MRI features of renal oncocytoma and chromophobe renal cell carcinoma. AJR Am J Roentgenol. 2010;195:W421-W427. [PMID: 21098174 DOI: 10.2214/ajr.10.4718]

Urologic Imaging of the Kidneys: Cancers and Mimics

Renal cell carcinoma (RCC) is a growing problem in global oncology, with a steadily increasing incidence, especially in developed regions. Its different histologic subtypes present different challenges in diagnosis and management. Advanced imaging techniques have a crucial role in distinguishing between these subtypes by highlighting unique radiographic features such as exophytic growth patterns, cystic components, and enhancement patterns. Practical suggestions discussed in this review include using chemical shift MRI to differentiate fat-poor angiomyolipomas from RCC, recognizing specific imaging markers such as pseudo-capsule in papillary RCC, and understanding the implications of negative pixel count in computed tomography scans.

MRI-based radiomics machine learning model to differentiate non-clear cell renal cell carcinoma from benign renal tumors

Purpose

We aim to develop an MRI-based radiomics model to improve the accuracy of differentiating non-ccRCC from benign renal tumors preoperatively.

Methods

The retrospective study included 195 patients with pathologically confirmed renal tumors (134 non-ccRCCs and 61 benign renal tumors) who underwent preoperative renal mass protocol MRI examinations. The patients were divided into a training set (n = 136) and test set (n = 59). Simple t-test and the Least Absolute Shrink and Selection Operator (LASSO) were used to select the most valuable features and the rad-scores of them were calculated. The clinicoradiologic models, single-sequence radiomics models, multi-sequence radiomics models and combined models for differentiation were constructed with 2 classifiers (support vector machine (SVM), logistic regression (LR)) in the training set and used for differentiation in the test set. Ten-fold cross validation was applied to obtain the optimal hyperparameters of the models. The performances of the models were evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). Delong's test was performed to compare the performances of models.

Results

After univariate and multivariate logistic regression analysis, the independent risk factors to differentiate non-ccRCC from benign renal tumors were selected as follows: age, tumor region, hemorrhage, pseudocapsule and enhancement degree. Among the 14 machine learning classification models constructed, the combined model with LR has the highest efficiency in differentiating non-ccRCC from benign renal tumors. The AUC in the training set is 0.964, and the accuracy is 0.919. The AUC in the test set is 0.936, and the accuracy is 0.864.

Conclusion

The MRI-based radiomics machine learning is feasible to differentiate non-ccRCC from benign renal tumors, which could improve the accuracy of clinical diagnosis.

Tumor morphology evaluation using 3D-morphometric features of renal masses

OBJECTIVE

To assess the correlation between the general (gender, age, and maximum tumor size) and 3D morphotopometric features of the renal tumor node, following the MSCT data post-processing, and the tumor histological structure; to propose an equation allowing for kidney malignancy assessment based on general and morphometric features.

MATERIALS AND METHODS

In total, 304 patients with unilateral solitary renal neoplasms underwent laparoscopic (retroperitoneoscopic) or robotic partial or radical nephrectomy. Before the procedure, kidney contrast-enhanced MSCT followed by the tumor 3D-modeling was performed. 3D model of the kidney tumor, and its morphotopometric features, and histological structure were analyzed. The morphotopometric ones include the side of the lesion, location by segments, the surface where the tumor, the depth of the tumor invasion into the kidney, and the shape of tumor.

RESULTS

Out of 304 patients, 254 (83.6%) had malignant kidney tumors and 50 (16.4%) benign kidney tumors. In total, 231 patients, out of 254 (90.9%) were assessed for the degree of malignant tumor differentiation. Malignant tumors were more frequent in men than in women (p < 0.001). Mushroom-shaped tumors were the most common shapes among benign renal masses (35.2%). The most common malignant kidney tumors had spherical with a partially uneven surface (27.6%), multinodular (tuberous (27.2%)), and spherical with a conical base (24.8%) shapes. Logistic regression model enabled the development of prognostic equation for tumor malignancy prediction ("low" or "high"). The univariate analysis revealed the correlation only between high differentiation (G1) and a spherical tumor with a conical base (p = 0.029).

CONCLUSION

The resulting logistic model, based on the analysis of such predictors as gender and form of kidney lesions, demonstrated a large share (87.6%) of correct predictions of the kidney tumor malignancy.

Advances in Molecular Mechanisms of Kidney Disease: Integrating Renal Tumorigenesis of Hereditary Cancer Syndrome

Renal cell carcinoma (RCC) comprises various histologically distinct subtypes, each characterized by specific genetic alterations, necessitating individualized management and treatment strategies for each subtype. An exhaustive search of the PubMed database was conducted without any filters or restrictions. Inclusion criteria encompassed original English articles focusing on molecular mechanisms of kidney cancer. On the other hand, all non-original articles and articles published in any language other than English were excluded. Hereditary kidney cancer represents 5-8% of all kidney cancer cases and is associated with syndromes such as von Hippel-Lindau syndrome, Birt-Hogg-Dubè syndrome, succinate dehydrogenase-deficient renal cell cancer syndrome, tuberous sclerosis complex, hereditary papillary renal cell carcinoma, fumarate hydratase deficiency syndrome, BAP1 tumor predisposition syndrome, and other uncommon hereditary cancer syndromes. These conditions are characterized by distinct genetic mutations and related extra-renal symptoms. The majority of renal cell carcinoma predispositions stem from loss-of-function mutations in tumor suppressor genes. These mutations promote malignant advancement through the somatic inactivation of the remaining allele. This review aims to elucidate the main molecular mechanisms underlying the pathophysiology of major syndromes associated with renal cell carcinoma. By providing a comprehensive overview, it aims to facilitate early diagnosis and to highlight the principal therapeutic options available.

Comparison of conventional diffusion-weighted imaging and intravoxel incoherent motion in differentiating between chromophobe renal cell carcinoma and renal oncocytoma: a preliminary study

OBJECTIVE

Quantitative comparison of the diagnostic efficacy of conventional diffusion-weighted imaging (DWI) and intravoxel incoherent motion (IVIM) in differentiating between chromophobe renal cell carcinoma (ChRCC) from renal oncocytoma (RO).

