Grade Heterogeneity in Small Renal Masses: Potential Implications for Renal Mass Biopsy

Tumour size is associated with growth rates of >0.5 cm/year and delayed intervention in small renal masses in patients on active surveillance

OBJECTIVE

To evaluate the association between tumour size and the growth rate (GR) of small renal masses (SRMs) in patients managed by active surveillance (AS).

MATERIALS AND METHODS

We queried the prospective, multi-institutional Delayed Intervention and Surveillance for Small Renal Masses (DISSRM) registry for patients on AS with an imaging interval of ≥6 months, identifying 456 patients. We tracked tumour size over time; a GR >0.5 cm/year was defined as a GR event. We used multivariable recurrent events and time-to-event Cox regression modelling to evaluate the association between tumour size and GR events (primary outcome) and tumour size and delayed intervention (DI; secondary outcome). We tested tumour size as a continuous variable and dichotomised tumour size by predefined (2-cm) and calculated (2.9-cm) cutoffs. We calculated the cutoff using maximally selected rank statistics and time to progression, defined according to the DISSRM registry.

RESULTS

The median (interquartile range) follow-up of patients on AS was 40.1 (26.4-71.2) months, during which 128 patients (28%) had ≥1 GR event, and 80 (18%) underwent DI. Larger tumour size was an independent predictor for GR events and DI when tested as a continuous and a dichotomous variable in multivariable analyses (all P < 0.05). The association was strongest when accounting for the change in tumour size over time and when applying the 2.9-cm cutoff. The study is limited by the mixed tumour pathology inert to SRMs.

CONCLUSION

Larger tumour size was independently associated with GR events and DI for patients with SRMs on AS. A 2.9-cm cutoff may provide valuable information for patient counselling.

In Pursuit of KI-RADS: Toward a Single, Evidence-based Imaging Classification of Renal Masses

Despite the successful application of Imaging Reporting and Data Systems to improve the radiologic description and management of disease in many organs, one does not yet exist for the kidney. Instead, the radiologic approach to the kidney has focused on the Bosniak classification system, which is based on imaging characteristics for cystic renal masses, and detecting macroscopic fat within solid renal masses. Radiologically, cystic and solid renal masses are categorized and evaluated separately because of historical precedent, differences in appearance at imaging, and differences in biologic behavior. However, the World Health Organization classification of renal neoplasms does not support such separation. Further, the primary goal has been cancer diagnosis. Differentiating benign from malignant masses is important, but data show that many renal cancers, particularly when small, will not cause harm. Therefore, a critical goal of any unifying, single, imaging-based classification of kidney masses (ie, a Kidney Imaging Reporting and Data System) should be predicting the biologic behavior or aggressiveness of suspected kidney cancer. This system could inform the need for treatment or active surveillance and reduce prevalent overdiagnosis and overtreatment. This review describes the rationale for and challenges in creating such a system and the research needed for it to be developed.

Contrast-Enhanced CT-Based Radiomics Nomogram for Prediction of Pathologic T3a Upstaging in Clinical T1 RCC

Background/Objectives: To develop a nomogram for the preoperative prediction of pathologic T3a (pT3a) upstaging in patients with clinical T1(cT1) renal cell carcinoma (RCC). Methods: A total of 169 cT1 patients with RCC with preoperative contrast-enhanced CT (CECT) and clinical data were enrolled in this study. Afterwards, the sample was split randomly into training and testing sets in a 7:3 ratio. Radiomics features were extracted and selected from the whole primary tumor on CECT images to develop radiomics signatures. The nomogram was constructed using the obtained radiomics signature and clinical risk factors. The predictive performance of different models was evaluated and visualized using receiver operator characteristic (ROC) curves. Results: In total, 26 radiomics features were selected for the radiomics signature construction. The radiomics signature yielded area under the curve (AUC) values of 0.945 and 0.873 in the training and testing sets, respectively. The nomogram integrating radiomics signature and predictive clinical factors, including tumor size and neutrophil-lymphocyte ratio (NLR), achieved higher predictive performance in the training [AUC, 0.958; 95% confidence interval (CI): 0.921, 0.995] and testing (AUC, 0.913; 95% CI: 0.814, 1.000) sets. Good calibration was achieved for the nomogram in both the training and testing sets (Brier score = 0.082 and 0.098). Decision curve analysis (DCA) demonstrated that the nomogram was clinically useful in predicting pT3a upstaging, with a corresponding net benefit of 0.378. Conclusions: The proposed nomogram can preoperatively predict pT3a upstaging in cT1 RCC and serve as a non-invasive imaging marker to guide individualized treatment.

Percutaneous microwave ablation of cT1b renal cell carcinoma: safety and oncologic efficacy in a large, single-center elderly and comorbid cohort

PURPOSE

To evaluate safety and oncologic efficacy of percutaneous microwave ablation (MWA) for treating clinically localized T1b (cT1b) renal cell carcinoma (RCC).

METHODS

This single-center retrospective study was performed under a waiver of informed consent. Seventy-four consecutive patients (49M/25F) with 76 cT1b RCC (median tumor diameter 4.5 cm) were treated with percutaneous MWA between 5/2012 and 8/2020. Patients were stratified into two groups by technique, depending on whether antennas were repositioned for additional ablation or not. Primary efficacy, complications, and local tumor progression (LTP) were compared using the Wilcoxon rank sum and Fisher's exact tests. The Kaplan Meier method was used for survival analysis.

RESULTS

Patients were elderly (median age 69.5), obese (median BMI 34.5), and comorbid (Charlson Comorbidity Index = 4). Most tumors were low-grade (grade 1-2) (67/89, 88%) and clear cell RCC was the most common histology (62/76, 82%). A median of three MWA antennas were powered at 65 W for 7 min for treatment. Renal masses were larger (4.6 vs 4.5 cm, p = 0.01) and procedure times longer (100 min vs 80.5 min, p = 0.04) for the antenna reposition cohort (n = 34, 45%). Primary efficacy and high-grade complication rates were 93% and 8%, respectively. The local tumor progression rate (LTP), at a median follow-up was 28.2 months, was 16%. Primary efficacy, low and high-grade complications, change in estimated glomerular filtration rate and LTP were similar between cohorts (p = 0.20-0.55).

CONCLUSION

Percutaneous MWA for cT1b RCC is safe in elderly and comorbid patients with acceptable oncologic efficacy. Repeat ablation is well-tolerated and can improve oncologic efficacy.

Exploring the value of arterial spin labeling and six diffusion MRI models in differentiating solid benign and malignant renal tumors

OBJECTIVE

To explore the value of three-dimensional arterial spin labeling (ASL) and six diffusion magnetic resonance imaging (MRI) models in differentiating solid benign and malignant renal tumors.

