Renal Masses in the Adult Patient: The Role of Percutaneous Biopsy

Peak early-phase enhancement ratio on contrast-enhanced MRI to differentiate chromophobe renal cell carcinoma from oncocytoma

Objectives

To evaluate the feasibility of using the peak early-phase enhancement ratio (PEER) of tumour to renal cortex measured on contrast-enhanced magnetic resonance imaging (MRI) to distinguish between chromophobe renal cell carcinoma (chRCC) and oncocytoma, which are difficult to differentiate on renal mass biopsy.

Patients and Methods

A consecutive case-control study was conducted of patients with chRCC or oncocytoma based on surgical pathology (2006-2020). Two radiologists blinded to pathology results independently measured PEER values on MRI for each tumour. PEER values were compared with surgical pathology results.

Results

For the 18 renal tumours evaluated, PEER values were higher for the 7 oncocytomas than for the 11 chRCCs (median 1.33 versus 0.55, p < 0.001). Agreement between the image interpreters was high (Pearson's: 0.90). PEER cutoff values ranging from 0.98 to 1.05 provided high performance in identifying chRCC. A PEER cutoff value of ≤1.05 had sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 100% for the averaged PEER measurements between the two radiologists. High accuracy in identifying chRCC was also achieved for each individual image interpreter using the cutoff value of ≤1.05, with sensitivity of 100%, specificity of 85.7%, PPV of 91.7% and NPV of 100% for radiologist #1 and sensitivity of 90.9%, specificity of 85.7%, PPV of 90.9% and NPV of 85.7% for radiologist #2.

Conclusion

Differentiating chRCCs from oncocytomas using PEER measurements obtained from contrast-enhanced MRI is feasible and reproducible between radiologists. We identified an accurate range for PEER cutoff values (0.98 to 1.05) requiring validation and adjustment in additional cohorts to maintain high sensitivity for detecting chRCC and negative predictive value. Using MRI PEER to evaluate oncocytic tumours with a differential diagnosis of chRCC versus oncocytoma based on biopsy pathology may help avoid unnecessary intervention for oncocytomas.

Artificial intelligence links CT images to pathologic features and survival outcomes of renal masses

Treatment decisions for an incidental renal mass are mostly made with pathologic uncertainty. Improving the diagnosis of benign renal masses and distinguishing aggressive cancers from indolent ones is key to better treatment selection. We analyze 13261 pre-operative computed computed tomography (CT) volumes of 4557 patients. Two multi-phase convolutional neural networks are developed to predict the malignancy and aggressiveness of renal masses. The first diagnostic model designed to predict the malignancy of renal masses achieves area under the curve (AUC) of 0.871 in the prospective test set. This model surpasses the average performance of seven seasoned radiologists. The second diagnostic model differentiating aggressive from indolent tumors has AUC of 0.783 in the prospective test set. Both models outperform corresponding radiomics models and the nephrometry score nomogram. Here we show that the deep learning models can non-invasively predict the likelihood of malignant and aggressive pathology of a renal mass based on preoperative multi-phase CT images.

Differentiating mixed epithelial and stromal tumor family from predominantly cystic renal cell carcinoma using magnetic resonance imaging-based Bosniak classification system version 2019

PURPOSE

To differentiate mixed epithelial and stromal tumor family (MESTF) of the kidney from predominantly cystic renal cell carcinoma (RCC) using the magnetic resonance imaging (MRI)-based Bosniak classification system version 2019 (v2019).

MATERIALS AND METHODS

The study included 36 consecutive patients with MESTF and 77 with predominantly cystic RCC who underwent preoperative renal MRI. One radiologist evaluated and documented the clinical and MRI characteristics (age, sex, laterality, R.E.N.A.L. Nephrometry Score [RNS], surgical approach, the signal intensity on T2-weighted imaging, restricted diffusion and enhancement features in corticomedullary phase). Blinded to clinical and pathological information, another two radiologists independently evaluated Bosniak category of all masses. Interobserver agreement based on Bosniak classification system v2019 was measured by the weighted Cohen/Conger's Kappa coefficient. Furthermore, predominantly cystic RCCs and MESTFs were divided into low (categories I, II, and IIF) and high-class (categories III, and IV) tumors. The independent sample t test (Mann-Whitney U test) or Pearson Chi-square test (Fisher's exact probability test) was utilized to compare clinical and imaging characteristics between MESTFs and predominantly cystic RCCs. The performance of the Bosniak classification system v2019 in distinguishing MESTF from predominantly cystic RCC was investigated via receiver operating characteristic curve analysis.

RESULTS

MESTF and predominantly cystic RCC groups significantly differed in terms of age, lesion size, RNS, restricted diffusion, and obvious enhancement in corticomedullary phase, but not sex, laterality, surgical approach, and the signal intensity on T2WI. Interobserver agreement was substantially based on the Bosniak classification system v2019. There were 24 low-class tumors and 12 high-class tumors in the MESTF group. Meanwhile, 13 low-class tumors and 64 high-class tumors were observed in the predominantly cystic RCC group. The distribution of low- or high-class tumors significantly differed between the MESTF and predominantly cystic RCC groups. Bosniak classification system v2019 had excellent discrimination (cutoff value = category III), and an area under curve value was 0.81; accuracy, 80.5%; sensitivity, 87.0%; and specificity, 66.7%.

CONCLUSION

The MRI-based Bosniak classification system v2019 can effectively distinguish MESTF from predominantly cystic RCC if category III was used as a cutoff reference.

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.

Clinical T1/2 renal cell carcinoma: multiparametric dynamic contrast-enhanced MRI features-based model for the prediction of individual adverse pathology

BACKGROUND

The detection of renal cell carcinoma (RCC) has been rising due to the enhanced utilization of cross-sectional imaging and incidentally discovered lesions with adverse pathology demonstrate potential for metastasis. The purpose of our study was to determine the clinical and multiparametric dynamic contrast-enhanced magnetic resonance imaging (CEMRI) associated independent predictors of adverse pathology for cT1/2 RCC and develop the predictive model.

