Heterogeneity and renal mass biopsy: a review of its role and reliability

Robot-assisted partial nephrectomy in patients aged 75 years or older - comparing the risk of complications with their younger counterparts

BACKGROUND & AIM

More elderly patients are diagnosed with kidney tumors where partial nephrectomy is technically possible. We investigated whether patients ≥ 75 years old had an increased risk of complications following robot-assisted partial nephrectomy (RAPN) compared to younger patients.

METHODS

Retrospective, consecutive study including patients who underwent RAPN between May 2016 - April 2023. Preoperative data, operative data and complications within 90 days were recorded by patient record review. Complications were classified according to Clavien-Dindo (CD).

RESULTS

451 patients underwent RAPN and a postoperative complication was recorded in 131 (29%) patients of which 28 (6%) were CD ≥ III. Any postoperative complication was recorded in 24/113 patients (21%) < 55 years, 40/127 patients (31%) 55-64 years, 45/151 patients (42%) 65-74 years, and 22/60 patients (37%) ≥ 75 years. Comparable numbers for a CD ≥ III postoperative complication were 2/113 (2%) < 55 years, 6/127 (7%) 55-64 years, 12/151 (8%) 65-74 years, and 5/60 (8%) ≥ 75 years. In multivariate logistic regression analysis, patients ≥ 75 years had a non-significant increased risk of complications when controlling for preoperative variables (OR 1.82 [95% CI 0.80-4.13]) or perioperative variables (OR 1.98 [95% CI 0.86-4.58]) compared to patients < 55 years. Two patients died postoperatively. Both were ≥ 75 years (2/60, 3%).

DISCUSSION AND CONCLUSIONS

Selected patients ≥ 75 years can undergo RAPN without a significantly increased risk of postoperative complications. However, a mortality rate of 3% in this age group indicates that these patients are frail when postoperative complications occur.

Prediction of Fuhrman pathological grade of renal clear cell carcinoma based on CT texture analysis

OBJECTIVE

To study the predictive performance of the imaging model based on the texture analysis of CT plain scan in distinguishing between low (grade I and II) and high (grade III and IV) of Fuhrman pathological grade of renal clear cell carcinoma.

METHODS

The clinical data of 94 patients with ccRCC who underwent CT scan and were confirmed by biopsy or surgery in TCGA-KIRC public database were retrospectively analyzed. There were 32 cases of low-grade ccRCC and 62 cases of high-grade ccRCC. The patients were randomly divided into training set and verification set according to the proportion of 7:3 by stratified sampling method. The imaging characteristics of ccRCC were calculated in the plain CT images. Lasso regression was used to reduce the dimensionality of the imaging characteristics of the training set, and binary logistic regression was used to construct the prediction model. Bootstrap method was used to verify the training set model and the validation set model, and the area under the receiver operating characteristic (ROC) curve (AUC) was calculated respectively.

RESULTS

Binary logistic regression showed that only imaging features were independent risk factors for predicting the Furhman classification of ccRCC. The predictive model was y = 1/[1 + exp (-z)], z = 1.274 × imaging risk score + 0.072. The results of bootstrap internal validation showed that the AUC of the training group was 0.961 (95% CI: 0.900-0.913). The Hosmer-Lemeshow goodness of fit test showed that the prediction model had a good calibration in the training group (P = 0.416). The AUC of prediction model in validation group was 0.731 (95% CI: 0.500-1.000). The Hosmer-Lemeshow goodness of fit test results showed that the prediction model had a good calibration in the validation group (P = 0.592).

CONCLUSION

The model based on CT texture analysis has a good predictive effect in differentiating low-grade and high-grade ccRCC and can provide reference for the treatment and prognosis of patients.

The success rate of small renal mass core needle biopsy and its impact on lowering benign resection rate

BACKGROUND

Small renal mass (SRM) biopsy remains under-utilized due to stigma. Meanwhile, the alarmingly high benign findings in resected kidney masses highlight the need for improved preoperative diagnosis and patient selection.

METHODS

The purpose of this study is to review the success rate of SRM biopsy and to evaluate its impact on patient management. A total of 168 percutaneous image-guided core needle biopsies (CNBs) of SRMs were retrieved at a tertiary academic center between 2015 and 2019. Subsequent treatment choices, side effects and outcomes were retrospectively reviewed.

RESULTS

The diagnostic rate of CNB was 86.9%. Benign neoplasms accounted for a significant portion (14.3%) of SRM. Renal cell carcinomas (RCCs) were the most common diagnoses (69.6%) as expected. In biopsy-resection correlation, the positive predictive value of CNB was 100%. Tumor typing and subtyping by CNB were highly accurate, 100% and 98.3% respectively. Nuclear grading for clear cell RCC was accurate in 83.8% cases. The CNB results had significant impact on treatment. Most patients with RCCs underwent either resection (54.1%) or ablation (33.9%), in contrast to observation in benign neoplasms (90.5%). Most importantly, the benign resection rate (3.2%) in this series was much lower than the national average.

