PROGNOSTIC ROLE OF FUHRMAN GRADE AND VASCULAR ENDOTHELIAL GROWTH FACTOR IN pT1a CLEAR CELL CARCINOMA IN PARTIAL NEPHRECTOMY SPECIMENS

Machine Learning-Enabled Fuhrman Grade in Clear-cell Renal Carcinoma Prediction Using Two-dimensional Ultrasound Images

OBJECTIVE

Accurate assessment of Fuhrman grade is crucial for optimal clinical management and personalized treatment strategies in patients with clear cell renal cell carcinoma (CCRCC). In this study, we developed a predictive model using ultrasound (US) images to accurately predict the Fuhrman grade.

METHODS

Between March 2013 and July 2023, a retrospective analysis was conducted on the US imaging and clinical data of 235 patients with pathologically confirmed CCRCC, including 67 with Fuhrman grades Ⅲ and Ⅳ. This study included 201 patients from Hospital A who were divided into training set (n = 161) and an internal validation set (n = 40) in an 8:2 ratio. Additionally, 34 patients from Hospital B were included for external validation. US images were delineated using ITK software, and radiomics features were extracted using PyRadiomics software. Subsequently, separate models for clinical factors, radiomics features, and their combinations were constructed. The model's performance was assessed by calculating the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve analysis (DCA).

RESULTS

In total, 235 patients diagnosed with CCRCC, comprising 168 low-grade and 67 high-grade tumors, were included in this study. A comparison of the predictive performances of different models revealed that the logistic regression model exhibited relatively good stability and robustness. The AUC of the combined model for the training, internal validation and external validation sets were 0.871, 0.785 and 0.826, respectively, which were higher than those of the clinical and imaging histology models. Furthermore, the calibration curve demonstrated excellent concordance between the predicted Fuhrman grade probability of CCRCC using the combined model and the observed values in both the training and validation sets. Additionally, within the threshold range of 0-0.93, the combined model demonstrated substantial clinical utility, as evidenced by DCA.

CONCLUSION

The application of US radiomics techniques enabled objective prediction of Fuhrman grading in patients with CCRCC. Nevertheless, certain clinical indicators remain indispensable, underscoring the pressing need for their integrated use in clinical practice.

ADVANCES IN KNOWLEDGE

Previous studies have predominantly focused on using computed tomography or magnetic resonance imaging modalities to predict the Fuhrman grade of CCRCC. Our findings demonstrate that a prediction model based on US images is more cost-effective, easily accessible and exhibits commendable performance. Consequently, this study offers a promising approach to maximizing the use of US examinations in future research.

Aggressiveness classification of clear cell renal cell carcinoma using registration-independent radiology-pathology correlation learning

BACKGROUND

Renal cell carcinoma (RCC) is a common cancer that varies in clinical behavior. Clear cell RCC (ccRCC) is the most common RCC subtype, with both aggressive and indolent manifestations. Indolent ccRCC is often low-grade without necrosis and can be monitored without treatment. Aggressive ccRCC is often high-grade and can cause metastasis and death if not promptly detected and treated. While most RCCs are detected on computed tomography (CT) scans, aggressiveness classification is based on pathology images acquired from invasive biopsy or surgery.

PURPOSE

CT imaging-based aggressiveness classification would be an important clinical advance, as it would facilitate non-invasive risk stratification and treatment planning. Here, we present a novel machine learning method, Correlated Feature Aggregation By Region (CorrFABR), for CT-based aggressiveness classification of ccRCC.

METHODS

CorrFABR is a multimodal fusion algorithm that learns from radiology and pathology images, and clinical variables in a clinically-relevant manner. CorrFABR leverages registration-independent radiology (CT) and pathology image correlations using features from vision transformer-based foundation models to facilitate aggressiveness assessment on CT images. CorrFABR consists of three main steps: (a) Feature aggregation where region-level features are extracted from radiology and pathology images at widely varying image resolutions, (b) Fusion where radiology features correlated with pathology features (pathology-informed CT biomarkers) are learned, and (c) Classification where the learned pathology-informed CT biomarkers, together with clinical variables of tumor diameter, gender, and age, are used to distinguish aggressive from indolent ccRCC using multi-layer perceptron-based classifiers. Pathology images are only required in the first two steps of CorrFABR, and are not required in the prediction module. Therefore, CorrFABR integrates information from CT images, pathology images, and clinical variables during training, but for inference, it relies solely on CT images and clinical variables, ensuring its clinical applicability. CorrFABR was trained with heterogenous, publicly-available data from 298 ccRCC tumors (136 indolent tumors, 162 aggressive tumors) in a five-fold cross-validation setup and evaluated on an independent test set of 74 tumors with a balanced distribution of aggressive and indolent tumors. Ablation studies were performed to test the utility of each component of CorrFABR.

RESULTS

CorrFABR outperformed the other classification methods, achieving an ROC-AUC (area under the curve) of 0.855 ± 0.0005 (95% confidence interval: 0.775, 0.947), F1-score of 0.793 ± 0.029, sensitivity of 0.741 ± 0.058, and specificity of 0.876 ± 0.032 in classifying ccRCC as aggressive or indolent subtypes. It was found that pathology-informed CT biomarkers learned through registration-independent correlation learning improves classification performance over using CT features alone, irrespective of the kind of features or the classification model used. Tumor diameter, gender, and age provide complementary clinical information, and integrating pathology-informed CT biomarkers with these clinical variables further improves performance.

CONCLUSION

CorrFABR provides a novel method for CT-based aggressiveness classification of ccRCC by enabling the identification of pathology-informed CT biomarkers, and integrating them with clinical variables. CorrFABR enables learning of these pathology-informed CT biomarkers through a novel registration-independent correlation learning module that considers unaligned radiology and pathology images at widely varying image resolutions.

Machine Learning-Based Magnetic Resonance Radiomics Analysis for Predicting Low- and High-Grade Clear Cell Renal Cell Carcinoma

PURPOSE

To explore whether high- and low-grade clear cell renal cell carcinomas (ccRCC) can be distinguished using radiomics features extracted from magnetic resonance imaging.

METHODS

In this retrospective study, 154 patients with pathologically proven clear ccRCC underwent contrast-enhanced 3 T magnetic resonance imaging and were assigned to the development (n = 122) and test (n = 32) cohorts in a temporal-split setup. A total of 834 radiomics features were extracted from whole-tumor volumes using 3 sequences: T2-weighted imaging (T2WI), diffusion-weighted imaging, and contrast-enhanced T1-weighted imaging. A random forest regressor was used to extract important radiomics features that were subsequently used for model development using the random forest algorithm. Tumor size, apparent diffusion coefficient value, and percentage of tumor-to-renal parenchymal signal intensity drop in the tumors were recorded by 2 radiologists for quantitative analysis. The area under the receiver operating characteristic curve (AUC) was generated to predict ccRCC grade.

RESULTS

In the development cohort, the T2WI-based radiomics model demonstrated the highest performance (AUC, 0.82). The T2WI-based radiomics and radiologic feature hybrid model showed AUCs of 0.79 and 0.83, respectively. In the test cohort, the T2WI-based radiomics model achieved an AUC of 0.82. The range of AUCs of the hybrid model of T2WI-based radiomics and radiologic features was 0.73 to 0.80.

CONCLUSION

Magnetic resonance imaging-based classifier models using radiomics features and machine learning showed satisfactory diagnostic performance in distinguishing between high- and low-grade ccRCC, thereby serving as a helpful noninvasive tool for predicting ccRCC grade.

The Use of Radiomic Tools in Renal Mass Characterization

The incidence of renal mass detection has increased during recent decades, with an increased diagnosis of small renal masses, and a final benign diagnosis in some cases. To avoid unnecessary surgeries, there is an increasing interest in using radiomics tools to predict histological results, using radiological features. We performed a narrative review to evaluate the use of radiomics in renal mass characterization. Conventional images, such as computed tomography (CT) and magnetic resonance (MR), are the most common diagnostic tools in renal mass characterization. Distinguishing between benign and malignant tumors in small renal masses can be challenging using conventional methods. To improve subjective evaluation, the interest in using radiomics to obtain quantitative parameters from medical images has increased. Several studies have assessed this novel tool for renal mass characterization, comparing its ability to distinguish benign to malign tumors, the results in differentiating renal cell carcinoma subtypes, or the correlation with prognostic features, with other methods. In several studies, radiomic tools have shown a good accuracy in characterizing renal mass lesions. However, due to the heterogeneity in the radiomic model building, prospective and external validated studies are needed.

Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study

Purpose

To investigate the predictive performance of machine learning-based CT radiomics for differentiating between low- and high-nuclear grade of clear cell renal cell carcinomas (CCRCCs).

Methods

This retrospective study enrolled 406 patients with pathologically confirmed low- and high-nuclear grade of CCRCCs according to the WHO/ISUP grading system, which were divided into the training and testing cohorts. Radiomics features were extracted from nephrographic-phase CT images using PyRadiomics. A support vector machine (SVM) combined with three feature selection algorithms such as least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE), and ReliefF was performed to determine the most suitable classification model, respectively. Clinicoradiological, radiomics, and combined models were constructed using the radiological and clinical characteristics with significant differences between the groups, selected radiomics features, and a combination of both, respectively. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses.

Results

SVM-ReliefF algorithm outperformed SVM-LASSO and SVM-RFE in distinguishing low- from high-grade CCRCCs. The combined model showed better prediction performance than the clinicoradiological and radiomics models (p < 0.05, DeLong test), which achieved the highest efficacy, with an area under the ROC curve (AUC) value of 0.887 (95% confidence interval [CI] 0.798–0.952), 0.859 (95% CI 0.748–0.935), and 0.828 (95% CI 0.731–0.929) in the training, validation, and testing cohorts, respectively. The calibration and decision curves also indicated the favorable performance of the combined model.

Conclusion

A combined model incorporating the radiomics features and clinicoradiological characteristics can better predict the WHO/ISUP nuclear grade of CCRCC preoperatively, thus providing effective and noninvasive assessment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13244-021-01107-1.

Can texture analysis based on single unenhanced CT accurately predict the WHO/ISUP grading of localized clear cell renal cell carcinoma?

OBJECTIVE

The purpose was to investigate the value of texture analysis in predicting the World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grading of localized clear cell renal cell carcinoma (ccRCC) based on unenhanced CT (UECT).

MATERIALS AND METHODS

Pathologically confirmed subjects (n = 104) with localized ccRCC who received UECT scanning were collected retrospectively for this study. All cases were classified into low grade (n = 53) and high grade (n = 51) according to the WHO/ISUP grading and were randomly divided into training set and test set as a ratio of 7:3. Using 3D-ROI segmentation on UECT images and extracted ninety-three texture features (first-order, gray-level co-occurrence matrix [GLCM], gray-level run length matrix [GLRLM], gray-level size zone matrix [GLSZM], neighboring gray tone difference matrix [NGTDM] and gray-level dependence matrix [GLDM] features). Univariate analysis and the least absolute shrinkage selection operator (LASSO) regression were used for feature dimension reduction, and logistic regression classifier was used to develop the prediction model. Using receiver operating characteristic (ROC) curve, bar chart and calibration curve to evaluate the performance of the prediction model.

RESULTS

Dimension reduction screened out eight optimal texture features (maximum, median, dependence variance [DV], long run emphasis [LRE], run entropy [RE], gray-level non-uniformity [GLN], gray-level variance [GLV] and large area low gray-level emphasis [LALGLE]), and then the prediction model was developed according to the linear combination of these features. The accuracy, sensitivity, specificity, and AUC of the model in training set were 86.1%, 91.4%, 81.1%, and 0.937, respectively. The accuracy, sensitivity, specificity, and AUC of the model in test set were 81.2%, 81.2%, 81.2%, and 0.844, respectively. The calibration curves showed good calibration both in training set and test set (P > 0.05).

CONCLUSION

This study has demonstrated that the radiomics model based on UECT texture analysis could accurately evaluate the WHO/ISUP grading of localized ccRCC.

Enhanced computed tomography radiomics-based machine-learning methods for predicting the Fuhrman grades of renal clear cell carcinoma

OBJECTIVE

To develop and test an optimal machine learning model based on the enhanced computed tomography (CT) to preoperatively predict pathological grade of clear cell renal cell carcinoma (ccRCC).

METHODS

A retrospective analysis of 53 pathologically confirmed cases of ccRCC was performed and 25 consecutive ccRCC cases were selected as a prospective testing set. All patients underwent routine preoperative abdominal CT plain and enhanced scans. Renal tumor lesions were segmented on arterial phase images and 396 radiomics features were extracted. In the training set, seven discrimination classifiers for high- and low-grade ccRCCs were constructed based on seven different machine learning models, respectively, and their performance and stability for predicting ccRCC grades were evaluated through receiver operating characteristic (ROC) analysis and cross-validation. Prediction accuracy and area under ROC curve were used as evaluation indices. Finally, the diagnostic efficacy of the optimal model was verified in the testing set.

RESULTS

The accuracies and AUC values achieved by support vector machine with radial basis function kernel (svmRadial), random forest and naïve Bayesian models were 0.860±0.158 and 0.919±0.118, 0.840±0.160 and 0.915±0.138, 0.839±0.147 and 0.921±0.133, respectively, which showed high predictive performance, whereas K-nearest neighborhood model yielded lower accuracy of 0.720±0.188 and lower AUC value of 0.810±0.150. Additionally, svmRadial had smallest relative standard deviation (RSD, 0.13 for AUC, 0.17 for accuracy), which indicates higher stability.

CONCLUSION

svmRadial performs best in predicting pathological grades of ccRCC using radiomics features computed from the preoperative CT images, and thus may have high clinical potential in guiding preoperative decision.

Differentiation of low and high grade renal cell carcinoma on routine MRI with an externally validated automatic machine learning algorithm

Pre-treatment determination of renal cell carcinoma aggressiveness may help guide clinical decision-making. We aimed to differentiate low-grade (Fuhrman I-II) from high-grade (Fuhrman III-IV) renal cell carcinoma using radiomics features extracted from routine MRI. 482 pathologically confirmed renal cell carcinoma lesions from 2008 to 2019 in a multicenter cohort were retrospectively identified. 439 lesions with information on Fuhrman grade from 4 institutions were divided into training and test sets with an 8:2 split for model development and internal validation. Another 43 lesions from a separate institution were set aside for independent external validation. The performance of TPOT (Tree-Based Pipeline Optimization Tool), an automatic machine learning pipeline optimizer, was compared to hand-optimized machine learning pipeline. The best-performing hand-optimized pipeline was a Bayesian classifier with Fischer Score feature selection, achieving an external validation ROC AUC of 0.59 (95% CI 0.49-0.68), accuracy of 0.77 (95% CI 0.68-0.84), sensitivity of 0.38 (95% CI 0.29-0.48), and specificity of 0.86 (95% CI 0.78-0.92). The best-performing TPOT pipeline achieved an external validation ROC AUC of 0.60 (95% CI 0.50-0.69), accuracy of 0.81 (95% CI 0.72-0.88), sensitivity of 0.12 (95% CI 0.14-0.30), and specificity of 0.97 (95% CI 0.87-0.97). Automated machine learning pipelines can perform equivalent to or better than hand-optimized pipeline on an external validation test non-invasively predicting Fuhrman grade of renal cell carcinoma using conventional MRI.

