Characterization of small (<4cm) solid renal masses by computed tomography and magnetic resonance imaging: Current evidence and further development

Low-grade oncocytic tumor of the kidney: imaging features of a novel tumor entity

PURPOSES

Low-grade oncocytic tumor (LOT) is a rare renal tumor that has emerged from the spectrum of eosinophilic/oncocytic renal tumors and poses a diagnostic challenge due to its similarity to chromophobe renal cell carcinoma (CHRCC) and renal oncocytoma (RO). The imaging features of this novel tumor entity have not yet been clearly described. The purpose of this study was to describe the imaging features of LOT with radiologic-pathologic correlation.

METHODS

We conducted a retrospective observational study involving two expert centers. We identified 12 pathologically proven LOT with preoperative imaging available, including at least computed tomography (CT) or magnetic resonance imaging (MRI), from the past 12 years. Three experienced radiologists performed the imaging analysis independently.

RESULTS

All tumors presented well-defined borders. Nine of the 12 LOT exhibited an early peripheral enhancement with complete or almost complete centripetal fill-in on nephrographic or delayed phases without any particular shape. Three showed a homogeneous contrast enhancement. Macroscopic fat and calcifications were not observed in any of the tumors.

CONCLUSION

Early peripheral enhancement with complete or almost complete centripetal fill-in on nephrographic or delayed phases without any particular shape suggests a LOT diagnosis. Further analyses involving larger studies are needed to fully confirm these imaging characteristics. To date, a percutaneous biopsy should be performed before considering management.

Molecular imaging for non-invasive risk stratification of renal masses

Anatomic imaging with contrast-enhanced computed tomography (CT) and magnetic resonance imaging (MRI) has long been the mainstay of renal mass characterization. However, those modalities are often unable to adequately characterize indeterminate, solid, enhancing renal masses - with some exceptions, such as the development of the clear-cell likelihood score on multi-parametric MRI. As such, molecular imaging approaches have gained traction as an alternative to anatomic imaging. Mitochondrial imaging with 99mTc-sestamibi single-photon emission computed tomography/CT is a cost-effective means of non-invasively identifying oncocytomas and other indolent renal masses. On the other end of the spectrum, carbonic anhydrase IX agents, most notably the monoclonal antibody girentuximab - which can be labeled with positron emission tomography radionuclides such as zirconium-89 - are effective at identifying renal masses that are likely to be aggressive clear cell renal cell carcinomas. Renal mass biopsy, which has a relatively high non-diagnostic rate and does not definitively characterize many oncocytic neoplasms, nonetheless may play an important role in any algorithm targeted to renal mass risk stratification. The combination of molecular imaging and biopsy in selected patients with other advanced imaging methods, such as artificial intelligence/machine learning and the abstraction of radiomics features, offers the optimal way forward for maximization of the information to be gained from risk stratification of indeterminate renal masses. With the proper application of those methods, inappropriately aggressive therapy for benign and indolent renal masses may be curtailed.

To characterize small renal cell carcinoma using diffusion relaxation correlation spectroscopic imaging and apparent diffusion coefficient based histogram analysis: a preliminary study

PURPOSE

To study the capability of diffusion-relaxation correlation spectroscopic imaging (DR-CSI) on subtype classification and grade differentiation for small renal cell carcinoma (RCC). Histogram analysis for apparent diffusion coefficient (ADC) was studied for comparison.

MATERIALS AND METHODS

A total of 61 patients with small RCC (< 4 cm) were included in the retrospective study. MRI data were reviewed, including a multi-b (0-1500 s/mm2) multi-TE (51-200 ms) diffusion weighted imaging (DWI) sequence. Region of interest (ROI) was delineated manually on DWI to include solid tumor. For each patient, a D-T2 spectrum was fitted and segmented into 5 compartments, and the volume fractions VA, VB, VC, VD, VE were obtained. ADC mapping was calculated, and histogram parameters ADC 90th, 10th, median, standard deviation, skewness and kurtosis were obtained. All MRI metrices were compared between clear cell RCC (ccRCC) and non-ccRCC group, and between high-grade and low-grade group. Receiver operator curve analysis was used to assess the corresponding diagnostic performance.

RESULTS

Significantly higher ADC 90th, ADC 10th and ADC median, and significantly lower DR-CSI VB was found for ccRCC compared to non-ccRCC. Significantly lower ADC 90th, ADC median and significantly higher VB was found for high-grade RCC compared to low-grade. For identifying ccRCC from non-ccRCC, VB showed the highest area under curve (AUC, 0.861) and specificity (0.882). For differentiating high- from low-grade, ADC 90th showed the highest AUC (0.726) and specificity (0.786), while VB also displayed a moderate AUC (0.715).

CONCLUSION

DR-CSI may offer improved accuracy in subtype identification for small RCC, while do not show better performance for small RCC grading compared to ADC histogram.

Development and validation of a clinical and ultrasound features-based nomogram for preoperative differentiation of renal urothelial carcinoma and central renal cell carcinoma

PURPOSE

This study aimed to develop and validate an ultrasound (US)-based nomogram for the preoperative differentiation of renal urothelial carcinoma (rUC) from central renal cell carcinoma (c-RCC).

METHODS

Clinical data and US images of 655 patients with 655 histologically confirmed malignant renal tumors (521 c-RCCs and 134 rUCs) were collected and divided into training (n = 455) and validation (n = 200) cohorts according to examination dates. Conventional US and contrast-enhanced US (CEUS) tumor features were analyzed to determine those that could discriminate rUC from c-RCC. Least absolute shrinkage and selection operator regression was applied to screen clinical and US features for the differentiation of rUC from c-RCC. Using multivariate logistic regression analysis, a diagnostic model of rUC was constructed and visualized as a nomogram. The diagnostic model's performance was assessed in the training and validation cohorts by calculating the area under the receiver operating characteristic curve (AUC) and calibration plot. Decision curve analysis (DCA) was used to assess the clinical usefulness of the US-based nomogram.

RESULTS

Seven features of both clinical features and ultrasound imaging were selected to build the diagnostic model. The nomogram achieved favorable discrimination in the training (AUC = 0.996, 95% CI: 0.993-0.999) and validation (AUC = 0.995, 95% CI: 0.974, 1.000) cohorts, and good calibration (Brier scores: 0.019 and 0.016, respectively). DCA demonstrated the clinical usefulness of the US-based nomogram.