METHODS

We recruited 105 cT1/2 RCC patients between 2018 and 2022, all of whom underwent preoperative CEMRI and had complete clinicopathological data. Adverse pathology was defined as RCC patients with nuclear grade III-IV; pT3a upstage; type II papillary RCC, collecting duct or renal medullary carcinoma, unclassified RCC; sarcomatoid/rhabdoid features. The qualitative and quantitative CEMRI parameters were independently reviewed by two radiologists. Univariate and multivariate binary logistic regression analyses were utilized to determine the independent predictors of adverse pathology for cT1/2 RCC and construct the predictive model. The receiver operating characteristic (ROC) curve, confusion matrix, calibration plot, and decision curve analysis (DCA) were conducted to compare the diagnostic performance of different predictive models. The individual risk scores and linear predicted probabilities were calculated for risk stratification, and the Kaplan-Meier curve and log-rank tests were used for survival analysis.

RESULTS

Overall, 45 patients were pathologically confirmed as RCC with adverse pathology. Clinical characteristics, including gender, and CEMRI parameters, including RENAL score, tumor margin irregularity, necrosis, and tumor apparent diffusion coefficient (ADC) value were identified as independent predictors of adverse pathology for cT1/2 RCC. The clinical-CEMRI predictive model yielded an area under the curve (AUC) of the ROC curve of 0.907, which outperformed the clinical model or CEMRI signature model alone. Good calibration, better clinical usefulness, excellent risk stratification ability of adverse pathology and prognosis were also achieved for the clinical-CEMRI predictive model.

CONCLUSIONS

The proposed clinical-CEMRI predictive model offers the potential for preoperative prediction of adverse pathology for cT1/2 RCC. With the ability to forecast adverse pathology, the predictive model could significantly benefit patients and clinicians alike by providing enhanced guidance for treatment planning and decision-making.

Contrast enhanced ultrasound of cystic renal lesions, from diagnosis up to treatment

Ultrasound is the most used interdisciplinary imaging technique in clinical routine for assessment of renal pathologies. This includes the monitoring of cystic renal lesions, which can be classified as non-complicated or complicated and by means of occurrence as solitary or multifocal lesions. The Bosniak-classification (I-IV) classifies renal cysts in 5 different categories and is used for decisions of further clinical treatment. This classification was developed for computed tomography and has been adopted for magnetic resonance imaging as well as contrast-enhanced ultrasound. In the following review article, cystic kidney lesions and their differentiation using contrast-enhanced ultrasound are presented and an overview of the therapy options is given. In interventional procedures, CEUS can make a valuable contribution in histological sampling, reduce radiation exposure and, under certain circumstances, the number of interventions for the patient.

[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.

Differentiation of Small Clear Renal Cell Carcinoma and Oncocytoma through Magnetic Resonance Imaging-Based Radiomics Analysis: Toward the End of Percutaneous Biopsy

PURPOSE

The aim of this study was to ascertain whether radiomics data can assist in differentiating small (<4 cm) clear cell renal cell carcinomas (ccRCCs) from small oncocytomas using T2-weighted magnetic resonance imaging (MRI).

MATERIAL AND METHODS

This retrospective study incorporated 48 tumors, 28 of which were ccRCCs and 20 were oncocytomas. All tumors were less than 4 cm in size and had undergone pre-biopsy or pre-surgery MRI. Following image pre-processing, 102 radiomics features were evaluated. A univariate analysis was performed using the Wilcoxon rank-sum test with Bonferroni correction. We compared multiple radiomics pipelines of normalization, feature selection, and machine learning (ML) algorithms, including random forest (RF), logistic regression (LR), AdaBoost, K-nearest neighbor, and support vector machine, using a supervised ML approach.

RESULTS

No statistically significant features were identified via the univariate analysis with Bonferroni correction. The most effective algorithm was identified using a pipeline incorporating standard normalization, RF-based feature selection, and LR, which achieved an area under the curve (AUC) of 83%, accuracy of 73%, sensitivity of 79%, and specificity of 65%. Subsequently, the most significant features were identified from this algorithm, and two groups of uncorrelated features were established based on Pearson correlation scores. Using these features, an algorithm was established after a pipeline of standard normalization and LR, achieving an AUC of 90%, an accuracy of 77%, sensitivity of 83%, and specificity of 69% for distinguishing ccRCCs from oncocytomas.

CONCLUSIONS

Radiomics analysis based on T2-weighted MRI can aid in distinguishing small ccRCCs from small oncocytomas. However, it is not superior to standard multiparameter renal MRI and does not yet allow us to dispense with percutaneous biopsy.

"Could Patient Age and Gender, along with Mass Size, Be Predictive Factors for Benign Kidney Tumors?": A Retrospective Analysis of 307 Consecutive Single Renal Masses Treated with Partial or Radical Nephrectomy

Due to the increased use of common and non-invasive abdominal imaging techniques over the last few decades, the diagnosis of about 60% of renal tumors is incidental. Contrast-enhancing renal nodules on computed tomography are diagnosed as malignant tumors, which are often removed surgically without first performing a biopsy. Most kidney nodules are renal cell carcinoma (RCC) after surgical treatment, but a non-negligible rate of these nodules may be benign on final pathology; as a result, patients undergo unnecessary surgery with an associated significant morbidity. Our study aimed to identify a subgroup of patients with higher odds of harboring benign tumors, who would hence benefit from further diagnostic examinations (such as renal biopsy) or active surveillance. We performed a retrospective review of the medical data, including pathology results, of patients undergoing surgery for solid renal masses that were suspected to be RCCs (for a total sample of 307 patients). Owing to the widespread use of common and non-invasive imaging techniques, the incidental diagnosis of kidney tumors has become increasingly common. Considering that a non-negligible rate of these tumors is found to be benign after surgery at pathological examination, it is crucial to identify features that can correctly diagnose a mass as benign or not. According to our study results, female sex and tumor size ≤ 3 cm were independent predictors of benign disease. Contrary to that demonstrated by other authors, increasing patient age was also positively linked to a greater risk of malign pathology.

Diagnostic performance of multiparametric magnetic resonance imaging in the differentiation of clear cell renal cell cancer

PURPOSE

This study aimed to evaluate the diagnostic performance of multiparametric magnetic resonance imaging (mpMRI) in the differentiation of renal cell carcinoma (RCC) subtypes.

