes presented as tiled images, were submitted to the final layer of the neural network we achieved a PPV of 82.5%.

CONCLUSIONS

The best classification result was obtained in the EX phase among the thirteen classification methods tested. Our proof of concept study is the first step towards understanding the utility of machine learning in the differentiation of ccRCC from ONC on routine CT images. We hope this could lead to future investigation into the development of a multivariate machine learning model which may augment our ability to accurately predict renal lesion histology on imaging.

Cystic renal oncocytoma mimicking renal cell carcinoma

Cystic renal lesions are one of the commonly encountered urological conditions. They can be either benign or malignant. The Bosniak classification is employed to differentiate benign cysts from the malignant ones and to recommend treatment options. Bosniak type 4 cysts are mostly malignant. Rarely, benign tumors can be encountered in Bosniak type 4 cysts. We present a 59-year-old female who presented with a hilar Bosniak type 4 cyst in the right kidney. She underwent open exploration of the right renal tumor. The tumor was infiltrating into the renal vessels and could not be separated from the renal vein. In view of preoperative and intraoperative suspicion of malignancy, radical nephrectomy was done. Postoperative histopathological examination revealed the tumor to be an oncocytoma. The benign nature of the cyst could not be conclusively determined by preoperative investigations and intraoperative findings. Postoperative histological examination uncovered the rare cystic presentation of this benign tumor.

Differentiation of Predominantly Solid Enhancing Lipid-Poor Renal Cell Masses by Use of Contrast-Enhanced CT: Evaluating the Role of Texture in Tumor Subtyping

OBJECTIVE

The purpose of this study was to assess the accuracy of a panel of texture features extracted from clinical CT in differentiating benign from malignant solid enhancing lipid-poor renal masses.

MATERIALS AND METHODS

In a retrospective case-control study of 174 patients with predominantly solid nonmacroscopic fat-containing enhancing renal masses, 129 cases of malignant renal cell carcinoma were found, including clear cell, papillary, and chromophobe subtypes. Benign renal masses-oncocytoma and lipid-poor angiomyolipoma-were found in 45 patients. Whole-lesion ROIs were manually segmented and coregistered from the standard-of-care multiphase contrast-enhanced CT (CECT) scans of these patients. Pathologic diagnosis of all tumors was obtained after surgical resection. CECT images of the renal masses were used as inputs to a CECT texture analysis panel comprising 31 texture metrics derived with six texture methods. Stepwise logistic regression analysis was used to select the best predictor among all candidate predictors from each of the texture methods, and their performance was quantified by AUC.

RESULTS

Among the texture predictors aiding renal mass subtyping were entropy, entropy of fast-Fourier transform magnitude, mean, uniformity, information measure of correlation 2, and sum of averages. These metrics had AUC values ranging from good (0.80) to excellent (0.98) across the various subtype comparisons. The overall CECT-based tumor texture model had an AUC of 0.87 (p < 0.05) for differentiating benign from malignant renal masses.

CONCLUSION

The CT texture statistical model studied was accurate for differentiating benign from malignant solid enhancing lipid-poor renal masses.

The different faces of renal angiomyolipomas on radiologic imaging: a pictorial review

Renal angiomyolipoma (AML) is an uncommon renal tumour, generally composed of mature adipose tissue, dysmorphic blood vessels and smooth muscle. Identification of intratumoral fat on unenhanced CT images is the most reliable finding for establishing the diagnosis of renal AML. However, AMLs sometimes exhibit atypical findings, including cystic as well as solid forms; some of these variants overlap with the appearance of other renal tumours. A rare type of AML, the epithelioid type, possesses malignant potential. The aim of this pictorial review is to gather the different imaging features of AMLs including the classic and fat-poor types, AMLs with epithelial cysts, epithelioid AML, AML associated with tuberous sclerosis, haemorrhagic AML and large AMLs mimicking retroperitoneal liposarcomas. The diagnostic clues that help to distinguish AMLs from other renal tumours are also described in the review.

Imaging and Screening of Hereditary Cancer Syndromes

There is a wide spectrum of mendelian disorders that predispose patients to an increased risk of benign as well as malignant tumors. Hereditary cancer syndromes are characterized by the early onset of diverse, frequently advanced malignancies in specific organ systems in multiple family members, posing significant challenges to diagnosis and management. A better understanding of the genetic abnormalities and pathophysiology that underlie these disorders has led to contemporary paradigms to screen, allowing early diagnosis, and has improved targeted therapies to aid in management. This article reviews select hereditary cancer syndromes with an emphasis on imaging-based screening and surveillance strategies.

The ABCs of BHD: An In-Depth Review of Birt-Hogg-Dubé Syndrome

OBJECTIVE

Birt-Hogg-Dubé (BHD) syndrome is an autosomal dominant inherited syndrome involving multiple organs. In young patients, renal neoplasms that are multiple, bilateral, or both, such as oncocytomas, chromophobe renal cell carcinoma (RCC), hybrid chromophobe RCC-oncocytomas, clear cell RCC, and papillary RCC, can suggest BHD syndrome. Extrarenal findings, including dermal lesions, pulmonary cysts, and spontaneous pneumothoraces, also aid in diagnosis.

CONCLUSION

Radiologists may be one of the first medical specialists to suggest the diagnosis of BHD syndrome. Knowledge of pathogenesis and management, including the importance of the types of renal neoplasms in a given patient, is needed to properly recognize this rare condition.

