Imaging of Solid Renal Masses

The evolving management of small renal masses

Small renal masses (SRMs) are a heterogeneous group of tumours with varying metastatic potential. The increasing use and improving quality of abdominal imaging have led to increasingly early diagnosis of incidental SRMs that are asymptomatic and organ confined. Despite improvements in imaging and the growing use of renal mass biopsy, diagnosis of malignancy before treatment remains challenging. Management of SRMs has shifted away from radical nephrectomy, with active surveillance and nephron-sparing surgery taking over as the primary modalities of treatment. The optimal treatment strategy for SRMs continues to evolve as factors affecting short-term and long-term outcomes in this patient cohort are elucidated through studies from prospective data registries. Evidence from rapidly evolving research in biomarkers, imaging modalities, and machine learning shows promise in improving understanding of the biology and management of this patient cohort.

Differentiation between renal epithelioid angiomyolipoma and clear cell renal cell carcinoma using clear cell likelihood score

PURPOSE

Clear cell likelihood score (ccLS) may be a reliable diagnostic method for distinguishing renal epithelioid angiomyolipoma (EAML) and clear cell renal cell carcinoma (ccRCC). In this study, we aim to explore the value of ccLS in differentiating EAML from ccRCC.

METHODS

We performed a retrospective analysis in which 27 EAML patients and 60 ccRCC patients underwent preoperative magnetic resonance imaging (MRI) at our institution. Two radiologists trained in the ccLS algorithm scored independently and the consistency of their interpretation was evaluated. The difference of the ccLS score was compared between EAML and ccRCC in the whole study cohort and two subgroups [small renal masses (SRM; ≤ 4 cm) and large renal masses (LRM; > 4 cm)].

RESULTS

In total, 87 patients (59 men, 28 women; mean age, 55±11 years) with 90 renal masses (EAML: ccRCC = 1: 2) were identified. The interobserver agreement of two radiologists for the ccLS system to differentiate EAML from ccRCC was good (k = 0.71). The ccLS score in the EAML group and the ccRCC group ranged from 1 to 5 (73.3% in scores 1-2) and 2 to 5 (76.7% in scores 4-5), respectively, with statistically significant differences (P < 0.001). With the threshold value of 2, ccLS can distinguish EAML from ccRCC with the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 87.8%, 95.0%, 73.3%, 87.7%, and 88.0%, respectively. The AUC (area under the curve) was 0.913. And the distribution of the ccLS score between the two diseases was not affected by tumor size (P = 0.780).

CONCLUSION

The ccLS can distinguish EAML from ccRCC with high accuracy and efficiency.

Style Transfer-assisted Deep Learning Method for Kidney Segmentation at Multiphase MRI

Purpose

To develop and validate a semisupervised style transfer-assisted deep learning method for automated segmentation of the kidneys using multiphase contrast-enhanced (MCE) MRI acquisitions.

Materials and Methods

This retrospective, Health Insurance Portability and Accountability Act-compliant, institutional review board-approved study included 125 patients (mean age, 57.3 years; 67 male, 58 female) with renal masses. Cohort 1 consisted of 102 coronal T2-weighted MRI acquisitions and 27 MCE MRI acquisitions during the corticomedullary phase. Cohort 2 comprised 92 MCE MRI acquisitions (23 acquisitions during four phases each, including precontrast, corticomedullary, early nephrographic, and nephrographic phases). The kidneys were manually segmented on T2-weighted images. A cycle-consistent generative adversarial network (CycleGAN) was trained to generate anatomically coregistered synthetic corticomedullary style images using T2-weighted images as input. Synthetic images for precontrast, early nephrographic, and nephrographic phases were then generated using the synthetic corticomedullary images as input. Mask region-based convolutional neural networks were trained on the four synthetic phase series for kidney segmentation using T2-weighted masks. Segmentation performance was evaluated in a different cohort of 20 originally acquired MCE MRI examinations by using Dice and Jaccard scores.

Results

The CycleGAN network successfully generated anatomically coregistered synthetic MCE MRI-like datasets from T2-weighted acquisitions. The proposed deep learning approach for kidney segmentation achieved high mean Dice scores in all four phases of the original MCE MRI acquisitions (0.91 for precontrast, 0.92 for corticomedullary, 0.91 for early nephrographic, and 0.93 for nephrographic).

Conclusion

The proposed deep learning approach achieved high performance in kidney segmentation on different MCE MRI acquisitions.Keywords: Kidney Segmentation, Generative Adversarial Network, CycleGAN, Convolutional Neural Network, Transfer Learning Supplemental material is available for this article. Published under a CC BY 4.0 license.

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.

Automatic renal mass segmentation and classification on CT images based on 3D U-Net and ResNet algorithms

Purpose

To automatically evaluate renal masses in CT images by using a cascade 3D U-Net- and ResNet-based method to accurately segment and classify focal renal lesions.

Material and Methods

We used an institutional dataset comprising 610 CT image series from 490 patients from August 2009 to August 2021 to train and evaluate the proposed method. We first determined the boundaries of the kidneys on the CT images utilizing a 3D U-Net-based method to be used as a region of interest to search for renal mass. An ensemble learning model based on 3D U-Net was then used to detect and segment the masses, followed by a ResNet algorithm for classification. Our algorithm was evaluated with an external validation dataset and kidney tumor segmentation (KiTS21) challenge dataset.

Results

The algorithm achieved a Dice similarity coefficient (DSC) of 0.99 for bilateral kidney boundary segmentation in the test set. The average DSC for renal mass delineation using the 3D U-Net was 0.75 and 0.83. Our method detected renal masses with recalls of 84.54% and 75.90%. The classification accuracy in the test set was 86.05% for masses (<5 mm) and 91.97% for masses (≥5 mm).

Conclusion

We developed a deep learning-based method for fully automated segmentation and classification of renal masses in CT images. Testing of this algorithm showed that it has the capability of accurately localizing and classifying renal masses.

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 radiomics for differentiating oncocytoma from renal cell carcinomas: Systematic review and meta-analysis

BACKGROUND

Radiomics is a type of quantitative analysis that provides a more objective approach to detecting tumor subtypes using medical imaging. The goal of this paper is to conduct a comprehensive assessment of the literature on computed tomography (CT) radiomics for distinguishing renal cell carcinomas (RCCs) from oncocytoma.