METHODS

This is a retrospective diagnostic performance study, in which the diagnostic performances of mpMRI features were evaluated to differentiate clear cell RCC (ccRCC) from non-clear cell RCC (non-ccRCC). Adult patients who were evaluated using a 3-Tesla dynamic contrast-enhanced mpMRI before undergoing partial or radical nephrectomy for possible malignant renal tumors were included in the study. Signal intensity change percentages (SICP) between contrast-enhanced phases and pre-administration period for both the tumor and normal renal cortex, and tumor-to-cortex enhancement index (TCEI); tumor apparent diffusion coefficient (ADC) values; tumor-to-cortex ADC ratio; and a scale which was developed according to the tumor signal intensities on the axial fat-suppressed T2-weighted Half-Fourier Acquisition Single-shot Turbo spin Echo (HASTE) images were used in ROC analysis to estimate the presence of ccRCC in the patients. The reference test positivity was the histopathologic examination of the surgical specimens.

RESULTS

Ninety-eight tumors from 91 patients were included in the study, and 59 of them were ccRCC, 29 were pRCC, and 10 were chRCC. The mpMRI features that had the three highest sensitivity rates were excretory phase SICP, T2-weighted HASTE scale score, and corticomedullary phase TCEI (93.2%, 91.5%, and 86.4%, respectively). However, those with the three highest specificity rates were nephrographic phase TCEI, excretory phase TCEI, and tumor ADC value (94.9%, 94.9%, and 89.7%, respectively).

CONCLUSION

Several parameters on mpMRI showed an acceptable performance to differentiate ccRCC from non-ccRCC.

The Changing Role of Renal Mass Biopsy

The incidence and prevalence of small renal masses (SRMs) continues to rise and with increased detection comes increases in surgical management, although the probability of an SRM being benign is upward of 30%. An extirpative treatment first diagnose-later strategy persists and clinical tools for risk stratification such as renal mass biopsy remain severely underutilized. The overtreatment of SRMs has multiple detrimental effects including surgical complications, psychosocial stress, financial loss, and reduced renal function leading to downstream effects such as the need for dialysis and cardiovascular disease.

Diagnostic Accuracy of MRI for Solid Renal Masses: A Systematic Review and Meta-analysis

BACKGROUND

Biparametric (bp)-MRI and multiparametric (mp)-MRI may improve the diagnostic accuracy of renal mass histology.

PURPOSE

To evaluate the available evidence on the diagnostic accuracy of bp-MRI and mp-MRI for solid renal masses in differentiating malignant from benign, aggressive from indolent, and clear cell renal cell carcinoma (ccRCC) from other histology.

STUDY TYPE

Systematic review.

POPULATION

MEDLINE, EMBASE, and CENTRAL up to January 11, 2022 were searched.

FIELD STRENGTH/SEQUENCE

1.5 or 3 Tesla.

ASSESSMENT

Eligible studies evaluated the accuracy of MRI (with at least two sequences: T2, T1, dynamic contrast and diffusion-weighted imaging) for diagnosis of solid renal masses in adult patients, using histology as reference standard. Risk of bias and applicability were assessed using QUADAS-2.

STATISTICAL TESTS

Meta-analysis using a bivariate logitnormal random effects model.

RESULTS

We included 10 studies (1239 masses from approximately 1200 patients). The risk of bias was high in three studies, unclear in five studies and low in two studies. The diagnostic accuracy of malignant (vs. benign) masses was assessed in five studies (64% [179/281] malignant). The summary estimate of sensitivity was 95% (95% confidence interval [CI]: 77%-99%), and specificity was 63% (95% CI: 46%-77%). No study assessed aggressive (vs. indolent) masses. The diagnostic accuracy of ccRCC (vs. other subtypes) was evaluated in six studies (47% [455/971] ccRCC): the summary estimate of sensitivity was 85% (95% CI: 77%-90%) and specificity was 77% (95% CI: 73%-81%).

DATA CONCLUSION

Our study reveals deficits in the available evidence on MRI for diagnosis of renal mass histology. The number of studies was limited, at unclear/high risk of bias, with heterogeneous definitions of solid masses, imaging techniques, diagnostic criteria, and outcome measures.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Percutaneous Thermal Ablation for Managing Small Renal Metastatic Tumors

PURPOSE

To retrospectively evaluate the treatment outcomes of thermal ablation for renal metastatic tumors.

MATERIALS AND METHODS

Thirteen consecutive patients with small renal metastatic tumors (≤3 cm), who underwent thermal ablation between 2009 and 2020, were included in this study. Eight patients had extra-renal tumors during renal ablation. The primary tumors were adenoid cystic carcinoma in four patients, lung cancer in three, hemangiopericytoma in three, leiomyosarcoma in two, and thyroid cancer in one. The therapeutic effects, safety, survival rate, prognostic factor, and renal function were evaluated.

RESULTS

We performed 18 ablation sessions (cryoablation, n = 13; radiofrequency ablation, n = 5) on 19 renal metastases with a mean diameter of 1.7 cm, which resulted in a primary technique efficacy rate of 100% without procedure-related deaths or major complications. Renal function significantly declined 6 months after ablation (P = 0.0039). During the mean follow-up period of 31.2 ± 22.4 months (range, 2.7-71.4 months), one patient had local tumor progression at 11.9 months following radiofrequency ablation. The overall survival rates at 1 and 3 years after ablation were 76.9% (95% confidence interval [CI], 54.0%-99.8%) and 59.3% (95% CI, 31.3%-87.3%), respectively. Tumor size ≥ 2 cm (P = 0.02) and metastasis from non-small cell lung cancer (P = 0.001) were significant worse prognostic factors in univariate analysis, and metastasis from non-small cell lung cancer (P = 0.01) was significant in multivariate analysis.

CONCLUSIONS

Percutaneous thermal ablation for small renal metastases is safe and feasible and can control local tumors.

A newly developed image fusion algorithm between CECT image and CT image: A feasibility study

Cancer cases have been on the rise over the world. Cancer treatment can benefit from an early accurate diagnosis. Percutaneous needle biopsy under the guidance of CT images is the most common method to obtain tumor samples for accurate diagnosis. However, due to the lack of vascular information in the CT images, the biopsy procedure is at great risk, especially for the tumor surrounded by vessels. In this study, a biomechanical model and surface elastic registration-based fusion algorithm were developed to map the vessels from contrast-enhanced CT images of the liver and lung to the corresponded CT image. Radiologists could observe vessels in the CT images during the biopsy procedure so that the risk can be decreased. The developed algorithm was tested through 20 groups of lung data and 16 groups of liver data. The results show that the fusion errors (mean ± standard deviation) were 2.35 ± 0.85, 2.08 ± 0.41, 2.31 ± 0.49, and 2.37 ± 0.62 mm for portal vein, hepatic vein, pulmonary artery, and pulmonary vein, respectively. The accuracy of this method was satisfied in clinical application

A comprehensive texture feature analysis framework of renal cell carcinoma: pathological, prognostic, and genomic evaluation based on CT images

OBJECTIVES

We tried to realize accurate pathological classification, assessment of prognosis, and genomic molecular typing of renal cell carcinoma by CT texture feature analysis. To determine whether CT texture features can perform accurate pathological classification and evaluation of prognosis and genomic characteristics in renal cell carcinoma.