A novel definition of microvessel density in renal cell carcinoma: Angiogenesis plus vasculogenic mimicry

The present study proposed the novel concept of total microvessel density (TMVD), which is the combination of the MVD and the vasculogenic mimicry (VM) status, and evaluated its clinical significance in patients with renal cell carcinoma (RCC). For that purpose, tumor samples from 183 patients with primary RCC were examined by CD34 single or periodic acid Schiff (PAS)/CD34 dual histology staining. MVD and VM were determined according to previous literature. Clinical information (tumor stage and grade, and duration of survival) was retrieved and analyzed. Survival information and VM-associated gene expression data of patients with RCC were also retrieved from The Cancer Genome Atlas (TCGA) database and the clinical significance of each individual gene was analyzed. The results indicated that MVD exhibited obvious differences among patients with RCC; however, it was not correlated with the stage/grade or length of survival in patients with RCC. In total, 81 patients (44.3%) were CD34(−)/PAS(+) and defined as VM(+), and they had a significantly shorter survival compared with that of VM(−) patients (P=0.0002). VM was not associated with MVD. TMVD was able to distinguish between patients with high and low MVD in terms of survival, thus TMVD was better compared with MVD alone at distinguishing between patients with different survival prognoses. TCGA data analysis revealed that among the VM-associated genes, nodal growth differentiation factor, caspase-3, matrix metalloproteinase-9 and galectin-3 had a statistically significant impact on the overall/disease-free survival of patients with RCC. In conclusion, the TMVD concept may be more appropriate and sensitive compared with the MVD or VM alone in predicting tumor aggressiveness and patient survival, particularly in RCC, which is a highly vascularized, VM-rich neoplasm, and certain VM formation-associated genes are negatively associated with the survival of patients with RCC.

Machine Learning in Radiomic Renal Mass Characterization: Fundamentals, Applications, Challenges, and Future Directions

OBJECTIVE. The purpose of this study is to provide an overview of the traditional machine learning (ML)-based and deep learning-based radiomic approaches, with focus placed on renal mass characterization. CONCLUSION. ML currently has a very low barrier to entry into general medical practice because of the availability of many open-source, free, and easy-to-use toolboxes. Therefore, it should not be surprising to see its related applications in renal mass characterization. A wider picture of the previous works might be beneficial to move this field forward.

Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer

PURPOSE

The purpose of the study was to provide a comprehensive review of recent machine learning (ML) and deep learning (DL) applications in urological practice. Numerous studies have reported their use in the medical care of various urological disorders; however, no critical analysis has been made to date.

METHODS

A detailed search of original articles was performed using the PubMed MEDLINE database to identify recent English literature relevant to ML and DL applications in the fields of urolithiasis, renal cell carcinoma (RCC), bladder cancer (BCa), and prostate cancer (PCa).

RESULTS

In total, 43 articles were included addressing these four subfields. The most common ML and DL application in urolithiasis is in the prediction of endourologic surgical outcomes. The main area of research involving ML and DL in RCC concerns the differentiation between benign and malignant small renal masses, Fuhrman nuclear grade prediction, and gene expression-based molecular signatures. BCa studies employ radiomics and texture feature analysis for the distinction between low- and high-grade tumors, address accurate image-based cytology, and use algorithms to predict treatment response, tumor recurrence, and patient survival. PCa studies aim at developing algorithms for Gleason score prediction, MRI computer-aided diagnosis, and surgical outcomes and biochemical recurrence prediction. Studies consistently found the superiority of these methods over traditional statistical methods.

CONCLUSIONS

The continuous incorporation of clinical data, further ML and DL algorithm retraining, and generalizability of models will augment the prediction accuracy and enhance individualized medicine.

Association among the R.E.N.A.L. nephrometry score and clinical outcomes in patients with small renal masses treated with percutaneous contrast enhanced ultrasound radiofrequency ablation

Introduction

An association between the R.E.N.A.L. nephrometry score (RNS) and clinical outcomes in patients with a small renal mass (SRM) has been proposed. We analyzed clinical outcomes according to the RNS in patients with a SRM treated with percutaneous contrast enhanced ultrasound (CEUS) radiofrequency ablation (RFA).

Material and methods

Patients with a SRM, who underwent RFA between January 2005 and March 2015, were retrospectively identified. The association between RNS and clinical outcomes was evaluated using parametric and non-parametric analysis.

Results

We analyzed 163 SRMs in 149 consecutive patients. The mean age was 71.7 years. Mean follow-up time was 33.3 months ±20.6 (2-102). The mean RNS was 5.6 ±1.52 (4-11). A total of 121 (74.2%) cases were of low complexity and 42 (25.8%) were medium complexity. We identified 11 cases of tumor persistence (6.7%). The mean RNS was 5.58 in the cases with no persistence and 5.73 in the cases with persistence (p = 0.788). We identified 15 (9.2%) cases of recurrence. The mean RNS was 5.57 ±0.1 (4-11) in the cases without recurrence and 5.73 ±0.4 (4-9) in recurrence cases (p = 0.804). Of the 76 biopsy proven RCC cases, 8 (10.5%) cases of recurrence were observed, 5 in the low complexity group and 3 in the medium complexity group (p = 0.690). A total of 9 (5.5%) cases of complications were observed, with 5 (4.3%) in the low complexity group and 4 cases in the medium complexity group (p = 0.23). The mean length of stay was 1.5 days with a significant difference between low and medium complexity groups (1.3 vs. 2.1 days, p = 0.02). The mean difference between preoperative eGFR and estimated eGFRat 12 months was -3.08 mL / min ±13.3 (-49.4-34.1) and was significant (p = 0.008).However, this variation did not show significant differences between the low and medium complexity groups (p = 0.936). All-cause mortality was 11.7%, 14 cases (11.6%) in the low complexity group and 5 (11.9%) in the medium complexity group (p = 1.0). No cases of renal cell carcinoma (RCC) specific mortality were identified.

Conclusions

The RNS was not associated with tumor persistence, recurrence, cancer specific mortality, complications or renal function 12 months after the first treatment, showing significant difference only in length of hospital stay between low and medium complexity groups.

Unenhanced CT Texture Analysis of Clear Cell Renal Cell Carcinomas: A Machine Learning-Based Study for Predicting Histopathologic Nuclear Grade

OBJECTIVE. The purpose of this study is to investigate the predictive performance of machine learning (ML)-based unenhanced CT texture analysis in distinguishing low (grades I and II) and high (grades III and IV) nuclear grade clear cell renal cell carcinomas (RCCs). MATERIALS AND METHODS. For this retrospective study, 81 patients with clear cell RCC (56 high and 25 low nuclear grade) were included from a public database. Using 2D manual segmentation, 744 texture features were extracted from unenhanced CT images. Dimension reduction was done in three consecutive steps: reproducibility analysis by two radiologists, collinearity analysis, and feature selection. Models were created using artificial neural network (ANN) and binary logistic regression, with and without synthetic minority oversampling technique (SMOTE), and were validated using 10-fold cross-validation. The reference standard was histopathologic nuclear grade (low vs high). RESULTS. Dimension reduction steps yielded five texture features for the ANN and six for the logistic regression algorithm. None of clinical variables was selected. ANN alone and ANN with SMOTE correctly classified 81.5% and 70.5%, respectively, of clear cell RCCs, with AUC values of 0.714 and 0.702, respectively. The logistic regression algorithm alone and with SMOTE correctly classified 75.3% and 62.5%, respectively, of the tumors, with AUC values of 0.656 and 0.666, respectively. The ANN performed better than the logistic regression (p < 0.05). No statistically significant difference was present between the model performances created with and without SMOTE (p > 0.05). CONCLUSION. ML-based unenhanced CT texture analysis using ANN can be a promising noninvasive method in predicting the nuclear grade of clear cell RCCs.

Renal cell carcinoma: preoperative evaluate the grade of histological malignancy using volumetric histogram analysis derived from magnetic resonance diffusion kurtosis imaging

Background

To investigate the value of histogram analysis of magnetic resonance (MR) diffusion kurtosis imaging (DKI) in the assessment of renal cell carcinoma (RCC) grading before surgery.

Methods

A total of 73 RCC patients who had undergone preoperative MR imaging and DKI were classified into either a low- grade group or a high-grade group. Parametric DKI maps of each tumor were obtained using in-house software, and histogram metrics between the two groups were analyzed. Receiver operating characteristic (ROC) curve analysis was used for obtaining the optimum diagnostic thresholds, the area under the ROC curve (AUC), sensitivity, specificity and accuracy of the parameters.