METHODS

A total of 48 patients with renal tumours who had undergone DWI and IVIM were divided into two groups-ChRCC (n = 28) and RO (n = 20) groups, and the apparent diffusion coefficient (ADC), true diffusivity (D), pseudo-diffusion coefficient (D*), perfusion fraction (f) and their diagnostic efficacy were compared between the two groups.

RESULTS

The D* values were higher in the ChRCCs group compared to the RO groups (0.019 ± 0.003 mm2/s vs 0.008 ± 0.002 mm2/s, P < .05). Moreover, the ADC, D and f values were higher in ROs compared to ChRCCs (0.61 ± 0.08 × 10-3 mm2/s vs 0.51 ± 0.06 × 10-3 mm2/s, 1.02 ± 0.15 × 10-3 mm2/s vs 0.86 ± 0.07 × 10-3 mm2/s, 0.41 ± 0.05 vs 0.28 ± 0.02, P < .05). The areas of the ADC, D, D* and f values under the ROC curves in differentiating ChRCCs from ROs were 0.713, 0.839, 0.856 and 0.906, respectively. The cut-off values of ADC, D, D* and f were 0.54, 0.91, 0.013 and 0.31, respectively. The AUC, sensitivity, specificity and accuracy of the f values were 0.906, 89.3%, 80.0% and 89.6%, respectively. For pairwise comparisons of ROC curves and diagnostic efficacy, IVIM parameters, that is, D, D* and f offered better diagnostic accuracy than ADC in differentiating ChRCCs from ROs (P = .013, .016, and .008) with f having the highest diagnostic accuracy.

CONCLUSION

IVIM parameters presented better performance than ADC in differentiating ChRCCs from ROs.

ADVANCES IN KNOWLEDGE

(1) D* values of ChRCCs were higher, while ADC, D and f values were lower than those of RO tumours. (2) f values had the highest diagnostic efficacy in differentiating ChRCC from RO. (3) IVIM parameters, that is, D, D* and f offered better diagnostic accuracy than ADC in differentiating ChRCC from RO (P=.013, .016, and .008).

Update on Renal Cell Carcinoma Diagnosis with Novel Imaging Approaches

This review highlights recent advances in renal cell carcinoma (RCC) imaging. It begins with dual-energy computed tomography (DECT), which has demonstrated a high diagnostic accuracy in the evaluation of renal masses. Several studies have suggested the potential benefits of iodine quantification, particularly for distinguishing low-attenuation, true enhancing solid masses from hyperdense cysts. By determining whether or not a renal mass is present, DECT could avoid the need for additional imaging studies, thereby reducing healthcare costs. DECT can also provide virtual unenhanced images, helping to reduce radiation exposure. The review then provides an update focusing on the advantages of multiparametric magnetic resonance (MR) imaging performance in the histological subtyping of RCC and in the differentiation of benign from malignant renal masses. A proposed standardized stepwise reading of images helps to identify clear cell RCC and papillary RCC with a high accuracy. Contrast-enhanced ultrasound may represent a promising diagnostic tool for the characterization of solid and cystic renal masses. Several combined pharmaceutical imaging strategies using both sestamibi and PSMA offer new opportunities in the diagnosis and staging of RCC, but their role in risk stratification needs to be evaluated. Although radiomics and tumor texture analysis are hampered by poor reproducibility and need standardization, they show promise in identifying new biomarkers for predicting tumor histology, clinical outcomes, overall survival, and the response to therapy. They have a wide range of potential applications but are still in the research phase. Artificial intelligence (AI) has shown encouraging results in tumor classification, grade, and prognosis. It is expected to play an important role in assessing the treatment response and advancing personalized medicine. The review then focuses on recently updated algorithms and guidelines. The Bosniak classification version 2019 incorporates MRI, precisely defines previously vague imaging terms, and allows a greater proportion of masses to be placed in lower-risk classes. Recent studies have reported an improved specificity of the higher-risk categories and better inter-reader agreement. The clear cell likelihood score, which adds standardization to the characterization of solid renal masses on MRI, has been validated in recent studies with high interobserver agreement. Finally, the review discusses the key imaging implications of the 2017 AUA guidelines for renal masses and localized renal cancer.

Diagnostic Value of Clear Cell Likelihood Score v1.0 and v2.0 for Common Subtypes of Small Renal Masses: A Multicenter Comparative Study

BACKGROUND

Clear cell likelihood score (ccLS) is reliable for diagnosing small renal masses (SRMs). However, the diagnostic value of Clear cell likelihood score version 1.0 (ccLS v1.0) and v2.0 for common subtypes of SRMs might be a potential score extension.

PURPOSE

To compare the diagnostic performance and interobserver agreement of ccLS v1.0 and v2.0 for characterizing five common subtypes of SRMs.

STUDY TYPE

Retrospective.

POPULATION

797 patients (563 males, 234 females; mean age, 53 ± 12 years) with 867 histologically proven renal masses.

FIELD STRENGTH/SEQUENCES

3.0 and 1.5 T/T2 weighted imaging, T1 weighted imaging, diffusion-weighted imaging, a dual-echo chemical shift (in- and opposed-phase) T1 weighted imaging, multiphase dynamic contrast-enhanced imaging.

ASSESSMENT

Six abdominal radiologists were trained in the ccLS algorithm and independently scored each SRM using ccLS v1.0 and v2.0, respectively. All SRMs had definite pathological results. The pooled area under curve (AUC), accuracy, sensitivity, and specificity were calculated to evaluate the diagnostic performance of ccLS v1.0 and v2.0 for characterizing common subtypes of SRMs. The average κ values were calculated to evaluate the interobserver agreement of the two scoring versions.

STATISTICAL TESTS

Random-effects logistic regression; Receiver operating characteristic analysis; DeLong test; Weighted Kappa test; Z test. The statistical significance level was P < 0.05.