METHODS

Patients with renal cell carcinoma from five open-source cohorts were analyzed retrospectively in this study. These data were randomly split to train and test machine learning algorithms to segment the lesion, predict the histological subtype, tumor stage, and pathological grade. Dice coefficient and performance metrics such as accuracy and AUC were calculated to evaluate the segmentation and classification model. Quantitative decomposition of the predictive model was conducted to explore the contribution of each feature. Besides, survival analysis and the statistical correlation between CT texture features, pathological, and genomic signatures were investigated.

RESULTS

A total of 569 enhanced CT images of 443 patients (mean age 59.4, 278 males) were included in the analysis. In the segmentation task, the mean dice coefficient was 0.96 for the kidney and 0.88 for the cancer region. For classification of histologic subtype, tumor stage, and pathological grade, the model was on a par with radiologists and the AUC was 0.83 [Formula: see text] 0.1, 0.80 [Formula: see text] 0.1, and 0.77 [Formula: see text] 0.1 at 95% confidence intervals, respectively. Moreover, specific quantitative CT features related to clinical prognosis were identified. A strong statistical correlation (R² = 0.83) between the feature crosses and genomic characteristics was shown. The structural equation modeling confirmed significant associations between CT features, pathological (β =  - 0.75), and molecular subtype (β =  - 0.30).

CONCLUSIONS

The framework illustrates high performance in the pathological classification of renal cell carcinoma. Prognosis and genomic characteristics can be inferred by quantitative image analysis.

KEY POINTS

• The analytical framework exhibits high-performance pathological classification of renal cell carcinoma and is on a par with human radiologists. • Quantitative decomposition of the predictive model shows that specific texture features contribute to histologic subtype and tumor stage classification. • Structural equation modeling shows the associations of genomic characteristics to CT texture features. Overall survival and molecular characteristics can be inferred by quantitative CT texture analysis in renal cell carcinoma.

Ultrasonography findings for the diagnosis of renal oncocytoma

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

Robustness and performance of radiomic features in diagnosing cystic renal masses

PURPOSE

We study the inter-reader variability in manual delineation of cystic renal masses (CRMs) presented in computerized tomography (CT) images and its effect on the classification performance of a machine learning algorithm in distinguishing benign from potentially malignant CRMs. In addition, we assessed whether the inclusion of higher-order robust radiomic features improves the classification performance over the use of first-order features.

METHODS

230 CRMs were independently delineated by two radiologists. Through a combination of random fluctuations, dilation, and erosion operations over the original region of interests (ROIs), we generated four additional sets of synthetic ROIs to capture the inter-reader variability realistically, as confirmed by dice coefficient measurements and visual assessment. We then identified the robust features based on the intra-class coefficient (ICC > 0.85) across these datasets. We applied a tenfold stratified cross-validation (CV) to train and test the performance of the random forest model for the classification of CRMs into benign and potentially malignant.

RESULTS

The mean area under the curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value were 0.87, 0.82, 0.90, 0.85, and 0.93, respectively. With the usage of first-order features alone, the corresponding values were nearly identical.

CONCLUSION

AUC ranged for the robust and uncorrelated features from 0.83 ± 0.09 to 0.93 ± 0.04 and for the first-order features from 0.84 ± 0.09 to 0.91 ± 0.04. Our study indicates that the first-order features alone are sufficient for the classification of CRMs, and that inclusion of higher-order features does not necessarily improve performance.

Computed Tomography-guided Core Needle Biopsy for Renal Tumors: A Review

Small renal tumors are sometimes challenging to diagnose accurately through imaging alone, and image-guided biopsies are performed when histological diagnoses are needed. Although ultrasound guidance is usually chosen for renal tumor biopsies, computed tomography guidance is preferred for selected cases; e.g., obese patients or when the target is undetectable by ultrasound (as those in the upper pole). In the 14 recently published studies covering ≥50 procedures, computed tomography-guided renal tumor biopsies had a wide range diagnostic yield (67.4%-97.4%). Complications often occurred; however, most were minor and asymptomatic. No biopsy-related deaths and tumor seeding occurred. This study aimed to review the advantages and disadvantages, procedure techniques, diagnostic yields, and complications of core needle biopsies for renal tumors under computed tomography guidance.

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.

Ultrasound-guided percutaneous renal biopsy at a university hospital: retrospective analysis of success and complication rates

OBJECTIVE

To evaluate the success and complication rates of ultrasound-guided renal biopsy at a tertiary care hospital.

MATERIALS AND METHODS

This was a retrospective analysis of 97 ultrasound-guided renal biopsies, all performed by the same radiologist, between 1 March, 2017 and 31 October, 2019.

RESULTS

Of the 97 biopsies evaluated, 87 had a definitive pathological diagnosis. In five cases (5.4%), the biopsy results were inconclusive and a second procedure was required. In seven procedures (7.6%), there were complications, all of which were properly resolved.

CONCLUSION

Ultrasound-guided renal biopsy has proven to be a safe, effective method for the diagnosis of nephropathies, with high success rates.

Value of Quantitative CTTA in Differentiating Malignant From Benign Bosniak III Renal Lesions on CT Images

OBJECTIVE

The aim of this study was to investigate whether computed tomography texture analysis can differentiate malignant from benign Bosniak III renal lesions on computed tomography (CT) images.

METHODS

This retrospective case-control study included 45 patients/lesions (22 benign and 23 malignant lesions) with Bosniak III renal lesions who underwent CT examination. Axial image slices in the unenhanced phase, corticomedullary phase, and nephrographic phase were selected and delineated manually. Computed tomography texture analysis was performed on each lesion during these 3 phases. Histogram-based, gray-level co-occurrence matrix, and gray-level run-length matrix features were extracted using open-source software and analyzed. In addition, receiver operating characteristic curve was constructed, and the area under the receiver operating characteristic curve (AUC) of each feature was constructed.