METHODS

This retrospective study included 89 patients with renal tumors. All patients underwent ASL and ZOOMit diffusion-weighted imaging (DWI) examinations and were divided into three groups: clear cell renal cell carcinoma (ccRCC), non-ccRCC, and benign renal tumors (BRT). The mean and peak renal blood flow (RBFmean and RBFpeak) from ASL and fourteen diffusion parameters from mono-exponential DWI (Mono_DWI), intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), stretched exponential model (SEM), fractional order calculus (FROC), and continuous-time random-walk (CTRW) model were analyzed. Binary logistic regression was used to determine the optimal parameter combinations. The diagnostic performance of various MRI-derived parameters and their combinations was compared.

RESULTS

Among the six diffusion models, the SEM model achieved the highest performance in differentiating ccRCC from non-ccRCC (area under the receiver operating characteristic curve [AUC] 0.880) and from BRT (AUC 0.891). IVIM model achieved the highest AUC (0.818) in differentiating non-ccRCC from BRT. Among all the MRI-derived parameters, RBFpeak combined with DKI_MK yielded the highest AUC (0.970) in differentiating ccRCC from non-ccRCC, and the combination of RBFpeak, SEM_DDC, and FROC_μ yielded the highest AUC (0.992) for differentiating ccRCC from BRT.

CONCLUSION

ASL and all diffusion models showed similar diagnostic performance in differentiating ccRCC from non-ccRCC or BRT, while the IVIM model performed better in distinguishing non-ccRCC from BRT. Combining ASL with diffusion models can provide additional value in predicting ccRCC.

RELEVANCE STATEMENT

Considering the increasing detection rate of incidental renal masses, accurate discrimination of benign and malignant renal tumors is crucial for decision-making. Combining ASL with diffusion MRI models offers a promising solution to this clinical issue.

KEY POINTS

All assessed models were effective for differentiating ccRCC from non-ccRCC or BRT. ASL and all diffusion models showed similar performance in differentiating ccRCC from non-ccRCC or BRT. Combining ASL with diffusion models significantly improved diagnostic efficacy in predicting ccRCC. IVIM model could better differentiate non-ccRCC from BRT.

Unmet needs in the management of patients with bilateral synchronous renal masses: the rationale for clinical decision-making

BACKGROUND

Bilateral synchronous renal masses (BSRMs) are a rare finding, and the optimal treatment strategy remains undetermined. This study depicts the management of BSRM at eight European high-volume centers.

METHODS

This is a retrospective analysis of prospective institutional databases collecting all patients presenting with clinical T1-2 N0 M0 BSRMs between 1993 and 2020 at 8 tertiary referral high-volume centers for renal cancer treatment in Europe. The treatment options included active surveillance (AS), tumor ablation (TA) and surgery (partial and radical nephrectomy).

RESULTS

Overall, 134 patients were analyzed. Renal mass biopsy prior treatment was performed in 8% of cases. 15%, 4%, and 81% of patients underwent AS, a combination of surgery and TA, and bilateral (one-stage or two-stage) surgery. Among patients undergoing bilateral surgery (N.=109), a staged approach was chosen in 78% (N.=85) of cases treating the lower complexity tumor first in 51/85 (60%) cases and in 34/85 (40%) treating the higher complexity tumor first. Concordance of the histological analysis was found in 77% of patients with 10% of bilateral benign masses.

CONCLUSIONS

Even if considering only referral centers, a high heterogeneity for decision-making in the treatment of BSRM should be expected. Advances in genetic diagnosis, the implementation of novel imaging technologies, and the strengthening role of alternative treatment, may lead to a standardized decision-making process in the setting of BSRMs.

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

Purpose

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

Methods

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

Results

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

Conclusion

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

The evolving management of small renal masses

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

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.

The Utility of Renal Mass Biopsy in Large Renal Masses

Objectives

The role of needle core renal biopsy in large renal masses, defined as lesions larger than 4 cm, is debatable, as larger renal masses are associated with malignant histology. We aim to review the safety and impact of renal biopsy on the management of large renal masses.

Methods

A retrospective, single-center review of all renal biopsies performed between January 2011 and December 2020 at Royal Perth Hospital was conducted. Indications for biopsy, complications and final management plans were correlated to assess the value of biopsies in large renal masses.

Results

In total, 126 biopsies were performed. Indeterminate imaging findings and comorbidities were the main indications for biopsies. We identified 116 (92.1%) diagnostic biopsies and 10 (8.0%) non-diagnostic biopsies due to insufficient samples or inflammatory tissue. Of the diagnostic biopsies, 99 (78.6%) were malignant and 17 (13.5%) were benign. Unnecessary extirpative surgery was avoided in 17 patients. Histology included renal cell carcinoma (96%) and other malignancies such as urothelial carcinoma (3%) and non-Hodgkin's lymphoma (1%). Benign biopsies identified histology including angiomyolipoma (35.3%) and oncocytoma (52.5%). The median follow-up time was 68 months (range 19-132 months).

Conclusion

Renal biopsies in large renal masses may aid in preventing unnecessary surgery, especially in situations where imaging findings are equivocal or in patients with many comorbidities.

Multi-phase-combined CECT radiomics models for Fuhrman grade prediction of clear cell renal cell carcinoma

Objective

This study aimed to evaluate the effectiveness of multi-phase-combined contrast-enhanced CT (CECT) radiomics methods for noninvasive Fuhrman grade prediction of clear cell renal cell carcinoma (ccRCC).

Methods

A total of 187 patients with four-phase CECT images were retrospectively enrolled and then were categorized into training cohort (n=126) and testing cohort (n=61). All patients were confirmed as ccRCC by histopathological reports. A total of 110 3D classical radiomics features were extracted from each phase of CECT for individual ccRCC lesion, and contrast-enhanced variation features were also calculated as derived radiomics features. These features were concatenated together, and redundant features were removed by Pearson correlation analysis. The discriminative features were selected by minimum redundancy maximum relevance method (mRMR) and then input into a C-support vector classifier to build multi-phase-combined CECT radiomics models. The prediction performance was evaluated by the area under the curve (AUC) of receiver operating characteristic (ROC).

Results

The multi-phase-combined CECT radiomics model showed the best prediction performance (AUC=0.777) than the single-phase CECT radiomics model (AUC=0.711) in the testing cohort (p value=0.039).

Conclusion

The multi-phase-combined CECT radiomics model is a potential effective way to noninvasively predict Fuhrman grade of ccRCC. The concatenation of first-order features and texture features extracted from corticomedullary phase and nephrographic phase are discriminative feature representations.

"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.