Imaging features of solid renal masses

The widespread use of abdominal imaging techniques has increased the detection of solid renal masses over the past years. Imaging plays a crucial role in the management and surveillance and in determining which lesions need treatment. The "classical angiomyolipoma" is the only benign solid renal mass that can be characterized with confidence by imaging through the detection of a fat-containing lesion without calcifications. There is a large overlap of imaging features between benign and malignant renal masses that often makes difficult a correct characterization of these lesions. In this review, we discuss the imaging features of the main solid renal masses that may suggest a likely benign diagnosis.

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.

Oncocytoma: A Differential Consideration for an Incidentally Detected FDG-Avid Renal Mass on PET/CT

Renal oncocytoma is a benign renal neoplasm that is often discovered incidentally and closely mimics renal cell carcinoma on common imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI). Due to the inability to reliably distinguish between these benign and malignant lesions with imaging, both are typically treated as if they are malignant. Hypermetabolic activity of renal oncocytomas is not frequently encountered because positron emission tomography (PET) is not a standard modality for imaging primary renal tumors. We present a case of a 65 year-old female with a history of thyroid cancer who had an incidentally discovered hypermetabolic renal mass on surveillance PET-CT imaging. Due to the concern for a primary renal malignancy or metastatic disease, the mass was resected and proven to be an oncocytoma on pathologic review.

Voxel-based whole-lesion enhancement parameters: a study of its clinical value in differentiating clear cell renal cell carcinoma from renal oncocytoma

PURPOSE

The purpose of this study was to compare whole-lesion (WL) enhancement parameters to single region of interest (ROI)-based enhancement in discriminating clear cell renal cell carcinoma (ccRCC) from renal oncocytoma.

MATERIALS AND METHODS

In this IRB-approved retrospective study, the surgical database was queried to derive a cohort of 94 postnephrectomy patients with ccRCC or oncocytoma (68 ccRCC, 26 oncocytoma), who underwent preoperative multiphase contrast-enhanced computed tomography (CECT) between June 2009 and August 2013. CT acquisitions were transferred to a three-dimensional workstation, and WL ROIs were manually segmented. WL enhancement and histogram distribution parameters skewness, kurtosis, standard deviation (SD), and interquartile range (IQR) were calculated. WL enhancement parameters were compared to single ROI-based enhancement using receiver operating characteristic (ROC) analysis.

RESULTS

Oncocytoma had significantly higher WL enhancement than ccRCC in nephrographic (mean, p = 0.02; median, p = 0.03) and excretory phases (mean, p = 0.03; median p < 0.01). ccRCC had significantly higher kurtosis than oncocytoma in corticomedullary (p = 0.03) and excretory phases (p < 0.01), and significantly higher SD and IQR than oncocytoma in all postcontrast phases: corticomedullary (SD, p = 0.02; IQR, p < 0.01), nephrographic (SD, p = 0.01; IQR, p = 0.03), and excretory (SD, p < 0.01; IQR, p < 0.01). When compared to single ROI-based enhancement, WL enhancement alone did not demonstrate a statistical advantage in discriminating between ccRCC and oncocytoma (area under ROC curve of 0.78 and 0.72 respectively), but when combined with histogram distribution parameters (area under ROC curve of 0.86), it did demonstrate a slight improvement.

CONCLUSION

Our study suggests that voxel-based WL enhancement parameters provide only a slight improvement over single ROI-based enhancement techniques in differentiating between ccRCC and renal oncocytoma.

Usefulness of MDCT to Differentiate Between Renal Cell Carcinoma and Oncocytoma: Development of a Predictive Model

OBJECTIVE

The objective of our study was to identify the most useful parameters to differentiate between renal cell carcinoma (RCC) and oncocytoma using four-phase CT.

MATERIALS AND METHODS

Ninety-seven patients with solid renal lesions who underwent surgery with four-phase preoperative CT evaluation and with pathologic diagnosis of RCC or oncocytoma were included in the study. Features of tumors and the enhancement pattern in the four CT phases were evaluated and analyzed. Logistic regression models were used to assess independent predictors for malignancy.

RESULTS

Histopathologically, 13 tumors were oncocytomas and 84 were RCCs. RCCs were larger (6.20 cm vs 3.21 cm, p = 0.0004) and more often enhanced heterogeneously (66 vs 6, p = 0.02). Lesions that were larger than 4 cm showed a significantly higher risk of malignancy (p = 0.0046). Significant differences were found in intensity of nodule enhancement between the nephrographic and the excretory phases with respect to the unenhanced phase (p = 0.003 and p = 0.0026). At multivariate analysis, parameters that were independent predictors of malignancy were enhancement pattern, with RCCs more often having heterogeneous enhancement than oncocytomas (odds ratio [OR], 0.18; 95% CI, 0.04-0.90), and nodule enhancement in the excretory phase in relation to the unenhanced phase, with RCCs showing lower enhancement (OR, 0.93; 95% CI, 0.88-0.97), and a size larger than 4 cm (OR, 4.01; 95% CI, 0.70-23.14).

CONCLUSION

The combination of different CT parameters including lesion size larger than 4 cm, lesion enhancement in the excretory phase in relation to the unenhanced phase, and heterogeneous enhancement pattern helps distinguish RCC from oncocytoma.