RESULTS

Of the 33 extracted features, 16 features showed significant differences (P < 0.05). Eight features were significantly different between the 2 groups after Holm-Bonferroni correction, including 3 histogram-based, 4 gray-level co-occurrence matrix, and 1 gray-level run-length matrix features (P < 0.01). The texture features resulted in the highest AUC of 0.769 ± 0.074. Renal cell carcinomas were labeled with a higher degree of lesion gray-level disorder and lower lesion homogeneity, and a model incorporating the 3 most discriminative features resulted in an AUC of 0.846 ± 0.058.

CONCLUSIONS

The results of this study showed that CT texture features were related to malignancy in Bosniak III renal lesions. Computed tomography texture analysis might help in differentiating malignant from benign Bosniak III renal lesions on CT images.

Diagnostic performance of single-phase dual-energy CT to differentiate vascular and nonvascular incidental renal lesions on portal venous phase: comparison with CT

OBJECTIVES

To determine whether single-phase dual-energy CT (DECT) differentiates vascular and nonvascular renal lesions in the portal venous phase (PVP). Optimal iodine threshold was determined and compared to Hounsfield unit (HU) measurements.

METHODS

We retrospectively included 250 patients (266 renal lesions) who underwent a clinically indicated PVP abdominopelvic CT on a rapid-kilovoltage-switching single-source DECT (rsDECT) or a dual-source DECT (dsDECT) scanner. Iodine concentration and HU measurements were calculated by four experienced readers. Diagnostic accuracy was determined using biopsy results and follow-up imaging as reference standard. Area under the curve (AUC) was calculated for each DECT scanner to differentiate vascular from nonvascular lesions and vascular lesions from hemorrhagic/proteinaceous cysts. Univariable and multivariable logistic regression analyses evaluated the association between variables and the presence of vascular lesions.

RESULTS

A normalized iodine concentration threshold of 0.25 mg/mL yielded high accuracy in differentiating vascular and nonvascular lesions (AUC 0.93, p < 0.001), with comparable performance to HU measurements (AUC 0.93). Both iodine concentration and HU measurements were independently associated with vascular lesions when adjusted for age, gender, body mass index, and lesion size (AUC 0.95 and 0.95, respectively). When combined, diagnostic performance was higher (AUC 0.96). Both absolute and normalized iodine concentrations performed better than HU measurements (AUC 0.92 vs. AUC 0.87) in differentiating vascular lesions from hemorrhagic/proteinaceous cysts.

CONCLUSION

A single-phase (PVP) DECT scan yields high accuracy to differentiate vascular from nonvascular renal lesions. Iodine concentration showed a slightly higher performance than HU measurements in differentiating vascular lesions from hemorrhagic/proteinaceous cysts.

KEY POINTS

• A single-phase dual-energy CT scan in the portal venous phase differentiates vascular from nonvascular renal lesions with high accuracy (AUC 0.93). • When combined, iodine concentration and HU measurements showed the highest diagnostic performance (AUC 0.96) to differentiate vascular from nonvascular renal lesions. • Compared to HU measurements, iodine concentration showed a slightly higher performance in differentiating vascular lesions from hemorrhagic/proteinaceous cysts.

Sporadic multiple renal angiomyolipoma with lymph node involvement: a case report and literature review

Angiomyolipoma (AML) is a benign tumor that mainly occurs in the kidneys. Simultaneous involvement of the kidney and local regional lymph nodes is very rare and might be misdiagnosed as a metastasizing malignant cancer. In the present study, a 50-year-old woman was referred to our hospital after a routine health screening ultrasound. Sporadic multiple renal AML with lymph node involvement was suspected based on the clinical manifestations and radiologic features. Partial nephrectomy was performed and a para-inferior vena cava lymph node was removed. The pathologic results confirmed multiple AML with lymph node invasion. We also reviewed the English-language literature regarding renal AML with lymph node involvement. We found that middle-aged women were likely to develop this disease and that loin pain was the main presenting feature. Most patients had no history of tuberous sclerosis complex. Radical nephrectomy was the predominant treatment. No local recurrence or distant metastasis occurred in any patients after radical nephrectomy or partial nephrectomy. In conclusion, renal AML with lymph node involvement is rare but can occur in both patients with tuberous sclerosis complex and those with multiple sporadic AML. Partial nephrectomy should be the first-line treatment, after which further treatment is not necessary.

Stratification of cystic renal masses into benign and potentially malignant: applying machine learning to the bosniak classification

PURPOSE

To create a CT texture-based machine learning algorithm that distinguishes benign from potentially malignant cystic renal masses as defined by the Bosniak Classification version 2019.

METHODS

In this IRB-approved, HIPAA-compliant study, 4,454 adult patients underwent renal mass protocol CT or CT urography from January 2011 to June 2018. Of these, 257 cystic renal masses were included in the final study cohort. Each mass was independently classified using Bosniak version 2019 by three radiologists, resulting in 185 benign (Bosniak I or II) and 72 potentially malignant (Bosniak IIF, III or IV) masses. Six texture features: mean, standard deviation, mean of positive pixels, entropy, skewness, kurtosis were extracted using commercial software TexRAD (Feedback PLC, Cambridge, UK). Random forest (RF), logistic regression (LR), and support vector machine (SVM) machine learning algorithms were implemented to classify cystic renal masses into the two groups and tested with tenfold cross validations.

RESULTS

Higher mean, standard deviation, mean of positive pixels, entropy, skewness were statistically associated with the potentially malignant group (P ≤ 0.0015 each). Sensitivity, specificity, positive predictive value, negative predictive value, and area under curve of RF model was 0.67, 0.91, 0.75, 0.88, 0.88; of LR model was 0.63, 0.93, 0.78, 0.86, 0.90, and of SVM model was 0.56, 0.91, 0.71, 0.84, 0.89, respectively.

CONCLUSION

Three CT texture-based machine learning algorithms demonstrated high discriminatory capability in distinguishing benign from potentially malignant cystic renal masses as defined by the Bosniak Classification version 2019. If validated, CT texture-based machine learning algorithms may help reduce interreader variability when applying the Bosniak classification.