Ultrasound Image-Based Deep Features and Radiomics for the Discrimination of Small Fat-Poor Angiomyolipoma and Small Renal Cell Carcinoma

We evaluated the performance of ultrasound image-based deep features and radiomics for differentiating small fat-poor angiomyolipoma (sfp-AML) from small renal cell carcinoma (SRCC). This retrospective study included 194 patients with pathologically proven small renal masses (diameter ≤4 cm; 67 in the sfp-AML group and 127 in the SRCC group). We obtained 206 and 364 images from the sfp-AML and SRCC groups with experienced radiologist identification, respectively. We extracted 4024 deep features from the autoencoder neural network and 1497 radiomics features from the Pyradiomics toolbox; the latter included first-order, shape, high-order, Laplacian of Gaussian and Wavelet features. All subjects were allocated to the training and testing sets with a ratio of 3:1 using stratified sampling. The least absolute shrinkage and selection operator (LASSO) regression model was applied to select the most diagnostic features. Support vector machine (SVM) was adopted as the discriminative classifier. An optimal feature subset including 45 deep and 7 radiomics features was screened by the LASSO model. The SVM classifier achieved good performance in discriminating between sfp-AMLs and SRCCs, with areas under the curve (AUCs) of 0.96 and 0.85 in the training and testing sets, respectively. The classifier built using deep and radiomics features can accurately differentiate sfp-AMLs from SRCCs on ultrasound imaging.

Diagnostic Accuracy of 99mTc-Sestamibi SPECT/CT for Characterization of Solid Renal Masses

Our objective was to assess the diagnostic accuracy of ^99mTc-sestamibi SPECT/CT for characterizing solid renal masses. Methods: Imaging and clinical records of patients who underwent ^99mTc-sestamibi SPECT/CT for clinical work-up of their solid renal masses from September 2018 to October 2021 were retrospectively reviewed. Histopathology formed the reference standard, and the diagnoses were categorized as malignant/concerning (renal cell carcinomas [RCCs] other than chromophobe histology) and benign/nonconcerning (oncocytic tumors including chromophobe RCC, other benign diagnoses) to calculate the sensitivity and specificity of ^99mTc-sestamibi SPECT/CT and contrast-enhanced CT (ceCT). The clinical reads of the SPECT/CT images were used for visual classification of the lesions. Additionally, the SPECT images were manually segmented to obtain the maximum and mean counts of the lesion and adjacent renal cortex and maximum and mean lesion Hounsfield units. Results: ^99mTc-sestamibi SPECT/CT was performed on 42 patients with 62 renal masses. A histopathologic diagnosis was available for 27 patients (18 male, 9 female) with 36 solid renal masses. ceCT findings were available for 20 of these patients. The most commonly identified single histologic type was clear cell RCC (13/36; 36.1%). Oncocytic tumors were the most common group of nonconcerning lesions (15/36), with oncocytoma as the predominant histologic type (n = 6). The sensitivity and specificity of SPECT/CT for diagnosing a nonconcerning lesion were 66.7% and 89.5%, respectively, compared with 10% and 75%, respectively, for ceCT. The lesion-to-kidney ratios for maximum and mean counts and maximum lesion Hounsfield units showed significant differences between the 2 groups (P < 0.05). The lesion-to-kidney mean count ratio at a cutoff of 0.46 showed a sensitivity and specificity of 87.5% and 86.67%, respectively, for detecting nonconcerning lesions, which was significantly higher than that of ceCT. Conclusion: The current literature on the utility of ^99mTc-sestamibi SPECT/CT for characterization of solid renal masses is limited. We offer additional evidence of the incremental value of ^99mTc-sestamibi SPECT/CT over ceCT for differentiating malignant or aggressive renal tumors from benign or indolent ones, thereby potentially avoiding overtreatment and its associated complications. Quantitative assessment can further increase the diagnostic accuracy of SPECT/CT and may be used in conjunction with visual interpretation.

Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review

Radiomics and artificial intelligence (AI) may increase the differentiation of benign from malignant kidney lesions, differentiation of angiomyolipoma (AML) from renal cell carcinoma (RCC), differentiation of oncocytoma from RCC, differentiation of different subtypes of RCC, to predict Fuhrman grade, to predict gene mutation through molecular biomarkers and to predict treatment response in metastatic RCC undergoing immunotherapy. Neural networks analyze imaging data. Statistical, geometrical, textural features derived are giving quantitative data of contour, internal heterogeneity and gray zone features of lesions. A comprehensive literature review was performed, until July 2022. Studies investigating the diagnostic value of radiomics in differentiation of renal lesions, grade prediction, gene alterations, molecular biomarkers and ongoing clinical trials have been analyzed. The application of AI and radiomics could lead to improved sensitivity, specificity, accuracy in detecting and differentiating between renal lesions. Standardization of scanner protocols will improve preoperative differentiation between benign, low-risk cancers and clinically significant renal cancers and holds the premises to enhance the diagnostic ability of imaging tools to characterize renal lesions.

Does histologic subtype impact overall survival in observed T1a kidney cancers compared with competing risks? Implications for biopsy as a risk stratification tool

OBJECTIVES

We sought to assess if adding a biopsy proven histologic subtype to a model that predicts overall survival that includes variables representing competing risks in observed, biopsy proven, T1a renal cell carcinomas, enhances the model's performance.

METHODS

The National Cancer Database was assessed (years 2004-2015) for patients with observed T1a renal cell carcinoma who had undergone renal mass biopsy. Kaplan-Meier curves were utilized to estimate overall survival stratified by histologic subtype. We utilized C-index from a Cox proportional hazards model to evaluate the impact of adding histologic subtypes to a model to predict overall survival for each stage.

RESULTS

Of 132 958 T1a renal masses identified, 1614 had biopsy proven histology and were managed non-operatively. Of those, 61% were clear cell, 33% papillary, and 6% chromophobe. Adjusted Kaplan-Meier curves demonstrated a difference in overall survival between histologic subtypes (P = 0.010) with greater median overall survival for patients with chromophobe (85.1 months, hazard rate 0.45, P = 0.005) compared to clear cell (64.8 months, reference group). Adding histology to a model with competing risks alone did not substantially improve model performance (C-index 0.65 vs 0.64 respectively).

CONCLUSIONS

Incorporation of histologic subtype into a risk stratification model to determine prognostic overall survival did not improve modeling of overall survival compared with variables representing competing risks in patients with T1a renal cell carcinoma managed with observation. These results suggest that performing renal mass biopsy in order to obtain tumor histology may have limited utility. Future studies should further investigate the overall utility of renal mass biopsy for observed T1a kidney cancers.

Diagnosis and Treatment of Small Renal Masses: Where Do We Stand?

PURPOSE OF REVIEW

To present an overview of the current evidence-based studies covering diagnostic and management of SRM.

RECENT FINDINGS

Renal cell carcinoma (RCC) represents 3% of the cancers. Nowadays, partial nephrectomy (PN) represents gold standard treatment. New nephron-sparing approaches such as active surveillance and ablative therapies have been increasingly used as an alternative to surgical intervention. Due to novel comprehension of RCC and widespread use of imaging techniques, diagnosis at early stage in elderly patients has increased. Treatment decision-making should be based on patient and tumour characteristics. With expanding treatment options, the management of SRMs has become a debate and should be adjusted to patient and tumour characteristics. In a shared decision manner, both active surveillance with possible delayed intervention and focal therapy should be discussed with the patient as an alternative to partial nephrectomy.