CONCLUSION

A noninvasive clinical and US-based nomogram combining conventional US and CEUS features possesses good predictive value for differentiating rUC from c-RCC.

Renal venous sampling assisted the diagnosis of juxtaglomerular cell tumor: a case report and literature review

Juxtaglomerular cell tumor (JCT) is an endocrine tumor marked by elevated renin levels and high blood pressure. This case report presents the clinical findings of a 47-year-old woman with a history of recurrent hypokalemia, headaches, hypertension, and increased plasma renin activity (PRA). Dynamic enhanced magnetic resonance imaging (MRI) revealed a small nodule on the upper part of the right kidney. Selective renal venous sampling indicated a higher PRA only in the right upper pole renal vein. The patient underwent surgical removal of the right kidney mass, and the pathology results confirmed the diagnosis of JCT. This case underscores the importance of conducting selective renal venous sampling for accurate JCT diagnosis.

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.

[Renal Biopsy]

The extent of renal biopsy indication is being widened because of the increasing incidence of incidental renal masses; the increasing treatment options for renal cell carcinoma, including ablation therapy and novel targeted treatment; and the increasing incidence of kidney transplantation. However, percutaneous renal biopsy is technically difficult, particularly for beginners, because the skin-to-organ distance is relatively longer than those associated with other organs. In the present review, we will discuss the indications, technical considerations, efficacy, and complications of renal biopsy. Furthermore, we share practical tips of renal biopsy through many examples to help radiologists perform renal biopsy safely and effectively in various situations.

MRI for the detection of small malignant renal masses: a systematic review and meta-analysis

Objective

We aimed to review the available evidence on the diagnostic performance of magnetic resonance imaging in differentiating malignant from benign small renal masses.

Methods

An electronic literature search of Web of Science, MEDLINE (Ovid and PubMed), Cochrane Library, EMBASE, and Google Scholar was performed to identify relevant articles up to 31 January 2023. We included studies that reported the diagnostic accuracy of using magnetic resonance imaging to differentiate small (≤4 cm) malignant from benign renal masses. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were calculated using the bivariate model and the hierarchical summary receiver operating characteristic model. The study quality evaluation was performed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool.

Results

A total of 10 studies with 860 small renal masses (815 patients) were included in the current meta-analysis. The pooled sensitivity and specificity of the studies for the detection of malignant masses were 0.85 (95% CI 0.79-0.90) and 0.83 (95% CI 0.67-0.92), respectively.

Conclusions

MRI had a moderate diagnostic performance in differentiating small malignant renal masses from benign ones. Substantial heterogeneity was observed between studies for both sensitivity and specificity.

Deep learning techniques for imaging diagnosis of renal cell carcinoma: current and emerging trends

This study summarizes the latest achievements, challenges, and future research directions in deep learning technologies for the diagnosis of renal cell carcinoma (RCC). This is the first review of deep learning in RCC applications. This review aims to show that deep learning technologies hold great promise in the field of RCC diagnosis, and we look forward to more research results to meet us for the mutual benefit of renal cell carcinoma patients. Medical imaging plays an important role in the early detection of renal cell carcinoma (RCC), as well as in the monitoring and evaluation of RCC during treatment. The most commonly used technologies such as contrast enhanced computed tomography (CECT), ultrasound and magnetic resonance imaging (MRI) are now digitalized, allowing deep learning to be applied to them. Deep learning is one of the fastest growing fields in the direction of medical imaging, with rapidly emerging applications that have changed the traditional medical treatment paradigm. With the help of deep learning-based medical imaging tools, clinicians can diagnose and evaluate renal tumors more accurately and quickly. This paper describes the application of deep learning-based imaging techniques in RCC assessment and provides a comprehensive review.

MR Virtual Biopsy of Solid Renal Masses: An Algorithmic Approach

Between 1983 and 2002, the incidence of solid renal tumors increased from 7.1 to 10.8 cases per 100,000. This is in large part due to the increase in the volume of ultrasound and cross-sectional imaging, although a majority of solid renal tumors are still found incidentally. Ultrasound and computed tomography (CT) have been the mainstay of renal mass screening and diagnosis but recent advances in magnetic resonance (MR) technology have made this the optimal choice when diagnosing and staging renal tumors. Our purpose in writing this review is to survey the modern MR imaging approach to benign and malignant solid renal tumors, consolidate the various imaging findings into an easy-to-read reference, and provide an imaging-based, algorithmic approach to renal mass characterization for clinicians. MR is at the forefront of renal mass characterization, surpassing ultrasound and CT in its ability to describe multiple tissue parameters and predict tumor biology. Cutting-edge MR protocols and the integration of diagnostic algorithms can improve patient outcomes, allowing the imager to narrow the differential and better guide oncologic and surgical management.

Qualitative Assessment of Contrast-Enhanced Ultrasound in Differentiating Clear Cell Renal Cell Carcinoma and Oncocytoma

BACKGROUND

We aimed to assess whether clear cell renal cell carcinoma (ccRCC) can be differentiated from renal oncocytoma (RO) on a contrast-enhanced ultrasound (CEUS).

METHODS

Between January 2021 and October 2022, we retrospectively queried and analyzed our prospectively maintained dataset. Renal mass features were scrutinized with conventional ultrasound imaging (CUS) and CEUS. All lesions were confirmed by histopathologic diagnoses after nephron-sparing surgery (NSS). A multivariable analysis was performed to identify the potential predictors of ccRCC. The area under the curve (AUC) was depicted in order to assess the diagnostic accuracy of the multivariable model.

RESULTS

A total of 126 renal masses, including 103 (81.7%) ccRCC and 23 (18.3%) RO, matched our inclusion criteria. Among these two groups, we found significant differences in terms of enhancement (homogeneous vs. heterogeneous) (p < 0.001), wash-in (fast vs. synchronous/slow) (p = 0.004), wash-out (fast vs. synchronous/slow) (p = 0.001), and rim-like enhancement (p < 0.001). On the multivariate logistic regression, heterogeneous enhancement (OR: 19.37; p = <0.001) and rim-like enhancement (OR: 3.73; p = 0.049) were independent predictors of ccRCC. Finally, these two variables had an AUC of 82.5% and 75.3%, respectively.