CONCLUSION

CNB provided accurate diagnoses for the majority of SRMs and revealed benign diagnoses in a subset of clinically suspicious lesions. Employment of CNB in suspicious SRM may help avoid overtreatment for benign lesions.

Accurate detection of benign and malignant renal tumor subtypes with MethylBoostER: An epigenetic marker-driven learning framework

Current gold standard diagnostic strategies are unable to accurately differentiate malignant from benign small renal masses preoperatively; consequently, 20% of patients undergo unnecessary surgery. Devising a more confident presurgical diagnosis is key to improving treatment decision-making. We therefore developed MethylBoostER, a machine learning model leveraging DNA methylation data from 1228 tissue samples, to classify pathological subtypes of renal tumors (benign oncocytoma, clear cell, papillary, and chromophobe RCC) and normal kidney. The prediction accuracy in the testing set was 0.960, with class-wise ROC AUCs >0.988 for all classes. External validation was performed on >500 samples from four independent datasets, achieving AUCs >0.89 for all classes and average accuracies of 0.824, 0.703, 0.875, and 0.894 for the four datasets. Furthermore, consistent classification of multiregion samples (N = 185) from the same patient demonstrates that methylation heterogeneity does not limit model applicability. Following further clinical studies, MethylBoostER could facilitate a more confident presurgical diagnosis to guide treatment decision-making in the future.

Differentiation of benign from malignant solid renal lesions with MRI-based radiomics and machine learning

BACKGROUND

Solid renal masses are often indeterminate for benignity versus malignancy on magnetic resonance imaging. Such masses are typically evaluated with either percutaneous biopsy or surgical resection. Percutaneous biopsy can be non-diagnostic and some surgically resected lesions are inadvertently benign.

PURPOSE

To assess the performance of ten machine learning (ML) algorithms trained with MRI-based radiomics features in distinguishing benign from malignant solid renal masses.

METHODS

Patients with solid renal masses identified on pre-intervention MRI were curated from our institutional database. Masses with a definitive diagnosis via imaging (for angiomyolipomas) or via biopsy or surgical resection (for oncocytomas or renal cell carcinomas) were selected. Each mass was segmented for both T2- and post-contrast T1-weighted images. Radiomics features were derived from the segmented masses for each imaging sequence. Ten ML algorithms were trained with the radiomics features gleaned from each MR sequence, as well as the combination of MR sequences.

RESULTS

In total, 182 renal masses in 160 patients were included in the study. The support vector machine algorithm trained on radiomics features from T2-weighted images performed superiorly, with an accuracy of 0.80 and an area under the curve (AUC) of 0.79. Linear discriminant analysis (accuracy = 0.84 and AUC = 0.77) and logistic regression (accuracy = 0.78 and AUC = 0.78) algorithms trained on T2-based radiomics features performed similarly. ML algorithms trained on radiomics features from post-contrast T1-weighted images or the combination of radiomics features from T2- and post-contrast T1-weighted images yielded lower performance.

CONCLUSION

Machine learning models trained with radiomics features derived from T2-weighted images can provide high accuracy for distinguishing benign from malignant solid renal masses.

CLINICAL IMPACT

Machine learning models derived from MRI-based radiomics features may improve the clinical management of solid renal masses and have the potential to reduce the frequency with which benign solid renal masses are biopsied or surgically resected.

Two-dimensional correlated spectroscopy distinguishes clear cell renal cell carcinoma from other kidney neoplasms and non-cancer kidney

Background

Routinely used clinical scanners, such as computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound (US), are unable to distinguish between aggressive and indolent tumor subtypes in masses localized to the kidney, often leading to surgical overtreatment. The results of the current investigation demonstrate that chemical differences, detected in human kidney biopsies using two-dimensional COrrelated SpectroscopY (2D L-COSY) and evaluated using multivariate statistical analysis, can distinguish these subtypes.

Methods

One hundred and twenty-six biopsy samples from patients with a confirmed enhancing kidney mass on abdominal imaging were analyzed as part of the training set. A further forty-three samples were used for model validation. In patients undergoing radical nephrectomy, biopsies of non-cancer kidney cortical tissue were also collected as a non-cancer control group. Spectroscopy data were analyzed using multivariate statistical analysis, including principal component analysis (PCA) and orthogonal projection to latent structures with discriminant analysis (OPLS-DA), to identify biomarkers in kidney cancer tissue that was also classified using the gold-standard of histopathology.

Results

The data analysis methodology showed good separation between clear cell renal cell carcinoma (ccRCC) versus non-clear cell RCC (non-ccRCC) and non-cancer cortical tissue from the kidneys of tumor-bearing patients. Variable Importance for the Projection (VIP) values, and OPLS-DA loadings plots were used to identify chemical species that correlated significantly with the histopathological classification. Model validation resulted in the correct classification of 37/43 biopsy samples, which included the correct classification of 15/17 ccRCC biopsies, achieving an overall predictive accuracy of 86%, Those chemical markers with a VIP value >1.2 were further analyzed using univariate statistical analysis. A subgroup analysis of 47 tumor tissues arising from T1 tumors revealed distinct separation between ccRCC and non-ccRCC tissues.