Results

Significant differences were observed in 3 metrics of ADC histogram parameters and 8 metrics of DKI histogram parameters (P<0.05). ROC curve analyses showed that K_app mean had the highest diagnostic efficacy in differentiating RCC grades. The AUC, sensitivity, and specificity of the K_app mean were 0.889, 87.9% and 80%, respectively.

Conclusions

DKI histogram parameters can effectively distinguish high- and low- grade RCC. K_app mean is the best parameter to distinguish RCC grades.

CT texture analysis: a potential tool for predicting the Fuhrman grade of clear-cell renal carcinoma

BACKGROUND

The purpose of this study was to analyze the image heterogeneity of clear-cell renal-cell carcinoma (ccRCC) by computer tomography texture analysis and to provide new objective quantitative imaging parameters for the pre-operative prediction of Fuhrman-grade ccRCC.

METHODS

A retrospective analysis of 131 cases of ccRCCs was performed by manually depicting tumor areas. Then, histogram-based texture parameters were calculated. The texture-feature values between Fuhrman low- (Grade I-II) and high-grade (Grade III-IV) ccRCCs were compared by two independent sample t-tests (False Discovery Rate correction), and receiver operating characteristic curve (ROC) was used to evaluate the efficacy of using texture features to predict Fuhrman high- and low-grade ccRCCs.

RESULTS

There were no statistical differences for any texture parameters without filtering (p > 0.05). There was a statistically significant difference between the entropy (fine) of the corticomedullary phase and the entropy (fine and coarse) of the nephrographic phase after Laplace of Gaussian filtering. The area under the ROC of the entropy was between 0.74 and 0.83.

CONCLUSIONS

Computer tomography texture features can predict the Fuhrman grading of ccRCC pre-operatively, with entropy being the most important imaging marker for clinical application.

Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade

OBJECTIVE

To evaluate the performance of quantitative computed tomography (CT) texture analysis using different machine learning (ML) classifiers for discriminating low and high nuclear grade clear cell renal cell carcinomas (cc-RCCs).

MATERIALS AND METHODS

This retrospective study included 53 patients with pathologically proven 54 cc-RCCs (31 low-grade [grade 1 or 2]; 23 high-grade [grade 3 or 4]). In one patient, two synchronous cc-RCCs were included in the analysis. Mean age was 57.5 years. Thirty-four (64.1%) patients were male and 19 were female (35.9%). Mean tumour size based on the maximum diameter was 57.4 mm (range, 16-145 mm). Forty patients underwent radical nephrectomy and 13 underwent partial nephrectomy. Following pre-processing steps, two-dimensional CT texture features were extracted using portal-phase contrast-enhanced CT. Reproducibility of texture features was assessed with the intra-class correlation coefficient (ICC). Nested cross-validation with a wrapper-based algorithm was used in feature selection and model optimisation. The ML classifiers were support vector machine (SVM), multilayer perceptron (MLP, a sort of neural network), naïve Bayes, k-nearest neighbours, and random forest. The performance of the classifiers was compared by certain metrics.

RESULTS

Among 279 texture features, 241 features with an ICC equal to or higher than 0.80 (excellent reproducibility) were included in the further feature selection process. The best model was created using SVM. The selected subset of features for SVM included five co-occurrence matrix (ICC range, 0.885-0.998), three run-length matrix (ICC range, 0.889-0.992), one gradient (ICC = 0.998), and four Haar wavelet features (ICC range, 0.941-0.997). The overall accuracy, sensitivity (for detecting high-grade cc-RCCs), specificity (for detecting high-grade cc-RCCs), and overall area under the curve of the best model were 85.1%, 91.3%, 80.6%, and 0.860, respectively.

CONCLUSIONS

The ML-based CT texture analysis can be a useful and promising non-invasive method for prediction of low and high Fuhrman nuclear grade cc-RCCs.

KEY POINTS

• Based on the percutaneous biopsy literature, ML-based CT texture analysis has a comparable predictive performance with percutaneous biopsy. • Highest predictive performance was obtained with use of the SVM. • SVM correctly classified 85.1% of cc-RCCs in terms of nuclear grade, with an AUC of 0.860.

Radiofrequency Ablation of Small Renal Masses: Outcomes, Complications and Effects on Renal Function

Introduction

To describe oncological outcomes, effects on renal function and complications with radiofrequency ablation (RFA) of T1 renal tumors in an 8-year experience.

Materials and Methods

A retrospective study of RFA in 89 consecutive patients between 2005 and 2013 was undertaken. Those with metastatic disease, incomplete follow-up, genetic pre-disposition to renal tumors and biopsy proven benign pathology were excluded, with 79 patients meeting inclusion criteria. Data was collected on demographics, oncological outcomes, complications and effects on renal function.

Results

We demonstrate 94% disease-free survival at median follow-up of 29 months in a population consisting of 42 T1a and 37 T1b tumors. No disease related deaths were recorded in the follow-up period. Post-RFA decline in renal function was shown to correlate with tumor size and increased age (p = 0.0009/0.0021). Pre-existing renal impairment was a risk for post-RFA function decline (p < 0.005). Two complications were encountered in the series.

Conclusion

RFA produces durable oncological outcomes in T1 tumors with a minimal effect on renal function and low risk of complications. Patients at risk of developing renal impairment can be identified from described risk factors.

Comparison of biexponential and monoexponential DWI in evaluation of Fuhrman grading of clear cell renal cell carcinoma

PURPOSE

Clear cell renal cell carcinoma (ccRCC) is the most common primary malignant urologic tumor. The Fuhrman grading system is an independent indicator for aggressiveness and prognosis of ccRCC. We aimed to assess the possible diagnostic role of biexponentially and monoexponentially fitted signal attenuation for the Fuhrman grading.

METHODS

A total of 33 patients with ccRCC underwent multiple b values (0, 20, 50, 100, 150, 250, 400, 600, 800, 1000 s/mm2) diffusion-weighted imaging (DWI). Biexponential parameters (fast ADC [ADCf], slow ADC [ADCs], and fraction of ADCf [f]) and monoexponential apparent diffusion coefficient were calculated, and correlated with the Fuhrman grade of ccRCC respectively. The performance of biexponential parameters in differentiating Fuhrman low- and high-grade tumors was assessed and compared with ADC value by receiver operating characteristic analysis.

RESULTS

Qualified images and diffusion-weighted parameters were obtained for all patients. The ADCf and f value were positively correlated, whereas ADCs and ADC value were negatively correlated with Fuhrman grade. Significant differences were observed in ADCf (P < 0.001), ADCs (P = 0.005), and f values (P < 0.001) of high- and low-grade ccRCCs. When differentiating Fuhrman low-grade tumors from high-grade, the ADCf revealed an area under receiver operating characteristic curve of 0.959, which was higher than the ADC value (0.789; P = 0.046), while ADCs (0.807) and f (0.833) showed no significant difference from ADC (P = 0.85 for ADCs, P = 0.73 for f).

CONCLUSION

Biexponential DWI provides additional parameters for ccRCC. ADCf is more accurate compared with the ADC value in characterizing Fuhrman grade of ccRCC.

Perioperative epidural analgesia is not associated with increased survival from renal cell cancer, but overall survival may be improved: a retrospective chart review

PURPOSE

We investigated the possible association between perioperative epidural and both cancer-specific survival (CSS) and overall survival (OS) in patients undergoing partial or radical nephrectomy for localized renal cell carcinoma (RCC).

METHODS

A retrospective chart review was performed on patients who underwent complete surgical resection of localized RCC from 1994-2008 at our institution. Baseline demographics and pathological and survival data were collected. Patients with clinically or pathologically positive lymph nodes or metastatic disease at the time of surgery were excluded. Patients with pathologically positive surgical margins were also excluded. Patients were divided into two groups, systemic analgesia and epidural analgesia. Multivariable Cox regression analysis was used to determine CSS and OS, and survival curves were generated using the Kaplan-Meier method.