CONCLUSIONS

Diagnostic imaging for presurgical planning is crucial in the choice of either conservative or radical management. CEUS, with its unique features, revealed its usefulness in differentiating ccRCC from RO.

Deep-learning-based ensemble method for fully automated detection of renal masses on magnetic resonance images

Purpose

Accurate detection of small renal masses (SRM) is a fundamental step for automated classification of benign and malignant or indolent and aggressive renal tumors. Magnetic resonance image (MRI) may outperform computed tomography (CT) for SRM subtype differentiation due to improved tissue characterization, but is less explored compared to CT. The objective of this study is to autonomously detect SRM on contrast-enhanced magnetic resonance images (CE-MRI).

Approach

In this paper, we described a novel, fully automated methodology for accurate detection and localization of SRM on CE-MRI. We first determine the kidney boundaries using a U-Net convolutional neural network. We then search for SRM within the localized kidney regions using a mixture-of-experts ensemble model based on the U-Net architecture. Our dataset contained CE-MRI scans of 118 patients with different solid kidney tumor subtypes including renal cell carcinomas, oncocytomas, and fat-poor renal angiomyolipoma. We evaluated the proposed model on the entire CE-MRI dataset using 5-fold cross validation.

Results

The developed algorithm reported a Dice similarity coefficient of 91.20 ± 5.41 % (mean ± standard deviation) for kidney segmentation from 118 volumes consisting of 25,025 slices. Our proposed ensemble model for SRM detection yielded a recall and precision of 86.2% and 83.3% on the entire CE-MRI dataset, respectively.

Conclusions

We described a deep-learning-based method for fully automated SRM detection using CE-MR images, which has not been studied previously. The results are clinically important as SRM localization is a pre-step for fully automated diagnosis of SRM subtypes.

MRI Characteristics of Pediatric and Young-Adult Renal Cell Carcinoma: A Single-Center Retrospective Study and Literature Review

Pediatric renal cell carcinoma (RCC) is a rare malignancy. Magnetic resonance imaging (MRI) is the preferred imaging modality for assessment of these tumors. The previous literature has suggested that cross-sectional-imaging findings differ between RCC and other pediatric renal tumors and between RCC subtypes. However, studies focusing on MRI characteristics are limited. Therefore, this study aims to identify MRI characteristics of pediatric and young-adult RCC, through a single-center case series and literature review. Six identified diagnostic MRI scans were retrospectively assessed, and an extensive literature review was conducted. The included patients had a median age of 12 years (63-193 months). Among other subtypes, 2/6 (33%) were translocation-type RCC (MiT-RCC) and 2/6 (33%) were clear-cell RCC. Median tumor volume was 393 cm³ (29-2191 cm³). Five tumors had a hypo-intense appearance on T2-weighted imaging, whereas 4/6 were iso-intense on T1-weighted imaging. Four/six tumors showed well-defined margins. The median apparent diffusion coefficient (ADC) values ranged from 0.70 to 1.20 × 10^-3 mm²/s. In thirteen identified articles focusing on MRI characteristics of MiT-RCC, the majority of the patients also showed T2-weighted hypo-intensity. T1-weighted hyper-intensity, irregular growth pattern and limited diffusion-restriction were also often described. Discrimination of RCC subtypes and differentiation from other pediatric renal tumors based on MRI remains difficult. Nevertheless, T2-weighted hypo-intensity of the tumor seems a potential distinctive characteristic.

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.

Imaging Recommendations for Diagnosis, Staging, and Management of Renal Tumors

Renal cell carcinomas accounts for 2% of all the cancers globally. Most of the renal tumors are detected incidentally. Ultrasound remains the main screening modality to evaluate the renal masses. A multi -phase contrast enhanced computer tomography is must for characterizing the renal lesions. Imaging plays an important role in staging, treatment planning and follow up of renal cancers. In this review , we discuss the imaging guidelines for the management of renal tumors.

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.

Multiparametric magnetic resonance imaging for characterizing renal tumors: A validation study of the algorithm presented by Cornelis et al

Objectives

In the last decade, the incidence of renal cell carcinoma (RCC) has been rising, with the greatest increase observed for solid tumors. Magnetic resonance imaging (MRI) protocols and algorithms have recently been available for classifying RCC subtypes and benign subtypes. The objective of this study was to prospectively validate the MRI algorithm presented by Cornelis et al. for RCC classification.

Material and Methods

Over a 7-month period, 38 patients with 44 renal tumors were prospectively included in the study and received an MRI examination in addition to the conventional investigation program. The MRI sequences were: T2-weighted, dual chemical shift MRI, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced T1-weighted in wash-in and wash-out phases. The images were evaluated according to the algorithm by two experienced, blinded radiologists, and the histopathological diagnosis served as the gold standard.

Results

Of 44 tumors in 38 patients, only 8 tumors (18.2%) received the same MRI diagnosis according to the algorithm as the histopathological diagnosis. MRI diagnosed 16 angiomyolipoma, 14 clear cell RCC (ccRCC), 12 chromophobe RCC (chRCC), and two papillary RCC (pRCC), while histopathological examination diagnosed 24 ccRCC, four pRCC, one chRCC, and one mixed tumor of both pRCC and chRCC. Malignant tumors were statistically significantly larger than the benign (3.16 ± 1.34 cm vs. 2.00 ± 1.04 cm, P = 0.006).

Conclusion

This prospective study could not reproduce Cornelis et al.'s results and does not support differentiating renal masses using multiparametric MRI without percutaneous biopsy in the future. The MRI algorithm showed few promising results to categorize renal tumors, indicating histopathology for clinical decisions and follow-up regimes of renal masses are still required.