Conclusions

This study provides metabolic insights that could have future diagnostic and/or clinical value. The results of this work demonstrate a clear separation between clear cell and non-ccRCC and non-cancer kidney tissue from tumor-bearing patients. The clinical translation of these results will now require the development of a one-dimensional (1D) magnetic resonance spectroscopy (MRS) protocol, for the kidney, using an in vivo clinical MRI scanner.

An Ultrasonic-Based Radiomics Nomogram for Distinguishing Between Benign and Malignant Solid Renal Masses

Objectives

This study was conducted in order to develop and validate an ultrasonic-based radiomics nomogram for diagnosing solid renal masses.

Methods

Six hundred renal solid masses with benign renal lesions (n = 204) and malignant renal tumors (n = 396) were divided into a training set (n = 480) and a validation set (n = 120). Radiomics features were extracted from ultrasound (US) images preoperatively and then a radiomics score (RadScore) was calculated. By integrating the RadScore and independent clinical factors, a radiomics nomogram was constructed. The diagnostic performance of junior physician, senior physician, RadScore, and radiomics nomogram in identifying benign from malignant solid renal masses was evaluated based on the area under the receiver operating characteristic curve (ROC) in both the training and validation sets. The clinical usefulness of the nomogram was assessed using decision curve analysis (DCA).

Results

The radiomics signature model showed satisfactory discrimination in the training set [area under the ROC (AUC), 0.887; 95% confidence interval (CI), 0.860–0.915] and the validation set (AUC, 0.874; 95% CI, 0.816–0.932). The radiomics nomogram also demonstrated good calibration and discrimination in the training set (AUC, 0.911; 95% CI, 0.886–0.936) and the validation set (AUC, 0.861; 95% CI, 0.802–0.921). In addition, the radiomics nomogram model showed higher accuracy in discriminating benign and malignant renal masses compared with the evaluations by junior physician (DeLong p = 0.004), and the model also showed significantly higher specificity than the senior and junior physicians (0.93 vs. 0.57 vs. 0.46).

Conclusions

The ultrasonic-based radiomics nomogram shows favorable predictive efficacy in differentiating solid renal masses.

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.

Utilization of Renal Mass Biopsy for T1 Renal Lesions across Michigan: Results from MUSIC-KIDNEY, A Statewide Quality Improvement Collaborative

BACKGROUND

Renal mass biopsy (RMB) has had limited and varied utilization to guide management of renal masses (RM).

OBJECTIVE

To evaluate utilization of RMB for newly diagnosed cT1 RMs across diverse practice types and assess associations of outcomes with RMB.

DESIGN SETTING AND PARTICIPANTS

MUSIC-KIDNEY commenced data collection in September 2017 for all newly presenting patients with a cT1 RM at 14 diverse practices. Patients were assessed at ≥120 d after initial evaluation.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Demographics and outcomes were compared for patients undergoing RMB versus no RMB. Clinical and demographic characteristics were summarized by RMB status using a χ2 test for categorical variables and Student t test for continuous variables. A mixed-effects logistic regression model was constructed to identify associations with RMB receipt.

RESULTS AND LIMITATIONS

RMB was performed in 15.5% (n = 282) of 1808 patients with a cT1 RM. Practice level rates varied from 0% to 100% (p = 0.001), with only five of 14 practices using RMB in >20% of patients. On multivariate analysis, predictors of RMB included greater comorbidity (Charlson comorbidity index ≥2 vs 0: odds ratio [OR] 1.44; p = 0.025) and solid lesion type (cystic vs solid: OR 0.17; p = 0.001; indeterminate vs solid: OR 0.58; p = 0.01). RMB patients were less likely to have benign pathology at intervention (5.0% vs 13.5%; p = 0.01). No radical nephrectomies were performed for patients with benign histology at RMB. The limitations include short follow-up and inclusion of practices with low numbers of RMBs.

CONCLUSIONS

Utilization of RMB varied widely across practices. Factors associated with RMB include comorbidities and lesion type. Patients undergoing RMB were less likely to have benign histology at intervention.

PATIENT SUMMARY

Current use of biopsy for kidney tumors is low and varies across our collaborative. Biopsy was performed in patients with greater comorbidity (more additional medical conditions) and for solid kidney tumors. Pretreatment biopsy is associated with lower nonmalignant pathology detected at treatment.

Monocarboxylate transporter 9 (MCT9) is down-regulated in renal cell carcinoma

BACKGROUND

The renal cell carcinoma (RCC) incidences are continuously increasing, however, their proper characterization remains difficult. Mammalian kidneys require large amounts of energy, and monocarboxylate transporter (MCT) gene family is responsible for the transport of monocarboxylic compounds across plasma membranes.

OBJECTIVE

A total of 14 MCT members have been identified in humans, which show highly distinct substrate affinities and tissue distributions. To understand the yet-uncharacterized renal cancer-specific role of MCTs, we identified MCT members that are differentially regulated during the renal tumor progression.