RESULTS

Four hundred thirty-eight patients were included in the analysis. Baseline characteristics of both groups were similar. Median follow-up was 77 months. On multivariable analysis, patient age (hazard ratio [HR], 1.04; 95% confidence interval [CI], 1.02 to 1.07), epidural status (HR, 0.5; 95% CI, 0.4 to 0.8), year of surgery (HR, 0.9; 95% CI, 0.89 to 0.95), and pathologic T-stage (pT-stage) ≥ 2 (pT-stage2: HR, 2.2; 95% CI, 1.2 to 4.1 and pT-stage3: HR, 3.1; 95% CI, 2.0 to 4.7) were independent predictors of OS. Nevertheless, epidural status did not significantly predict CSS (P = 0.73), while T-stage and year of surgery maintained their respective predictive significance. Tumour grade did not significantly affect OS or CSS.

CONCLUSIONS

Our retrospective analysis suggests that epidural at the time of surgical excision of localized RCC does not significantly impact CSS. Nevertheless, use of epidural was associated with significantly improved OS. Future prospective clinical and laboratory studies are warranted in order to characterize these associations further and determine the underlying mechanisms.

Lesion Size and Iodine Quantification to Distinguish Low-Grade From High-Grade Clear Cell Renal Cell Carcinoma Using Dual-Energy Spectral Computed Tomography

OBJECTIVE

The aim of this study was to assess the utility of lesion size and iodine quantification using dual-energy spectral computed tomography to distinguish between low-grade and high-grade clear cell renal cell carcinomas (ccRCCs).

METHODS

Spectral parameters of 75 patients with pathologically proven ccRCCs who underwent preoperative dual-energy spectral computed tomography examinations were divided into low-grade and high-grade groups. Independent sample t test, receiver operating characteristic curve analysis, and Spearman rank correlation were analyzed.

RESULTS

The lesion size was significantly smaller, and spectral parameters were significantly higher in the low-grade ccRCC. The significant correlation (r = -0.412, P < 0.001) by the Spearman rank correlation was between the normalized iodine concentration and lesion size. The receiver operating characteristic analysis demonstrated that 0.710 was the optimal cutoff value, which yielded the following: sensitivity, 97.6%; specificity, 97.1%; positive predictive value, 97.6%; negative predictive value, 97.1%; and accuracy, 97.3%.

CONCLUSIONS

Iodine quantification can play an important role in distinguishing low-grade from high-grade ccRCC.

The evolving role of monoclonal antibodies in the treatment of patients with advanced renal cell carcinoma: a systematic review

INTRODUCTION

While the majority of the vascular endothelial growth factor (VEGF) and mammalian target of rapamycin (mTOR) inhibitors currently used for the therapy of metastatic renal cell carcinoma (mRCC) are small molecule agents inhibiting multiple targets, monoclonal antibodies are inhibitors of specific targets, which may decrease off-target effects while preserving on-target activity. A few monoclonal antibodies have already been approved for mRCC (bevacizumab, nivolumab), while many others may play an important role in the therapeutic scenario of mRCC.

AREAS COVERED

This review describes emerging monoclonal antibodies for treating RCC. Currently, bevacizumab, a VEGF monoclonal antibody, is approved in combination with interferon for the therapy of metastatic RCC, while nivolumab, a Programmed Death (PD)-1 inhibitor, is approved following prior VEGF inhibitor treatment. Other PD-1 and PD-ligand (L)-1 inhibitors are undergoing clinical development.

EXPERT OPINION

Combinations of inhibitors of the PD1/PD-L1 axis with VEGF inhibitors or cytotoxic T-lymphocyte antigen (CTLA)-4 inhibitors have shown promising efficacy in mRCC. The development of biomarkers predictive for benefit and rational tolerable combinations are both important pillars of research to improve outcomes in RCC.

Port-site metastasis as a primary complication following retroperitoneal laparoscopic radical resection of renal pelvis carcinoma or nephron-sparing surgery: A report of three cases and review of the literature

The present study reports the clinical data of two patients with renal pelvis carcinoma and one patient with renal carcinoma who developed port-site metastasis following retroperitoneal laparoscopic surgery. The current study aimed to identify the cause and prognosis of the occurrence of port-site metastasis subsequent to laparoscopic radical resection of renal pelvis carcinoma and nephron-sparing surgery. Post-operative pathology confirmed the presence of high-grade urothelial cell carcinoma in two patients and Fuhrman grade 3 renal clear cell carcinoma in one patient. Port-site metastasis was initially detected 1–7 months post-surgery. The two patients with renal pelvis carcinoma succumbed to the disease 2 and 4 months following the identification of the port-site metastasis, respectively, whereas the patient with renal carcinoma survived with no disease progression during the targeted therapy period. The occurrence of port-site metastasis may be attributed to systemic and local factors. Measures to reduce the development of this complication include strict compliance with the operating guidelines for tumor surgery, avoidance of air leakage at the port-site, complete removal of the specimen with an impermeable bag, irrigation of the laparoscopic instruments and incisional wound with povidone-iodine when necessary, and enhancement of the body's immunity. Close post-operative follow-up observation for signs of recurrence or metastasis is essential, and systemic chemotherapy may be required in patients with high-grade renal pelvis carcinoma and renal carcinoma in order to prolong life expectancy.

Recent aspects of sunitinib therapy in patients with metastatic clear-cell renal cell carcinoma: a systematic review of the literature

Sunitinib is an orally available inhibitor of multiple tyrosine-kinase receptors approved for the treatment of advanced clear-cell renal cell carcinoma (ccRCC), a disease which has habitually had a very poor patient survival rate. Although it has become the most widely used drug for this disease, it remains not completely clear the best treatment strategy with these agent. The aim of this review is to highlight the most recent and interesting aspects of the research on treatment of advanced ccRCC with sunitinib and eventually determine alternative treatment schedule to reduce the incidence of side effects; we also wanted to review recent biomarkers able to predict response to therapy and also to point out the mechanism of acquired resistance to this drug.

Characterization of clear cell renal cell carcinoma by gene expression profiling

OBJECTIVES

Use global gene expression to characterize differences between high-grade and low-grade clear cell renal cell carcinoma (ccRCC) compared with normal and benign renal tissue.

METHODS

Tissue samples were collected from patients undergoing surgical resection for ccRCC. Affymetrix gene expression arrays were used to examine global gene expression patterns in high- (n = 16) and low-grade ccRCC (n = 13) as well as in samples from normal kidney (n =14) and benign kidney disease (n = 6). Differential gene expression was determined by analysis of variance with a false discovery rate of 1% and a 2-fold cutoff.

RESULTS

Comparing high-grade ccRCC with each of normal and benign kidney resulted in 1,833 and 2,208 differentially expressed genes, respectively. Of these, 930 were differentially expressed in both comparisons. In order to identify genes most related to progression of ccRCC, these differentially expressed genes were filtered to identify genes that showed a pattern of expression with a magnitude of change greater in high-grade ccRCC in the comparison to low-grade ccRCC. This resulted in the identification of genes such as TMEM45A, ceruloplasmin, and E-cadherin that were involved in cell processes of cell differentiation and response to hypoxia. Additionally changes in HIF1α and TNF signaling are highly represented by changes between high- and low-grade ccRCC.

CONCLUSIONS

Gene expression differences between high-grade and low-grade ccRCC may prove to be valuable biomarkers for advanced ccRCC. In addition, altered signaling between grades of ccRCC may provide important insight into the biology driving the progression of ccRCC and potential targets for therapy.

Biology of renal tumour cancer stem cells applied in medicine

The present article highlights the diverse role of stem cells in normal kidney and renal cancer, with special emphasis on surface markers. Proteins such as CD105 and CD133 have been reported as being significant in clear cell renal cell carcinoma (ccRCC) cancer stem cells. The role of normal kidney progenitor cells and their surface markers is compared with the role of those surface markers in ccRCC. Subsequently, we state the current hypothesis about origin of tumour-initiating cells along with their clinical and prognostic potential in RCC. Finally, we present future perspectives with respect to recent studies.

Comparison of utility of tumor size and apparent diffusion coefficient for differentiation of low- and high-grade clear-cell renal cell carcinoma

BACKGROUND

There is a significant correlation between tumor size and tumor grade for clear-cell renal cell carcinoma (RCC) in pathology. Thus, apparent diffusion coefficient (ADC) of clear-cell RCC might be influenced by tumor size.