Defining the Magnetic Resonance Features of Renal Lesions and Their Response to Everolimus in a Transgenic Mouse Model of Tuberous Sclerosis Complex

Tuberous sclerosis complex (TSC) is an inherited genetic disorder characterized by mutations in TSC1 or TSC2 class of tumor suppressers which impact several organs including the kidney. The renal manifestations are usually in the form of angiomyolipoma (AML, in 80% of the cases) and cystadenomas. mTOR inhibitors such as rapamycin and everolimus have shown efficacy in reducing the renal tumor burden. Early treatment prevents the progression of AML; however, the tumors regrow upon cessation of therapy implying a lifelong need for monitoring and management of this morbid disease. There is a critical need for development of imaging strategies to monitor response to therapy and progression of disease which will also facilitate development of newer targeted therapy. In this study we evaluated the potential of multiparametric ¹H magnetic resonance imaging (mpMRI) to monitor tumor response to therapy in a preclinical model of TSC, the transgenic mouse A/J Tsc2^+/-. We found 2-dimensional T₂-weighted sequence with 0.5 mm slice thickness to be optimal for detecting renal lesions as small as 0.016 mm³. Baseline characterization of lesions with MRI to assess physiological parameters such as cellularity and perfusion is critical for distinguishing between cystic and solid lesions. Everolimus treatment for three weeks maintained tumor growth at 36% from baseline, while control tumors displayed steady growth and were 70% larger than baseline at the end of therapy. Apparent diffusion coefficient, T₁ values and normalized T₂ intensity changes were also indictive of response to treatment. Our results indicate that standardization and implementation of improved MR imaging protocols will significantly enhance the utility of mpMRI in determining the severity and composition of renal lesions for better treatment planning.

Long Non-Coding RNAs as Novel Biomarkers in the Clinical Management of Papillary Renal Cell Carcinoma Patients: A Promise or a Pledge?

Papillary renal cell carcinoma (pRCC) represents the second most common subtype of renal cell carcinoma, following clear cell carcinoma and accounting for 10-15% of cases. For around 20 years, pRCCs have been classified according to their mere histopathologic appearance, unsupported by genetic and molecular evidence, with an unmet need for clinically relevant classification. Moreover, patients with non-clear cell renal cell carcinomas have been seldom included in large clinical trials; therefore, the therapeutic landscape is less defined than in the clear cell subtype. However, in the last decades, the evolving comprehension of pRCC molecular features has led to a growing use of target therapy and to better oncological outcomes. Nonetheless, a reliable molecular biomarker able to detect the aggressiveness of pRCC is not yet available in clinical practice. As a result, the pRCC correct prognosis remains cumbersome, and new biomarkers able to stratify patients upon risk of recurrence are strongly needed. Non-coding RNAs (ncRNAs) are functional elements which play critical roles in gene expression, at the epigenetic, transcriptional, and post-transcriptional levels. In the last decade, ncRNAs have gained importance as possible biomarkers for several types of diseases, especially in the cancer universe. In this review, we analyzed the role of long non-coding RNAs (lncRNAs) in the prognosis of pRCC, with a particular focus on their networking. In fact, in the competing endogenous RNA hypothesis, lncRNAs can bind miRNAs, resulting in the modulation of the mRNA levels targeted by the sponged miRNA, leading to additional regulation of the target gene expression and increasing complexity in the biological processes.

Comparison of cortico-medullary phase contrast-enhanced MDCT and T2-weighted MR imaging in the histological subtype differentiation of renal cell carcinoma: radiology-pathology correlation

Purpose

Renal cell carcinoma (RCC) subtype differentiation is of crucial importance in the management and prognosis of these patients. In this study, we investigated the usefulness of unenhanced and cortico-medullary phase contrast-enhanced multidetector-row computed tomography (MDCT) and T2-weighted fast spin-echo (FSE) magnetic resonance imaging (MRI) modalities in the discrimination of the 3 main subtype RCC patients in correlation with their histopathological findings.

Material and methods

A total of 80 pathologically proven RCC patients who had undergone either partial or total nephrectomy were retrospectively investigated in this study. Their histological subtypes were 54 clear cell renal cell carcinoma (ccRCC), 15 papillary renal cell carcinoma (pRCC), and 11 chromophobe renal cell carcinoma (cRCC), based on pathological evaluation. There were 62 male (77.5%) and 18 female (22.5%) patients. Among the 54 ccRCC patients, 29 patients had both non-contrast and cortico-medullary phase CT, 1 had only non-contrast CT, 5 only had cortico-medullary phase CT, and 38 had MRI examination. In the pRCC group, 10 patients had both non-contrast and cortico-medullary phase CT, 1 had only non-contrast CT, 1 had only cortico-medullary phase CT, and 12 had MRI. Finally, in the remaining 11 cRCC patients, 9 had both non-contrast and cortico-medullary phase CT, and only 5 had MRI. We calculated both tumour attenuation values as HU (Hounsfield units) on unenhanced and cortico-medullary phase MDCT images and also tumour mean signal intensity values on FSE T2-weighted MRI images by using the region of interest (ROI) including normal renal cortex measurements. Besides quantitative evaluation, we also performed qualitative visual assessment of tumours on contrast-enhanced MDCT and FSE T2-weighted MRI.

Results

There was no statistically significant difference among the attenuation values of the 3 tumour subtypes on pre-contrast CT images. ccRCC demonstrated a prominent degree of contrast enhancement compared to the chromophobe and papillary ones on cortico-medullary phase MDCT. We found no statistically significant difference between chromophobe and papillary subtypes, although chromophobe tumours showed slightly higher attenuation values compared to papillary ones. ccRCCs usually demonstrated a heterogenous contrast enhancement on cortico-medullary phase CT images, while the papillary subtype usually had a homogenous appearance on visual assessment. On FSE T2-weighted MR images, the signal intensity values of ccRCC patients were found to be significantly higher than both chromophobe and papillary subtypes. Although cRCC patients had a prominently lower T2 signal intensity than clear cell subtype, there was no statistically significant signal intensity difference between chromophobe and papillary subtypes. Regarding visual assessment, papillary subtype tumours showed a mostly homogenous appearance on T2-weighted images and a statistically significant difference was present. On the other hand, there was no significant difference of visual assessment of the clear cell and chromophobe subtypes.

Conclusions

The measurement of the attenuation values on cortico-medullary phase MDCT and the mean signal intensity values on FSE T2-weighted MRI can provide useful information in the differentiation of RCC main subtypes. Also, visual assessment of tumours on both modalities can contribute to this issue by providing additional imaging properties.