METHODS

From February 15th 2012 to 2022, we conducted a broad search of the current literature using the PubMed/MEDLINE, Google scholar, Cochrane Library, Embase, and Web of Science. A meta-analysis of radiomics studies concentrating on discriminating between oncocytoma and RCCs was performed, and the risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies method. The pooled sensitivity, specificity, and diagnostic odds ratio were evaluated via a random-effects model, which was applied for the meta-analysis. This study is registered with PROSPERO (CRD42022311575).

RESULTS

After screening the search results, we identified 6 studies that utilized radiomics to distinguish oncocytoma from other renal tumors; there were a total of 1064 lesions in 1049 patients (288 oncocytoma lesions vs 776 RCCs lesions). The meta-analysis found substantial heterogeneity among the included studies, with pooled sensitivity and specificity of 0.818 [0.619-0.926] and 0.808 [0.537-0.938], for detecting different subtypes of RCCs (clear cell RCC, chromophobe RCC, and papillary RCC) from oncocytoma. Also, a pooled sensitivity and specificity of 0.83 [0.498-0.960] and 0.92 [0.825-0.965], respectively, was found in detecting oncocytoma from chromophobe RCC specifically.

CONCLUSIONS

According to this study, CT radiomics has a high degree of accuracy in distinguishing RCCs from RO, including chromophobe RCCs from RO. Radiomics algorithms have the potential to improve diagnosis in scenarios that have traditionally been ambiguous. However, in order for this modality to be implemented in the clinical setting, standardization of image acquisition and segmentation protocols as well as inter-institutional sharing of software is warranted.

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.

Diagnostic Performance and Interreader Agreement of the MRI Clear Cell Likelihood Score for Characterization of cT1a and cT1b Solid Renal Masses: An External Validation Study

BACKGROUND. The clear cell likelihood score (ccLS) has been proposed for the noninvasive differentiation of clear cell renal cell carcinoma (ccRCC) from other renal neoplasms on multiparametric MRI (mpMRI), though further external validation remains needed. OBJECTIVE. The purpose of our study was to evaluate the diagnostic performance and interreader agreement of the ccLS version 2.0 (v2.0) for characterizing solid renal masses as ccRCC. METHODS. This retrospective study included 102 patients (67 men, 35 women; mean age, 56.9 ± 12.8 [SD] years) who underwent mpMRI between January 2013 and February 2018, showing a total of 108 (≥ 25% enhancing tissue) solid renal masses measuring 7 cm or smaller (83 cT1a [≤ 4 cm] and 25 cT1b [> 4 cm and ≤ 7 cm]), all with a histologic diagnosis. Three abdominal radiologists independently reviewed the MRI examinations using ccLS v2.0. Median reader sensitivity, specificity, and accuracy were computed for predicting ccRCC by ccLS of 4 or greater, and individual reader AUCs were derived. The percentage of masses that were ccRCC was calculated, stratified by ccLS. Interobserver agreement was assessed by the Fleiss kappa statistic. RESULTS. The sample included 45 ccRCCs (34 cT1a, 11 cT1b), 30 papillary renal cell carcinomas (RCCs), 13 chromophobe RCCs, 14 oncocytomas, and six fat-poor angiomyolipomas. Median reader sensitivity, specificity, and accuracy for predicting ccRCC by ccLS of 4 or greater were 85%, 82%, and 83% among cT1a masses and 82%, 100%, and 92% among cT1b masses. The three readers' AUCs for predicting ccRCC by ccLS for cT1a masses were 0.90, 0.84, and 0.89 and for cT1b masses were 0.99, 0.97, and 0.92. Across readers, the percentage of masses that were ccRCC among cT1a masses was 0%, 0%, 20%, 68%, and 93% for ccLS of 1, 2, 3, 4, and 5, respectively; among cT1b masses, the percentage of masses that were ccRCC was 0%, 0%, 32%, 90%, and 100% for ccLS of 1, 2, 3, 4, and 5, respectively. Interobserver agreement among cT1a and cT1b masses for ccLS of 4 or greater was 0.82 and 0.83 and for ccLS of 1-5 overall was 0.65 and 0.62, respectively. CONCLUSION. This study provides external validation of the ccLS, finding overall high measures of diagnostic performance and interreader agreement. CLINICAL IMPACT. The ccLS provides a standardized approach to the noninvasive diagnosis of ccRCC by MRI.

Systematic review and meta-analysis of multiparametric MRI clear cell likelihood scores for classification of small renal masses

Purpose

To systematically assess the multiparametric MRI clear cell likelihood score (ccLS) algorithm for the classification of small renal masses (SRM).

Methods

We conducted an electronic literature search on Web of Science, MEDLINE (Ovid and PubMed), Cochrane Library, EMBASE, and Google Scholar to identify relevant articles from 2017 up to June 30, 2022. We included studies reporting the diagnostic performance of the ccLS for characterization of solid SRM. The bivariate model and hierarchical summary receiver operating characteristic (HSROC) model were used to pool sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR−), and diagnostic odds ratio (DOR). The quality evaluation was performed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool.

Results

A total of 6 studies with 825 renal masses (785 patients) were included in the current meta-analysis. The pooled sensitivity and specificity for cT1a renal masses were 0.80 (95% CI 0.75–0.85) and 0.74 (95% CI 0.65–0.81) at the threshold of ccLS ≥4, the pooled LR+, LR−, and DOR were 3.04 (95% CI 2.34-3.95), 0.27 (95% CI 0.22–0.33), and 11.4 (95% CI 8.2-15.9), respectively. The area under the HSROC curve was 0.84 (95% CI 0.81–0.87). For all cT1 renal masses, the pooled sensitivity and specificity were 0.80 (95% CI 0.74–0.85) and 0.76 (95% CI 0.67–0.83).

Conclusions

The ccLS had moderate to high accuracy for identifying ccRCC from other RCC subtypes and with a moderate inter-reader agreement. However, its diagnostic performance remain needs multi-center, large cohort studies to validate in the future.

Differentiation of renal masses with multi-parametric MRI: the de Silva St George classification scheme

Purpose

To develop a system for multi-parametric MRI to differentiate benign from malignant solid renal masses and assess its accuracy compared to the gold standard of histopathological diagnosis.