PURPOSE

To compare the utility of tumor size and ADC for distinguishing low-grade from high-grade clear-cell RCC.

MATERIAL AND METHODS

Forty-nine patients undergoing preoperative magnetic resonance imaging were retrospectively assessed. ADC values were calculated using b-value combinations of 0 and 800 s/mm(2) at 1.5 T. Two radiologists in consensus measured ADC values via small region of interest (ROI) (mean ROI area, 88.8 mm(2); range, 80-108 mm(2)) placement on an area of solid tumor on a single slice. Maximum tumor diameter was measured at the maximum tumor area. A single pathologist reviewed all pathological slides to determine the nuclear grade according to the Fuhrman classification. The utility of ADC, tumor size, and ADC/size ratio for distinguishing low-grade from high-grade tumors was assessed. Receiver-operating characteristic (ROC) analysis and regression analysis of the each index were performed. The correlation between ADC and tumor size was also investigated.

RESULTS

The 49 clear-cell RCC included 34 low-grade and 15 high-grade tumors. The differences of ADC, tumor size, and ADC/size ratio between high-grade and low-grade tumors were statistically significant (P <0.05). The area under the ROC curve of ADC, tumor size, and ADC/size ratio were 0.802, 0.763, and 0.804 respectively. However, using regression analysis, only ADC (P <0.05) was statistically significant index as independent risk factors for high-grade clear-cell RCC. Moreover, weak significant correlation was observed between tumor size and ADC (R(2) = 0.3865, P <0.01).

CONCLUSION

There was a weak significant correlation between tumor size and ADC value of clear-cell RCC. Using ROC and regression analysis, ADC was statistically significant index for distinguishing low-grade from high-grade clear-cell RCC more than tumor size and ADC/size ratio.

Tumor VEGF expression correlates with tumor stage and identifies prognostically different groups in patients with clear cell renal cell carcinoma

OBJECTIVES

Vascular endothelial growth factor (VEGF) is a potent inducer of tumor angiogenesis and represents the key element in the pathogenesis of clear cell renal cell carcinoma (ccRCC). The aim of this study was to investigate the use of tumor VEGF expression as a parameter to identify tumor stage and prognostically different patient groups.

METHODS AND MATERIALS

We retrospectively collected clinical data of 137 patients treated with partial or radical nephrectomy at our institutions for organ-confined, locally advanced, and metastatic ccRCCs between 1984 and 2013. Tumor cell VEGF immunohistochemical expression was compared with pathological and clinical features including age, sex, tumor stage, and Fuhrman grade. Comparison of VEGF expression levels between tumor stages was performed via Kruskal-Wallis nonparametric test. Survival analysis was conducted via Kaplan-Meier product-limit method, and Mantel-Haenszel log-rank test was employed to compare survival among groups.

RESULTS

Median age at diagnosis was 61 years (range: 33-85 y). Tumor stage was pT1N0M0 in 67 patients (49%), pT2N0M0 in 5 (4%), and pT3N0M0 in 25 (18%), while 40 patients (29%) had metastatic tumors at diagnosis. Fuhrman nuclear grade was G1 in 22 patients (16%), G2 in 60 (44%), G3 in 33 (24%), G4 in 13 patients (9%), and unknown in 9 patients. Tumor VEGF was differentially expressed among different stages (P<0.001) and in low (G1-2) and high (G3-4) Fuhrman grade tumors (P<0.001). No significant differences were found when stratifying by sex (P = 0.06) or age (P = 0.29). Median overall survival (OS) from partial or radical nephrectomy was 161 months (range: 1-366). We observed a significantly longer OS in patients with low (<25%) vs. high (>25%) VEGF expression levels (median OS 206 vs. 65 mo, P<0.001).

CONCLUSIONS

Our data show that tumor cell VEGF expression is significantly associated with tumor stage and Fuhrman grade and is able to predict patient outcome, suggesting a potential use of this parameter in identifying prognostically different patients with ccRCC.

Tumoral CD105 is a novel independent prognostic marker for prognosis in clear-cell renal cell carcinoma

Background:

Angiogenesis is essential for tumour growth and metastasis. There are conflicting reports as to whether microvessel density (MVD) using the endothelial marker CD105 (cluster of differentiation molecule 105) in clear-cell renal cell carcinomas (ccRCC) is associated with prognosis. Recently, CD105 has been described as a RCC cancer stem cell marker.

Methods:

A total of 102 ccRCC were analysed. Representative tumour sections were stained for CD105. Vascularity (endothelial CD105) was quantified by MVD. The immunohistochemistry analysis detected positive (if present) or negative (if absent) CD105 tumoral staining. This retrospective population-based study was evaluated using Kaplan–Meier method, t-test and Cox proportional hazard model.

Results:

We found that the expression of endothelial CD105 (MVD) negatively correlated with nuclear grade (P<0.001), tumour stage (P<0.001) and Leibovitch score (P<0.001), whereas the expression of tumoral CD105 positively correlated with these three clinicopathological factors (P<0.001). In multivariate analysis, tumoral CD105 was found to be an independent predictor of poor overall survival (P=0.002).

Conclusions:

We have shown for the first time that tumoral CD105 is an independent predictive marker for death risk and unfavourable prognosis in patients with ccRCC after curative resection.

Prognostic biomarkers in renal cell carcinoma

A hallmark of renal cell carcinoma is its variable prognosis. Surgical resection of primary renal cell carcinoma can be curative when the disease is localized. However, approximately 20% of patients with early stages of localized renal cell carcinomas subsequently develop metastasis after the primary tumor is removed. The median survival for patients with metastatic disease is approximately 13 months. Therefore, there is a great need for biomarkers to predict metastasis and prognosis. Many prognostic biomarkers were studied in the past decade. In recent years, several promising biomarkers, including CAIX, B7-H1 and IMP3, have also been identified by large retrospective studies. Further validation of these biomarkers is essential to transfer the research data into clinical practice. Eventually, an outcome prediction model with biomarkers, staging system and other risk factors will identify high-risk patients with likelihood of progression and formulate different follow-up protocols or systematic treatments for these patients.

The clinical significance of lymphangiogenesis in renal cell carcinoma

Background

The formation of lymphatic vessels (lymphangiogenesis) occurs in tumor tissues and is crucial for tumor development and progression in some cancers. Lymphangiogenesis and its clinical effect on renal cell carcinoma have been less thoroughly investigated in comparison with angiogenesis. The aim of this study was to evaluate the role of lymphangiogenesis as a prognostic factor in renal cell carcinoma (RCC).

Material/Methods

The expression of peritumoral/intratumoral lymphatics was studied by immunohistochemical methods in paraffin-embedded nephrectomy specimens from 133 patients with clear cell carcinoma. Patients were divided into 3 groups depending on postoperative follow-up: I) patients without metastases, II) patients with metastases during follow-up, and III) patients with metastases during the operation. Peritumoral lymphatics (PTL) and intratumoral lymphatics (ITL) were immunostained with a D2-40 antibody.

Results

The mean number of PTL present in each group was I=14.1, II=10.6, III=12.1. The mean number of ITL present in each group was I=0.7, II=2.3, III=2.3. The 3 groups showed statistically significant differences only in the case of ITL. A mean count of ITL ≥1 is significantly associated with an increased risk of regional lymph node involvement and distant metastasis. Patients with expression ITL >0.2 and PTL ≤15.2 had a significantly shorter cancer-specific survival.

Conclusions

The number of ITL showed an association with more aggressive cases of RCC and progression of disease. Therefore, the level of expression ITL, together with stage and histological grading, may provide valuable predictive information about the outcome of treatment.