RESULTS

The pooled AUCs of clear cell likelihood score version 2.0 (ccLS v2.0) were statistically superior to those of ccLS v1.0 for diagnosing clear cell renal cell carcinoma (ccRCC) (0.907 vs. 0.851), papillary renal cell carcinoma (pRCC) (0.926 vs. 0.888), renal oncocytoma (RO) (0.745 vs. 0.679), and angiomyolipoma without visible fat (AMLwvf) (0.826 vs. 0.766). Interobserver agreement for SRMs between ccLS v1.0 and v2.0 is comparable and was not statistically significant (P = 0.993).

CONCLUSION

The diagnostic performance of ccLS v2.0 surpasses that of ccLS v1.0 for characterizing ccRCC, pRCC, RO, and AMLwvf. Especially, the standardized algorithm has optimal performance for ccRCC and pRCC. ccLS has potential as a supportive clinical tool.

EVIDENCE LEVEL

4.

TECHNICAL EFFICACY

Stage 2.

Improving CNNs classification with pathologist-based expertise: the renal cell carcinoma case study

The prognosis of renal cell carcinoma (RCC) malignant neoplasms deeply relies on an accurate determination of the histological subtype, which currently involves the light microscopy visual analysis of histological slides, considering notably tumor architecture and cytology. RCC subtyping is therefore a time-consuming and tedious process, sometimes requiring expert review, with great impact on diagnosis, prognosis and treatment of RCC neoplasms. In this study, we investigate the automatic RCC subtyping classification of 91 patients, diagnosed with clear cell RCC, papillary RCC, chromophobe RCC, or renal oncocytoma, through deep learning based methodologies. We show how the classification performance of several state-of-the-art Convolutional Neural Networks (CNNs) are perfectible among the different RCC subtypes. Thus, we introduce a new classification model leveraging a combination of supervised deep learning models (specifically CNNs) and pathologist's expertise, giving birth to a hybrid approach that we termed ExpertDeepTree (ExpertDT). Our findings prove ExpertDT's superior capability in the RCC subtyping task, with respect to traditional CNNs, and suggest that introducing some expert-based knowledge into deep learning models may be a valuable solution for complex classification cases.

A pilot radiometabolomics integration study for the characterization of renal oncocytic neoplasia

Differentiating benign renal oncocytic tumors and malignant renal cell carcinoma (RCC) on imaging and histopathology is a critical problem that presents an everyday clinical challenge. This manuscript aims to demonstrate a novel methodology integrating metabolomics with radiomics features (RF) to differentiate between benign oncocytic neoplasia and malignant renal tumors. For this purpose, thirty-three renal tumors (14 renal oncocytic tumors and 19 RCC) were prospectively collected and histopathologically characterised. Matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) was used to extract metabolomics data, while RF were extracted from CT scans of the same tumors. Statistical integration was used to generate multilevel network communities of -omics features. Metabolites and RF critical for the differentiation between the two groups (delta centrality > 0.1) were used for pathway enrichment analysis and machine learning classifier (XGboost) development. Receiver operating characteristics (ROC) curves and areas under the curve (AUC) were used to assess classifier performance. Radiometabolomics analysis demonstrated differential network node configuration between benign and malignant renal tumors. Fourteen nodes (6 RF and 8 metabolites) were crucial in distinguishing between the two groups. The combined radiometabolomics model achieved an AUC of 86.4%, whereas metabolomics-only and radiomics-only classifiers achieved AUC of 72.7% and 68.2%, respectively. Analysis of significant metabolite nodes identified three distinct tumour clusters (malignant, benign, and mixed) and differentially enriched metabolic pathways. In conclusion, radiometabolomics integration has been presented as an approach to evaluate disease entities. In our case study, the method identified RF and metabolites important in differentiating between benign oncocytic neoplasia and malignant renal tumors, highlighting pathways differentially expressed between the two groups. Key metabolites and RF identified by radiometabolomics can be used to improve the identification and differentiation between renal neoplasms.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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

ADVANCES IN KNOWLEDGE

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

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.

Evaluation of testicles by sonoelastography in men recovering after Covid-19 disease

INTRODUCTION

Testicular cells, seminiferous tubule cells, spermatogonia, Leydig and Sertoli cells showing angiotensin-converting enzyme 2 expression have the potential to be targets and to be damaged by the coronavirus. We aimed to use Two-Dimensional Shear Wave Elastography (2D-SWE) as an effective technique to identify parenchymal damage in the testicles of patients recovering from COVID-19 infection.

METHODS

35 Male patients (group 1) who recovered after COVID-19 infection between 4 and 12 weeks were included in this prospective study. Before 2D-SWE, these male patients were confirmed with control Rt-PCR test negativity. In addition, the first Rt-PCR test positivity of these patients was confirmed. A control group was formed of 31 healthy subjects (group 2). These two groups were compared in terms of age, volume of each testis, and SWE values. Ultrasound including SWE was applied to all the testes. A total of 9 measurements were taken as 3 SWE measurements from each third of the testis (superior, mid, inferior) and the average of these was calculated. Data obtained in the study were analyzed statistically. A value of p < 0.05 was accepted as statistically significant.

RESULTS

The mean SWE values for the right testis and the left testis were determined to be statistically significantly higher in Group 1 than in Group 2, respectively (p < 0.001, p < 0.001).

CONCLUSION

There is an increase in testicular stiffness in males who have recovered from COVID-19 infection. The underlying cause of testicular damage is changes at the cellular level. The 2D-SWE technique can predict potential testicular parenchymal damage in male patients recovering from COVID-19 infection.

IMPLICATIONS FOR PRACTICE

Two-Dimensional Shear Wave Elastography (2D-SWE) seems to be a promising imaging technique in the evaluation of testis parenchyma.

Transarterial ethiodised oil marking before CT-guided renal cryoablation: evaluation of tumour visibility in various renal cell carcinoma subtypes

AIM

To evaluate ethiodised oil retention of transarterial embolisation using ethiodised oil (ethiodised oil marking) before computed tomography (CT)-guided percutaneous cryoablation (PCA) according to renal cell carcinoma (RCC) subtype.