Asian Conference on Tumor Ablation guidelines for renal cell carcinoma

Thermal ablation has been established as an alternative treatment for renal cell carcinoma (RCC) in patients who are poor candidates for surgery. However, while American and European guidelines have been established for American and European patients, respectively, no ablation guidelines for Asian patients with RCCs have been established many years after the Asian Conference on Tumor Ablation (ACTA) had been held. Given that Western guidelines are difficult to apply to Asian patients due to differences in body habitus, economic status, and insurance systems, the current review sought to establish the first version of the ACTA guidelines for treating a RCC with thermal ablation.

Characterization of solid renal neoplasms using MRI-based quantitative radiomics features

PURPOSE

To assess the diagnostic value of magnetic resonance imaging (MRI)-based radiomics features using machine learning (ML) models in characterizing solid renal neoplasms, in comparison/combination with qualitative radiologic evaluation.

METHODS

Retrospective analysis of 125 patients (mean age 59 years, 67% males) with solid renal neoplasms that underwent MRI before surgery. Qualitative (signal and enhancement characteristics) and quantitative radiomics analyses (histogram and texture features) were performed on T2-weighted imaging (WI), T1-WI pre- and post-contrast, and DWI. Mann-Whitney U test and receiver-operating characteristic analysis were used in a training set (n = 88) to evaluate diagnostic performance of qualitative and radiomics features for differentiation of renal cell carcinomas (RCCs) from benign lesions, and characterization of RCC subtypes (clear cell RCC [ccRCC] and papillary RCC [pRCC]). Random forest ML models were developed for discrimination between tumor types on the training set, and validated on an independent set (n = 37).

RESULTS

We assessed 104 RCCs (51 ccRCC, 29 pRCC, and 24 other subtypes) and 21 benign lesions in 125 patients. Significant qualitative and quantitative radiomics features (area under the curve [AUC] between 0.62 and 0.90) were included for ML analysis. Models with best diagnostic performance on validation sets showed AUC of 0.73 (confidence interval [CI] 0.5-0.96) for differentiating RCC from benign lesions (using combination of qualitative and radiomics features); AUC of 0.77 (CI 0.62-0.92) for diagnosing ccRCC (using radiomics features), and AUC of 0.74 (CI 0.53-0.95) for diagnosing pRCC (using qualitative features).

CONCLUSION

ML models incorporating MRI-based radiomics features and qualitative radiologic assessment can help characterize renal masses.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

ADVANCES IN KNOWLEDGE

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

Recommendations for the management of renal involvement in the tuberous sclerosis complex

Tuberous sclerosis complex (TSC) is a rare, hereditary, multisystemic disease with a broad phenotypic spectrum. Its management requires the collaboration of multiple specialists. Just as in the paediatric age, the paediatric neurologist takes on special importance; in adulthood, renal involvement is the cause of the greatest morbidity and mortality. There are several recommendations on the general management of patients with TSC but none that focuses on renal involvement. These recommendations respond to the need to provide guidelines to facilitate a better knowledge and diagnostic-therapeutic management of the renal involvement of TSC through a rational use of complementary tests and the correct use of available treatments. Their elaboration has been based on consensus within the hereditary renal diseases working group of the SEN/REDINREN (Spanish Society of Nephrology/Kidney Research Network). It has also counted on the participation of non-nephrologist specialists in TSC in order to expand the vision of the disease.

Giant renal leiomyoma: A case report

Renal leiomyoma is a rare benign mesenchymal tumor. It arises from the smooth muscle cells of the kidney and renal capsule is its most common location. Small tumor may be asymptomatic and usually appears as a well circumscribed peripherally located solid mass. Large tumor may manifest with pain, palpable flank mass or hematuria. Intersecting fascicles of spindle cells showing immunoreactivity to actin or desmin are characteristic histologic features. We present a case of giant renal leiomyoma in a 20-year-old female with chief complaints of abdominal discomfort and lump in her left side of abdomen. AP radiograph showed a large abdominopelvic soft tissue opacity. Contrast-enhanced computed tomography scan revealed a massive well circumscribed exophytic complex solid cystic mass of size 17 cm × 15 cm × 13 cm arising from upper pole of left kidney. The role of percutaneous biopsy is limited in such lesions and surgery is the only therapeutic option.

Predictive Value of In Vivo MR Spectroscopy With Semilocalization by Adiabatic Selective Refocusing in Differentiating Clear Cell Renal Cell Carcinoma From Other Subtypes

OBJECTIVE. The purpose of this study is to evaluate the diagnostic value of in vivo MR spectroscopy (MRS) with semilocalization by adiabatic selective refocusing (semi-LASER MRS) in differentiating clear cell renal cell carcinoma (RCC) from the non-clear cell subtype. SUBJECTS AND METHODS. Sixteen patients with biopsy-proven RCC or masses highly suspicious for RCC were prospectively recruited to participate in the study. Single-voxel ¹H spectra were acquired using a 3-T MRI system, with a semi-LASER sequence acquired for renal tumors in 14 patients and for healthy renal tissue (control tissue) in 12 patients. Offline processing of the MR spectra was performed. MRI and spectra analysis were performed independently by radiologists who were blinded to the reference histopathologic findings. RESULTS. Semi-LASER MRS was diagnostic for nine of 11 patients (82%) with histopathologically proven clear cell RCC, showing a strong lipid peak in seven patients and a weaker lipid resonance in two others, whereas control spectra showed weakly positive findings in only one patient. MRS findings were negative for lipid resonance in two of three patients (67%) with non-clear cell tumors and were weakly positive in another patient. Semi-LASER MRS had a high sensitivity and positive predictive value of 82% and 90%, respectively, in addition to a specificity of 67%, a negative predictive value of 50%, and overall accuracy of 79% for the detection of clear cell RCC. Lipid resonance was detected by MRS for four of six clear cell RCCs with no intravoxel fat on chemical-shift MRI. CONCLUSION. The preliminary results of the present study show that semi-LASER MRS is promising for the noninvasive discrimination of clear cell RCC from non-clear cell RCC on the basis of detection of lipid resonance and that it provides an incremental yield compared with chemical-shift MRI.

Deep Learning to Distinguish Benign from Malignant Renal Lesions Based on Routine MR Imaging

PURPOSE

With increasing incidence of renal mass, it is important to make a pretreatment differentiation between benign renal mass and malignant tumor. We aimed to develop a deep learning model that distinguishes benign renal tumors from renal cell carcinoma (RCC) by applying a residual convolutional neural network (ResNet) on routine MR imaging.