Novel Imaging Methods for Renal Mass Characterization: A Collaborative Review

CONTEXT

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

OBJECTIVE

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

EVIDENCE ACQUISITION

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

EVIDENCE SYNTHESIS

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

CONCLUSIONS

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

PATIENT SUMMARY

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

Improving Renal Tumor Biopsy Prognostication With BAP1 Analyses

CONTEXT.—

Active surveillance of small renal masses highlights the need for accurate prognostication of biopsies.

OBJECTIVE.—

To comprehensively evaluate the accuracy of biopsies in assessing known prognostic parameters including histologic subtype by comparison with subsequent nephrectomy samples.

DESIGN.—

We retrospectively identified patients at University of Texas Southwestern Medical Center, Dallas, Texas, who had a biopsy for a renal mass between 2004-2018. Biopsy samples were evaluated for known prognostic factors such as tumor grade, necrosis, sarcomatoid/rhabdoid change, and BRCA1-associated protein-1 (BAP1) status, which we previously showed is an independent prognostic factor for clear cell renal cell carcinoma. Accuracy was determined by comparison with subsequent analyses of nephrectomy specimens. Statistical analyses were performed to assess biopsy accuracy and correlation with tumor size and pathologic stage.

RESULTS.—

From 805 biopsies with a diagnosis of renal neoplasm, 178 had subsequent resection of the biopsied tumor. Concordance rate for histologic subtype was 96.9% (κ [w], 0.90; 95% CI, 0.82-0.99) and excellent for small renal masses (98.8%; κ [w], 0.97; 95% CI, 0.90-1). Amongst the prognostic variables evaluated, BAP1 immunohistochemistry in clear cell renal cell carcinoma had the highest agreement (94.8%; κ [w], 0.83; 95% CI, 0.66-0.99). The presence of 1 or more aggressive features (grade 3-4, tumor necrosis, BAP1 loss, sarcomatoid/rhabdoid change) in a biopsy significantly correlated with pT stage (P = .004).

CONCLUSIONS.—

Biopsy analyses showed high accuracy for subtyping renal tumors, but it underestimated several poor prognostic features. Addition of BAP1 for clear cell renal cell carcinoma may increase prognostic accuracy. If validated, routine incorporation of BAP1 immunohistochemistry in clear cell renal cell carcinoma biopsies may refine prognosis and aid in the selection of patients for active surveillance.

Active Surveillance of Renal Masses: The Role of Radiology

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

Renal Tumor Biopsy: Rationale to Avoid Surgery in Small Renal Masses

PURPOSE OF REVIEW

The use of renal tumor biopsy (RTB) for small renal masses (SRMs) in daily practice, although safe and accurate, is unusual. Considering the large number of benign tumors in patients with renal masses < 4 cm, some patients with benign tumors are directly referred for surgery instead. This study aimed to report the diagnostic rates of RTB, determine the concordance with surgical pathology, and assess the number of procedures that could have been avoided. We retrospectively studied 255 patients who underwent RTB at our institution in 2010-2019. Of them, 73 were excluded from the analysis (exclusion criteria: > 4 cm, cystic lesion, missing data). The remaining 182 with undetermined SRMs ≤ 4 cm underwent RTB under computed tomography guidance.

RECENT FINDINGS

Biopsies were diagnostic in 154/182 (84.6%) cases. Of the non-diagnostic biopsies, 11 were diagnostic when repeated. When RTB was performed of all undetermined SRMs, active treatment (surgery or cryotherapy) was avoided in 50/182 patients (27.5%) because of a benign diagnosis, while 9/182 patients (4.9%) underwent surveillance after a shared multidisciplinary decision. The overall diagnostic rate was 90.6%. All adverse events (approximately 4%) were Clavien-Dindo grade I and did not require active treatment. RTB histology results and nuclear grade were highly concordant with the final pathology (96% and 86.6%, respectively). On univariate logistic regression analysis, male sex was the only contributing factor of diagnostic biopsy. RTB of SRMs should be performed more frequently as part of a multidisciplinary decision-making process since it avoided unnecessary surgical treatment in 1 of 3 patients in our institution.

Prediction of histologic grade and type of small (< 4 cm) papillary renal cell carcinomas using texture and neural network analysis: a feasibility study

OBJECTIVE

To predict the histologic grade and type of small papillary renal cell carcinomas (pRCCs) using texture analysis and machine learning algorithms.

METHODS

This was a retrospective HIPAA-compliant study. 24 noncontrast (NC), 22 corticomedullary (CM) phase, and 24 nephrographic (NG) phase CTs of small (< 4 cm) surgically resected pRCCs were identified. Surgical pathology classified the tumors as low- or high-Fuhrman histologic grade and type 1 or 2. The axial image with the largest cross-sectional tumor area was exported and segmented. Six histogram and 31 texture (20 gray-level co-occurrences and 11 gray-level run-lengths) features were calculated for each tumor in each phase. Feature values in low- versus high-grade and type 1 versus 2 pRCCs were compared. Area under the receiver operating curve (AUC) was calculated for each feature to assess prediction of histologic grade and type of pRCCs in each phase. Histogram, texture, and combined histogram and texture feature sets were used to train and test three classification algorithms (support vector machine (SVM), random forest, and histogram-based gradient boosting decision tree (HGBDT)) with stratified shuffle splits and threefold cross-validation; AUCs were calculated for each algorithm in each phase to assess prediction of histologic grade and type of pRCCs.

RESULTS

Individual histogram and texture features did not have statistically significant differences between low- and high-grade or type 1 and type 2 pRCCs across all phases. Individual features had low predictive power for tumor grade or type in all phases (AUC < 0.70). HGBDT was highly accurate at predicting pRCC histologic grade and type using histogram, texture or combined histogram and texture feature data from the CM phase (AUCs = 0.97-1.0). All algorithms had highest AUCs using CM phase feature data sets; AUCs decreased using feature sets from NC or NG phases.

CONCLUSIONS

The histologic grade and type of small pRCCs can be predicted with classification algorithms using CM histogram and texture features, which outperform NC and NG phase image data. The accurate prediction of pRCC histologic grade and type may be able to further guide management of patients with small (< 4 cm) pRCCs being considered for active surveillance.

Active Surveillance of Small Renal Masses: A Systematic Review

BACKGROUND: Our goal is to review current literature regarding active surveillance (AS) of small renal masses (SRMs) and identify trends in survival outcomes, factors that predict the need for further intervention, and quality of life (QOL). METHODS: We performed a comprehensive literature search in PubMed and EMBASE and identified 194 articles. A narrative summary was performed in lieu of a meta-analysis due to the heterogeneity of selected studies. RESULTS: Seventeen articles were chosen to be featured in this review. Growth rate (GR) was not an accurate predictor of malignancy, although it was the characteristic most commonly used to trigger delayed intervention (DI). The mean 5-year overall survival (OS) of all studies was 73.6% ±1.7% for AS groups. The combined cancer specific survival (CSS) for AS is 97.1% ±0.6%, compared to 98.6% ±0.4% for the primary intervention (PI) groups, (p = 0.038). CONCLUSIONS: Short and intermediate-term data demonstrate that AS with the option for DI is a management approach whose efficacy (in terms of CSS) approaches that of PI at 5 years, is cost effective, and prevents overtreatment, especially in patients with significant comorbidities.