MATERIALS AND METHODS

Ethiodised oil marking was performed 1-3 days before PCA in 99 patients with 99 RCCs from 2016 to 2020. Ethiodised oil retention on CT images was evaluated retrospectively and CT attenuation values in the tumour were measured. Regions of interest (ROI) were placed on the tumours to calculate: average (ROI-average), maximal (ROI-max), minimum (ROI-min), and standard deviation (ROI-SD). Qualitative scores comprising a five-point scale (5, excellent; 1, poor) were evaluated for the retention scores (RS) of ethiodised oil in the tumour (ethiodised oil-RS) and the visualisation scores (VS) of the boundary between the tumour and renal parenchyma (boundary-VS).

RESULTS

The histological subtypes comprised clear cell (ccRCC; n=85), papillary (pRCC; n=6), and chromophobe/oncocytoma renal cell carcinoma (chrRCC; n=8). The mean ROI-average, ROI-max, and ROI-SD were significantly higher in ccRCCs than in chrRCCs and pRCCs (p<0.05). The mean ethiodised oil-RS was significantly lower in pRCCs than in ccRCCs (p=0.039), and the mean boundary-VS was >4 in all subtypes. Even with poor intratumour ethiodised oil retention (n=6), sufficient boundary-VS was obtained due to "inverted marking." All PCA procedures were completed without additional intravenous contrast material injection at the time of PCA.

CONCLUSION

Regardless of the tumour subtypes, ethiodised oil marking aids in visualising the boundary between the tumour and parenchyma on non-contrast CT in PCA.

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.

"To Be or Not to Be Benign" at Partial Nephrectomy for Presumed RCC Renal Masses: Single-Center Experience with 195 Consecutive Patients

In daily medical practice, an increasing number of kidney masses are being incidentally detected using common imaging techniques, owing to the improved diagnostic accuracy and increasingly frequent use of these techniques. As a consequence, the rate of detection of smaller lesions is increasing considerably. According to certain studies, following surgical treatment, up to 27% of small enhancing renal masses are identified as benign tumors at the final pathological examination. This high rate of benign tumors challenges the appropriateness of surgery for all suspicious lesions, given the morbidity associated with such an intervention. The objective of the present study was, therefore, to determine the incidence of benign tumors at partial nephrectomy (PN) for a solitary renal mass. To meet this end, a total of 195 patients who each underwent one PN for a solitary renal lesion with the intent to cure RCC were included in the final retrospective analysis. A benign neoplasm was identified in 30 of these patients. The age of the patients ranged from 29.9-79 years (average: 60.9 years). The tumor size range was 1.5-7 cm (average: 3 cm). All the operations were successful using the laparoscopic approach. The pathological results were renal oncocytoma in 26 cases, angiomyolipomas in two cases, and cysts in the remaining two cases. In conclusion, we have shown in our present series the incidence rate of benign tumors in patients who have been subjected to laparoscopic PN due to a suspected solitary renal mass. Based on these results, we advise that the patient should be counseled not only about the intra- and post-operative risks of nephron-sparing surgery but also about its dual therapeutic and diagnostic role. Therefore, the patients should be informed of the considerably high probability of a benign histological result.

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.

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.

The Role of CT Imaging in Characterization of Small Renal Masses

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

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

BACKGROUND

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

CASE SUMMARY

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

CONCLUSION

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

Renal tumor biopsy in patients with cT1b-T4-M0 disease susceptible to radical nephrectomy: analysis of safety, accuracy and clinical impact on definitive management

PURPOSE

Renal tumor biopsy was provided in patients candidate to radical nephrectomy for a renal mass ≥4 cm, to evaluate treatment deviation.

METHODS

Between 2008 and 2017, 102 patients with a solid renal mass ≥4 cm with no distant metastases underwent preliminary renal tumor biopsy. We investigated the proportion of patients who proceeded with radical nephrectomy, variables predicting non-renal cell carcinoma (RCC) and concordance between biopsy findings and definitive pathology.

RESULTS

Median tumor size was 70 mm (IQR 55-110). Clinical stage was cT1b in 41, cT2 in 33, cT3 in 25 and cT4 in three patients. A median of three (IQR 2-3) renal tumor biopsies were taken with 16/18 Gauge needles in 97% of cases. Clavien grade I complications occurred in five cases. Malignant tumors were documented in 84 patients: 78 RCCs and six non-RCCs. Fifteen biopsies documented oncocytoma and three were non-diagnostic. Grade was reported in 50 RCCs: 42 (84%) were low and eight (16%) high grade. Eighty-three patients proceeded with radical nephrectomy; six non-RCC malignant tumors underwent combined and/or intensified treatment; 13 of 15 patients with oncocytoma did not undergo radical nephrectomy (eight underwent observation). Definitive pathology confirmed diagnosis in all cases. Grade concordance was 84%, considering two tiers (high vs low grade). No preoperative clinical variable predicted definitive pathology.

CONCLUSIONS

Renal tumor biopsy is a safe procedure that leads to radical nephrectomy in most tumors ≥4 cm. Nonetheless, 20% of patients exhibited non-RCC histology. Renal tumor biopsy should be considered in this setting.

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.