METHODS

We examined the expression level of MCT members in renal cell tumors and their relationship with survival rate of patients using a public database. Quantitative RT-PCR and northern blotting were performed to validate the expression of MCTs. Anti-MCT9 antiserum was raised in rabbit and used to examine MCT9 expression in normal and tumor tissue arrays. Effect of MCT9 overexpression on cell proliferation was measured using renal cancer cell lines.

RESULTS

MCT9 was found to be abundantly and exclusively expressed in human kidney cells, and was highly downregulated in renal cancers. Kaplan-Meier plotter analysis revealed an increased survival rate of MCT9 high-expressing RCC patients. MCT9 proteins were detected in normal kidney tissue sections and their overexpression clearly attenuated renal cell proliferation.

CONCLUSIONS

MCT9 was identified as a novel highly downregulated gene in renal cell cancer, and its overexpression clearly attenuated RCC cell proliferation. Thus, functional analysis of MCT9 may help in deciphering a yet-undiscovered kidney-specific energy metabolism during renal tumor progression.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

KEY POINTS

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

Imaging guided percutaneous renal biopsy: do it or not?

Since its first reported application, renal biopsy became an important part of the diagnostic algorithm, considered advantages and risks, to better manage therapeutic options. The biopsy can be performed with different techniques (open, laparoscopic, transjugular, transurethral and percutaneous). Currently, the percutaneous approach is the modality of choice. Percutaneous biopsy can be performed under CT or US guidance, but critical benefits and disadvantages have to be considered. Core needle biopsy is usually preferred to fine-needle aspiration because of the sample quality, usually obtaining multiple cores, especially in heterogeneous tumors. Principal complications are hematuria (1-10%), perinephric hematoma (10-90%), pneumothorax (0,6%), clinically significant pain (1,2%).

Analysis of the concordance of 20 immunohistochemical tissue markers in metastasectomy lesions in patients with metastatic renal cell carcinoma: A retrospective study using tissue microarray

Purpose

This study aimed to characterize the different expressions of 20 tissue markers in multiple metastatic lesions and organs in patients with metastatic renal cell carcinoma (mRCC).

Materials and Methods

Sixty-six patients with mRCC, harboring 162 metastasectomy tissue lesions (MTLs), were enrolled. Immunohistochemical analysis for the following tissue markers was performed: BAP1; CD31; CD 34; HIF1α and 2α; Ki67; pS6; PBRM1; PDGFRα and β; PDL1; PSMA; PTEN; α-SMA; TGase2; VEGFR1, 2, and 3; VHL loss; and CA9. Cases were identified pathologically using the semi-quantitative H-score (0–300), including the intensity score (0, 1, 2, 3). The concordance rate was calculated as the number of patients with concordant binary score out of the total number of patients in that comparison.

Results

The specimens from 66 patients were divided into those from the same organs and those from different organs. Forty-two patients (44 cases) with 96 MTLs and 39 with 83 MTLs were examined. Among the 20 tissue markers, only BAP1, PSMA, VEGFR3, PDGFRα, and pS6 tissue showed high concordance ratio (>0.7) regardless of different metastatic tissues and different metastatic lesions within the tumor.

Conclusions

The study demonstrated the intratumoral heterogeneity of mRCC with a low-concordance index of most tissue markers. However, some had high concordance with a similar expression regardless of the metastatic organs, metastatic sites, or presence of recurrence.

Active Surveillance for Small Renal Masses

Active surveillance (AS) is a safe and reasonable management strategy for many patients with small renal masses (SRM) suspicious for a clinical T1a renal cell carcinoma based on excellent metastasis-free and cancer-specific survival. However, the expansion of robotic extirpation of SRM has outpaced the adoption of AS, resulting in the possibility of overtreatment for select patients with SRM, especially the elderly and comorbid. In this review of AS for SRM, with a focus on the Delayed Intervention and Surveillance for Small Renal Masses (DISSRM) Registry, we detail the rationale behind AS, review lessons learned from the past decades of literature, and offer suggestions for appropriate patient selection and follow-up. An improved understanding of the data supporting AS will empower physicians and patients to more comfortably pursue AS to avoid over-treatment and provide individualized care to patients with SRM.

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

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

The Impact of Tumor Eco-Evolution in Renal Cell Carcinoma Sampling

Malignant tumors behave dynamically as cell communities governed by ecological principles. Massive sequencing tools are unveiling the true dimension of the heterogeneity of these communities along their evolution in most human neoplasms, clear cell renal cell carcinomas (CCRCC) included. Although initially thought to be purely stochastic processes, very recent genomic analyses have shown that temporal tumor evolution in CCRCC may follow some deterministic pathways that give rise to different clones and sub-clones randomly spatially distributed across the tumor. This fact makes each case unique, unrepeatable and unpredictable. Precise and complete molecular information is crucial for patients with cancer since it may help in establishing a personalized therapy. Intratumor heterogeneity (ITH) detection relies on the correctness of tumor sampling and this is part of the pathologist’s daily work. International protocols for tumor sampling are insufficient today. They were conceived decades ago, when ITH was not an issue, and have remained unchanged until now. Noteworthy, an alternative and more efficient sampling method for detecting ITH has been developed recently. This new method, called multisite tumor sampling (MSTS), is specifically addressed to large tumors that are impossible to be totally sampled, and represent an opportunity to improve ITH detection without extra costs.