Differentiation of renal angiomyolipoma without visible fat from small clear cell renal cell carcinoma by using specific region of interest on contrast-enhanced CT: a new combination of quantitative tools

Background

To investigate the value of using specific region of interest (ROI) on contrast-enhanced CT for differentiating renal angiomyolipoma without visible fat (AML.wovf) from small clear cell renal cell carcinoma (ccRCC).

Methods

Four-phase (pre-contrast phase [PCP], corticomedullary phase [CMP], nephrographic phase [NP], and excretory phase [EP]) contrast-enhanced CT images of AML.wovf (n = 31) and ccRCC (n = 74) confirmed by histopathology were retrospectively analyzed. The CT attenuation value of tumor (AVT), net enhancement value (NEV), relative enhancement ratio (RER), heterogeneous degree of tumor (HDT) and standardized heterogeneous ratio (SHR) were obtained by using different ROIs [small: ROI (1), smaller: ROI (2), large: ROI (3)], and the differences of these quantitative data between AML.wovf and ccRCC were statistically analyzed. Multivariate regression was used to screen the main factors for differentiation in each scanning phase, and the prediction models were established and evaluated.

Results

Among the quantitative parameters determined by different ROIs, the degree of enhancement measured by ROI (2) and the enhanced heterogeneity measured by ROI (3) performed better than ROI (1) in distinguishing AML.wovf from ccRCC. The receiver operating characteristic (ROC) curves showed that the area under the curve (AUC) of RER_CMP (2), RER_NP (2) measured by ROI (2) and HDT_CMP and SHR_CMP measured by ROI (3) were higher (AUC = 0.876, 0.849, 0.837 and 0.800). Prediction models that incorporated demographic data, morphological features and quantitative data derived from the enhanced phase were superior to quantitative data derived from the pre-contrast phase in differentiating between AML.wovf and ccRCC. Among them, the model in CMP was the best prediction model with the highest AUC (AUC = 0.986).

Conclusion

The combination of quantitative data obtained by specific ROI in CMP can be used as a simple quantitative tool to distinguish AML.wovf from ccRCC, which has a high diagnostic value after combining demographic data and morphological features.

A 25 year perspective on the evolution and advances in an understanding of the biology, evaluation and treatment of kidney cancer

The diagnosis, evaluation and management of patients with renal cell carcinoma has transformed in the 21st century. Utilizing biological discoveries and technological advances, the field has moved from blunt surgical and largely ineffective medical treatments, to nuanced and fine-tuned approaches based on biology, extent of disease and patient preferences. In this review we will summarize the last 25 years of progress in kidney cancer.

Comparison of CT Texture Analysis Software Platforms in Renal Cell Carcinoma: Reproducibility of Numerical Values and Association With Histologic Subtype Across Platforms

OBJECTIVE. The purpose of this article is to evaluate interobserver, intraobserver, and interplatform variability and compare the previously established association between texture metrics and tumor histologic subtype using three commercially available CT texture analysis (CTTA) software platforms on the same dataset of large (> 7 cm) renal cell carcinomas (RCCs). MATERIALS AND METHODS. CT-based texture analysis was performed on contrast-enhanced MDCT images of large (> 7 cm) untreated RCCs in 124 patients (median age, 62 years; 82 men and 42 women) using three different software platforms. Using this previously studied cohort, texture features were compared across platforms. Features were correlated with histologic subtype, and strength of association was compared between platforms. Single-slice and volumetric measures from one platform were compared. Values for interobserver and intraobserver variability on a tumor subset (n = 30) were assessed across platforms. RESULTS. Metrics including mean gray-level intensity, SD, and volume correlated fairly well across platforms (concordance correlation coefficient [CCC], 0.66-0.99; mean relative difference [MRD], 0.17-5.97%). Entropy showed high variability (CCC, 0.04; MRD, 44.5%). Mean, SD, mean of positive pixels (MPP), and entropy were associated with clear cell histologic subtype on almost all platforms (p < .05). Mean, SD, entropy, and MPP were highly reproducible on most platforms on both interobserver and intraobserver analysis. CONCLUSION. Select texture metrics were reproducible across platforms and readers, but other metrics were widely variable. If clinical models are developed that use CTTA for medical decision making, these differences in reproducibility of some features across platforms need to be considered, and standardization is critical for more widespread adaptation and implementation.

Clinical, surgical, pathological and follow-up features of kidney cancer patients with Von Hippel-Lindau syndrome: novel insights from a large consortium

PURPOSE

To investigate the natural history and follow-up after kidney tumor treatment of Von Hippel-Lindau (VHL) patients.

MATERIALS AND METHODS

A multi-institutional European consortium of patients with VHL syndrome included 96 non-metastatic patients treated at 9 urological departments (1987-2018). Descriptive and survival analyses were performed.

RESULTS AND LIMITATIONS

Median age at VHL diagnosis was 34 years (IQR 25-43). Two patients (2.1%) showed only renal manifestations at VHL diagnosis. Concomitant involvement of Central Nervous System (CNS) vs. pancreas vs. eyes vs. adrenal gland vs. others were present in 60.4 vs. 68.7 vs. 30.2 vs. 15.6 vs. 15.6% of patients, respectively. 45% of patients had both CNS and pancreatic diseases alongside kidney. The median interval between VHL diagnosis and renal cancer treatment resulted 79 months (IQR 0-132), and median index tumor size leading to treatment was 35.5 mm (IQR 28-60). Of resected malignant tumours, 73% were low grade. Of high-grade tumors, 61.1% were large > 4 cm. With a median follow-up of 8 years, clinical renal progression rate was 11.7% and 29.3% at 5 and 10 years, respectively. Overall mortality was 4% and 7.5% at 5 and 10 years, respectively. During the follow-up, 50% of patients did not receive a second active renal treatment. Finally, 25.3% of patients had CKD at last follow-up.

CONCLUSIONS

Mean period between VHL diagnosis and renal cancer detection is roughly three years, with significant variability. Although, most renal tumors are small low-grade, clinical progression and mortality are not negligible. Moreover, kidney function represents a key issue in VHL patients.

Computerized Image Analysis of Tumor Cell Nuclear Morphology Can Improve Patient Selection for Clinical Trials in Localized Clear Cell Renal Cell Carcinoma

Background:

Clinicopathological scores are used to predict the likelihood of recurrence-free survival for patients with clear cell renal cell carcinoma (ccRCC) after surgery. These are fallible, particularly in the middle range. This inevitably means that a significant proportion of ccRCC patients who will not develop recurrent disease enroll into clinical trials. As an exemplar of using digital pathology, we sought to improve the predictive power of “recurrence free” designation in localized ccRCC patients, by precise measurement of ccRCC nuclear morphological features using computational image analysis, thereby replacing manual nuclear grade assessment.