Multicenter Evaluation of Multiparametric MRI Clear Cell Likelihood Scores in Solid Indeterminate Small Renal Masses

Background Solid small renal masses (SRMs) (≤4 cm) represent benign and malignant tumors. Among SRMs, clear cell renal cell carcinoma (ccRCC) is frequently aggressive. When compared with invasive percutaneous biopsies, the objective of the proposed clear cell likelihood score (ccLS) is to classify ccRCC noninvasively by using multiparametric MRI, but it lacks external validation. Purpose To evaluate the performance of and interobserver agreement for ccLS to diagnose ccRCC among solid SRMs. Materials and Methods This retrospective multicenter cross-sectional study included patients with consecutive solid (≥25% approximate volume enhancement) SRMs undergoing multiparametric MRI between December 2012 and December 2019 at five academic medical centers with histologic confirmation of diagnosis. Masses with macroscopic fat were excluded. After a 1.5-hour training session, two abdominal radiologists per center independently rendered a ccLS for 50 masses. The diagnostic performance for ccRCC was calculated using random-effects logistic regression modeling. The distribution of ccRCC by ccLS was tabulated. Interobserver agreement for ccLS was evaluated with the Fleiss κ statistic. Results A total of 241 patients (mean age, 60 years ± 13 [SD]; 174 men) with 250 solid SRMs were evaluated. The mean size was 25 mm ± 8 (range, 10-39 mm). Of the 250 SRMs, 119 (48%) were ccRCC. The sensitivity, specificity, and positive predictive value for the diagnosis of ccRCC when ccLS was 4 or higher were 75% (95% CI: 68, 81), 78% (72, 84), and 76% (69, 81), respectively. The negative predictive value of a ccLS of 2 or lower was 88% (95% CI: 81, 93). The percentages of ccRCC according to the ccLS were 6% (range, 0%-18%), 38% (range, 0%-100%), 32% (range, 60%-83%), 72% (range, 40%-88%), and 81% (range, 73%-100%) for ccLSs of 1-5, respectively. The mean interobserver agreement was moderate (κ = 0.58; 95% CI: 0.42, 0.75). Conclusion The clear cell likelihood score applied to multiparametric MRI had moderate interobserver agreement and differentiated clear cell renal cell carcinoma from other solid renal masses, with a negative predictive value of 88%. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Mileto and Potretzke in this issue.

Novel Imaging Methods for Renal Mass Characterization: A Collaborative Review

CONTEXT

The incidental detection of localized renal masses has been rising steadily, but a significant proportion of these tumors are benign or indolent and, in most cases, do not require treatment. At the present time, a majority of patients with an incidentally detected renal tumor undergo treatment for the presumption of cancer, leading to a significant number of unnecessary surgical interventions that can result in complications including loss of renal function. Thus, there exists a clinical need for improved tools to aid in the pretreatment characterization of renal tumors to inform patient management.

OBJECTIVE

To systematically review the evidence on noninvasive, imaging-based tools for solid renal mass characterization.

EVIDENCE ACQUISITION

The MEDLINE database was systematically searched for relevant studies on novel imaging techniques and interpretative tools for the characterization of solid renal masses, published in the past 10 yr.

EVIDENCE SYNTHESIS

Over the past decade, several novel imaging tools have offered promise for the improved characterization of indeterminate renal masses. Technologies of particular note include multiparametric magnetic resonance imaging of the kidney, molecular imaging with targeted radiopharmaceutical agents, and use of radiomics as well as artificial intelligence to enhance the interpretation of imaging studies. Among these, 99mTc-sestamibi single photon emission computed tomography/computed tomography (CT) for the identification of benign renal oncocytomas and hybrid oncocytic chromophobe tumors, and positron emission tomography/CT imaging with radiolabeled girentuximab for the identification of clear cell renal cell carcinoma, are likely to be closest to implementation in clinical practice.

CONCLUSIONS

A number of novel imaging tools stand poised to aid in the noninvasive characterization of indeterminate renal masses. In the future, these tools may aid in patient management by providing a comprehensive virtual biopsy, complete with information on tumor histology, underlying molecular abnormalities, and ultimately disease prognosis.

PATIENT SUMMARY

Not all renal tumors require treatment, as a significant proportion are either benign or have limited metastatic potential. Several innovative imaging tools have shown promise for their ability to improve the characterization of renal tumors and provide guidance in terms of patient management.

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

OBJECTIVE

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

Imaging findings of spontaneous intraabdominal hemorrhage: neoplastic and non-neoplastic causes

Contrary to traumatic and iatrogenic intraabdominal hemorrhages, spontaneous intraabdominal hemorrhage is a challenging clinical situation. A variety of neoplastic and non-neoplastic conditions may cause spontaneous intraabdominal bleeding. Imaging findings vary depending on the source of bleeding and the underlying cause. In this article, we aim to increase the awareness of imagers to the most common causes of spontaneous intraabdominal hemorrhage by using representative cases.

Apparent Diffusion Coefficient Map-Based Texture Analysis for the Differentiation of Chromophobe Renal Cell Carcinoma from Renal Oncocytoma

Preoperative imaging differentiation between ChRCC and RO is difficult with conventional subjective evaluation, and the development of quantitative analysis is a clinical challenge. Forty-nine patients underwent partial or radical nephrectomy preceded by MRI and followed by pathological diagnosis with ChRCC or RO (ChRCC: n = 41, RO: n = 8). The whole-lesion volume of interest was set on apparent diffusion coefficient (ADC) maps of 1.5T-MRI. The importance of selected texture features (TFs) was evaluated, and diagnostic models were created using random forest (RF) analysis. The Mean Decrease Gini as calculated through RF analysis was the highest for mean_ADC_value. ChRCC had a significantly lower mean_ADC_value than RO (1.26 vs. 1.79 × 10−3 mm2/s, p < 0.0001). Feature selection by the Boruta method identified the first-quartile ADC value and GLZLM_HGZE as important features. ROC curve analysis showed that there was no significant difference in the classification performances between the mean_ADC_value-only model and the Boruta model (AUC: 0.954 vs. 0.969, p = 0.236). The mean ADC value had good predictive ability for the distinction between ChRCC and RO, comparable to that of the combination of TFs optimized for the evaluated cohort. The mean ADC value may be useful in distinguishing between ChRCC and RO.