Methods

This is a retrospective analysis of patients who underwent 3 Tesla mpMRI for further assessment of small renal tumours with specific scanning and reporting protocol incorporating T2 HASTE signal intensity, contrast enhancement ratios, apparent diffusion coefficient and presence of microscopic/macroscopic fat. All MRIs were reported prior to comparison with histopathologic diagnosis and a reporting scheme was developed. 2 × 2 contingency table analysis (sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV)), Fisher Exact test were used to assess the association between suspicion of malignancy on mpMRI and histopathology, and descriptive statistics were performed.

Results

67 patients were included over a 5-year period with a total of 75 renal masses. 70 masses were confirmed on histopathology (five had pathognomonic findings for angiomyolipomas; biopsy was therefore considered unethical, so these were included without histopathology). Three patients were excluded due to a non-diagnostic result, non-standardised imaging and one found to be an organising haematoma rather than a mass. Therefore 72 cases were included in analysis (in 64 patients, with seven patients having multiple tumours). Unless otherwise specified, all further statistics refer to individual tumours rather than patients. 52 (72.2%) were deemed ‘suspicious or malignant’ and 20 (27.8%) were deemed ‘benign’ on mpMRI. 51 cases (70.8%) had renal cell carcinoma confirmed. The sensitivity, NPV, specificity and PPV for MRI for detecting malignancy were 96.1%, 90%, 85.7% and 94.2% respectively, Fisher’s exact test demonstrated p < 0.0001 for the association between suspicion of malignancy on MRI and histopathology.

Conclusion

The de Silva St George classification scheme performed well in differentiating benign from malignant solid renal masses, and may be useful in predicting the likelihood of malignancy to determine the need for biopsy/excision. Further validation is required before this reporting system can  be recommended for clinical use.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12894-022-01082-9.

Comparative Analysis for the Distinction of Chromophobe Renal Cell Carcinoma from Renal Oncocytoma in Computed Tomography Imaging Using Machine Learning Radiomics Analysis

Background: ChRCC and RO are two types of rarely occurring renal tumors that are difficult to distinguish from one another based on morphological features alone. They differ in prognosis, with ChRCC capable of progressing and metastasizing, but RO is benign. This means discrimination of the two tumors is of crucial importance. Objectives: The purpose of this research was to develop and comprehensively evaluate predictive models that can discriminate between ChRCC and RO tumors using Computed Tomography (CT) scans and ML-Radiomics texture analysis methods. Methods: Data were obtained from 78 pathologically confirmed renal masses, scanned at two institutions. Data from the two institutions were combined to form a third set resulting in three data cohorts, i.e., cohort 1, 2 and combined. Contrast-enhanced scans were used and the axial cross-sectional slices of each tumor were extracted from the 3D data using a semi-automatic segmentation technique for both 2D and 3D scans. Radiomics features were extracted before and after applying filters and the dimensions of the radiomic features reduced using the least absolute shrinkage and selection operator (LASSO) method. Synthetic minority oversampling technique (SMOTE) was applied to avoid class imbalance. Five ML algorithms were used to train models for predictive classification and evaluated using 5-fold cross-validation. Results: The number of selected features with good model performance was 20, 40 and 6 for cohorts 1, 2 and combined, respectively. The best model performance in cohorts 1, 2 and combined had an excellent Area Under the Curve (AUC) of 1.00 ± 0.000, 1.00 ± 0.000 and 0.87 ± 0.073, respectively. Conclusions: ML-based radiomics signatures are potentially useful for distinguishing ChRCC and RO tumors, with a reliable level of performance for both 2D and 3D scanning.

Artificial Intelligence in Kidney Cancer

Artificial intelligence is rapidly expanding into nearly all facets of life, particularly within the field of medicine. The diagnosis, characterization, management, and treatment of kidney cancer is ripe with areas for improvement that may be met with the promises of artificial intelligence. Here, we explore the impact of current research work in artificial intelligence for clinicians caring for patients with renal cancer, with a focus on the perspectives of radiologists, pathologists, and urologists. Promising preliminary results indicate that artificial intelligence may assist in the diagnosis and risk stratification of newly discovered renal masses and help guide the clinical treatment of patients with kidney cancer. However, much of the work in this field is still in its early stages, limited in its broader applicability, and hampered by small datasets, the varied appearance and presentation of kidney cancers, and the intrinsic limitations of the rigidly structured tasks artificial intelligence algorithms are trained to complete. Nonetheless, the continued exploration of artificial intelligence holds promise toward improving the clinical care of patients with kidney cancer.

Small Renal Masses without Gross Fat: What Is the Role of Contrast-Enhanced MDCT?

Increased detection of small renal masses (SRMs) has encouraged research for non-invasive diagnostic tools capable of adequately differentiating malignant vs. benign SRMs and the type of the tumour. Multi-detector computed tomography (MDCT) has been suggested as an alternative to intervention, therefore, it is important to determine both the capabilities and limitations of MDCT for SRM evaluation. In our study, two abdominal radiologists retrospectively blindly assessed MDCT scan images of 98 patients with incidentally detected lipid-poor SRMs that did not present as definitely aggressive lesions on CT. Radiological conclusions were compared to histopathological findings of materials obtained during surgery that were assumed as the gold standard. The probability (odds ratio (OR)) in regression analyses, sensitivity (SE), and specificity (SP) of predetermined SRM characteristics were calculated. Correct differentiation between malignant vs. benign SRMs was detected in 70.4% of cases, with more accurate identification of malignant (73%) in comparison to benign (65.7%) lesions. The radiological conclusions of SRM type matched histopathological findings in 56.1%. Central scarring (OR 10.6, p = 0.001), diameter of lesion (OR 2.4, p = 0.003), and homogeneous accumulation of contrast medium (OR 3.4, p = 0.03) significantly influenced the accuracy of malignant diagnosis. SE and SP of these parameters varied from 20.6% to 91.3% and 22.9% to 74.3%, respectively. In conclusion, MDCT is able to correctly differentiate malignant versus uncharacteristic benign SRMs in more than 2/3 of cases. However, frequency of the correct histopathological SRM type MDCT identification remains low.