Renal mass biopsy using Raman spectroscopy identifies malignant and benign renal tumors: potential for pre-operative diagnosis

The accuracy of renal mass biopsy to diagnose malignancy can be affected by multiple factors. Here, we investigated the feasibility of Raman spectroscopy to distinguish malignant and benign renal tumors using biopsy specimens. Samples were collected from 63 patients who received radical or partial nephrectomy, mass suspicious of cancer and distal parenchyma were obtained from resected kidney using an 18-gauge biopsy needle. Four Raman spectra were obtained for each sample, and Discriminant Analysis was applied for data analysis. A total of 383 Raman spectra were eventually gathered and each type of tumor had its characteristic spectrum. Raman could separate tumoral and normal tissues with an accuracy of 82.53%, and distinguish malignant and benign tumors with a sensitivity of 91.79% and specificity of 71.15%. It could classify low-grade and high-grade tumors with an accuracy of 86.98%. Besides, clear cell renal carcinoma was differentiated with oncocytoma and angiomyolipoma with accuracy of 100% and 89.25%, respectively. And histological subtypes of cell carcinoma were distinguished with an accuracy of 93.48%. When compared with final pathology and biopsy, Raman spectroscopy was able to correctly identify 7 of 11 “missed” biopsy diagnoses. These results suggested that Raman may serve as a promising non-invasive approach in the future for pre-operative diagnosis.

Diagnostic Performance and Interreader Agreement of a Standardized MR Imaging Approach in the Prediction of Small Renal Mass Histology

Purpose To assess the diagnostic performance and interreader agreement of a standardized diagnostic algorithm in determining the histologic type of small (≤4 cm) renal masses (SRMs) with multiparametric magnetic resonance (MR) imaging. Materials and Methods This single-center retrospective HIPAA-compliant institutional review board-approved study included 103 patients with 109 SRMs resected between December 2011 and July 2015. The requirement for informed consent was waived. Presurgical renal MR images were reviewed by seven radiologists with diverse experience. Eleven MR imaging features were assessed, and a standardized diagnostic algorithm was used to determine the most likely histologic diagnosis, which was compared with histopathology results after surgery. Interreader variability was tested with the Cohen κ statistic. Regression models using MR imaging features were used to predict the histopathologic diagnosis with 5% significance level. Results Clear cell renal cell carcinoma (RCC) and papillary RCC were diagnosed, with sensitivities of 85% (47 of 55) and 80% (20 of 25), respectively, and specificities of 76% (41 of 54) and 94% (79 of 84), respectively. Interreader agreement was moderate to substantial (clear cell RCC, κ = 0.58; papillary RCC, κ = 0.73). Signal intensity (SI) of the lesion on T2-weighted MR images and degree of contrast enhancement (CE) during the corticomedullary phase were independent predictors of clear cell RCC (SI odds ratio [OR]: 3.19; 95% confidence interval [CI]: 1.4, 7.1; P = .003; CE OR, 4.45; 95% CI: 1.8, 10.8; P < .001) and papillary RCC (CE OR, 0.053; 95% CI: 0.02, 0.2; P < .001), and both had substantial interreader agreement (SI, κ = 0.69; CE, κ = 0.71). Poorer performance was observed for chromophobe histology, oncocytomas, and minimal fat angiomyolipomas, (sensitivity range, 14%-67%; specificity range, 97%-99%), with fair to moderate interreader agreement (κ range = 0.23-0.43). Segmental enhancement inversion was an independent predictor of oncocytomas (OR, 16.21; 95% CI: 1.0, 275.4; P = .049), with moderate interreader agreement (κ = 0.49). Conclusion The proposed standardized MR imaging-based diagnostic algorithm had diagnostic accuracy of 81% (88 of 109) and 91% (99 of 109) in the diagnosis of clear cell RCC and papillary RCC, respectively, while achieving moderate to substantial interreader agreement among seven radiologists. ^© RSNA, 2018 Online supplemental material is available for this article.

The kidney cancer research priority-setting partnership: Identifying the top 10 research priorities as defined by patients, caregivers, and expert clinicians

It is critically important to define disease-specific research priorities to better allocate limited resources. There is growing recognition of the value of involving patients and caregivers, as well as expert clinicians in this process. To our knowledge, this has not been done this way for kidney cancer. Using the transparent and inclusive process established by the James Lind Alliance, the Kidney Cancer Research Network of Canada (KCRNC) sponsored a collaborative consensus-based priority-setting partnership (PSP) to identify research priorities in the management of kidney cancer. The final result was identification of 10 research priorities for kidney cancer, which are discussed in the context of current initiatives and gaps in knowledge. This process provided a systematic and effective way to collaboratively establish research priorities with patients, caregivers, and clinicians, and provides a valuable resource for researchers and funding agencies.