Renal Oncocytoma: The Diagnostic Challenge to Unmask the Double of Renal Cancer

Renal oncocytoma represents the most common type of benign neoplasm that is an increasing concern for urologists, oncologists, and nephrologists due to its difficult differential diagnosis and frequent overtreatment. It displays a variable neoplastic parenchymal and stromal architecture, and the defining cellular element is a large polygonal, granular, eosinophilic, mitochondria-rich cell known as an oncocyte. The real challenge in the oncocytoma treatment algorithm is related to the misdiagnosis due to its resemblance, at an initial radiological assessment, to malignant renal cancers with a completely different prognosis and medical treatment. Unfortunately, percutaneous renal biopsy is not frequently performed due to the possible side effects related to the procedure. Therefore, the majority of oncocytoma are diagnosed after the surgical operation via partial or radical nephrectomy. For this reason, new reliable strategies to solve this issue are needed. In our review, we will discuss the clinical implications of renal oncocytoma in daily clinical practice with a particular focus on the medical diagnosis and treatment and on the potential of novel promising molecular biomarkers such as circulating microRNAs to distinguish between a benign and a malignant lesion.

Expanding the Role of Ultrasound for the Characterization of Renal Masses

The incidental detection of renal masses has been steadily rising. As a significant proportion of renal masses that are surgically treated are benign or indolent in nature, there is a clear need for better presurgical characterization of renal masses to minimize unnecessary harm. Ultrasound is a widely available and relatively inexpensive real-time imaging technique, and novel ultrasound-based applications can potentially aid in the non-invasive characterization of renal masses. Evidence acquisition: We performed a narrative review on novel ultrasound-based techniques that can aid in the non-invasive characterization of renal masses. Evidence synthesis: Contrast-enhanced ultrasound (CEUS) adds significant diagnostic value, particularly for cystic renal masses, by improving the characterization of fine septations and small nodules, with a sensitivity and specificity comparable to magnetic resonance imaging (MRI). Additionally, the performance of CEUS for the classification of benign versus malignant renal masses is comparable to that of computed tomography (CT) and MRI, although the imaging features of different tumor subtypes overlap significantly. Ultrasound molecular imaging with targeted contrast agents is being investigated in preclinical research as an addition to CEUS. Elastography for the assessment of tissue stiffness and micro-Doppler imaging for the improved detection of intratumoral blood flow without the need for contrast are both being investigated for the characterization of renal masses, though few studies have been conducted and validation is lacking. Conclusions: Several novel ultrasound-based techniques have been investigated for the non-invasive characterization of renal masses. CEUS has several advantages over traditional grayscale ultrasound, including the improved characterization of cystic renal masses and the potential to differentiate benign from malignant renal masses to some extent. Ultrasound molecular imaging offers promise for serial disease monitoring and the longitudinal assessment of treatment response, though this remains in the preclinical stages of development. While elastography and emerging micro-Doppler techniques have shown some encouraging applications, they are currently not ready for widespread clinical use.

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.

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.

Kidney tumor diffusion-weighted magnetic resonance imaging derived ADC histogram parameters combined with patient characteristics and tumor volume to discriminate oncocytoma from renal cell carcinoma

PURPOSE

To assess the ability to discriminate oncocytoma from RCC based on a model using whole tumor ADC histogram parameters with additional use of tumor volume and patient characteristics.

METHOD

In this prospective study, 39 patients (mean age 65 years, range 28-79; 9/39 (23%) female) with 39 renal tumors (32/39 (82%) RCC and 7/39 (18%) oncocytoma) underwent multiparametric MRI between November 2014 and June 2018. Two regions of interest (ROIs) were drawn to cover both the entire tumor volume and a part of healthy renal cortex. ROI ADC maps were calculated using a mono-exponential model and ADC histogram distribution parameters were calculated. A logistic regression model was created using ADC histogram parameters, radiographic and patient characteristics that were significantly different between oncocytoma and RCC. A ROC curve of the model was constructed and the AUC, sensitivity and specificity were calculated. Furthermore, differences in intra-patient ADC histogram parameters between renal tumor and healthy cortex were calculated. A separate ROC curve was constructed to differentiate oncocytoma from RCC using statistically significant intra-patient parameter differences.

RESULTS

ADC standard deviation (p = 0.008), entropy (p = 0.010), tumor volume (p = 0.012), and patient sex (p = 0.018) were significantly different between RCC and oncocytoma. The regression model of these parameters combined had an ROC-AUC of 0.91 with a sensitivity of 86% and specificity of 84%. Intra-patient difference in ADC 25th percentile (p < 0.01) and entropy (p = 0.030) combined had a ROC-AUC of 0.86 with a sensitivity and specificity of 86%, and 81%, respectively.

CONCLUSION

A model combining ADC standard deviation and entropy with tumor volume and patient sex has the highest diagnostic value for discrimination of oncocytoma. Although less accurate, intra-patient difference in ADC 25th percentile and entropy between renal tumor and healthy cortex can also be used. Although the results of this preliminary study do not yet justify clinical use of the model, it does stimulate further research using whole tumor ADC histogram parameters.

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.

Active Surveillance of Renal Masses: The Role of Radiology

Active surveillance of renal masses, which includes serial imaging with the possibility of delayed treatment, has emerged as a viable alternative to immediate therapeutic intervention in selected patients. Active surveillance is supported by evidence that many benign masses are resected unnecessarily, and treatment of small cancers has not substantially reduced cancer-specific mortality. These data are a call to radiologists to improve the diagnosis of benign renal masses and differentiate cancers that are biologically aggressive (prompting treatment) from those that are indolent (allowing treatment deferral). Current evidence suggests that active surveillance results in comparable cancer-specific survival with a low risk of developing metastasis. Radiology is central in this. Imaging is used at the outset to estimate the probability of malignancy and degree of aggressiveness in malignant masses and to follow up masses for growth and morphologic change. Percutaneous biopsy is used to provide a more definitive histologic diagnosis and to guide treatment decisions, including whether active surveillance is appropriate. Emerging applications that may improve imaging assessment of renal masses include standardized assessment of cystic and solid masses and radiomic analysis. This article reviews the current and future role of radiology in the care of patients with renal masses undergoing active surveillance.

Percutaneous biopsy of small renal mass: can diagnostic accuracy be affected by hospital volume?

Introduction

High diagnostic performance and low morbidity for renal tumor biopsy (RTB) have been described in highly experienced centers. Here we present the five-year experience of our institute in performing RTB. The protocol used, the safety profile and the diagnostic accuracy obtained were analyzed.