EXPERIMENTAL DESIGN

Preoperative MR images (T2-weighted and T1-postcontrast sequences) of 1,162 renal lesions definitely diagnosed on pathology or imaging in a multicenter cohort were divided into training, validation, and test sets (70:20:10 split). An ensemble model based on ResNet was built combining clinical variables and T1C and T2WI MR images using a bagging classifier to predict renal tumor pathology. Final model performance was compared with expert interpretation and the most optimized radiomics model.

RESULTS

Among the 1,162 renal lesions, 655 were malignant and 507 were benign. Compared with a baseline zero rule algorithm, the ensemble deep learning model had a statistically significant higher test accuracy (0.70 vs. 0.56, P = 0.004). Compared with all experts averaged, the ensemble deep learning model had higher test accuracy (0.70 vs. 0.60, P = 0.053), sensitivity (0.92 vs. 0.80, P = 0.017), and specificity (0.41 vs. 0.35, P = 0.450). Compared with the radiomics model, the ensemble deep learning model had higher test accuracy (0.70 vs. 0.62, P = 0.081), sensitivity (0.92 vs. 0.79, P = 0.012), and specificity (0.41 vs. 0.39, P = 0.770).

CONCLUSIONS

Deep learning can noninvasively distinguish benign renal tumors from RCC using conventional MR imaging in a multi-institutional dataset with good accuracy, sensitivity, and specificity comparable with experts and radiomics.

Recommendations for the management of renal involvement in the tuberous sclerosis complex

Tuberous sclerosis complex (TSC) is a rare, hereditary, multisystemic disease with a broad phenotypic spectrum. Its management requires the collaboration of multiple specialists. Just as in the paediatric age, the paediatric neurologist takes on special importance; in adulthood, renal involvement is the cause of the greatest morbidity and mortality. There are several recommendations on the general management of patients with TSC but none that focuses on renal involvement. These recommendations respond to the need to provide guidelines to facilitate a better knowledge and diagnostic-therapeutic management of the renal involvement of TSC through a rational use of complementary tests and the correct use of available treatments. Their elaboration has been based on consensus within the hereditary renal diseases working group of the SEN/REDINREN (Spanish Society of Nephrology/Kidney Research Network). It has also counted on the participation of non-nephrologist specialists in TSC in order to expand the vision of the disease.

[Concordances and predictors of biopsies in renal tumors]

AIM

Evaluate the concordance between the renal lesions biopsy's histology and the final histology of the surgical specimen according to histological subtype, and search for predictive factors of non-concordance.

MATERIAL

We performed a monocentric retrospective study that included 156 patients suffering from a renal tumor that benefited a lesion biopsy before surgical treatment. Sensibility and specificity of the renal lesion's biopsy for histological diagnostic of the different renal tumors where calculated.

RESULTS

One hundred and fifty-eight renal tumor biopsies were realized between 2001 and 2016. One hundred and forty-three renal cell carcinoma were found on the surgical piece, 135 were diagnosed on prior biopsy. Global concordance rate was 88%. For the establishment of the nuclear Fuhrmann grade, the concordance rate (low vs. high grade) was 72.9%. The cohort was divided into 2 groups according to the existence (group 1, n=139) or the absence (group 2, n=19) of concordance. Group 1 and 2 differed by the predominance of men in group 1 (66% vs. 37%, P=0.013), distance between the sinus and the tumor above 4mm (65% vs. 42%, P=0.05).

CONCLUSION

In renal tumor care, renal biopsy is a reliable testing. However, some factors most likely linked to the tumor anatomy (intra-sinusal tumor) and their histological composition were involved in the lack of non-contribution to the diagnosis.

LEVEL OF EVIDENCE

4.

Computed Tomography-Guided Kidney Transplant Biopsy Outcomes: A Single-Center Experience

OBJECTIVES

Percutaneous kidney transplant biopsy is typically performed using ultrasonographic guidance; computed tomography is an alternative modality used to obtain kidney allografttissuewhen ultrasonographyguided percutaneous kidney transplant biopsy is technically challenging. Studies examining postbiopsy outcomes in kidney transplant patients using a computed tomography-guided approach are scarce. Our goal was to reportthe incidence of nonsevere and severe complications in computed tomographyguided percutaneous kidney transplant biopsies and the potential risk factors.

MATERIALS AND METHODS

We retrospectively reviewed computed tomography-guided percutaneous kidney transplant biopsies in patients undergoing work-up for kidney allograft rejection between 2013 and 2017. Demographics, comorbidities, laboratory data, history of antiplatelet and/or anticoagulant use, and complications were assessed.

RESULTS

: During the study period, 28 patients underwent computed tomography-guided percutaneous kidney transplant biopsies; mean age was 57.5 ± 15.5 years, and 12 (43%)werewomen.Twenty-three patients (82%) were obese, with a body mass index greater than 30 kg/m². Our cohort of kidney transplant recipients included 21 (75%) from deceased donors and 7 (25%) from living-related donors. At the time of biopsy, 6 patients (21%) had elevated blood pressure (defined as > 160/90 mm Hg). One patient had severe complications, which included a significant decrease in hemoglobin requiring transfusion and a perinephric hematoma with worsening renal function. This was a morbidly obese patient whose blood pressure was elevated at the time of biopsy with a platelet count of 93 × 10³/mm³ and international normalized ratio of 1.21.

CONCLUSIONS

A computed tomography-guided percutaneous kidney transplant biopsy is a safe and effective alternative to obtain kidney tissue in the obese population and is associated with low rates of complications. In this study, we highlighted the need to achieve adequate blood pressure control and assess bleeding risk factors, such as platelet count and international normalized ratio, prior to biopsy.

Bosniak Classification of Cystic Renal Masses, Version 2019: An Update Proposal and Needs Assessment

Cystic renal cell carcinoma (RCC) is almost certainly overdiagnosed and overtreated. Efforts to diagnose and treat RCC at a curable stage result in many benign neoplasms and indolent cancers being resected without clear benefit. This is especially true for cystic masses, which compared with solid masses are more likely to be benign and, when malignant, less aggressive. For more than 30 years, the Bosniak classification has been used to stratify the risk of malignancy in cystic renal masses. Although it is widely used and still effective, the classification does not formally incorporate masses identified at MRI or US or masses that are incompletely characterized but are highly likely to be benign, and it is affected by interreader variability and variable reported malignancy rates. The Bosniak classification system cannot fully differentiate aggressive from indolent cancers and results in many benign masses being resected. This proposed update to the Bosniak classification addresses some of these shortcomings. The primary modifications incorporate MRI, establish definitions for previously vague imaging terms, and enable a greater proportion of masses to enter lower-risk classes. Although the update will require validation, it aims to expand the number of cystic masses to which the Bosniak classification can be applied while improving its precision and accuracy for the likelihood of cancer in each class.