A Gene Module-Based eQTL Analysis Prioritizing Disease Genes and Pathways in Kidney Cancer

Clear cell renal cell carcinoma (ccRCC) is the most common and most aggressive form of renal cell cancer (RCC). The incidence of RCC has increased steadily in recent years. The pathogenesis of renal cell cancer remains poorly understood. Many of the tumor suppressor genes, oncogenes, and dysregulated pathways in ccRCC need to be revealed for improvement of the overall clinical outlook of the disease. Here, we developed a systems biology approach to prioritize the somatic mutated genes that lead to dysregulation of pathways in ccRCC. The method integrated multi-layer information to infer causative mutations and disease genes. First, we identified differential gene modules in ccRCC by coupling transcriptome and protein-protein interactions. Each of these modules consisted of interacting genes that were involved in similar biological processes and their combined expression alterations were significantly associated with disease type. Then, subsequent gene module-based eQTL analysis revealed somatic mutated genes that had driven the expression alterations of differential gene modules. Our study yielded a list of candidate disease genes, including several known ccRCC causative genes such as BAP1 and PBRM1, as well as novel genes such as NOD2, RRM1, CSRNP1, SLC4A2, TTLL1 and CNTN1. The differential gene modules and their driver genes revealed by our study provided a new perspective for understanding the molecular mechanisms underlying the disease. Moreover, we validated the results in independent ccRCC patient datasets. Our study provided a new method for prioritizing disease genes and pathways.

Addressing metabolic heterogeneity in clear cell renal cell carcinoma with quantitative Dixon MRI

BACKGROUND

Dysregulated lipid and glucose metabolism in clear cell renal cell carcinoma (ccRCC) has been implicated in disease progression, and whole tumor tissue-based assessment of these changes is challenged by the tumor heterogeneity. We studied a noninvasive quantitative MRI method that predicts metabolic alterations in the whole tumor.

METHODS

We applied Dixon-based MRI for in vivo quantification of lipid accumulation (fat fraction [FF]) in targeted regions of interest of 45 primary ccRCCs and correlated these MRI measures to mass spectrometry-based lipidomics and metabolomics of anatomically colocalized tissue samples isolated from the same tumor after surgery.

RESULTS

In vivo tumor FF showed statistically significant (P < 0.0001) positive correlation with histologic fat content (Spearman correlation coefficient, ρ = 0.79), spectrometric triglycerides (ρ = 0.56) and cholesterol (ρ = 0.47); it showed negative correlation with free fatty acids (ρ = -0.44) and phospholipids (ρ = -0.65). We observed both inter- and intratumoral heterogeneity in lipid accumulation within the same tumor grade, whereas most aggressive tumors (International Society of Urological Pathology [ISUP] grade 4) exhibited reduced lipid accumulation. Cellular metabolites in tumors were altered compared with adjacent renal parenchyma.

CONCLUSION

Our results support the use of noninvasive quantitative Dixon-based MRI as a biomarker of reprogrammed lipid metabolism in ccRCC, which may serve as a predictor of tumor aggressiveness before surgical intervention.

FUNDING

NIH R01CA154475 (YZ, MF, PK, IP), NIH P50CA196516 (IP, JB, RJD, JAC, PK), Welch Foundation I-1832 (JY), and NIH P01HL020948 (JGM).

Update on Renal Mass Biopsy

Renal masses are diagnosed with an increasing frequency. However, a significant proportion of these masses are benign, and the majority of malignant tumors are biologically indolent. Furthermore, renal tumors are often harbored by the elderly and comorbid patients. As such, matching of renal tumor biology to appropriate treatment intensity is an urgent clinical need. Renal mass biopsy is currently a very useful clinical tool that can assist with critical clinical decision-making in patients with renal mass. Yet, renal mass biopsy is associated with limitations and, as such, may not be appropriate for all patients.

Diagnostic Accuracy of Multiparametric Magnetic Resonance Imaging to Identify Clear Cell Renal Cell Carcinoma in cT1a Renal Masses

PURPOSE

The detection of small renal masses is increasing with the use of cross-sectional imaging, although many incidental lesions have negligible metastatic potential. Among malignant masses clear cell renal cell carcinoma is the most prevalent and aggressive subtype. A method to identify such histology would aid in risk stratification. Our goal was to evaluate a likelihood scale for multiparametric magnetic resonance imaging in the diagnosis of clear cell histology.

MATERIALS AND METHODS

We retrospectively reviewed the records of patients with cT1a masses who underwent magnetic resonance imaging and partial or radical nephrectomy from December 2011 to July 2015. Seven radiologists with different levels of experience who were blinded to final pathology findings independently reviewed studies based on a predefined algorithm. They applied a clear cell likelihood score, including 1-definitely not, 2-probably not, 3-equivocal, 4-probably and 5-definitely. Binary classification was used to determine the accuracy of clear cell vs all other histologies. Interobserver agreement was calculated with the weighted κ statistic.