Material and methods

The study is a retrospective single-institution clinical data review of 84 consecutive RTB of small renal masses. Post-biopsy complications were reported using the Clavien-Dindo system. To measure the concordance between biopsy and nephrectomy specimens regarding histological subtype and International Society of Urological Pathology/World Health Organization (ISUP/WHO) renal cell carcinoma grade, the kappa coefficient of Cohen was used.

Results

Median (IQR) follow-up time was 44 (29–58) months. In total, 94% of RTB procedures were free of complications; when complications did occur, 80% were grade I and 20% were grade II. No cases of tumor seeding were observed. Combining the first and repeated biopsies the overall diagnostic rate was 85.8%. Overall, 79.1% of diagnostic RTB were malignant. In 42 surgically treated patients, the concordance between the histological results of biopsies and surgical specimens was very good for histological subtypes (k = 0.87) and moderate for tumor grade (k = 0.51).

Conclusions

RTB resulted in a high safety profile. The overall diagnostic rate was 85% and an unnecessary intervention was avoided in 21% of patients. RTB showed a very good accuracy in determining the histological subtype of renal cancer while it was moderate for the tumor grade. These results are similar to those reported in larger series and support feasibility of this procedure in low-volume centers.

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

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

Chromophobe Renal Cell Carcinoma with Radiologic-Pathologic Correlation

Renal cell carcinoma (RCC) is a heterogeneous group of neoplasms derived from the renal tubular epithelial cells. Chromophobe RCC (chRCC) is the third most common subtype of RCC, accounting for 5% of cases. chRCC may be detected as an incidental finding or less commonly may manifest with clinical symptoms. The mainstay of therapy for chRCC is surgical resection. chRCC has a better prognosis compared with the more common clear cell RCC. At gross pathologic analysis, chRCC is a solid well-defined mass with lobulated borders. Histologic findings vary by subtype but include large pale polygonal cells with abundant transparent cytoplasm, crinkled "raisinoid" nuclei with perinuclear halos, and prominent cell membranes. Pathologic analysis reveals only moderate vascularity. The most common imaging pattern is a predominantly solid renal mass with circumscribed margins and enhancement less than that of the renal cortex. The authors discuss chRCC with emphasis on correlative pathologic findings and illustrate the multimodality imaging appearances of chRCC by using cases from the Radiologic Pathology Archives of the American Institute for Radiologic Pathology. ^©RSNA, 2021.

Validation of aorta-lesion-attenuation difference on preoperative contrast-enhanced computed tomography scan to differentiate between malignant and benign oncocytic renal tumors

OBJECTIVES

We previously noted that the aorta-lesion-attenuation difference (ALAD) determined on CT scan discriminated well between chromophobe RCC and oncocytoma. The current evaluation seeks to validate these initial findings in a second cohort of nephrectomy patients.

METHODS

A retrospective review of preoperative CT scans and surgical pathology was performed on patients undergoing nephrectomy for small, 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 malignant pathology from oncocytoma was evaluated by sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under curve (AUC) using ROC analysis.

RESULTS

Twenty-one preoperative CT scans and corresponding pathology reports were reviewed and included in the validation cohort. ALAD values were calculated during the excretory and nephrographic phases. Compared to the training cohort, patients in the validation cohort were significantly older (62 versus 59 years old), had larger tumors (3.7 versus 2.7 cm), and higher stage disease (59% versus 79% T1a disease). Nephrographic ALAD was able to differentiate malignant pathology from oncocytoma in the training and validation cohorts with a sensitivity of 84% versus 73%, specificity of 86% and 67%, PPV of 98% versus 91%, and NPV of 33% versus 35%. The AUC for malignant pathology versus oncocytoma in the validation cohort was 0.72 (95% CI 0.63-0.82). Nephrographic ALAD was able to differentiate chromophobe RCC from oncocytoma in the training and validation cohorts with a sensitivity of 100% versus 67%, specificity of 86% versus 67%, PPV of 75% versus 43%, and NPV of 100% versus 84%. The AUC for chromophobe RCC versus oncocytoma in the validation cohort was 0.72 (95% CI 0.48-0.96).

CONCLUSIONS

The ability of ALAD to discriminate between chromophobe RCC and oncocytoma was diminished in the validation cohort compared to the training cohort, but remained significant. The current findings support further investigation in the role of ALAD in the management of patients with indeterminate diagnoses of oncocytic neoplasm.

18Fluorodeoxyglucose-positron emission tomography/computed tomography for differentiation of renal tumors in hereditary kidney cancer syndromes

PURPOSE

To assess differences in FDG-PET/CT uptake among four subtypes of renal tumors: clear cell RCC (ccRCC), papillary type I and II RCC (pRCC), and oncocytoma.

METHODS

This retrospective study investigated 33 patients with 98 hereditary renal tumors. Lesions greater than 1 cm and patients with a timeframe of less than 18 months between preoperative imaging and surgery were considered. FDG-PET/CT images were independently reviewed by two nuclear medicine physicians, blinded to clinical information. Volumetric lesion SUV_mean was measured and used to calculate a target-to-background ratio respective to liver (TBR). The Shrout-Fleiss intra-class correlation coefficient was used to assess reliability between readers. A linear mixed effects model, accounting for within-patient correlations, was used to compare TBR values of primary renal lesions with and without distant metastasis.

RESULTS

The time interval between imaging and surgery for all tumors had a median of 77 (Mean: 139; Range: 1-512) days. Intra-class reliability of mean TBR resulted in a mean κ score of 0.93, indicating strong agreement between the readers. The mixed model showed a significant difference in mean TBR among the subtypes (p < 0.0001). Pairwise comparison showed significant differences between pRCC type II and ccRCC (p < 0.0001), pRCC type II and pRCC type I (p = 0.0001), and pRCC type II and oncocytoma (p = 0.0016). Furthermore, a significant difference in FDG uptake was present between primary pRCC type II renal lesions with and without distant metastasis (p = 0.023).