Association between nuclear grade of renal cell carcinoma and the aorta-lesion-attenuation-difference

INTRODUCTION AND BACKGROUND

Several features noted on renal mass biopsy (RMB) can influence treatment selection including tumor histology and nuclear grade. However, there is poor concordance between renal cell carcinoma (RCC) nuclear grade on RMB compared to nephrectomy specimens. Here, we evaluate the association of nuclear grade with aorta-lesion-attenuation-difference (ALAD) values determined on preoperative CT scan.

METHODS AND MATERIALS

A retrospective review of preoperative CT scans and surgical pathology was performed on patients undergoing nephrectomy for solid renal masses. ALAD was calculated by measuring the difference in Hounsfield units (HU) between the aorta and the lesion of interest on the same image slice on preoperative CT scan. The discriminative ability of ALAD to differentiate low-grade (nuclear grade 1 and 2) and high-grade (nuclear grade 3 and 4) tumors was evaluated by sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under curve (AUC) using ROC analysis. Sub-group analysis by histologic sub-type was also performed.

RESULTS

A total of 368 preoperative CT scans in patients with RCC on nephrectomy specimen were reviewed. Median patient age was 61 years (IQR 52-68). The majority of patients were male, 66% (243/368). Tumor histology was chromophobe RCC in 7.6%, papillary RCC in 15.5%, and clear cell RCC in 76.9%. The majority, 69.3% (253/365) of tumors, were stage T1a. Nuclear grade was grade 1 in 5.46% (19/348), grade 2 in 64.7% (225/348), grade 3 in 26.2% (91/348), and grade 4 in 3.2% (11/348). Nephrographic ALAD values for grade 1, 2, 3, and 4 were 73.7, 46.5, 36.4, and 43.1, respectively (p = 0.0043). Nephrographic ALAD was able to differentiate low-grade from high-grade RCC with a sensitivity of 32%, specificity of 89%, PPV of 86%, and NPV of 36%. ROC analysis demonstrated the predictive utility of nephrographic ALAD to predict high- versus low-grade RCC with an AUC of 0.60 (95% CI 0.51-0.69).

CONCLUSION

ALAD was significantly associated with nuclear grade in our nephrectomy series. Strong specificity and PPV for the nephrographic phrase demonstrate a potential role for ALAD in the pre-operative setting that may augment RMB findings in assessing nuclear grade of RCC. Although this association was statistically significant, the clinical utility is limited at this time given the results of the statistical analysis (relatively poor ROC analysis). Sub-group analysis by histologic subtype yielded very similar diagnostic performance and limitations of ALAD. Further studies are necessary to evaluate this relationship further.

Imaging considerations for thermal and radiotherapy ablation of primary and metastatic renal cell carcinoma

Ablative (percutaneous and stereotactic) thermal and radiotherapy procedures for management of both primary and metastatic renal cell carcinoma are increasing in popularity in clinical practice. Data suggest comparable efficacy with lower cost and morbidity compared to nephrectomy. Ablative therapies may be used alone or in conjunction with surgery or chemotherapy for treatment of primary tumor and metastatic disease. Imaging plays a crucial role in pre-treatment selection and planning of ablation, intra-procedural guidance, evaluation for complications, short- and long-term post-procedural surveillance of disease, and treatment response. Treatment response and disease recurrence may differ considerably after ablation, particularly for stereotactic radiotherapy, when compared to conventional surgical and chemotherapies. This article reviews the current and emerging role of imaging for ablative therapy of renal cell carcinoma.

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

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

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.

Facile Synthesis of Holmium-Based Nanoparticles as a CT and MRI Dual-Modal Imaging for Cancer Diagnosis

The rapid development of medical imaging has boosted the abilities of modern medicine. As single modality imaging limits complex cancer diagnostics, dual-modal imaging has come into the spotlight in clinical settings. The rare earth element Holmium (Ho) has intrinsic paramagnetism and great X-ray attenuation due to its high atomic number. These features endow Ho with good potential to be a nanoprobe in combined x-ray computed tomography (CT) and T₂-weighted magnetic resonance imaging (MRI). Herein, we present a facile strategy for preparing HoF₃ nanoparticles (HoF₃ NPs) with modification by PEG 4000. The functional PEG-HoF₃ NPs have good water solubility, low cytotoxicity, and biocompatibility as a dual-modal contrast agent. Currently, there is limited systematic and intensive investigation of Ho-based nanomaterials for dual-modal imaging. Our PEG-HoF₃ NPs provide a new direction to realize in vitro and vivo CT/MRI imaging, as well as validation of Ho-based nanomaterials will verify their potential for biomedical applications.

Differentiating Benign from Malignant Renal Tumors Using T2- and Diffusion-Weighted Images: A Comparison of Deep Learning and Radiomics Models Versus Assessment from Radiologists

BACKGROUND

Differentiating benign from malignant renal tumors is important for selection of the most effective treatment.

PURPOSE

To develop magnetic resonance imaging (MRI)-based deep learning (DL) models for differentiation of benign and malignant renal tumors and to compare their discrimination performance with the performance of radiomics models and assessment by radiologists.

STUDY TYPE

Retrospective.

POPULATION

A total of 217 patients were randomly assigned to a training cohort (N = 173) or a testing cohort (N = 44).

FIELD STRENGTH/SEQUENCE

Diffusion-weighted imaging (DWI) and fast spin-echo sequence T2-weighted imaging (T2WI) at 3.0T.

ASSESSMENT

A radiologist manually labeled the region of interest (ROI) on each image. Three DL models using ResNet-18 architecture and three radiomics models using random forest were developed using T2WI alone, DWI alone, and a combination of the two image sets to discriminate between benign and malignant renal tumors. The diagnostic performance of two radiologists was assessed based on professional experience. We also compared the performance of each model and the radiologists.

STATISTICAL TESTS

The area under the receiver operating characteristic (ROC) curve (AUC) was used to assess the performance of each model and the radiologists. P < 0.05 indicated statistical significance.