Materials and Methods:

TNM 8 UICC pathological stage pT1-pT3 ccRCC cases were recruited in Scotland and in Singapore. A Leibovich score (LS) was calculated. Definiens Tissue studio® (Definiens GmbH, Munich) image analysis platform was used to measure tumor nuclear morphological features in digitized hematoxylin and eosin (H&E) images.

Results:

Replacing human-defined nuclear grade with computer-defined mean perimeter generated a modified Leibovich algorithm, improved overall specificity 0.86 from 0.76 in the training cohort. The greatest increase in specificity was seen in LS 5 and 6, which went from 0 to 0.57 and 0.40, respectively. The modified Leibovich algorithm increased the specificity from 0.84 to 0.94 in the validation cohort.

Conclusions:

CcRCC nuclear mean perimeter, measured by computational image analysis, together with tumor stage and size, node status and necrosis improved the accuracy of predicting recurrence-free in the localized ccRCC patients. This finding was validated in an ethnically different Singaporean cohort, despite the different H and E staining protocol and scanner used. This may be a useful patient selection tool for recruitment to multicenter studies, preventing some patients from receiving unnecessary additional treatment while reducing the number of patients required to achieve adequate power within neoadjuvant and adjuvant clinical studies.

Differential diagnosis of hypervascular ultra-small renal cell carcinoma and renal angiomyolipoma with minimal fat in early stage by using thin-section multidetector computed tomography

PURPOSE

The purpose of this study was to investigate the difference between imaging features of ultra-small renal cell carcinoma (usRCC) and angiomyolipoma with minimal fat (mfAML) whose enhancement were both hypervascular by using multidetector computed tomography (MDCT).

MATERIALS AND METHODS

Confirmed by pathology, 40 cases of hypervascular usRCC and 21 cases of hypervascular mfAML both with diameter of 2 cm or less were compared and analyzed retrospectively, including traditional imaging features and thin-section computed tomography (CT) dynamic enhanced parameters. Meanwhile, receiver operating characteristic (ROC) curves were constructed to evaluate the diagnostic efficacy of each significant parameter and the information with diagnostic value was selected to construct the prediction model.

RESULTS

Comparison of traditional imaging features: the features, included age, shape, location, central location of tumor, wedge sign, renal cortex lift sign, black star sign, enhanced homogeneity in cortical phase (CP) and enhancement pattern had no significant difference between usRCC and mfAML (P > 0.05); sex, cystic degeneration or necrosis, pseudocapsule sign, and enhanced homogeneity in nephrographic phase (NP) had significant differences between usRCC and mfAML (P < 0.05). Comparison of CT dynamic enhanced parameters: the CT value, NEV and REV of usRCC were all higher than mfAML in both CP and NP (P < 0.01). Respectively, the area under the ROC curve (AUC) were 0.74, 0.75, 0.78, 0.83, 0.81 and 0.78. The sensitivity and specificity for differentiating ucRCC from mfAML were 85.0% and 76.2% respectively when NEV_NP was 73.6 HU as the critical value. Multivariate analysis showed that male, cystic degeneration or necrosis, and NEV_NP higher than 73.6 HU as an independent risk factor for usRCC (P < 0.01). The AUC value of the prediction model constructed by the combination was 0.94, the accuracy was 86.89%, the sensitivity was 82.50%, and the specificity was 95.24%.

CONCLUSION

Morphological characteristics in traditional diagnosis of small renal carcinoma (diameter of 4 cm or less) have certain significance in differentiating hypervascular usRCC and mfAML in early stage, but the diagnostic efficacy was limited. Sex, cystic degeneration or necrosis, and quantitative parameters measured after enhancement play an important role in differential diagnosis of hypervascular usRCC and mfAML, and the prediction model constructed by the combination has a good diagnostic performance.

Histologic Heterogeneity of Extirpated Renal Cell Carcinoma Specimens: Implications for Renal Mass Biopsy

Pathologic characteristics of extirpated renal cell carcinoma (RCC) specimens <7 cm were reviewed to get better information on technical nuances of renal mass biopsy (RMB). Specimens were stratified according to tumor stage, nuclear grade, size, histology, presence of lymphovascular invasion (LVI), necrosis, and sarcomatoid features. When considering pT1 (0–7 cm) tumors, pT1b (4–7 cm) RCC masses were more likely to have necrosis (43% vs 16%, P < 0.001), LVI (6% vs 2%, P = 0.024), high-grade nuclear elements (29% vs 17%, P < 0.001), and sarcomatoid features (2% vs 0%, P = 0.006) compared with pT1a (0–4 cm) tumors. Additionally, pT3a tumors were more highly associated with necrosis (P = 0.005), LVI, sarcomatoid features, and high-grade disease (P for all < 0.001) when compared to pT1 masses. For masses <4 cm, pT3a cancers were more likely to demonstrate necrosis (38% vs 16%, P < 0.001), LVI (22% vs 2%, P < 0.001), high-grade nuclear elements (45% vs 17%, P < 0.001), and sarcomatoid features (12% vs 0%, P < 0.001) compared to pT1a tumors. Similarly, for masses 4–7 cm, pathologic T3a tumors were significantly more likely to have sarcomatoid features (12% vs 2%, P = 0.006) and LVI (22% vs 6%, P = 0.003) compared to pT1b tumors. In summary, pT3a tumors and those RCC masses >4 cm exhibit considerable histologic heterogeneity and may harbor elements that are not easily appreciated with limited renal sampling. Therefore, if RMB is considered for renal masses greater than 4 cm or those that abut sinus fat, a multi-quadrant biopsy approach is necessary to ensure adequate sampling and characterization of the mass.

Identification of Immune-Related Cells and Genes in Tumor Microenvironment of Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is one of the most common tumors in the urinary system. Progression in immunotherapy has provided novel options for the ccRCC treatment. However, the understanding of the ccRCC microenvironment and the potential therapeutic targets in the microenvironment is still unclear. Here, we analyzed the gene expression profile of ccRCC tumors from the Cancer Genome Atlas (TCGA) and calculated the abundance ratios of immune cells for each sample. Then, seven types of immune cells were found to be correlated to overall survival, and 3863 immune-related genes were identified by analyzing differentially expressed genes. We also found that the function of immune-related genes was mainly focused on ligand-receptor binding and signaling pathway transductions. Additionally, we identified 13 hub genes by analyzing the protein-protein interaction network, and seven of them are related to overall survival. Our study not only expands the understanding of fundamental biological features of microenvironment but also provides potential therapeutic targets.

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.