Sperm-associated antigen 4, a novel hypoxia-inducible factor 1 target, regulates cytokinesis, and its expression correlates with the prognosis of renal cell carcinoma

Hypoxia plays a crucial role in many pathophysiological conditions, including cancer biology, and hypoxia-inducible factor (HIF) regulates transcriptional responses under hypoxia. To elucidate the cellular responses to hypoxia, we performed chromatin immunoprecipitation with deep sequencing in combination with microarray analysis and identified HIF-1 targets. We focused on one of the novel targets, sperm-associated antigen 4 (SPAG4), whose function was unknown. SPAG4, an HIF-1-specific target, is up-regulated in various cultured cells under hypoxia. Examination of SPAG4 expression using a tissue microarray consisting of 190 human renal cell carcinoma (RCC) samples revealed that SPAG4 is an independent prognostic factor of cancer-specific mortality. Live-cell imaging revealed localization of SPAG4 at the intercellular bridge in telophase. We also studied cells in which SPAG4 was knocked down. Hypoxia enhances tetraploidy, which disturbs cell proliferation, and knockdown of SPAG4 increased tetraploid formation and decreased cell proliferation under both normoxic and hypoxic conditions. Studies using deletion mutants of SPAG4 also suggested the involvement of SPAG4 in cytokinesis. Microarray analysis confirmed dysregulation of cytokinesis-related genes by knockdown of SPAG4. In conclusion, SPAG4 is an independent prognostic factor in RCC and plays a crucial role in cytokinesis to defend against hypoxia-induced tetraploid formation. This defensive mechanism may promote survival of cancer cells under hypoxic conditions, thus leading to poor prognosis.

Vasohibin-1 is a new predictor of disease-free survival in operated patients with renal cell carcinoma

Background

Vasohibin-1 (VASH1) is an endothelium-produced angiogenesis inhibitor. Renal cell carcinoma is highly vascularised, but the significance of endogenous VASH1 in renal cell carcinoma has not been defined.

Aims

To identify VASH1 expression and its possible relationship with various clinicopathological factors and prognosis in renal cell carcinoma.

Methods

A retrospective analysis of 122 tumours obtained from 118 consecutive patients with renal cell carcinoma was performed. The expression patterns of VASH1, CD31, vascular endothelial growth factor (VEGF) and VEGF receptor type 2 (VEGFR2) were examined immunohistochemically and their relationships with clinicopathological factors were analysed.

Results

Microvessel density, VASH1 and VEGFR2 expression were significantly higher in clear cell carcinoma than in other subtypes. The VEGF expression pattern differed significantly between clear cell carcinoma and other histological subtypes. VASH1, pT factor and TNM stage were significantly associated with disease-free survival (p=0.030, p = 0.0012 and p = 0.0018, respectively). Cox models of multivariable disease-free survival analyses indicated that VASH1 and stage are independent prognostic factors (p=0.019 and p = 0.024).

Conclusions

VASH1 expression may be useful for estimating the prognosis of renal cell carcinoma. Further studies of the role of VASH1 in renal cell carcinoma involving larger sample sizes are warranted.

Camera-port site metastasis of a renal-cell carcinoma after robot-assisted partial nephrectomy

BACKGROUND AND PURPOSE

Port-site metastasis (PSM) is a rare complication of laparoscopic intervention in urologic malignancies. Of the greater than 50 reported cases of PSM in the urologic oncology literature, only 9 have occurred after surgery for renal-cell carcinoma (RCC). We report a 10th instance of RCC metastasis-in this case to the camera-port site after robot-assisted partial nephrectomy (RAPN). To our knowledge, this case is the first reported PSM of RCC after RAPN.

PATIENT AND METHODS

A 68-year-old man underwent an uncomplicated right RAPN for a 4-cm right renal mass (stage T1aN0M0). Five months later, he was found to have metastatic disease with an isolated peritoneal recurrence at the camera-port site. Biopsy of the lesion confirmed RCC, and the lesion was surgically resected. A comprehensive MEDLINE search for all published studies of port-site recurrences after laparoscopic renal surgery for RCC was performed.

RESULTS

Nine cases of PSM after successful laparoscopic radical or partial nephrectomy for locally confined RCC have been reported. Proposed etiologic factors for port-site recurrence include biologic aggressiveness of the tumor, patient immunosuppression, local wound factors, and technique-related factors. We report an unusual case of PSM to a camera port that was not used for specimen manipulation or extraction.

CONCLUSION

PSM after laparoscopic renal surgery for RCC is a rare occurrence. Our case, in which PSM occurred without specimen bag rupture or extraction through the port in question, highlights the importance of local and systemic factors in contributing to PSM occurrence. We also demonstrate that when PSM is the only site of disease recurrence, it can be successfully managed with minimally invasive surgical resection.

Long-term oncologic outcomes after radiofrequency ablation for T1 renal cell carcinoma

BACKGROUND

Radiofrequency ablation (RFA) of renal cell carcinoma (RCC) is used to obtain local control of small renal masses. However, available long-term oncologic outcomes for RFA of RCC are limited by small numbers, short follow-up, and lack of pathologic diagnoses.

OBJECTIVE

To assess the oncologic effectiveness of RFA for the treatment of biopsy-proven RCC.

DESIGN, SETTING, AND PARTICIPANTS

Exclusion criteria included prior RCC or metastatic RCC, familial syndromes, or T2 RCC. We retrospectively reviewed long-term oncologic outcomes for 185 patients with sporadic T1 RCC. Median follow-up was 6.43 yr (interquartile range [IQR]: 5.3-7.7).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The chi-square test and Wilcoxon rank-sum tests were used to compare proportions and medians, respectively. Disease-specific survival and overall survival (OS) were calculated using Kaplan-Meier analysis, then stratified by tumor stage, and comparisons were made using log-rank analysis. The 5-yr disease-free survival (DFS) and OS rates are reported. A p value <0.05 was considered statistically significant.

RESULTS AND LIMITATIONS

Median tumor size was 3 cm (IQR: 2.1-3.9 cm). Tumor stage was T1a: 143 (77.3%) or T1b: 42 (22.7%). Twenty-four patients (13%) were retreated for residual disease. There were 12 local recurrences (6.5%), 6 recurrences in T1a disease (4.2%) and 6 in T1b disease (14.3%) (p=0.0196). Median time to recurrence was 2.5 yr. Local salvage RFA was performed in six patients, of whom five remain disease free at 3.8-yr median follow-up. Tumor stage was the only significant predictor of DFS on multivariate analysis. At last follow-up, 164 patients (88.6%) were disease free (T1a: n=132 [92.3%]; T1b: n=32 [76.2%]; p=0.0038). OS was similar regardless of stage (p=0.06). Five patients developed metachronous renal tumors (2.7%). Four patients developed extrarenal metastases (2.2%), three of whom died of metastatic RCC (1.6%).

CONCLUSIONS

In poor surgical candidates, RFA results in durable local control and low risk of recurrence in T1a RCC. Higher stage correlates with a decreased disease-free survival. Long-term surveillance is necessary following RFA. Patient selection based on tumor characteristics, comorbid disease, and life expectancy is of paramount importance.

Microvascular density as an independent predictor of clinical outcome in renal cell carcinoma: an automated image analysis study

Tumor microvascular density (MVD) has been shown to correlate with the aggressiveness of several cancers. With the introduction of targeted anti-angiogenic therapy, assessment of MVD has the potential not only as a prognostic but also as a therapeutic marker. The significance of tumor vascularity in clear cell renal cell carcinoma (ccRCC) has been debated, with studies showing contradictory results. Previous studies were limited by manual quantification of MVD within a small area of tumor. Since then, the validity of this method has been questioned. To avoid the inaccuracies of manual quantification, we employed a computerized image analysis, which allowed assessment of large areas of tumor and adjacent normal tissue. The latter was used as an internal reference for normalization. MVD and vascular endothelial growth factor (VEGF) were assessed in 57 cases of ccRCC. Sections were immunostained for CD34 and VEGF. Areas of ccRCC and normal kidney medulla were analyzed within scanned images using software that counted CD34-positive vessels and measured the intensity of VEGF staining. We obtained unadjusted values from tumoral areas and calculated adjusted values as tumor/normal ratios. Unadjusted MVD had no association with clinical outcome. However, similarly to tumor stage, higher adjusted MVD was associated with shorter disease-free survival (log-rank P=0.037, Cox P=0.02). This was significant in univariate and multivariate analyses. MVD did not correlate with tumor stage, pointing to its independent prognostic value. As expected due to the known molecular abnormalities in ccRCC, most tumors showed higher VEGF expression than normal tissue. Higher adjusted VEGF was associated with high tumor grade (P=0.049). The finding of increased MVD as an independent marker of tumor aggressiveness may prove useful in the development of new tests for prognostic and therapeutic guidance. Digital techniques can provide more accurate assessment of immunomarkers and may reveal less obvious associations.