How We Do It: Managing the Indeterminate Renal Mass with the MRI Clear Cell Likelihood Score

The widespread use of cross-sectional imaging has led to a continuous increase in the number of incidentally detected indeterminate renal masses. Frequently, these clinical scenarios involve an older patient with comorbidities and a small renal mass (≤4 cm). Despite aggressive treatment in early stages of the disease, a clear positive effect in reducing kidney cancer-specific mortality is lacking, indicating that many renal cancers exhibit an indolent oncologic behavior. Furthermore, in general, one in five small renal masses is histologically benign and may not benefit from aggressive treatment. Although active surveillance is increasingly recognized as a management option for some patients, the absence of reliable clinical and imaging predictive biologic markers of aggressiveness can contribute to patient anxiety and limit its use in clinical practice. A standardized approach to the image interpretation of solid renal masses has not been broadly implemented. The clear cell likelihood score (ccLS) derived from multiparametric MRI is useful in noninvasively identifying the clear cell subtype, the most common and aggressive form of kidney cancer. Herein, a review of the ccLS is presented, including a step-by-step guide for image interpretation and additional guidance for its implementation in clinical practice.

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.

Which criteria can be used to predict benignity in solid renal lesions lower-equal to 2 cm?

PURPOSE

To evaluate magnetic resonance imaging (MRI) criteria of solid renal lesions lower-equal to 2 cm to differentiate benign and malignant tumors, using histopathology as gold standard.

METHODS

Three radiologists independently evaluated objective and subjective MRI criteria of focal renal lesions. A total of 105 nodules of patients who had MRI and histopathological results in our institution were included. Subjective criteria evaluated were signal on T2-weighted imaging, presence of microscopic and macroscopic fat, hemosiderin, hemorrhage, central scar, segmented inversion enhancement and enhancement type; objective criteria were gender, ADC value, heterogeneity on T2-weighted imaging and proportion of enhancement in late post-contrast phases. Finally, the readers classified the lesions in probably benign or malignant. Interobserver agreement was evaluated by the Gwet method, and the quantitative variables by intraclass correlation coefficients. To adjust the predictive model, the logistic regression model was used considering the benignity variable as outcome.

RESULTS

A total of 26 nodules (24.5%) were benign and 79 (75.2%) were malignant, with size ranging from 7 to 20 mm (median: 14 mm). The most frequent subtype was papillary renal cell carcinoma (RCC) (35.2%), followed by clear-cell RCC (24.8%) and oncocytoma (12.4%). The univariate and multivariate analysis showed, among all categories evaluated, that microscopic fat (p: 0.072), intermediate (p: 0.004) and hyper-enhancement (p: 0.031) and female sex (p: 0.0047) had the best outcome for benignity, within odds ratios of 4.29, 5.75, 4.07 and 2.86, respectively.

CONCLUSION

In small solid renal lesions lower-equal to 2 cm, microscopic fat, moderate and hyper-enhancement and female sex were associated with benignity.

The Renal Vasculature: What the Radiologist Needs to Know

The physiologic role of the kidneys is dependent on the normal structure and functioning of the renal vasculature. Knowledge and understanding of the embryologic basis of the renal vasculature are necessary for the radiologist. Common anatomic variants involving the renal artery (supernumerary arteries and prehilar branching) and renal vein (supernumerary veins, delayed venous confluence, retroaortic or circumaortic vein) may affect procedures like renal transplantation, percutaneous biopsy, and aortic aneurysm repair. Venous compression syndromes (anterior and posterior nutcracker syndrome) can be symptomatic and can be diagnosed with a combination of radiologic features. Renal artery stenosis is commonly atherosclerotic and is diagnosed with Doppler US, CT angiography, or MR angiography. Fibromuscular dysplasia, the second most common cause of renal artery narrowing, has a characteristic string-of-beads appearance resulting from multifocal stenoses and dilatations. Manifestations of renal vasculitis differ depending on whether the affected vessels are large, medium, or small. Renal vascular injury is graded according to the American Association for the Surgery of Trauma (AAST) renal injury scale, which defines vascular injury and active bleeding in renal injuries. Both renal arteries and veins are affected by primary neoplasms or secondarily by neoplasms from adjacent structures. Differentiation between bland thrombus and tumor thrombus and the extent of involvement dictate management in malignancies, especially renal cell carcinoma. Aneurysms, pseudoaneurysms, arteriovenous malformations, and arteriovenous fistulas can affect renal vessels and can be diagnosed with specific imaging features. The radiologist has a critical role in identification of specific imaging characteristics and establishing the diagnosis in the varied pathologic conditions affecting the renal vasculature, which is critical for directing management. Thus, the renal vasculature should be an integral part of radiologists' checklist. ^©RSNA, 2021.

Association of Clear Cell Likelihood Score on MRI and Growth Kinetics of Small Solid Renal Masses on Active Surveillance

Background: The lack of validated imaging markers to characterize biologic aggressiveness of small renal masses (SRMs; cT1a, ≤4 cm) hinders medical decision making amongst available initial management strategies. Objective: To explore the association of the clear cell likelihood score (ccLS) on MRI with growth rates and progression of SRMs. Methods: This retrospective study included consecutive SRMs assigned a ccLS on clinical MRI examinations performed between June 2016 and November 2019 at an academic tertiary-care medical center or its affiliated safety net hospital system. The ccLS scores the likelihood that the SRM represents clear cell renal cell carcinoma (ccRCC) from 1 (very unlikely) to 5 (very likely). The ccLS was extracted from clinical reports. Tumor size measurements were extracted from available prior and follow-up cross-sectional imaging examinations, through June 2020. Serial tumor size measurements were fit to linear and exponential growth curves. Estimated growth rates were grouped by the assigned ccLS. Tumor progression was defined by development of large size (>4 cm in at least two measurements) and/or rapid growth (doubling of volume within 1 year). Differences among ccLS groups were evaluated using Kruskal-Wallis tests. Correlation between ccLS and growth rate were evaluated by Spearman correlation (ρ). Results: Growth rates of 386 SRMs (100 ccLS1-2, 75 ccLS3, and 211 ccLS4-5) from 339 patients (median age 65 years; 198 men, 141 women) were analyzed. Median follow-up was 1.16 years. The ccLS was correlated with growth rates by size (ρ=0.19, p<.001; ccLS4-5: 9%/year, ccLS1-2: 5%/year, p<.001) and by volume (ρ=0.136, p=.008; ccLS4-5: 29%/year, ccLS1-2: 16%/year, p<.001). Disease progression (observed in 49 SRMs) was not significantly associated with ccLS group (p=.61). Two patients (0.6%) developed metastases during active surveillance (AS): one ccLS1 that was a type 2 papillary renal cell carcinoma and one ccLS4 that was ccRCC. Conclusions: Growth is associated with ccLS in SRMs, with higher ccLS correlating with faster growth. Clinical Impact: The non-invasive clear cell likelihood score (ccLS), derived from MRI, correlates with growth rate of SRMs and may help guide personalized management. SRMs with lower ccLS may be considered for AS, whereas SRMs with higher ccLS may warrant earlier intervention.