Research progress on advanced renal cell carcinoma

Renal cell carcinoma (RCC) is a malignant tumor and the third most common urinary disease. It was estimated that RCC affected over 350,000 individuals in 2013, and there are nearly 140,000 deaths annually due to this disease. The initial masses in RCC patients are mostly confined to a single organ. However, due to the metastatic spread of cancer cells through the circulatory system, more than 30% of RCC patients relapse after surgery. The appearance of distant metastases often means that patients enter the advanced stage of cancer with low quality of life and a short expected survival time. This review aims to describe the extant research on advanced RCC, including its pathophysiology, heterogeneity, diagnosis, treatment, and prospects. We try to highlight the most suitable means of treating advanced RCC patients, focusing on comprehensive personalized treatments.

Prediction of High-Grade Clear Cell Renal Cell Carcinoma Based on Plasma mRNA Profiles in Patients with Localized Pathologic T1N0M0 Stage Disease

A high nuclear grade is crucial to predicting tumor recurrence and metastasis in clear cell renal cell carcinomas (ccRCCs). We aimed to compare the mRNA profiles of tumor tissues and preoperative plasma in patients with localized T1 stage ccRCCs, and to evaluate the potential of the plasma mRNA profile for predicting high-grade ccRCCs. Data from a prospective cohort (n = 140) were collected between November 2018 and November 2019. Frozen tumor tissues and plasma were used to measure PBRM1, BAP1, SET domain-containing 2 (SETD2), KDM5C, FOXC2, CLIP4, AQP1, DDX11, BAIAP2L1, and TMEM38B mRNA levels, and correlation with the Fuhrman grade was investigated. Multivariate logistic regression analysis revealed significant association between high-grade ccRCC and SETD2 and DDX11 mRNA levels in tissues (odds ratio (b) = 0.021, 95% confidence interval (CI): 0.001-0.466, p = 0.014; b = 6.116, 95% CI: 1.729-21.631, p = 0.005, respectively) and plasma (b = 0.028, 95% CI 0.007-0.119, p < 0.001; b = 1.496, 95% CI: 1.187-1.885, p = 0.001, respectively). High-grade ccRCC prediction models revealed areas under the curve of 0.997 and 0.971 and diagnostic accuracies of 97.86% and 92.86% for the frozen tissue and plasma, respectively. SETD2 and DDX11 mRNA can serve as non-invasive plasma biomarkers for predicting high-grade ccRCCs. Studies with long follow-ups are needed to validate the prognostic value of these biomarkers in ccRCCs.

Texture analysis and machine learning algorithms accurately predict histologic grade in small (< 4 cm) clear cell renal cell carcinomas: a pilot study

PURPOSE

To predict the histologic grade of small clear cell renal cell carcinomas (ccRCCs) using texture analysis and machine learning algorithms.

METHODS

Fifty-two noncontrast (NC), 26 corticomedullary (CM) phase, and 35 nephrographic (NG) phase CTs of small (< 4 cm) surgically resected ccRCCs were retrospectively identified. Surgical pathology classified the tumors as low- or high-Fuhrman histologic grade. The axial image with the largest cross-sectional tumor area was exported and segmented. Six histogram and 31 texture (gray-level co-occurrences (GLC) and gray-level run-lengths (GLRL)) features were calculated for each tumor in each phase. T testing compared feature values in low- and high-grade ccRCCs, with a (Benjamini-Hochberg) false discovery rate of 10%. Area under the receiver operating curve (AUC) was calculated for each feature to assess prediction of low- and high-grade ccRCCs in each phase. Histogram, texture, and combined histogram and texture data sets were used to train and test four algorithms (k-nearest neighbor (KNN), support vector machine (SVM), random forests, and decision tree) with tenfold cross-validation; AUCs were calculated for each algorithm in each phase to assess prediction of low- and high-grade ccRCCs.

RESULTS

Zero, 23, and 0 features in the NC, CM, and NG phases had statistically significant differences between low and high-grade ccRCCs. CM histogram skewness and GLRL short run emphasis had the highest AUCs (0.82) in predicting histologic grade. All four algorithms had the highest AUCs (0.97) predicting histologic grade using CM histogram features. The algorithms' AUCs decreased using histogram or texture features from NC or NG phases.

CONCLUSION

The histologic grade of small ccRCCs can be accurately predicted with machine learning algorithms using CM histogram features, which outperform NC and NG phase image data.

Development and validation of CT imaging-based preoperative nomogram in the prediction of unfavorable high-grade small renal masses

PURPOSE

In recent years, there has been an increase in the incidence of small renal masses (SRMs) and nephrectomy was the standard management of this disease in the past. Currently, the use of active surveillance has been recommended as an alternative option in the case of some patients with SRMs due to its heterogenicity. However, limited studies focused on the regarding risk stratification. Therefore, in the current study, we developed a nomogram for the purpose of predicting the presence of high-grade SRMs on the basis of the patient information provided (clinical information, hematological indicators, and CT imaging data).

PATIENTS AND METHODS

A total of 329 patients (consisting of development and validation cohort) who had undergone nephrectomy for SRMs between January 2013 and May 2016 retrospectively were recruited for the present study. All preoperative information, including clinical predictors, hematological indicators, and CT predictors, were obtained. Lasso regression model was used for data dimension reduction and feature selection. Multivariable logistic regression analysis was applied for the establishment of the predicting model. The performance of the nomogram was assessed with respect to its calibration and discrimination properties and externally validated.

RESULTS

The predictors used in the assessment of the nomogram included tumor size, CT tumor contour, CT necrosis, CT tumor exophytic properties, and CT collecting system oppression. Based on these parameters, the nomogram was evaluated to have an effective discrimination and calibration ability, and the C-index was found to be 0.883 after internal validation and 0.887 following external validation.

CONCLUSION

Based on the aforementioned findings, it can be concluded that CT imaging-based preoperative nomogram is an effective predictor of SRMs and hence can be used in the preoperative evaluation of SRMs, due to its calibration and discrimination abilities.

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.

Histologic Diagnosis of Renal Mass Biopsy

CONTEXT.—

Core biopsy has been increasingly used for clinical decision-making in the management of patients with renal masses. The sensitivity and specificity of histologic diagnoses of renal mass biopsies depend on many factors such as adequate sampling and tissue processing, diagnostic skill and experience, and appropriate use of ancillary techniques.

OBJECTIVE.—

To review the indications, emphasize the importance of obtaining adequate diagnostic material, and introduce a general diagnostic approach, in conjunction with immunohistochemistry, in diagnosis of renal mass biopsies.

DATA SOURCES.—

Literature review and personal experiences in daily practice and consultation diagnosis of renal masses in a large tertiary medical center.

CONCLUSIONS.—

For renal mass biopsies, it is critical to obtain adequate diagnostic material and establish a standard laboratory procedure in working with small biopsy specimens. The key for the diagnosis is to be familiar with different tumor entities with characteristic morphology and to understand the wide spectrum of tumor heterogeneity. By developing a systematic approach, one can categorize the tumor and create a sensible differential diagnosis based on the growth pattern and cellular morphology. Immunohistochemistry is particularly helpful for renal mass biopsy diagnosis in selected situations.