RESULTS

The AUC of the DL models based on T2WI, DWI, and the combination was 0.906, 0.846, and 0.925 in the testing cohorts, respectively. The AUC of the combination DL model was significantly better than that of the models based on individual sequences (0.925 > 0.906, 0.925 > 0.846). The AUC of the radiomics models based on T2WI, DWI, and the combination was 0.824, 0.742, and 0.826 in the testing cohorts, respectively. The AUC of two radiologists was 0.724 and 0.667 in the testing cohorts.

CONCLUSION

Thus, the MRI-based DL model is useful for differentiating benign from malignant renal tumors in clinic, and the DL model based on T2WI + DWI had the best performance.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2.

Radiomics to better characterize small renal masses

PURPOSE

Radiomics is a specific field of medical research that uses programmable recognition tools to extract objective information from standard images to combine with clinical data, with the aim of improving diagnostic, prognostic, and predictive accuracy beyond standard visual interpretation. We performed a narrative review of radiomic applications that may support improved characterization of small renal masses (SRM). The main focus of the review was to identify and discuss methods which may accurately differentiate benign from malignant renal masses, specifically between renal cell carcinoma (RCC) subtypes and from angiomyolipoma without visible fat (fat-poor AML) and oncocytoma. Furthermore, prediction of grade, sarcomatoid features, and gene mutations would be of importance in terms of potential clinical utility in prognostic stratification and selecting personalised patient management strategies.

METHODS

A detailed search of original articles was performed using the PubMed-MEDLINE database until 20 September 2020 to identify the English literature relevant to radiomics applications in renal tumour assessment. In total, 42 articles were included in the analysis in 3 main categories related to SRM: prediction of benign versus malignant SRM, subtypes, and nuclear grade, and other features of aggressiveness.

CONCLUSION

Overall, studies reported the superiority of radiomics over expert radiological assessment, but were mainly of retrospective design and therefore of low-quality evidence. However, it is clear that radiomics is an attractive modality that has the potential to improve the non-invasive diagnostic accuracy of SRM imaging and prediction of its natural behaviour. Further prospective validation studies of radiomics are needed to augment management algorithms of SRM.

The Global Reading Room: An Indeterminate Renal Mass

You are consulted regarding an 83-year-old healthy woman who underwent renal protocol CT for a 3.6 cm renal mass. The CT was degraded by motion artifact but showed indeterminate (17 HU) enhancement in the mass, reported as suspicious for papillary renal cell carcinoma. What do you recommend for further management?

The role of imaging in the management of renal masses

The wide availability of cross-sectional imaging is responsible for the increased detection of small, usually asymptomatic renal masses. More than 50 % of renal cell carcinomas (RCCs) represent incidental findings on noninvasive imaging. Multimodality imaging, including conventional US, contrast-enhanced US (CEUS), CT and multiparametric MRI (mpMRI) is pivotal in diagnosing and characterizing a renal mass, but also provides information regarding its prognosis, therapeutic management, and follow-up. In this review, imaging data for renal masses that urologists need for accurate treatment planning will be discussed. The role of US, CEUS, CT and mpMRI in the detection and characterization of renal masses, RCC staging and follow-up of surgically treated or untreated localized RCC will be presented. The role of percutaneous image-guided ablation in the management of RCC will be also reviewed.

Bosniak Classification of Cystic Renal Masses, Version 2019: A Pictorial Guide to Clinical Use

Cystic renal masses are commonly encountered in clinical practice. In 2019, the Bosniak classification of cystic renal masses, originally developed for CT, underwent a major revision to incorporate MRI and is referred to as the Bosniak Classification, version 2019. The proposed changes attempt to (a) define renal masses (ie, cystic tumors with less than 25% enhancing tissue) to which the classification should be applied; (b) emphasize specificity for diagnosis of cystic renal cancers, thereby decreasing the number of benign and indolent cystic masses that are unnecessarily treated or imaged further; (c) improve interobserver agreement by defining imaging features, terms, and classes of cystic renal masses; (d) reduce variation in reported malignancy rates for each of the Bosniak classes; (e) incorporate MRI and to some extent US; and (f) be applicable to all cystic renal masses encountered in clinical practice, including those that had been considered indeterminate with the original classification. The authors instruct how, using CT, MRI, and to some extent US, the revised classification can be applied, with representative clinical examples and images. Practical tips, pitfalls to avoid, and decision tree rules are included to help radiologists and other physicians apply the Bosniak Classification, version 2019 and better manage cystic renal masses. An online resource and mobile application are also available for clinical assistance. An invited commentary by Siegel and Cohan is available online. ^©RSNA, 2021.

Importance of phase enhancement for machine learning classification of solid renal masses using texture analysis features at multi-phasic CT

OBJECTIVE

To compare machine learning (ML) of texture analysis (TA) features for classification of solid renal masses on non-contrast-enhanced CT (NCCT), corticomedullary (CM) and nephrographic (NG) phase contrast-enhanced (CE) CT.

MATERIALS AND METHODS

With IRB approval, we retrospectively identified 177 consecutive solid renal masses (116 renal cell carcinoma [RCC]; 51 clear cell [cc], 40 papillary, 25 chromophobe and 61 benign tumors; 49 oncocytomas and 12 fat-poor angiomyolipomas) with renal protocol CT between 2012 and 2017. Tumors were independently segmented by two blinded radiologists. Twenty-five 2-dimensional TA features were extracted from each phase. Diagnostic accuracy for 1) RCC versus benign tumor and 2) cc-RCC versus other tumor was assessed using XGBoost.

RESULTS

ML of texture analysis features on different phases achieved mean area under the ROC curve (AUC [SD]), sensitivity/specificity for 1) RCC vs benign = 0.70(0.19), 96%/32% on CM-CECT and 0.71(0.14), 83%/58% on NG-CECT and; 2) cc-RCC vs other = 0.77(0.12), 49%/90% on CM-CECT and 0.71(0.16), 22%/94% on NG-CECT. There was no difference in AUC comparing CECT to NCCT (p = 0.058-0.54) and no improvement when combining data across all three phases compared single-phase assessment (p = 0.39-0.68) for either outcome. AUCs decreased when ML models were trained with one phase and tested on a different phase for both outcomes (RCC;p = 0.045-0.106, cc-RCC; < 0.001).

CONCLUSION

Accuracy of machine learning classification of renal masses using texture analysis features did not depend on phase; however, models trained using one phase performed worse when tested on another phase particularly when associating NCCT and CECT. These findings have implications for large registries which use varying CT protocols to study renal masses.