Retroperitoneal Sclerosing Angiomyolipoma with Long-Term Follow up: A Case Report with Unique Clinicopathologic and Genomic Profile

Sclerosing angiomyolipoma (sAML) is a rare variant of the perivascular epithelioid tumors exhibiting distinct morphology with extensive stromal hyalinization, which makes it challenging to recognize. It often lacks an adipose tissue component and melanocytic markers may be expressed only focally, further posing a diagnostic challenge. Here, we report a case of sAML of the left pararenal retroperitoneum in a 52-year-old woman with 92 months of clinical follow up and discuss the histologic features, immunoprofile, molecular alterations, and differential diagnoses that can aid in the diagnosis of this unique and rare entity.

Controversies in the diagnosis of renal cell carcinoma with tumor thrombus

Diagnosis and treatment of renal cell carcinoma with venous tumor thrombosis remains a challenge today, requiring multidisciplinary teams, mainly in tumor thrombus levels III-IV. Our objective is to present the various diagnostic techniques used and its controversies. A review of the most relevant related articles between January 2000 and August 2020 has been carried out in PubMed, EMBASE and Scielo. Continuous technological development has allowed progress in its detection, in the approximation of the histological subtype, and in the determination of tumor thrombus level. Regardless of the imaging technique used for its diagnosis (CT, MRI, TEE, ultrasound with contrast), the time elapsed until treatment is vitally important to reduce the risk of complications, some of them fatal, such as pulmonary thromboembolism.

Abdominal Recurrence after Robotic Nephron-Sparing Surgery for Wilms Tumor in an Adult Patient

A 31-year-old man was referred to an adult urologist for a renal polar mass that measured 7.2 cm in maximum diameter. Robotic assisted complete tumor excision for suspicious renal cell carcinoma was carried out, preserving the rest of the left kidney. Histopathology showed a Wilms tumor (WT) with positive margins. No postoperative therapy was made, and the patient shortly presented an abdominal recurrence. The patient was referred to our pediatric oncology unit; he received preoperative chemotherapy, followed by surgery (completion nephrectomy and removal of neoplastic deposits in the omentum and parietal peritoneum), postoperative chemotherapy, and abdomen radiotherapy. He is well at the 5-year follow-up. Peritoneal dissemination after laparoscopic nephron-sparing surgery (NSS) in a child with a 10-cm WT was previously reported. We suggest open NSS for large WT may be safer than laparoscopic or robotic NSS because carbon dioxide pneumoperitoneum and traumatic handling of tumor may predispose to tumor cell migration. An abdominal WT relapse in adults can be salvaged by multimodal therapy recommended by current pediatric WT guidelines.

Magnetic Resonance Imaging Radiomics Analyses for Prediction of High-Grade Histology and Necrosis in Clear Cell Renal Cell Carcinoma: Preliminary Experience

INTRODUCTION

Percutaneous renal mass biopsy results can accurately diagnose clear cell renal cell carcinoma (ccRCC); however, their reliability to determine nuclear grade in larger, heterogeneous tumors is limited. We assessed the ability of radiomics analyses of magnetic resonance imaging (MRI) to predict high-grade (HG) histology in ccRCC.

PATIENTS AND METHODS

Seventy patients with a renal mass underwent 3 T MRI before surgery between August 2012 and August 2017. Tumor length, first-order statistics, and Haralick texture features were calculated on T2-weighted and dynamic contrast-enhanced (DCE) MRI after manual tumor segmentation. After a variable clustering algorithm was applied, tumor length, washout, and all cluster features were evaluated univariably by receiver operating characteristic curves. Three logistic regression models were constructed to assess the predictability of HG ccRCC and then cross-validated.

RESULTS

At univariate analysis, area under the curve values of length, and DCE texture cluster 1 and cluster 3 for diagnosis of HG ccRCC were 0.7 (95% confidence interval [CI], 0.58-0.82, false discovery rate P = .008), 0.72 (95% CI, 0.59-0.84, false discovery rate P = .004), and 0.75 (95% CI, 0.63-0.87, false discovery rate P = .0009), respectively. At multivariable analysis, area under the curve for model 1 (tumor length only), model 2 (length + DCE clusters 3 and 4), and model 3 (DCE cluster 1 and 3) for diagnosis of HG ccRCC were 0.67 (95% CI, 0.54-0.79), 0.82 (95% CI, 0.71-0.92), and 0.81 (95% CI, 0.70-0.91), respectively.

CONCLUSION

Radiomics analysis of MRI images was superior to tumor size for the prediction of HG histology in ccRCC in our cohort.

Imaging Characterization of Renal Masses

The detection of a renal mass is a relatively frequent occurrence in the daily practice of any Radiology Department. The diagnostic approaches depend on whether the lesion is cystic or solid. Cystic lesions can be managed using the Bosniak classification, while management of solid lesions depends on whether the lesion is well-defined or infiltrative. The approach to well-defined lesions focuses mainly on the differentiation between renal cancer and benign tumors such as angiomyolipoma (AML) and oncocytoma. Differential diagnosis of infiltrative lesions is wider, including primary and secondary malignancies and inflammatory disease, and knowledge of the patient history is essential. Radiologists may establish a possible differential diagnosis based on the imaging features of the renal masses and the clinical history. The aim of this review is to present the contribution of the different imaging techniques and image guided biopsies in the diagnostic management of cystic and solid renal lesions.

Prediction of Benign and Malignant Solid Renal Masses: Machine Learning-Based CT Texture Analysis

RATIONALE AND OBJECTIVES

This study aimed to investigate whether benign and malignant renal solid masses could be distinguished through machine learning (ML)-based computed tomography (CT) texture analysis.