Challenges and opportunities in the proteomic characterization of clear cell renal cell carcinoma (ccRCC): A critical step towards the personalized care of renal cancers

Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney cancer, comprising approximately 75% of all kidney tumors. Recent the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) studies have significantly advanced the molecular characterization of RCC and facilitated the development of targeted therapies. Such advances have improved the median survival of patients with advanced disease from less than 10 months prior to 2004 to 30 months by 2011. However, approximately 30% of localized ccRCC patients will nevertheless develop recurrence or metastasis after surgical resection of their tumor. Therefore, it is critical to further analyze potential tumor-associated proteins and their profiles during disease progression. Over the past decade, tremendous effort has been focused on the study of molecular pathways, including genomics, transcriptomics, and proteomics in order to identify potential molecular biomarkers, as well as to facilitate early detection, monitor tumor progression and uncover potentially therapeutic targets. In this review, we focus on recent advances in the proteomic analysis of ccRCC, current strategies and challenges, and perspectives in the field. This insight will highlight the discovery of tumor-associated proteins, and their potential clinical impact on personalized precision-based care in ccRCC.

Value of global metabolomics in association with diagnosis and clinicopathological factors of renal cell carcinoma

Renal cell carcinoma (RCC) is a malignant tumor that currently lacks clinically useful biomarkers indicative of early diagnosis or disease status. RCC has commonly been diagnosed based on imaging results. Metabolomics offers a potential technology for discovering biomarkers and therapeutic targets by comprehensive screening of metabolites from patients with various cancers. We aimed to identify metabolites associated with early diagnosis and clinicopathological factors in RCC using global metabolomics (G-Met). Tumor and nontumor tissues were sampled from 20 cases of surgically resected clear cell RCC. G-Met was performed by liquid chromatography mass spectrometry and important metabolites specific to RCC were analyzed by multivariate statistical analysis for cancer diagnostic ability based on area under the curve (AUC) and clinicopathological factors (tumor volume, pathological T stage, Fuhrman grade, presence of coagulation necrosis and distant metastasis). We identified 58 metabolites showing significantly increased levels in tumor tissues, 34 of which showed potential early diagnostic ability (AUC >0.8), but 24 did not discriminate between tumor and nontumor tissues (AUC ≤0.8). We recognized 6 pathways from 9 metabolites with AUC >0.8 and 7 pathways from 10 metabolites with AUC ≤0.8 about malignant status. Clinicopathological factors involving malignant status correlated significantly with metabolites showing AUC ≤0.8 (p = 0.0279). The tricarboxylic acid cycle (TCA) cycle, TCA cycle intermediates, nucleotide sugar pathway and inositol pathway were characteristic pathways for the malignant status of RCC. In conclusion, our study found that metabolites and their pathways allowed discrimination between early diagnosis and malignant status in RCC according to our G-Met protocol.

Association of qualitative and quantitative imaging features on multiphasic multidetector CT with tumor grade in clear cell renal cell carcinoma

PURPOSE

The purpose of the study was to determine if enhancement features and qualitative imaging features on multiphasic multidetector computed tomography (MDCT) were associated with tumor grade in patients with clear cell renal cell carcinoma (ccRCC).

METHODS

In this retrospective, IRB approved, HIPAA-compliant, institutional review board-approved study with waiver of informed consent, 127 consecutive patients with 89 low grade (LG) and 43 high grade (HG) ccRCCs underwent preoperative four-phase MDCT in unenhanced (UN), corticomedullary (CM), nephrographic (NP), and excretory (EX) phases. Previously published quantitative (absolute peak lesion enhancement, absolute peak lesion enhancement relative to normal enhancing renal cortex, 3D whole lesion enhancement and the wash-in/wash-out of enhancement within the 3D whole lesion ROI) and qualitative (enhancement pattern; presence of necrosis; pattern of; tumor margin; tumor-parenchymal interface, tumor-parenchymal interaction; intratumoral vascularity; collecting system infiltration; renal vein invasion; and calcification) assessments were obtained for each lesion independently by two fellowship-trained genitourinary radiologists. Comparisons between variables included χ², ANOVA, and student t test. p values less than 0.05 were considered to be significant. Inter-reader agreement was obtained with the Gwet agreement coefficient (AC1) and standard error (SE) was reported.

RESULTS

No significant differences were observed between the LG and HG ccRCC cohorts with respect to absolute peak lesion enhancement and relative lesion enhancement ratio. There was a significant inverse correlation between low and high grade ccRCC and tumor enhancement the NP (71 HU vs. 54 HU, p < 0.001) and EX (52 HU vs. 39 HU, p < 0.001) phases using the 3D whole lesion ROI method. The percent wash-in of 3D enhancement from the UN to the CM phase was also significantly different between LG and HG ccRCCs (352% vs. 255%, p = 0.003). HG lesions showed significantly more calcification, necrosis, collecting system infiltration and ill-defined tumor margins (p < 0.05). Overall agreement between the two readers had a mean AC1 of 0.8172 (SE 0.0235).

CONCLUSIONS

Quantitatively, high grade ccRCC had significantly lower whole lesion enhancement in the NP and EX phases on MDCT. Qualitatively, high grade ccRCC were significantly more likely to be associated with calcifications, necrosis, collecting system infiltration, and an ill-defined tumor margin.

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

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

A Deep Learning-Based Radiomics Model for Differentiating Benign and Malignant Renal Tumors

OBJECTIVES: To investigate the effect of transfer learning on computed tomography (CT) images for the benign and malignant classification on renal tumors and to attempt to improve the classification accuracy by building patient-level models. METHODS: One hundred ninety-two cases of renal tumors were collected and identified by pathologic diagnosis within 15 days after enhanced CT examination (66% male, 70% malignant renal tumors, average age of 62.27 ± 12.26 years). The InceptionV3 model pretrained by the ImageNet dataset was cross-trained to perform this classification. Five image-level models were established for each of the Slice, region of interest (ROI), and rectangular box region (RBR) datasets. Then, two patient-level models were built based on the optimal image-level models. The network's performance was evaluated through analysis of the receiver operating characteristic (ROC) and five-fold cross-validation. RESULTS: In the image-level models, the test results of model trained on the Slice dataset [accuracy (ACC) = 0.69 and Matthews correlation coefficient (MCC) = 0.45] were the worst. The corresponding results on the ROI dataset (ACC = 0.97 and MCC = 0.93) were slightly better than those on the RBR dataset (ACC = 0.93 and MCC = 0.85) when freezing the weights before the mixed6 layer. Compared with the image-level models, both patient-level models could discriminate better (ACC increased by 2%-5%) on the RBR and Slice datasets. CONCLUSIONS: Deep learning can be used to classify benign and malignant renal tumors from CT images. Our patient-level models could benefit from 3D data to improve the accuracy.