Ensemble U-net-based method for fully automated detection and segmentation of renal masses on computed tomography images

PURPOSE

Detection and accurate localization of renal masses (RM) are important steps toward future potential classification of benign vs malignant RM. A fully automated algorithm for detection and localization of RM may eliminate the observer variability in the clinical workflow.

METHOD

In this paper, we describe a fully automated methodology for accurate detection and segmentation of RM from contrast-enhanced computed tomography (CECT) images. We first determine the boundaries of the kidneys on the CECT images utilizing a convolutional neural network-based method to be used as a region of interest to search for RM. We then employ a homogenous U-Net-based ensemble learning model to identify and delineate RM. We used an institutional dataset comprised of CECT images in 315 patients to train and evaluate the proposed method. We compared results of our method to those of three-dimensional (3D) U-Net for RM localization and further evaluated our algorithm using the kidney tumor segmentation (KiTS19) challenge dataset.

RESULTS

The developed algorithm reported a Dice similarity coefficient (DSC) of 95.79% ± 5.16% and 96.25 ± 3.37 (mean ± standard deviation) for segmentation accuracy of kidney boundary from 125 and 60 test images from institutional and KiTS19 datasets, respectively. Using our method, RM were detected in 125 and 52 test cases, which corresponds to 100% and 86.67% sensitivity at patient level in institutional and KiTS19 test images. Our ensemble method for RM localization yielded a mean DSC of 88.65% ± 7.31% and 87.91% ± 6.82% on the institutional and KiTS19 test datasets, respectively. The mean DSC for RM delineation from CECT institutional test images using 3D U-Net was 85.95% ± 1.46%.

CONCLUSION

We describe a method for automated localization of RM using CECT images. Our results are important in terms of clinical perspective as fully automated detection of RM is a fundamental step for further diagnosis of cystic vs solid RM and eventually benign vs malignant solid RM, that has not been reported previously.

Image-guided core biopsy of 2-cm or smaller renal tumors

PURPOSE

The purpose of this study was to retrospectively evaluate diagnostic yield, risk factors for diagnostic failure, and safety of image-guided core biopsy of renal tumors≤2cm.

MATERIALS AND METHODS

Eighty-four biopsies of 84 renal tumors (mean size, 1.5±0.4[SD] cm; range, 0.6-2.0cm) from 84 patients (53 men, 31 women; mean age, 61.7±12.7 [SD] years; age range, 34-87 years) were included. All adverse events (AEs) were evaluated based on the CIRSE classification. The 84 procedures were classified as diagnostic or nondiagnostic. Multiple variables related to the patients, tumors, and procedures were assessed to identify variables associated with diagnostic failure.

RESULTS

All 84 biopsies (100%) were technically successful, defined as penetration of the target and acquisition of some specimens. Eighty (80/84; 95.2%) biopsy procedures were diagnostic and four (4/84; 4.8%) procedures were nondiagnostic. Among 80 diagnosed renal tumors, 71/80 (88.8%) tumors were malignant (49 clear cell renal cell carcinomas [RCCs], 14 papillary RCCs, 3 chromophobe RCCs, 3 metastatic renal cancers, 1 lymphoma, and 1 unclassified RCC) and 9/80 (11.2%) lesions were benign (5 angiomyolipomas, 3 oncocytomas, and 1 inflammatory lesion). No significant differences existed in any variables between the two groups. A total of 57 (57/84; 67.9%) procedures resulted in 56 Grade 1, 2 Grade 2, and 1 Grade 3 AEs.

CONCLUSION

Image-guided biopsy of renal tumors≤2cm is safe and has a high diagnostic yield.

Automated classification of solid renal masses on contrast-enhanced computed tomography images using convolutional neural network with decision fusion

OBJECTIVES

To develop a deep learning-based method for automated classification of renal cell carcinoma (RCC) from benign solid renal masses using contrast-enhanced computed tomography (CECT) images.

METHODS

This institutional review board-approved retrospective study evaluated CECT in 315 patients with 77 benign (57 oncocytomas, and 20 fat-poor angiomyolipoma) and 238 malignant (RCC: 123 clear cell, 69 papillary, and 46 chromophobe subtypes) tumors identified consecutively between 2015 and 2017. We employed a decision fusion-based model to aggregate slice level predictions determined by convolutional neural network (CNN) via a majority voting system to evaluate renal masses on CECT. The CNN-based model was trained using 7023 slices with renal masses manually extracted from CECT images of 155 patients, cropped automatically around kidneys, and augmented artificially. We also examined the fully automated approach for renal mass evaluation on CECT. Moreover, a 3D CNN was trained and tested using the same datasets and the obtained results were compared with those acquired from slice-wise algorithms.

RESULTS

For differentiation of RCC versus benign solid masses, the semi-automated majority voting-based CNN algorithm achieved accuracy, precision, and recall of 83.75%, 89.05%, and 91.73% using 160 test cases, respectively. Fully automated pipeline yielded accuracy, precision, and recall of 77.36%, 85.92%, and 87.22% on the same test cases, respectively. 3D CNN reported accuracy, precision, and recall of 79.24%, 90.32%, and 84.21% using 160 test cases, respectively.

CONCLUSIONS

A semi-automated majority voting CNN-based methodology enabled accurate classification of RCC from benign neoplasms among solid renal masses on CECT.

KEY POINTS

• Our proposed semi-automated majority voting CNN-based algorithm achieved accuracy of 83.75% for the diagnosis of RCC from benign solid renal masses on CECT images. • A fully automated CNN-based methodology classified solid renal masses with moderate accuracy of 77.36% using the same test images. • Employing 3D CNN-based methodology yielded slightly lower accuracy for renal mass classification compared with the semi- automated 2D CNN-based algorithm (79.24%).

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.

Imaging protocols for renal multiparametric MRI and MR urography: results of a consensus conference from the French Society of Genitourinary Imaging

OBJECTIVES

To develop technical guidelines for magnetic resonance imaging aimed at characterising renal masses (multiparametric magnetic resonance imaging, mpMRI) and at imaging the bladder and upper urinary tract (magnetic resonance urography, MRU).