MATERIALS AND METHODS

Seventy-nine patients with 84 solid renal masses (21 benign; 63 malignant) from a single center were included in this retrospective study. Malignant masses included common renal cell carcinoma (RCC) subtypes: clear cell RCC, papillary cell RCC, and chromophobe RCC. Benign masses are represented by oncocytomas and fat-poor angiomyolipomas. Following preprocessing steps, a total of 271 texture features were extracted from unenhanced and contrast-enhanced CT images. Dimension reduction was done with a reliability analysis and then with a feature selection algorithm. A nested-approach was used for feature selection, model optimization, and validation. Eight ML algorithms were used for the classifications: decision tree, locally weighted learning, k-nearest neighbors, naive Bayes, logistic regression, support vector machine, neural network, and random forest.

RESULTS

The number of features with good reproducibility was 198 for unenhanced CT and 244 for contrast-enhanced CT. Random forest algorithm demonstrated the best predictive performance using five selected contrast-enhanced CT texture features. The accuracy and area under the curve metrics were 90.5% and 0.915, respectively. Having eliminated the highly collinear features from the analysis, the accuracy and area under the curve values slightly increased to 91.7% and 0.916, respectively.

CONCLUSION

ML-based contrast-enhanced CT texture analysis might be a potential method for distinguishing benign and malignant solid renal masses with satisfactory performance.

Predicting common solid renal tumors using machine learning models of classification of radiologist-assessed magnetic resonance characteristics

PURPOSE

Solid renal masses (SRM) are difficult to differentiate based on standard MR features. The purpose of this study was to assess MR imaging features of SRM to evaluate performance of ensemble methods of classifying SRM subtypes.

MATERIALS AND METHODS

MR images of SRM (n = 330) were retrospectively evaluated for standard and multiparametric (mp) features. Models of MR features for predicting malignant and benign lesions as well as subtyping SRM were developed using a training dataset and performance was evaluated in a test data-set using recursive partitioning (RP), gradient booting machine (GBM), and random forest (RF) methods.

RESULTS

In the test dataset, GBM and RF models demonstrated an accuracy of 86% (95% CI 75% to 93%) for predicting benign versus malignant SRM compared to 83% (95% CI 71% to 91%) for the RP model. RF had the greatest accuracy in predicting SRM subtypes, 81.2% (95% CI 69.5% to 89.9%) compared with GBM 73.4% (95% CI 60.9% to 83.7%) or RP 70.3% (95% CI 57.6% to 81.1%). Marginal homogeneity was reduced by the RF model compared with the RP model (P < 0.001), but not the GBM model (P = 0.135). All models had high sensitivity and specificity for clear cell and papillary renal cell carcinomas (RCC), but performed less well in differentiating chromophobe RCC, oncocytomas, and fat-poor angiomyolipomas.

CONCLUSION

Ensemble methods for prediction of SRM from radiologist-assessed image characteristics have high accuracy for distinguishing benign and malignant lesions. SRM subtype classification is limited by the ability to categorize chromophobe RCCs, oncocytomas, and fat-poor angiomyolipomas.

Prospective performance of clear cell likelihood scores (ccLS) in renal masses evaluated with multiparametric magnetic resonance imaging

OBJECTIVES

Solid renal masses have unknown malignant potential with commonly utilized imaging. Biopsy can offer a diagnosis of cancer but has a high non-diagnostic rate and complications. Reported use of multiparametric magnetic resonance imaging (mpMRI) to diagnose aggressive histology (i.e., clear cell renal cell carcinoma (ccRCC)) via a clear cell likelihood score (ccLS) was based on retrospective review of cT1a tumors. We aim to retrospectively assess the diagnostic performance of ccLS prospectively assigned to renal masses of all stages evaluated with mpMRI prior to histopathologic evaluation.

METHODS

In this retrospective cohort study from June 2016 to November 2019, 434 patients with 454 renal masses from 2 institutions with heterogenous patient populations underwent mpMRI with prospective ccLS assignment and had pathologic diagnosis. ccLS performance was assessed by contingency table analysis. The association between ccLS and ccRCC was assessed with logistic regression.

RESULTS

Mean age and tumor size were 60 ± 13 years and 5.4 ± 3.8 cm. Characteristics were similar between institutions except for patient age and race (both p < 0.001) and lesion laterality and histology (both p = 0.04). The PPV of ccLS increased with each increment in ccLS (ccLS1 5% [3/55], ccLS2 6% [3/47], ccLS3 35% [20/57], ccLS4 78% [85/109], ccLS5 93% [173/186]). Pooled analysis for ccRCC diagnosis revealed sensitivity 91% (258/284), PPV 87% (258/295) for ccLS ≥ 4, and specificity 56% (96/170), NPV 94% (96/102) for ccLS ≤ 2. Diagnostic performance was similar between institutions.

CONCLUSIONS

We confirm the optimal diagnostic performance of mpMRI to identify ccRCC in all clinical stages. High PPV and NPV of ccLS can help inform clinical management decision-making.

KEY POINTS

• The positive predictive value of the clear cell likelihood score (ccLS) for detecting clear cell renal cell carcinoma was 5% (ccLS1), 6% (ccLS2), 35% (ccLS3), 78% (ccLS4), and 93% (ccLS5). Sensitivity of ccLS ≥ 4 and specificity of ccLS ≤ 2 were 91% and 56%, respectively. • When controlling for confounding variables, ccLS is an independent risk factor for identifying clear cell renal cell carcinoma. • Utilization of the ccLS can help guide clinical care, including the decision for renal mass biopsy, reducing the morbidity and risk to patients.

Chemical shift imaging in the identification of those renal tumours that contain microscopic fat and the utility of multiparametric MRI in their differentiation

INTRODUCTION

The aim of this study was to assess the qualitative and MRI findings of renal tumours, to determine which lesions contain microscopic fat, one of the potential differentiating factors between tumour types.

METHODS

73 patients who underwent 3 Tesla MRI including chemical shift imaging, with subsequent biopsy or excision for histopathological diagnosis, were included in the study. The images were reviewed for a decrease in signal intensity (SI) on the opposed phase compared with the in-phase gradient echo T1 images, indicating the presence of microscopic fat. The chemical shift index was then calculated as a percentage of SI change and compared with the pathological diagnosis.

RESULTS

In total, 38 (52%) of lesions demonstrated a decrease in SI, consistent with microscopic fat. Microscopic fat was found in 28 (80%) clear cell renal cell carcinomas (RCCs), 6 (66.7%) angiomyolipomas, 2 (20%) papillary RCCs, 1 (20%) chromophobe RCC and 1 (9.1%) oncocytoma. Pairwise comparison of means indicated that the amount of microscopic fat was significantly larger only for angiomyolipomas compared with clear cell RCCs (P < 0.001) and other renal lesions (P < 0.001).