RESULTS

A total of 110 patients with 121 masses were identified. Mean tumor size was 2.4 cm and 50% of the lesions were clear cell. Defining clear cell as scores of 4 or greater demonstrated 78% sensitivity and 80% specificity while scores of 3 or greater showed 95% sensitivity and 58% specificity. Interobserver agreement was moderate to good with a mean κ of 0.53.

CONCLUSIONS

A clear cell likelihood score used with magnetic resonance imaging can reasonably identify clear cell histology in small renal masses and may decrease the number of diagnostic renal mass biopsies. Standardization of imaging protocols and reporting criteria is needed to improve interobserver reliability.

Proteotranscriptomic Analysis Reveals Stage Specific Changes in the Molecular Landscape of Clear-Cell Renal Cell Carcinoma

Renal cell carcinoma comprises 2 to 3% of malignancies in adults with the most prevalent subtype being clear-cell RCC (ccRCC). This type of cancer is well characterized at the genomic and transcriptomic level and is associated with a loss of VHL that results in stabilization of HIF1. The current study focused on evaluating ccRCC stage dependent changes at the proteome level to provide insight into the molecular pathogenesis of ccRCC progression. To accomplish this, label-free proteomics was used to characterize matched tumor and normal-adjacent tissues from 84 patients with stage I to IV ccRCC. Using pooled samples 1551 proteins were identified, of which 290 were differentially abundant, while 783 proteins were identified using individual samples, with 344 being differentially abundant. These 344 differentially abundant proteins were enriched in metabolic pathways and further examination revealed metabolic dysfunction consistent with the Warburg effect. Additionally, the protein data indicated activation of ESRRA and ESRRG, and HIF1A, as well as inhibition of FOXA1, MAPK1 and WISP2. A subset analysis of complementary gene expression array data on 47 pairs of these same tissues indicated similar upstream changes, such as increased HIF1A activation with stage, though ESRRA and ESRRG activation and FOXA1 inhibition were not predicted from the transcriptomic data. The activation of ESRRA and ESRRG implied that HIF2A may also be activated during later stages of ccRCC, which was confirmed in the transcriptional analysis. This combined analysis highlights the importance of HIF1A and HIF2A in developing the ccRCC molecular phenotype as well as the potential involvement of ESRRA and ESRRG in driving these changes. In addition, cofilin-1, profilin-1, nicotinamide N-methyltransferase, and fructose-bisphosphate aldolase A were identified as candidate markers of late stage ccRCC. Utilization of data collected from heterogeneous biological domains strengthened the findings from each domain, demonstrating the complementary nature of such an analysis. Together these results highlight the importance of the VHL/HIF1A/HIF2A axis and provide a foundation and therapeutic targets for future studies. (Data are available via ProteomeXchange with identifier PXD003271 and MassIVE with identifier MSV000079511.)

FOXM1 participates in PLK1-regulated cell cycle progression in renal cell cancer cells

The regulation of entry into and progression through mitosis is important for cell proliferation. Polo-like kinase 1 (PLK1) is involved in multiple stages of mitosis. Forkhead box protein M1 (FOXM1) has multiple functions in tumorigenesis and, in elevated levels, is frequently associated with cancer progression. The present study reports that FOXM1, a substrate of PLK1, controls the transcription mechanism that mediates the PLK1-dependent regulation of the cell cycle. The present study investigated the expression of PLK1 and FOXM1 in the clear renal cell carcinoma 769-P and ACHN cell lines, and indicated that the expression of PLK1 and FOXM1 are correlated in human renal cell cancer cell lines and that the suppression of PLK1 may decrease the expression of FOXM1. The knockdown of FOXM1 or PLK1 in renal cell cancer cell lines caused cell cycle progression to be blocked. As a result, the present study indicated the involvement of FOXM1 in PLK1-regulated cell cycle progression.

Imaging genomics in cancer research: limitations and promises

Recently, radiogenomics or imaging genomics has emerged as a novel high-throughput method of associating imaging features with genomic data. Radiogenomics has the potential to provide comprehensive intratumour, intertumour and peritumour information non-invasively. This review article summarizes the current state of radiogenomic research in tumour characterization, discusses some of its limitations and promises and projects its future directions. Semi-radiogenomic studies that relate specific gene expressions to imaging features will also be briefly reviewed.

Alternate Metabolic Programs Define Regional Variation of Relevant Biological Features in Renal Cell Carcinoma Progression

PURPOSE

Clear cell renal cell carcinoma (ccRCC) has recently been redefined as a highly heterogeneous disease. In addition to genetic heterogeneity, the tumor displays risk variability for developing metastatic disease, therefore underscoring the urgent need for tissue-based prognostic strategies applicable to the clinical setting. We have recently employed the novel PET/magnetic resonance (MR) image modality to enrich our understanding of how tumor heterogeneity can relate to gene expression and tumor biology to assist in defining individualized treatment plans.

EXPERIMENTAL DESIGN

ccRCC patients underwent PET/MR imaging, and these images subsequently used to identify areas of varied intensity for sampling. Samples from 8 patients were subjected to histologic, immunohistochemical, and microarray analysis.