METHODS

The French Society of Genitourinary Imaging organised a Delphi consensus conference with a two-round Delphi survey followed by a face-to-face meeting. Two separate questionnaires were issued for renal mpMRI and for MRU. Consensus was strictly defined using a priori criteria.

RESULTS

Forty-two expert uroradiologists completed both survey rounds with no attrition between the rounds. Fifty-six of 84 (67%) statements of the mpMRI questionnaire and 44/71 (62%) statements of the MRU questionnaire reached final consensus. For mpMRI, there was consensus that no injection of furosemide was needed and that the imaging protocol should include T2-weighted imaging, dual chemical shift imaging, diffusion-weighted imaging (use of multiple b-values; maximal b-value, 1000 s/mm²) and fat-saturated single-bolus multiphase (unenhanced, corticomedullary, nephrographic) contrast-enhanced imaging; late imaging (more than 10 min after injection) was judged optional. For MRU, the patients should void their bladder before the examination. The protocol must include T2-weighted imaging, anatomical fast T1/T2-weighted imaging, diffusion-weighted imaging (use of multiple b-values; maximal b-value, 1000 s/mm²) and fat-saturated single-bolus multiphase (unenhanced, corticomedullary, nephrographic, excretory) contrast-enhanced imaging. An intravenous injection of furosemide is mandatory before the injection of contrast medium. Heavily T2-weighted cholangiopancreatography-like imaging was judged optional.

CONCLUSION

This expert-based consensus conference provides recommendations to standardise magnetic resonance imaging of kidneys, ureter and bladder.

KEY POINTS

• Multiparametric magnetic resonance imaging (mpMRI) aims at characterising renal masses; magnetic resonance urography (MRU) aims at imaging the urinary bladder and the collecting systems. • For mpMRI, no injection of furosemide is needed. • For MRU, an intravenous injection of furosemide is mandatory before the injection of contrast medium; heavily T2-weighted cholangiopancreatography-like imaging is optional.

Current management of small renal masses

One of the consequences of the growing use of diagnostic imaging techniques is the notable growth in the detection of small renal masses presumably corresponding to localized tumors that are potentially curable with surgical treatment. When faced with the finding of a small renal mass, radiologists must determine whether it is benign or malignant, and if it is malignant, what subtype it belong to, and whether it should be managed with surgical treatment, with ablative techniques, or with watchful waiting with active surveillance. Small renal masses are now a clinical entity that require management different from the approaches used for classical renal cell carcinomas. In this scenario, radiologists are key because they are involved in all aspects of the management of these tumors, including in their diagnosis, treatment, and follow-up.

Update on Gadolinium-Based Contrast Agent-Enhanced Imaging in the Genitourinary System

OBJECTIVE. The purpose of this article is to review gadolinium-based contrast agent (GBCA)-enhanced MRI applications in the genitourinary system. CONCLUSION. Nephrogenic systemic fibrosis is rare or nonexistent with standard dosing of group II GBCAs. Gadolinium retention, cost, and examination times are emerging considerations affecting GBCA use. GBCA is unnecessary to diagnose adrenal adenomas, simple cysts, and some Bosniak category II cysts; however, it is required to determine solid or septal renal mass enhancement. Biparametric prostate MRI requires high-quality and reproducible DWI; therefore, dynamic contrast-enhanced MRI remains valuable in selected prostate MRI examinations.

Needle track seeding in renal mass biopsies

A review and analysis of the literature demonstrates that needle track seeding in renal mass biopsy has been reported 16 times. This complication occurs almost exclusively among patients with papillary renal cell carcinoma. The incidence is associated with multiple punctures of the mass, the use of core needles of ≥20 gauge, and lack of a coaxial sheath. Needle tract seeding may be associated with tumor upstaging and a worse prognosis. Fine-needle aspiration has a significantly lower rate of needle track seeding compared with large core needle biopsy (>20-gauge needle). A more formalized risk-based system for interpreting renal mass fine-needle aspiration may be useful as clinicians choose among an increasing number of therapeutic options.

Diagnostic Yield and Complication Rate in Percutaneous Needle Biopsy of Renal Hilar Masses With Comparison With Renal Cortical Mass Biopsies in a Cohort of 195 Patients

OBJECTIVE

The objective of this study was to compare diagnostic yield and complication rate in needle biopsy (NB) of renal hilar and cortical masses.

MATERIALS AND METHODS

With institutional review board approval, we retrospectively studied 195 patients (120 men, 75 women; mean age ± SD, 67 ± 13 years old) who underwent ultrasound-guided renal mass NB between January 2013 and December 2017. Operator years of experience, biopsy technique (coaxial or successive), needle gauge (22-gauge fine-needle aspiration, 18-gauge core-needle, or both), number of passes, postprocedural complication, and histopathologic diagnoses were recorded. A radiologist who was blinded to histopathologic diagnoses recorded mass location (upper pole, interpolar region, lower pole) and percentage of hilar involvement. Comparisons were performed using independent t and chi-square tests.

RESULTS

Of the masses biopsied, 5.6% (11/195) were 100% hilar (mean hilar involvement, 20.8% ± 29.8%; range, 0-100%). Mean lesion size was 44 ± 27 mm (range, 12-157 mm). NB diagnosis was established in 84.6% (165/195) of masses, and 15.4% (30/195) of biopsies were inconclusive, with no association with size (p = 0.55) or percentage of hilar involvement (p = 0.756). In the purely hilar masses, diagnosis was established in 72.7% (8/11) compared with 85.3% (157/184) with any cortical involvement (p = 0.265). There was no association between diagnosis and operator years of experience, biopsy technique, needle gauge, or number of passes (p > 0.05). Bleeding occurred after biopsy in 7.7% (15/195) of cases, was associated with percentage of hilar involvement (39.3% ± 44.9% vs 19.3% ± 27.8%; p = 0.012), and was more common in purely hilar masses (36.4% [4/11] vs 5.6% [11/195]; p < 0.001). Complications were not associated with any other feature (p > 0.05).

CONCLUSION

Percutaneous biopsy of renal hilar masses is technically feasible with diagnostic yield similar to that of cortical masses but with postprocedural bleeding more often than what is seen with cortical masses.