CONCLUSIONS

A decrease in SI on opposed phase compared with in-phase chemical shift imaging favours the diagnosis of either clear cell RCC or an angiomyolipoma. When combined with other parameters in mpMRI, this may aid differentiation of benign from malignant tumours and differentiation of aggressive from indolent RCC subtypes. This may be of value where biopsy is non-diagnostic, not feasible due to location or in high-risk patients.

Applications of radiomics in genitourinary tumors

Genitourinary tumors are heterogeneous groups of tumors with high morbidity and mortality rates. Confronted with existing problems in the management of genitourinary tumors, a personalized imaging method called radiomics shows great potential in areas including detection, grading, and treatment response assessment. Radiomics is characterized by extraction of quantitative imaging features which are not visible to the naked eye from conventional imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography-computed tomography (PET-CT), followed by data analysis and model building. It outperforms other invasive methods in terms of non-invasiveness, low cost and high efficiency. Recently, a number of studies have evaluated the application of radiomics in patients with genitourinary tumors with promising data. The combination of radiomics and clinical/laboratory factors provides added value in many studies. Despite this, there are limitations and challenges to be overcome before a more extensive clinical application in the future. In this article, we will introduce the concept, significance and workflow of radiomics, review their current applications in patients with genitourinary tumors and discuss limitations and future directions of radiomics. It would help multidisciplinary team involved in the treatment of patients with genitourinary tumors to achieve a better understanding of the results of radiomics study toward a personalized medicine.

Von Hippel-Lindau syndrome and renal tumours: radiological diagnostic and treatment options. A case report and literature review

Background

Von Hippel-Lindau disease (VHL) is a rare autosomal dominant syndrome diagnosed for 1 out of 36000-45000 newborns and 90% of the patients have a clinical manifestation before 65 years of age. Affected individuals have an increased risk of developing tumours in several organs or their systems. The most common tumours are retinal or central nervous system hemangioblastomas (60-80%) and VHL-associated renal lesions. Contrast-enhanced computer tomography (CECT) is the gold standard for the diagnosis and characterization of renal tumours. The best treatment option for VHL syndrome-caused renal tumours are nephron-sparing treatment techniques (cryotherapy, radiofrequency, or microwave ablation), which require imaging control. All these innovative treatment techniques are extremely important for VHL patients, because they increase the quality of life by staving off renal dialysis and preventing distant metastases.

Case report

Our case report presents a 16-year-old female with multiple renal cysts observed on ultrasound examination and clinically and molecularly diagnosed with Von Hippel-Lindau syndrome (deletion of the entire VHL gene). After that, for past 11 years multiple renal tumours were removed by cryoablation and patient monitoring on contrast-enhanced magnetic resonance (MRI) and CECT control scans was conducted.

Conclusions

Active multidisciplinary patient follow-up, routine radiological examinations, and correct treatment tactics allow controlling the  progression of renal cell carcinoma and other tumours associated with VHL syndrome, maintaining a normal organ function for a long time, and preventing distant metastases and fatal disease outcomes.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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

ADVANCES IN KNOWLEDGE

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

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.

Usefulness of CT texture analysis in differentiating benign and malignant renal tumours

AIM

To elucidate visually imperceptible differences between benign and malignant renal tumours using computed tomography texture analysis (CTTA) using filtration histogram based parameters.

MATERIALS AND METHODS

A retrospective study was performed by texture analysis of pretreatment contrast-enhanced CT examinations in 354 histopathologically confirmed renal cell carcinomas (RCCs) and 147 benign renal tumours. A region-of-interest was drawn encompassing the largest cross-section of the tumour on venous phase axial CT. CTTA features of entropy, kurtosis, mean positive pixel density, and skewness at different spatial filters were calculated and compared in an attempt to differentiate benign lesions from malignancy.

RESULTS

Entropy with fine spatial filter was significantly higher in RCC than benign renal tumours (p=0.022). Entropy with fine and medium filters was higher in RCC than lipid-poor angiomyolipoma (p=0.050 and 0.052, respectively). Entropy >5.62 had high specificity of 85.7%, but low sensitivity of 31.3%, respectively, for predicting RCC.

CONCLUSIONS

Differences in entropy were helpful in differentiating RCC from lipid-poor angiomyolipoma, and chromophobe RCC from oncocytoma. This technique may be useful to differentiate lesions that appear equivocal on visual assessment or alter management in poor surgical candidates.

Diagnostic performance of prospectively assigned clear cell Likelihood scores (ccLS) in small renal masses at multiparametric magnetic resonance imaging

INTRODUCTION

Detection of small renal masses (SRM) is increasing with the use of cross-sectional imaging, although many incidental lesions have negligible metastatic potential. A method to identify this subtype would aid in risk stratification. A previously reported clear cell likelihood score (ccLS; 1-very unlikely, 2-unlikely, 3-equivocal, 4-likely, and 5-very likely), based on retrospective review of multiparametric magnetic resonance imaging (mpMRI), predicted the likelihood of encountering clear cell renal cell carcinoma (ccRCC) at surgery. Here, we assess the performance of ccLS prospectively assigned for prediction of ccRCC.

METHODS

Patients with a known renal mass who underwent mpMRI at a single institution between June 2016 and April 2018 were prospectively assigned a ccLS as part of the clinical MRI report. These patients were retrospectively reviewed, and those with a cT1a lesion and available pathological tissue diagnosis (diagnostic biopsy or extirpative surgery) were selected for analysis.

RESULTS

In total, 57 patients (mean age 61.7 ± 14.9 years) with 63 cT1a renal masses were identified. Mean tumor size was 2.7 ± 0.7 cm. Defining ccLS 4-5 lesions as positive demonstrated an overall accuracy of 84%, sensitivity of 89%, specificity of 79%, positive predictive value of 84%, and negative predictive value of 86%. A ccLS of 1-2 demonstrates an 86% accuracy and 100% sensitivity/positive predictive value of identifying non-ccRCC histology.

CONCLUSIONS

Utilizing prospectively assigned ccLS, we confirm that mpMRI can reasonably identify ccRCC histology in cT1a renal masses. Standardization of imaging protocols and reporting criteria such as the ccLS can be used to aid in the diagnosis and management of small renal masses.