Update on Indications for Percutaneous Renal Mass Biopsy in the Era of Advanced CT and MRI

OBJECTIVE. The objective of this article is to review the burgeoning role of percutaneous renal mass biopsy (RMB). CONCLUSION. Percutaneous RMB is safe, accurate, and indicated for an expanded list of clinical scenarios. The chief scenarios among them are to prevent treatment of benign masses and help select patients for active surveillance (AS). Imaging characterization of renal masses has improved; however, management decisions often depend on a histologic diagnosis and an assessment of biologic behavior of renal cancers, both of which are currently best achieved with RMB.

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.

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

OBJECTIVE

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

CONCLUSION

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

[Biopsies of kidney lesions: when and how?]

The demand for image-guided renal biopsy has increased due to the better detection of renal lesions; however, despite modern imaging techniques many small renal tumors cannot be classified as benign because they cannot be differentiated from renal cell carcinoma. Ultrasound and computed tomography (CT)-guided kidney biopsy is a safe and accurate method in the diagnostics of renal lesions and can be helpful in the selection of new ablative and pharmaceutical forms of treatment and avoid unnecessary operations. This article describes the clinical indications for an image-guided biopsy and discusses factors which should be considered when performing a biopsy.

Adult Renal Neoplasms: Cytology, Immunohistochemistry, and Cytogenetic Characteristics

Tissue sampling of renal masses is traditionally performed using percutaneous sonographic or CT guidance core biopsy (CB) with or without touch preparation cytology and/or fine-needle aspiration cytology (FNAC). The combined used of CB and FNAC is expanding in clinical practice, especially in small renal masses and plays a pivotal role in therapeutic decision making. Grouping the renal neoplasms in differential diagnostic groups helps in choosing specific immunohistochemical markers and reaching an accurate diagnosis.

Image-Guided Renal Interventions

Image-guided renal biopsies have an increasing role in clinical practice. Renal mass and renal parenchymal biopsy indications, techniques, and other clinical considerations are reviewed in this article. Image-guided renal mass ablation shows significant promise and increasing clinical usefulness as more studies demonstrate its safety and efficacy. Renal mass ablation indications, techniques, and other considerations are also reviewed.

Percutaneous image-guided core biopsy of solid renal masses: analysis of safety, efficacy, pathologic interpretation, and clinical significance

PURPOSE

To determine the efficacy, safety and clinical utility of CT and US-guided percutaneous renal mass biopsy.

MATERIALS AND METHODS

A retrospective IRB-approved, HIPAA-compliant study of a cohort of 183 consecutive patients who underwent percutaneous, CT or US-guided renal mass biopsy (RMB) from March 2002 through December 2012 was performed. RMB was performed in 183 consecutive patients for suspected solid renal mass of whom 14/183 (7.7%) were excluded because biopsies were performed at an outside institution, medical records were incomplete, or lesions were poorly visualized. Ten patients had multiple biopsies for new growing masses. Using US, CT or CT/US fusion-guidance, a 17G or 19G cannula needle was placed at the margin of the mass and an 18G or 20G core biopsy gun was used to obtain several tissue cores. Renal parenchymal biopsies for medical renal diseases were excluded. Imaging variables (including size, location, and extent of disease), number of core biopsies, patient demographics (age, gender), clinical indication, final pathologic diagnosis, immunohistochemical (IHC) studies, and subsequent final pathological diagnosis on nephrectomy were evaluated.

RESULTS

Of the 169 patients with 184 RMB, 121/169 (71.6%) were male with a mean age of 67.5 years. Of 184 RMB, 126 were malignant [126/184 (68.5%)], 37 [37/184 (20.1%)], were benign, and 21 (21/184 (11.4%) were nondiagnostic. IHC was performed in 131 biopsies (71.1%) and was diagnostic in 88.5% of those cases. Twenty-eight patients underwent subsequent partial nephrectomy; in 27/27 (100%) cases, RMB was concordant with nephrectomy for malignancy and in 21/27 (77.8%) RMB was concordant for subtype of RCC. Overall, the RMB sensitivity for detection of malignancy, specificity, and positive predictive value were 100%. The negative predictive value of benign RMB diagnosis was also 100%. There was a total of 14 (7.6%) complications, 13 minor (7.1%) and 1 major (0.5%). Of the minor complications, ten (5.5%) were postprocedural minor hematomas that resolved conservatively; one (0.5%) postprocedural vasovagal reaction; one (0.5%) episode of hematuria; and one (0.5%) episode of nausea and abdominal discomfort. No cases of renal pseudoaneurysm or tumor seeding attributed to biopsy were identified.

CONCLUSION

Percutaneous image-guided RMB is safe and highly diagnostic when combined with IHC and supports a greater role of RMB and imaging in evaluating renal masses when rendering appropriate treatments.

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

Angiomyolipomas (AML) are benign tumors of the kidneys frequently encountered in radiologic practice in large tertiary centers. In comparison to renal cell carcinomas (RCC), AML are seldom treated unless they are large, undergo malignant transformation or develop complications like acute hemorrhage. The common garden triphasic (classic) AML is an easy diagnosis, however, some variants lack macroscopic fat in which case the radiologic differentiation from RCC becomes challenging. Several imaging features, both qualitative and quantitative, have been described in differentiating the 2 entities. Although minimal fat AML is not entirely a radiologic diagnosis, the suspicion raised on imaging necessitates sampling and potentially avoids an unwanted surgery. Recently a new variant, epitheloid AML has been described which often has atypical imaging features and is at a higher risk for malignant transformation. Apart from the diagnosis, the radiologist also needs to convey information regarding nephrometric scores which help in surgical decision-making. Recently, more and more AMLs are managed with selective arterial embolization and percutaneous ablation, both of which are associated with less morbidity when compared to surgery. The purpose of this article is to review the imaging and pathologic features of classic AML as well as the differentiation of minimal fat AML from RCC. In addition, an overview of nephrometric scoring and image-guided interventions is also provided.

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

OBJECTIVE

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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