RESULTS

Tumor subsamples displayed a range of heterogeneity for common features of hypoxia-inducible factor expression and microvessel density, as well as for features closely linked to metabolic processes, such as GLUT1 and FBP1. In addition, gene signatures linked with disease risk (ccA and ccB) also demonstrated variable heterogeneity, with most tumors displaying a dominant panel of features across the sampled regions. Intriguingly, the ccA- and ccB-classified samples corresponded with metabolic features and functional imaging levels. These correlations further linked a variety of metabolic pathways (i.e., the pentose phosphate and mTOR pathways) with the more aggressive, and glucose avid ccB subtype.

CONCLUSIONS

Higher tumor dependency on exogenous glucose accompanies the development of features associated with the poor risk ccB subgroup. Linking these panels of features may provide the opportunity to create functional maps to enable enhanced visualization of the heterogeneous biologic processes of an individual's disease. Clin Cancer Res; 22(12); 2950-9. ©2016 AACR.

A single-centre experience with tumour tract seeding associated with needle manipulation of renal cell carcinomas

With the rise in detection of incidental renal masses on imaging, there has been a commensurate rise in the use of percutaneous biopsies for evaluation of these tumours. Tumour tract seeding had previously been one of the most feared complications of percutaneous biopsy of renal cell carcinoma (RCC). Recently, less emphasis has been placed on this complication, with the assertion that it has only been reported eight times in literature, and thus must be exceedingly rare. However, we report two cases of tumour tract seeding associated with percutaneous biopsy and treatment of RCC over a short time period at a single institution. This report challenges the current extremely low estimates of the frequency of this complication and calls for a more realistic assessment.

Magnetic Resonance Imaging as a Biomarker for Renal Cell Carcinoma

As the most common neoplasm arising from the kidney, renal cell carcinoma (RCC) continues to have a significant impact on global health. Conventional cross-sectional imaging has always served an important role in the staging of RCC. However, with recent advances in imaging techniques and postprocessing analysis, magnetic resonance imaging (MRI) now has the capability to function as a diagnostic, therapeutic, and prognostic biomarker for RCC. For this narrative literature review, a PubMed search was conducted to collect the most relevant and impactful studies from our perspectives as urologic oncologists, radiologists, and computational imaging specialists. We seek to cover advanced MR imaging and image analysis techniques that may improve the management of patients with small renal mass or metastatic renal cell carcinoma.

Percutaneous biopsy for risk stratification of renal masses

The increased use of abdominal imaging has led to identification of more patients with incidental renal masses, and renal mass biopsy (RMB) has become a popular method to evaluate unknown renal masses prior to definitive treatment. Pathologic data obtained from biopsy may be used to guide decisions for treatment and may include the presence or absence of malignant tumor, renal cell cancer subtype, tumor grade and the presence of other aggressive pathologic features. However, prior to using RMB for risk stratification, it is important to understand whether RMB findings are equivalent to pathologic analysis of surgical specimens and to identify any potential limitations of this approach. This review outlines the advantages and limitations of the current studies that evaluate RMB as a guide for treatment decision in patients with unknown renal masses. In multiple series, RMB has demonstrated low morbidity and a theoretical reduction in cost, if patients with benign tumors are identified from biopsy and can avoid subsequent treatment. However, when considering the routine use of RMB for risk stratification, it is important to note that biopsy may underestimate risk in some patients by undergrading, understaging or failing to identify aggressive tumor features. Future studies should focus on developing treatment algorithms that integrate RMB to identify the optimal use in risk stratification of patients with unknown renal masses.

The Past, Present, and Future in Management of Small Renal Masses

Management of small renal masses (SRMs) is currently evolving due to the increased incidence given the ubiquity of cross-sectional imaging. Diagnosing a mass in the early stages theoretically allows for high rates of cure but simultaneously risks overtreatment. New consensus guidelines and treatment modalities are changing frequently. The multitude of information currently available shall be summarized in this review. This summary will detail the historic surgical treatment of renal cell carcinoma with current innovations, the feasibility and utility of biopsy, the efficacy of ablative techniques, active surveillance, and use of biomarkers. We evaluate how technology may be used in approaching the small renal mass in order to decrease morbidity, while keeping rates of overtreatment to a minimum.

Indications for biopsy and the current status of focal therapy for renal tumours

The increased detection of small renal masses (SRMs) has focused attention on their uncertain natural history. The development of treatment alternatives and the discovery of biologically targeted drugs have also raised interest. Renal mass biopsies (RMBs) have a crucial role as they provide the pathological, molecular and genetic information needed to classify these lesions and guide clinical management. The improved accuracy has improved our knowledge of the behaviour of different tumour histologies and opened the potential for risk-adapted individualized treatment approaches. To date, studies have demonstrated that percutaneous ablation is an effective therapy with acceptable outcomes and low risk in the appropriate clinical setting. Although partial nephrectomy (PN) is still considered the standard treatment for SRM, percutaneous ablation is increasingly being performed and if long-term efficacy is sustained, it may have a wider application for SRMs after biopsy characterization.