Role of Virtual Biopsy in the Management of Renal Masses

OBJECTIVE. Renal masses comprise a heterogeneous group of pathologic conditions, including benign and indolent diseases and aggressive malignancies, complicating management. In this article, we explore the emerging role of imaging to provide a comprehensive noninvasive characterization of a renal mass-so-called "virtual biopsy"-and its potential use in the management of patients with renal tumors. CONCLUSION. Percutaneous renal mass biopsy (RMB) remains a valuable method to provide a presurgical histopathologic diagnosis of renal masses, but it is an invasive procedure and is not always feasible. Accumulating data support the use of imaging features to predict histopathology of renal masses. Imaging may help address some of the inherent limitations of RMB, and in certain settings, a multimodal clinical approach may allow decreasing the need for RMB.

Role of Interface Sign and Diffusion-Weighted Magnetic Resonance Imaging in Differential Diagnosis of Exophytic Renal Masses

PURPOSE

We aimed to investigate the role of interfaces of exophytic solid and cystic renal masses on magnetic resonance imaging (MRI) and the added value of diffusion-weighted imaging in differentiating benign from malignant lesions.

METHODS

The Institutional Review Board approved this retrospective study, and informed consent was waived. A total of 265 patients (109 [41%] women and 156 [59%] men) with a mean age of 57 ± 12 (standard deviation) years were enrolled in this study. Preoperative MRI (n = 238) examinations of patients with solid or cystic renal masses and MRI (n = 27) examinations of patients with Bosniak IIF cysts without progression were reviewed. Solid/cystic pattern, interface types and apparent diffusion coefficient (ADC) values were recorded by 2 radiologists. The diagnostic performance of combining normalized ADC values with interface sign were evaluated.

RESULTS

Among 265 renal lesions (109 cystic and 156 solid), all malignant lesions (n = 192) had a round interface. No malignant lesions showed an angular interface. For prediction of benignity in cystic lesions, sensitivity (82.86% vs 56.16%), negative predictive value (92.50% vs 85.71%), and accuracy (94.50% vs 87.92%) ratios of angular interface were higher compared to all (solid plus cystic) lesions. The best normalized ADC cutoff values for predicting malignancy in lesions with round interface were as follows: for all (solid plus cystic), ≤ 0.75 (AUROC = 0.804); solid, ≤ 0.6 (AUROC = 0.819); and cystic, ≤ 0.8 (AUROC = 0.936).

CONCLUSIONS

Angular interface can be a predictor of benignity for especially cystic renal masses. The evaluation of interface type with normalized ADC value can be an important clue in differential diagnosis especially in patients avoiding contrast.

Imaging of Unusual Renal Tumors

PURPOSE OF REVIEW

Renal masses are a wide entity and a common finding in clinical practice. Detection of these masses has increased in the last years, yet mortality rates have slightly decreased.

RECENT FINDINGS

According to the World Health Organization classification, there are 8 types, 51 subtypes, and a lot more subsequent subclassifications of renal tumors. Histopathological analysis should always be assessed for final diagnosis of theses tumors. However, imaging can be an important diagnostic guidance. The most common diagnoses of renal tumor are clear cell carcinoma, papillary renal cell carcinoma, angiomyolipoma, and transitional cell carcinoma. Nonetheless, a considerable variety of particular tumors can arise from the kidney, challenging the expertise of radiologists and urologists on this subject. The awareness of these unusual entities is vital for professionals working at a complex medical facility with greater volume of patients. We hereby present uncommon renal tumors and its pathological and radiological features.

Current Management of Small Renal Masses, Including Patient Selection, Renal Tumor Biopsy, Active Surveillance, and Thermal Ablation

Renal cancer represents 2% to 3% of all cancers, and its incidence is rising. The increased use of ultrasonography and cross-sectional imaging has resulted in the clinical dilemma of incidentally detected small renal masses (SRMs). SRMs represent a heterogeneous group of tumors that span the full spectrum of metastatic potential, including benign, indolent, and more aggressive tumors. Currently, no composite model or biomarker exists that accurately predicts the diagnosis of kidney cancer before treatment selection, and the use of renal mass biopsy remains controversial. The management of SRMs has changed dramatically over the last two decades as our understanding of tumor biology and competing risks of mortality in this population has improved. In this review, we critically assess published consensus guidelines and recent literature on the diagnosis and management of SRMs, with a focus on patient treatment selection and use of renal mass biopsy, active surveillance, and thermal ablation. Finally, we highlight important opportunities for leveraging recent research discoveries to identify patients with SRMs at high risk for renal cell carcinoma-related mortality and minimize overtreatment and patient morbidity.

Pre-cryoablation Embolization of Renal Tumors: Decreasing Probes and Saving Loads

The use of adjuvant pre-ablation embolization for renal tumors has been reported in endophytic, centrally located lesions to reduce the risk of injuring the renal collecting system during subsequent cryoablation. In this technical report, we present another utilization of adjuvant pre-ablation embolization, applied for the purpose of decreasing the number of cryoablation probes needed in the ablation intervention. This novel procedural protocol not only decreases the cost of the procedure, but also preserves more normal renal parenchyma, and decreases the risk of injuries related to probe positioning.

Embolization-induced Renal Tumor Shrinkage Followed by Definitive Cryoablation

Significant incidental findings reported on computed tomography (CT) scans are common. This article describes a 72-year-old man evaluated for possible bowel obstruction in whom was found a 3.1-cm x 2.6-cm centrally located enhancing mass in the left kidney highly suspicious for renal cell carcinoma. Due to substantial medical comorbidities, the patient was deemed a poor surgical candidate for either partial or complete nephrectomy. Interventional radiology was consulted for a minimally invasive ablation procedure. The large size and central location of the tumor involving the renal collecting system initially precluded definitive percutaneous cryoablation. Intra-arterial embolization was used as neoadjuvant therapy to decrease tumor burden. Fluoroscopy-guided bland embolization was performed targeting the arterial supply of the mass until stagnation of flow was achieved. A subsequent two-month post-embolization follow-up CT scan showed a 30% reduction in tumor size. Shrinkage of the mass from a central to a more peripheral location allowed for a cryoablation approach that would avoid damage to the vulnerable collecting system. Cryoablation was performed, and intraoperative CT demonstrated complete coverage of the tumor by the ice ball with no damage to the renal collecting system. A follow-up CT scan four years later showed no residual malignancy at the ablation site.

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.