Assessment of the prognostic significance of endoglin (CD105) in clear cell renal cell carcinoma using automated image analysis

The behavior of clear cell renal cell carcinoma can be difficult to predict. Angiogenesis has proven to be a useful prognostic indicator in different malignancies. Endoglin (CD105) is a new marker of angiogenesis found to have prognostic utility in various tumors. Here, we provide the first automated digital assessment of intratumoral microvascular density in clear cell renal cell carcinoma using endoglin and CD31 and assess their utility as predictors of clinical outcome. Both endoglin and CD31 expression showed association with advanced tumor stage (P = .025 and P = .011, respectively). There was a significant correlation between CD31 and tumor grade (P = .034). Kaplan-Meier survival curves showed that patients with higher endoglin expression had significantly shorter progression-free survival (P = .010). Patients with higher CD31 expression tended to have a worse prognosis, although this was not statistically significant (P = .082). In univariate analysis using endoglin as a continuous variable, increased endoglin was strongly associated with reduced survival (hazard ratio, 1.74; 95% CI, 1.39-2.18; P = <.001). CD31 also correlated with poor outcomes (hazard ratio, 1.52; 95% CI, 1.24-1.86; P = .001). There was no correlation between CD31 and endoglin expression (r = -0.090, P = .541). Receiver operating characteristic analysis showed the area under the curve to be 0.749 for endoglin and 0.550 for CD31. In conclusion, increased endoglin and CD31 expression are associated with a higher tumor stage and decreased progression-free survival. Our automated approach overcomes many limitations of manual quantification. Advances in digital assessment of immunohistochemical markers can be helpful in standardizing the evaluation of tumor biomarkers.

Prognostic biomarkers of renal cell carcinoma: Recent advances

Recent advances in understanding the characteristics of renal cell carcinoma (RCC) have brought to our attention many prognostic markers that affect and predict the survival outcome of patients with the disease. For the moment, however, patients with RCC have not received any benefit from such markers. If a patient is diagnosed as “high risk” by using such prognostic markers, there is no promising systemic therapy available. In this review we mainly focus on biomarkers of RCC that can be applied for therapeutic use reported in recent publications. Several issues and limitations in the reported studies are also highlighted and discussed. Developing biomarkers from the viewpoint of therapeutic application will lead to improvement of the prognosis of RCC patients.

Do DNA-methylation and histone acetylation play a role in clear cell renal carcinoma? Analysis of radical nephrectomy specimens in a long-term follow-up

We investigated global methylation and histone acetylation in 50 conventional clear cell renal carcinomas (RCC), treated with radical nephrectomy, to assess their possible role as diagnostic biomarkers. The features considered in this study were patient age, tumor size and grade, percentage and intensity of 5-methylcytosine (5mc) and Acetyl-Histone (Lys 9) expression in tumor tissue. All considered parameters were correlated with patient specific survival. The mean percentage of global cellular methylation in tumoral tissue was significantly higher compared to normal peritumoral tissue (p<0.0001), while the intensity of cellular methylation was significantly higher in normal tissue than in tumoral tissue (p=0.001). The mean percentage of histone cellular acetylation in tumoral tissue was significantly lower compared to normal peritumoral tissue (p=0.0005), while the intensity of mean acetylation in neoplastic tissue was similar to the normal tissue. The percentage of global DNA methylation was significantly higher in grades 3 and 4 tumors (p=0.033). Global DNA methylation and histone acetylation in tumoral tissue did not correlate with survival. Fuhrman grade was statistically significant for prognosis (p=0.031). In conclusion, global hypermethylation and histone hypoacetylation play an important role in RCC carcinogenesis; Fuhrman grade is still considered the most important factor for patient survival; 5mc can have a role as markers of aggressiveness.

Active surveillance of renal cortical neoplasms: a contemporary review

INTRODUCTION

Over the past 2 decades, there has been a significant increase in the number of incidentally found small renal cortical neoplasms (RCNs). As more RCNs are being discovered in the elderly and infirmed patient populations, there has been a growing interest in the role of active surveillance (AS). Active surveillance is recommended for high surgical-risk patients and those with a reduced life expectancy. It is also an option for patients wishing to avoid surgery. We review the current literature on AS and highlight the natural history of disease, the important factors to evaluate during AS, and the contemporary role of biopsy.

METHODS AND MATERIALS

The MEDLINE database was searched using PubMed. Search terms included active surveillance, renal mass, natural history, and renal mass histology. From 1966 to present, 17 AS series were identified, all of which have been included in this summary. A summary was performed by compiling all available data and performing a weighted mean where applicable.

RESULTS

Initial tumor size does not correlate with growth rate or malignancy. The mean growth rate in large published series is low (0.28-0.34 cm/year). Tumors with high growth rates usually represent malignant lesions and typically undergo delayed intervention. Progression to metatatic disease is a low-probability event for tumors on AS (1.4%); however, this is still a risk that patients must be willing to accept. Larger tumors (cT1b and cT2) also demonstrate relatively low growth (0.57 cm/year); however, these tumors should be monitored carefully. Tumors followed for > 5 years demonstrate a low growth rate (0.15 cm/year), will not likely require intervention, and have a low chance of progression to metastatic disease.

CONCLUSION

For highly selected patients with RCN, AS is a reasonable treatment option. Age, surgical risk, comorbidities, and patient opinion must all factor into the final decision when considering a patient for AS.

Vitespen: a preclinical and clinical review

Vitespen is a heat shock protein (gp96)-peptide complex purified from resected autologous tumors, developed as a means of capturing the antigenic 'fingerprint' of a specific cancer for use as a patient-specific vaccine. Vitespen has been extensively assessed in animal models, and clinically in a range of cancers, including Phase I and II trials in colorectal cancer, glioblastoma, lung cancer, melanoma and renal cell carcinoma, and two Phase III studies in melanoma and renal cell carcinoma. Vitespen has shown itself capable of inducing major histocompatibility class I-restricted immune responses in a range of tumor types, and clinical responses in patients with earlier-stage disease, in line with previously published data on cancer vaccines. Vitespen is almost devoid of side effects aside from minor injection-site reactions.

Natural history and management of small renal masses

The recent stage migration observed for renal tumours is contributing to a significant increase in the diagnosis of small renal masses. This evolution has led to a significant change in the approach to renal masses. New options such as observation or energy ablation are gaining popularity in a subset of this patient population. In addition, the observed changes directly contribute to the increased use of nephron-sparing surgery. A better understanding of the various characteristics of these masses will allow for a better understanding of the natural history of these masses and for selection of the optimal therapeutic approach.

Surveillance as an option for the treatment of small renal masses

OBJECTIVES. To review the natural history and biological potential of small renal masses in order to evaluate surveillance as a treatment option. METHODS. Literature search of Medline and additional references from non-Medline-indexed publications concerning surveillance of small renal masses. RESULTS. The natural history and biological potential of small renal masses can still not be unambiguously predicted at present. There seems to be no clear correlation between tumour size and presence of benign histology. The majority of small renal masses grow and the majority are cancer, but one cannot safely assume that a lack of growth on serial CT scans is the confirmation of absence of malignancy. Needle core biopsies could be used to help in decision making. They show a high accuracy for histopathological tumour type but are less accurate in evaluating Fuhrman grade. CONCLUSIONS. At present, surveillance of small renal masses should only be considered in elderly and/or infirm patients with competing health risks, in those with a limited life expectancy, and in those for whom minimal invasive treatment or surgery is not an option. In all other patients, active surveillance should only be considered in the context of a study protocol. Long-term, prospective studies are needed to provide a more accurate assessment of the natural history and metastastic potential of small renal masses.