CONCLUSION

pRCC type II lesions demonstrated significantly higher FDG activity than ccRCC, pRCC type I, or oncocytoma. These findings indicate that FDG may prove useful in studying the metabolic activity of renal neoplasms, identifying lesions of highest clinical concern, and ultimately optimizing active surveillance, and personalizing management plans.

The Incidental Renal Mass- Update on Characterization and Management

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

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.

Multi-Phase Multiple Detector Computed Tomography (MDCT) Enhancement Patterns and Morphological Features of Chromophobe Renal Cell Carcinoma: An Analysis of 67 Cases

Background

Chromophobe renal cell carcinoma (ChRCC) is difficult to diagnose preoperatively. We investigated multiple detector computed tomography (MDCT) plain scan and multi-phase CT enhancement features to aid ChRCC preoperative diagnosis.

Material/Methods

MDCT data of patients with pathologically confirmed ChRCC were retrospectively analyzed. We calculated the ratios of the CT value for the solid part of the mass to those of the renal cortex, aorta, and inferior vena cava. These ratios were designated as L01–3 for the CT plain scan images, La1–3 for the cortical phase, Lv1–3 for the nephrographic phase, and Lp1–3 for the pelvic phase. We classified the masses into types I, II, III, and IV by type of enhancement.

Results

Sixty-eight masses were included and divided into 3 groups by tumor size (groups A, B, and C). Percentages of calcification, central scars, and small vessel signs were significantly different during the cortical phase for masses in all groups (all P<0.01). Significant differences in enhancement were observed between tumors with severe and mild degrees of enhancement (P<0.01); and among La1, Lv1, and Lp1; La2, Lv2, and Lp2; and La3, Lv3, and Lp3 after enhancement during the cortical, nephrographic, and renal pelvic phases (all P<0.01). The most common type of mass enhancement was type II, followed by type I, and differences between these 2 types were significant (P<0.001).

Conclusions

Although the MDCT features for ChRCC are diverse, MDCT helped preoperatively diagnose ChRCC. Multiple MDCT features are needed to improve the accuracy of preoperative diagnosis.

'Hybrid' oncocytoma: collecting duct (Bellini) carcinoma-the peril from this extremely rare intratumoural coexistence

We presented an extremely rare entity of 'hybrid' oncocytoma and collecting duct (Bellini) carcinoma. The intratumoural coexistence of benign and malignant cells may lead to false diagnosis and suboptimal treatment of an aggressive tumour. Diagnosis may be challenging if only based on imaging modalities. Even the established value of targeted renal biopsy may be questioned in such scarce cases. Consequently, active surveillance for small renal tumours shall not considered a widely safe management.

Can we Avoid the Unnecessary Loss of nephrons in the Management of Small Solid Renal Masses? Additional Clinical Parameters to Predict Benign-malign Distinction

Objectives:

We aimed to investigate the predictive value of additional parameters for distinguishing benign-malign tumors and to prevent the loss of nephrons in small (≤4 cm) solid renal masses.

Methods:

The data of 56 patients underwent partial or radical nephrectomy between September 2009 and December 2017 due to diagnosis of localized renal cell carcinoma were retrospectively analyzed. Demographic datas, histopathological tumor types, neutrophil/lymphocyte ratio (NLR), monocyte/lymphocyte ratio (MLR), platelet/lymphocyte ratio (PLR), red blood cell distribution width (RDW), mean platelet volume (MPV), the Framingham risk score and its components, postoperative follow-up results were recorded. Patients were divided into two groups as benign and malign.

Results:

Among 56 patients with a median age of 60 (min: 35-max: 74) years, 13 patients had benign and 43 patients had malign pathologies. MLR (p=0.011), NLR (p=0.032), PLR (p=0.006), MPV (p=0.025), eGFR (p=0.019) and the Framingham score (p=0.008) were significantly higher in malign group. Among the components constituting the Framingham score, only presence of smoking (p=0.032), presence of hypertension (p=0.041) and total cholesterol values (p=0.021) were significantly higher. In multivariate analysis, NLR>2.02 (OR: 7.184, p=0.037), PLR>109.65 (OR: 12.692, p=0.002), MPV>3.44 (OR: 10.543, p=0.046) and Framingham score >10.5 (OR: 12.287, p=0.007) were found as predictive factors for distinguishing small solid renal masses concerning malignancy.

Conclusion:

We think that NLR, PLR, MPV and the Framingham scores may be used in the clinical evaluation of small solid renal masses. In this way, we may prevent the unnecessary loss of nephrons in benign masses with suspicion of malignancy.

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.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Value of radiomics in differential diagnosis of chromophobe renal cell carcinoma and renal oncocytoma

Purpose

To explore the value of CT-enhanced quantitative features combined with machine learning for differential diagnosis of renal chromophobe cell carcinoma (chRCC) and renal oncocytoma (RO).

Methods

Sixty-one cases of renal tumors (chRCC = 44; RO = 17) that were pathologically confirmed at our hospital between 2008 and 2018 were retrospectively analyzed. All patients had undergone preoperative enhanced CT scans including the corticomedullary (CMP), nephrographic (NP), and excretory phases (EP) of contrast enhancement. Volumes of interest (VOIs), including lesions on the images, were manually delineated using the RadCloud platform. A LASSO regression algorithm was used to screen the image features extracted from all VOIs. Five machine learning classifications were trained to distinguish chRCC from RO by using a fivefold cross-validation strategy. The performance of the classifier was mainly evaluated by areas under the receiver operating characteristic (ROC) curve and accuracy.

Results

In total, 1029 features were extracted from CMP, NP, and EP. The LASSO regression algorithm was used to screen out the four, four, and six best features, respectively, and eight features were selected when CMP and NP were combined. All five classifiers had good diagnostic performance, with area under the curve (AUC) values greater than 0.850, and support vector machine (SVM) classifier showed a diagnostic accuracy of 0.945 (AUC 0.964 ± 0.054; sensitivity 0.999; specificity 0.800), showing the best performance.

Conclusions

Accurate preoperative differential diagnosis of chRCC and RO can be facilitated by a combination of CT-enhanced quantitative features and machine learning.